In certain cases, the proposed techniques achieve up to an 80% reduction in the correlation between masking shares. The PDN with two DLDOs and two-phase DLDO with 180t-test values remain under 4.5 with 100,000 traces of simulations. The security of the PDN improves if DLDO is placed closer to any one of the masking shares.A design space exploration of the countermeasures for hardware masking is proposed in this paper. The assumption of independence among shares used in hardware masking can be violated in practical designs. Recently, the security impact of noise coupling among multiple masking shares has been demonstrated both in practical FPGA implementations and with extensive transistor level simulations. Due to the highly sophisticated interactions in modern VLSI circuits, the interactions among multiple masking shares are quite challenging to model and thus information leakage from one share to another through noise coupling is difficult to mitigate. In this paper, the implications of utilizing on-chip voltage regulators to minimize the coupling among multiple masking shares through a shared power delivery network (PDN) are investigated. Specifically, different voltage regulator configurations where the power is delivered to different shares through various configurations are investigated. The placement of a voltage regulator relative to the masking shares is demonstrated to a have a significant impact on the coupling between masking shares. A PDN consisting of two shares is simulated with an ideal voltage regulator, strong DLDO, normal DLDO, weak DLDO, two DLDOs, and two DLDOs with 180 Modern computing devices consist of various circuit components to perform different tasks. The security and privacy of data processed and stored in these devices have become important with the proliferation of modern computing devices in our daily lives. Cryptographic modules that perform encryption/decryption operations are therefore utilized to improve the security and privacy of data. To perform the encryption/decryption in a lightweight, fast, and power efficient manner, various algorithms with unique implementations have been proposed. However, side-channel attacks still threaten the security of all of these cryptographic devices. Passive and non-invasive side-channel attacks use certain intermediate values of an encryption algorithm to obtain physical leakage signatures, correlate this leakage with certain predetermined models, and eventually determine the private keys or passwords stored in these devices. To protect private data, different types of countermeasures have been developed ,2. The wThe hiding countermeasures are difficult to implement since strict requirements such as aligned signal propagation and balanced routing are difficult to achieve at advanced technology nodes due to the increase in parasitic effects . An effidth order Boolean masking where the sensitive information is the Boolean addition of each share. The operations in each share are unmasked and typical dth order masking can be defeated by dth order attack to be successful on an encryption device with dth order masking. While hardware masking provides security by processing the sensitive data into multiple shares, the violation of the independence can lead to severe security vulnerabilities [Masking divides the sensitive information into a bilities . The masbilities . On the The practical implementations of hardware masking have certain challenges due to the parasitic impedances, and variations in the transistors and interconnections due to aging, temperature, or fabrication process, which make satisfying the independent masking share assumption quite difficult. The primary reasons for the gap between the theory and practice of the hardware masking due to the aforementioned design challenges are as follows: The Hamming distance leakage between hardware masking shares cannot be completely eliminated due to the shared architectural components between shares; the leakage between shares is dependent because of the nature of the chip manufacturing techniques; and the glitches propagate through the logic gates and between hardware masking shares. The interdependence of different shares of masking and potential countermeasures are studied in the literature ,11,12,13There are a small number of papers that investigate the security vulnerability of hardware masking due to the violation of independence assumption. An ASIC (Application Specific Integrated Circuit) design framework is proposed in  to decreThe existing literature provides a limited number of solutions to the problem of hardware masking because the existing circuit design, placement, and routing tools for power delivery network do not typically consider security hardware masking as a design target and therefore have limited capacity to evaluate the security of the designs in the preliminary stage. Additionally, the evaluation of the leakage in early stages of the design still requires a large number of measurements which take considerable time using existing EDA tools. The practical implementation of any design can potentially have security vulnerabilities which are typically only evaluated after manufacturing. A cost and time effective way to minimize this leakage among masking shares is to design the power delivery network (PDN) and on-chip voltage regulator in a leakage-cognizant way. Accordingly, on-chip voltage regulators are utilized in this paper as a countermeasure to mitigate the leakage between hardware masking shares. A design space exploration of the implications of different voltage regulator topologies and placement techniques for the voltage regulators and masking shares is performed to demonstrate the effectiveness towards closing the gap between theory and practice for hardware masking implementations.Motivation: Hardware masking aims to separate the sensitive information into multiple shares to improve the resistance against side-channel attacks . The maiThe non-independent leakage can be caused due to a shared PDN or certain logic circuitry  because Our Contribution: Although there are many countermeasures against side-channel attacks, there are a limited number of these countermeasures that specifically focus on the vulnerability of hardware masking. The voltage fluctuations in PDN have been extensively investigated; however, security implications of noise for hardware masking have typically not been considered ,19. In aFirst, for the first time, to the best of our knowledge, DLDO is used to improve the security vulnerability of hardware masking where DLDO inserts voltage fluctuations to improve the security of hardware masking. Second, we prove the methodology mentioned in  where thPaper Organization: The outline of the paper is provided as follows. A literature review/related works of on-chip voltage regulators and the specific voltage regulator that is used in this paper, and countermeasures against side-channel attacks are provided in A literature review/related works is provided for digital low dropout (DLDO) voltage regulators in N parallel PMOS transistors , switched capacitor (SC), and buck voltage regulators ,24. Altht (left) .Countermeasures can be categorized into two based on the implementation. Software countermeasures are designed at the software level typically in a micro-controller. Hardware countermeasures are implemented directly during the design process by modifying the circuits and a dedicated protection circuit is implemented as a countermeasure. There are therefore distinct differences between software and hardware countermeasures. The focus of this paper is primarily on hardware countermeasures; therefore, a brief background of hardware countermeasures is provided below.Hardware countermeasures can perform both hiding and masking of the private data. The primary hiding countermeasures are decoupling, minimization, randomization, desynchronization, and noise insertion . Shamir In addition to the aforementioned circuit level countermeasures that specifically target analog circuitry, several other countermeasures modify the digital portion of the circuitry. New logic families are introduced to balance and hide the power consumption of the logic core that implements the encryption circuitry. Sense-amplifier based logic , wave dyThere is also a variety of high-level architectural countermeasures that can typically work orthogonal with the countermeasures at different levels of design abstraction, including the circuit based countermeasures. The sensitive information is balanced between multiprocessors with an algorithmic level balancing algorithm to improve resistance against power side-channel attacks in , and a rHardware masking is a shuffling type of countermeasure and is the main concern of this paper. The basic developments can be summarized as the hardware masking being designed in FPGA and related security tests being implemented in . The impdth order Boolean masking, the sensitive information is divided into A hardware masking technique splits the sensitive information to be processed into multiple shares. Shamir’s secret sharing scheme provides an effective way to divide the sensitive information into multiple shares which are processed individually . These sAfter the proposal of Shamir’s secret sharing scheme, many hardware masking designs have been proposed . Prior wTI, one of the widely used masking schemes, overcomes the glitch propagation among shares with non-completeness, which means that any multiple component function should be independent of all unshared functions to achieve the security of hardware masking. The design becomes glitch-resistant because the components through which glitch impacts other shares becomes independent. The non-completeness can be formulated for a 2nd degree function with three shares asleteness .x such as dth order masking is secure against dth order attacks as long as each domain is independent. The implementation is glitch resistant since there is no common source between shares where the glitch can propagate. The required number of components is less than that of TI with an additional cost of an extra clock cycle [DOM is another type of hardware masking, which uses operation refreshing and share compression in two clock cycles. In the refreshing operation, the randomness is inserted during the multiplication process. In the share compression, all of the operations in a share are synchronized, and each share is implemented in a dedicated domain. For example, the individual shares of ck cycle .The independence of hardware masking is just an assumption when theoretically demonstrating the effectiveness of these masking techniques. However, practical designs may suffer due to the difficulty of designing actual circuits that have statistically non-significant coupling (and consequently leakage) between circuit blocks that share the same die .The existing studies ,15,16 deIn this paper, a first-order masking scheme is implemented such that the sensitive information is divided into two shares. The presence of noise coupled from neighboring circuitry to the masking shares is crucial to make the simulations more realistic and a better representation of a practical integrated circuit. Accordingly, a Fibonacci linear-feedback shift register (LFSR) is implemented to emulate the noise that stems from another circuitry. A 16-bit LFSR is used with four taps ,46.A t-shaped PDN is designed to represent the power delivery from an off-chip voltage regulator to the on-chip masking shares. The two masking shares are expected to exhibit significant noise coupling when connected directly to an external voltage regulator through this shared PDN. Accordingly, any one of the masking shares can potentially leak information to an adversary about the other shares related to the private information. The concurrent operation of the shares makes it slightly more difficult to extract sensitive information from a single share from another share. Additionally, the relative position of the shares with respect to the other shares and voltage regulator connections within the PDN are expected to significantly impact the coupling. A shunt resistor can be connected to the input power line of the circuit by the attacker to measure the power usage of the cryptographic device. The shares are represented with XOR gates as cryptographic circuits commonly utilize XOR gates to perform encryption operation .A basic schematic of the PDN with an off-chip power supply is illustrated in A quantitative analysis of the circuit is performed by deriving the related transfer functions. The According to Kirchhoff current law, With Kirchhoff voltage law,After inserting  into 2)2) and orThe relationship between After inserting  into 5)5), I1 caThe hardware masking consists of XOR gates and inverters at the last state of the circuit logic. Therefore, the assumption is made that a capacitive load is connected to the output of inverter. The CMOS logic consumes power during the operation of the encryption circuitry while charging the output capacitor during input logic changing from a one to zero state. This current passes through the PMOS transistor. Therefore, the relationship between the activity of the circuitry and current through the PMOS is modeled. The PMOS is assumed to work in the linear region during the switching activity. The modeling is performed according to the CMOS logic and can be applied to any MOSFET technology where the basic MOSFET equations are valid . The secRp=1βpI, 6), an, an6), aA similar analysis is performed with the decoupling capacitor with Kirchhoff current law yieldingWith Kirchhoff voltage law and 16)16) yieldThe relationship between the supply voltage values delivered to Shareerved in  and 17)1 and Sha2 nm PTM , and theThe parasitic impedance can be partially reduced using advanced placement and routing algorithms, and the design process requires iterative methods to minimize the effect of the parasitic impedance. Although the parasitic impedances cannot be eliminated completely, there are various techniques to minimize the detrimental effects of the interconnect parasitics on the system performance . The parSince several hundreds of thousands of simulations under different inputs and variations need to be performed to obtain meaningful results in the proposed statistical tests, a drastically improved simulation speed and capacity are required. Accordingly, Synopsys Finesim, a SPICE circuit simulator, is used throughout the paper to improve the simulation time. In addition, 32 nm PTM CMOS technology models have been used . The supi is the input size, j is the share number, The Boolean function of  circuit . A Boolet-test is applied to the proposed hardware masking design. Welch’s t-test is widely used to quantify security where the level of sensitive information leakage can be observed quantitatively. Typically, a t-test value of 4.5 and below is assumed to be secure since the amount of information leakage from one share to another share is considered negligible [In this article, the correlation between the node voltages gligible .t-test is used to check if the circuit behaves differently under two different inputs, e.g., one is fixed vs. one is random, and can be written asX and Y are two random distributions, E(X) and E(Y) are the expected value of X and Y, and X and Y. If t is lower than 4.5, the confidence interval of the test is 99.99%, meaning that X is statistically different than Y. Therefore, the t-test values below 4.5 are typically assumed to have no leakage [Welch’s  leakage ,14.Voltage fluctuations in the power delivery network  are the primary source of coupling between hardware masking shares. Therefore, voltage fluctuations are generally analyzed to measure the leakage. One of the sources of voltage fluctuations is the change in the current demand over time due to the switching activity of the circuits which are powered through a PDN that is comprised of a resistive and capacitive interconnection network. The voltage fluctuations can be as fast as the operating frequency of the load circuit, leading to considerable voltage fluctuations when the switching activity is higher.t-test and correlation. The leakage testing methodology is defined in [t-test is performed. The experiment setup is shown in Throughout this paper, the voltage fluctuations are used for evaluation by utilizing the previously known methods of side channel analysis, such as Welch’s fined in . Howeverfined in . The volhttps://github.com/sonersec/Exploiting-On-chip-Voltage-Regulators-for-Leakage-Reduction-in-Hardware-Masking, accessed on 16 August 2022.The source code is available at To validate the inputs that are used in the experiments, a side-channel attack is performed on a real encryption device when processing the same inputs. A 128-bit AES is run on an Atmega128 8-bit AVR device. In addition, 100,000 traces are collected with Chipwhisperer . The inpt-test evaluation. In the experiments, interconnect parasitic impedances are assumed as t-test is implemented throughout the experiments [A thorough analysis to evaluate the effects of the PDN parasitic impedance and different on-chip voltage regulator connection strategies is performed in this section. The target circuit consists of XOR gates with two masking shares. The simulations are performed using Synopsys Finesim, and the results are used in correlation analysis and eriments . The loaeriments . The 16-t-test [The effect of the PDN parasitic impedance is investigated under fixed input to Sharem t-test . The rant-test results, as shown in max(|t|), leading to a more secure design.The relationship between t-test. A detailed schematic of the simulation is shown in The implications of using a DLDO voltage regulator instead of an ideal supply voltage on the noise coupling from Sharet-test value decreases. The t-test value is at a minimum when t-test decrease. The output voltage of a DLDO has voltage fluctuations depending on the load current characteristics as compared to an ideal voltage regulator which does not have any voltage fluctuations at the output. These voltage fluctuations  due to the non-ideality of the DLDO has a positive impact on the security since t-test and correlation decrease as compared to the case when the voltage regulator is ideal [A similar PDN parasitic impedance evaluation is performed with a strong DLDO voltage regulator instead of an ideal voltage regulator used in the previous section. There are 64 PMOS transistors as the pass transistors within the strong DLDO. The correlation between is ideal .t-test value is observed when t-test values than the case when the voltage regulator is strong DLDO. An increase in the value of t-test value, which means the circuit becomes more resistant against side-channel attacks.To evaluate the effect of the size of the voltage regulators, a comparably smaller sized DLDO, which is called normal DLDO, is investigated in this section. The value of t-test value is observed when t-test values are higher for the weak DLDO than the strong and normal DLDO configurations, since the weak DLDO is smaller than the strong and normal DLDOs, there is more voltage fluctuations  in the output of the DLDO, leading to higher t-test values than the case when the voltage regulator is larger. An increase in the value of t-test.To further investigate the effect of the size of the voltage regulators, an even smaller sized DLDO, which is called weak DLDO, is investigated in this section. The value of t-test values remain under 4.5, which is assumed to be secure [t-test value remains constant when The implications of using two separate DLDO voltage regulators that provide power individually to two masking shares on the noise coupling mechanisms is investigated. Note that the DLDO voltage regulators are assumed to be integrated on-chip and their input sides are connected to the same external power supply. The information leakage from one masking share to another share becomes more difficult when the noise has to propagate through a higher number of circuit components and longer interconnect lengths. To simulate the effect of off-chip parasitic impedance, a common e secure  and implThe implications of using a shared DLDO to provide power to two masking shares is investigated. In this case, each share is connected to one of the two phases of the DLDO (i.e., the pass transistors that are controlled by one of the clock signals are connected to one of the shares, the remaining pass transistors that are controlled by the 180t-test result for a shared two-phase DLDO is shown in t-test results. The two phases of the DLDO are driven by clock signals which are 180t-test results below the threshold value of 4.5. The t-test value remains constant when The t-test results are compatible with correlation analyses.The summary of the implications of different PDN design choices on noise coupling is tabulated in t-test values. The leakage does not occur even with 100k traces when either two DLDOs or a two-phase DLDO with phase shift is utilized. The comparison of the other methods is given in X means that there is no corresponding results published in the work. The implementation of this work focuses on PDN on ASIC, thus the focus of comparison of this table is based on the PDN.The leakage occurs within the hardware masking typically after one million traces for FPGAs  using fi, , , or , as shown in , as tabulated in t-test value increases as the location of the DLDO is shifted to the far corner of the grid . Alternatively, the t-test value decreases as the DLDO connection location is shifted to the closest location to the ShareThe design of PDN has a significant impact on the security of the hardware masking, as discussed in the previous sections. To better analyze the implications of PDN and placement of the voltage regulator, a more realistic, 16 × 16 grid structure is investigated, as shown in t-test value is observed when DLDO is located in ; as this is the farthest location from Share, the effective resistance in the PDN corresponding to ; and , the effective resistance in the PDN corresponding to t-test values are lower when the DLDO is in ; than when the DLDO is in ;. Thus, the effective resistance between the two shares is lower when DLDO is connected to ; than when DLDO is connected to ;. The voltage drop in Sharet-test values decrease with the increase in the distance between shares similar to the case when t-test values decrease with the increase in The highest t-test are used to quantify the amount of noise coupling in hardware masking. The correlation between two masking shares reduces by 80% when a strong DLDO with t-test value remains below 4.5 when either two DLDOs separately provide power to the masking shares or different phases of a single DLDO provide power to the masking shares. The optimal placement of a single DLDO voltage regulator among the simulated nodes is determined which reduces correlation between two shares and t-test value. Accordingly, design guidelines are proposed that would minimize the gap between the theory and practical implementation of hardware masking.Hardware masking relies on the principal assumption that the masking shares are independent of each other. However, this independence assumption can be violated in practical implementations due to the parasitic impedance of on-chip interconnect and circuitry, and coupling between shares. Therefore, a feasible solution to this security vulnerability of hardware masking is proposed in this article. Five different DLDO configurations, strong DLDO, normal DLDO, weak DLDO, two individual DLDOs, and a single two-phase DLDO, are investigated to explore the security implications for hardware masking. Additionally, the noise coupling implications of the DLDO placement are explored when a single DLDO is connected to different locations in the PDN. The correlation between two masking shares and Welch’s The amount of data and simulation times are huge. Therefore, the fast simulators should be utilized due to the limitations of the computing resources and timing limitations. A fast simulator with more advanced technologies can be utilized and implemented. The effect of 3D integration with advanced technologies can be adapted to improve the security of the hardware masking.


Task offloading decision is one of the core technologies of vehicular edge computing. Efficient offloading decision can not only meet the requirements of complex vehicle tasks in terms of time, energy consumption and computing performance, but also reduce the competition and consumption of network resources. Traditional distributed task offloading decision is made by vehicles based on local states and can’t maximize the resource utilization of Mobile Edge Computing (MEC) server. Moreover, the mobility of vehicles is rarely taken into consideration for simplification. This article proposes a deep reinforcement learning based task offloading decision algorithm, namely Deep Reinforcement learning based offloading decision (DROD) for Vehicular Edge Computing (VEC). In this work, the mobility of vehicles and the signal blocking commonly in VEC circumstance are considered in our optimal problem of minimizing the system overhead. For resolving the optimal problem, the DROD employs Markov decision process to model the interactions between vehicles and MEC server, and an improved deep deterministic policy gradient algorithm called NLDDPG to train the model iteratively to obtain the optimal decision. The NLDDPG takes the normalized state space as input and introduces LSTM structure into the actor-critic network for improving the efficiency of learning. Finally, two series of experiments are conducted to explore DROD. Firstly, the influences of core hyper-parameters on the performances of DROD are discussed, and the optimal values are determined. Secondly, the DROD is compared with some other baseline algorithms, and the results show that DROD is 25% better than DQN, 10% better than NLDQN and 130% better than DDDPG. As a typical service scenario of mobile edge computing, the combination of Internet of Vehicles (IoV) and Mobile Edge Computing (MEC) is called the Vehicular Edge Computing (VEC). The Vehicular Edge Computing Network not only addresses the lack of computing power in vehicles themselves, but also the problems of high latency, high energy consumption and low security in cloud computing. As a result, VEC has become a hot topic today , and an offloading strategy based on Deep Q Network (DQN) is designed to dynamically adjust the offloading ratio to guarantee the latency and energy consumption of the system performance in In Aiming to VEC environment, the mobility of vehicles, the time-varying channel and the signal blocking are considered into our optimal problem of minimizing the system overhead which is defined as the weighted average of time and energy consumption.A DROD algorithm based on deep reinforcement learning is proposed to obtain a full type offloading decision, that is full local computing, full offloading computing or partial offloading computing. Moreover, for partial offloading computing, the optimal offloading proportion of task can be determined by MEC server through the interactions with vehicles in its cell.Tensorflow platform and verify the effectiveness of the algorithm.An improved deep deterministic policy gradient algorithm, named NLDDPG, is proposed to train the neural network model and obtain the optimal decision for the optimal problem. NLDDPG improves DDPG by taking the normalized state space as input and introduces the long short term memory (LSTM) structure into the actor-critic network. The normalized state space eliminates the difference of magnitudes of different original states and speeds up training. LSTM adds history state information into Markov decision system and upgrades the training effect. Experimental simulation based on Based on the above summary and analysis, we find that (1) traditional edge computing task offloading decisions are made independently by vehicles in a distributed mode, so MEC servers serve in first-in-first-service mode simply and the resource utilization efficiency is low. (2) Some characteristics of VEC should be taken into consideration, such as the high mobility of vehicles, the time-varying channel, and the signal blocking. (3) 0–1 task offloading decision is only suitable to undividable tasks, for those dividable tasks, partial offloading and the optimal offloading proportion are necessary to be considered. Therefore, this article proposes an improved DDPG based VEC-suitable central offloading decision algorithm, namely DROD. DROD can comprehensively solve the problems raised above compared with other works. The main contributions can be summarized as follows:The rest of article is organized as follows. In “System Model and Problem Formulation”, vehicular edge network system model and problem formulation are presented. “Deep Reinforcement learning based offloading decision (DROD) algorithm” introduces the offloading decision model based on NLDDPG algorithm. “Simulation and Result Analysis” shows the performances of NLDDPG algorithm based on simulations. Finally, “Conclusion” concludes this article.D. Considering the overlapping coverage area of adjacent BS, a square with side length L inscribed in the circle is used to approximate the coverage area of the MEC server as shown in The vehicular edge computing network structure is shown in x can be used to describe the task offloading decision result in the VEC system, as shown in The task offloading ratio Local computing: Tasks are all calculated on the vehicle.Full offloading: All tasks are offloaded to MEC server for calculation.Partial offloading: A portion of the task is computed on the local vehicle, while the remainder is offloaded to MEC server for processing.i, the MEC server is able to complete an offloading decision for a task request based on task characteristics and computing resources. In this way, the MEC server can choose to complete M task requests from a total of S tasks within the decision time I to make the VEC system optimal.Since the connection between tasks and tasks and between tasks and MEC servers has a significant impact on task offloading decision, this article proposes a centralized decision scheme performed by MEC server. Without loss of generality, we split the decision time I of the MEC server into time slots of equal length, and within any time slot T1 and local energy consumption E1 are shown in In local computing mode, the task is fully computed on vehicular Electronic Control Unit (ECU). So, the local delay j-th vehicle in i-th time slot. s is the CPU cycles required to compute each bit. P1 is the unit of power consumed by the vehicle calculation. At this point, x is 0.H is the height of the BS. If the vehicular location at time t can be written as  is as follows.Since the location of the MEC server is fixed , the loc at the current moment, and the coordinate  of the elapsed time interval ∆t are both within the coverage of the MEC server can it participate in the MEC server offloading computing.As shown in Considering that the signal could be blocked during actual transmission, the signal blocking flag is added to differentiate the signal transmission capability. Therefore, the wireless transmission rate  is shownα is the channel power gain at a reference distance 1 m. B is channel bandwidth. p is the transmission power of vehicle. T2 and the upward transmission energy consumption E2 shown in The task is transmitted through the wireless channel with the upward transmission delay P2 is the unit of power consumed by upward transmission. When x = 1, T3 and energy consumption E3 of edge computing are shown in The delay P3 is the unit of power consumed by the MEC server calculation.The time and energy consumption of partial offloading can be expressed as An optimization problem is given to realize the optimal offloading decision. Its goal is minimizing the VEC system overhead through the proposed task offloading decision algorithm as described in D is the system overhead. j-th task is offloaded or not. i. Fmax is the maximum computing capacity of the MEC server. x(i) and D is the smallest.Different from traditional distributed offloading decision, the DROD algorithm uses central decision mode and considers task characteristics and network states synchronously. With the rich information, it can realize an effective dynamic decision for the task computing request. The key works include Markov offloading decision model and NLDDPG based optimal decision.i.e., state, action, policy, reward and return. As shown in The Markov decision process  (MDP) ist, vehicle perceives the initial state St and implements the action At according to the policy. After the action has an effect on the edge environment, it enters a new state Rt to the vehicular edge environment. Subsequently, the vehicle adopts a new policy Gt of the tasks.At a certain time Based on the above model, this article proposes an improved deep deterministic policy gradient (NLDDPG) algorithm to solve the optimization problem given by The deep reinforcement learning method solves the problem with five basic elements.Normalized state space: In the VEC system, the state space consists of all kinds of states of MEC server and vehicle that can affect the offloading decisions.j-th vehicle in i-th time slot respectively. i-th time slot.In the training process of neural network, the large value range of task data slows down the training speed. Furthermore, the differences and uncertainties of the value ranges of different task types will lead to instability and poor convergence of the entire system. So, it is necessary to normalize the value range for NLDDPG. Then the normalized state can be expressed as:FMEC is the maximum computing resource of the MEC server and Msum is the sum of the tasks participating in the offloading at the current moment.Action space: According to the current state, the vehicle chooses a two-dimensional action. The action ai can be expressed as:i-th time slot and i-th time slot.Reward: Reward is a measure of the performance of the action selected by vehicles in the current state and it is the decisive factor for evaluating the performance of the algorithm. This article selects the action based on the merits of the reward. The goal of this article is to minimize the system overhead. According to the Policy: Policy can guide actions, so good policy can make vehicles produce good task offloading and improve vehicular edge computing environment. The deterministic policy μ adopted in this article is because the efficiency is dozens of times higher than the random policy, which greatly shortens the training time.Return: Return is the accumulation of rewards over time, and is the sum of all rewards on the timeline. The vehicle can learn the optimal policy through return. The goal of this article is to maximize return within each time slot.γ is the discount factor.The architecture diagram of NLDDPG algorithm is shown in Actor-Critic network: The role of the actor part is to define parameterized policies and generate actions based on the observed state of the environment, while the critic part is responsible for evaluating and criticizing the current policy by processing the rewards obtained from the environment.Due to the partial observability of environment, it is difficult for vehicles to obtain relevant information and MEC server develop successful decision when the deep reinforcement learning algorithm is trained in a complex dynamic environment. As a result, some processing of the neural network is required to produce better results. LSTM can synthesize historical and current information to handle complex state space data well. We introduce the LSTM structure into the actor-critic network, use memory reasoning to extract vehicle tasks and environmental information, and conduct effective network learning by observing the state space and analyzing data features comprehensively.Experience replay buffer: The experience replay buffer mechanism refers to using a fixed replay buffer to store the previous transition VEC: VEC is the vehicular edge computing environment including vehicles and MEC server etc. In VEC, the vehicles interact with the environment and learns actor-critic network parameters through experience replay buffer to find the action with the highest rewarding.E, time slot I, actor network learning rate R, noise n0 and mini-batch N, and the output is the optimal online policy network weight The purpose of the NLDDPG algorithm is to learn the optimal policy for maximizing long term return. The input of the NLDDPG algorithm is the vehicular edge computing environment parameters such as episodes lgorithm  is shownwhere, DROD is a task offloading decision algorithm using NLDDPG, and the details are shown in Tensorflow platform with Python programming. All simulation parameters used are shown in We have simulated a MEC environment with Python. In this environment, there are one MEC server and ten vehicles. The MEC server is allocated besides road, and all vehicles move along the road with a random chosen angle in the communication region of the MEC server. In the simulation, vehicles generate task and send requests to the MEC server. When the MEC server receives these requests, it makes an offloading decision according DROD algorithm. DROD was tested on In the NLDDPG algorithm, the setting of hyper-parameters is very important. Different hyper-parameters will affect the optimization, convergence and stability of the algorithm. Therefore, this article has done relevant experiments to determine the optimal hyper-parameters used in the algorithm.The network learning rate affects the training and updating of the neural network in the NLDDPG algorithm. A large network learning rate leads to poor optimization and stability of results, while a small network learning rate leads to poor optimization and slow convergence. In general, the actor network and critic network are symmetric, so the learning rate is the same. As shown in The experience replay buffer affects the training time, optimality, and convergence of the algorithm. As shown in To verify the performance of the NLDDPG algorithm, the DQN algorithm, the normalized DQN algorithm with LSTM network (NLDQN) and the DDPG algorithm were used for comparison experiments. To further examine the performance of the NLDDPG algorithm, it is compared to local computing, full offloading, and random computing. Random computing refers to the offloading method in which the offloading ratio is randomly selected within . In order to verify the stability and effectiveness of the algorithm, The task offloading decision for vehicular edge computing is one of the key technologies to improve the service capabilities of IoV. The main goal of the proposed task offloading decision algorithm is to be able to flexibly select the edge computing mode based on the task characteristics and network status. Different from the distributed decisions by different independent vehicles in existing work, this article proposes a central vehicular edge computing task decision algorithm based on deep reinforcement learning. Currently, deep reinforcement learning techniques are mostly used to solve discrete actions , such as DQN, in the field of offloading decisions for connected vehicle edge computing tasks. however, the algorithm proposed in this article can solve high dimensional continuous actions . Based on the task characteristics and network status, MEC server can make an optimal offloading decision for minimizing system overhead. The deep reinforcement learning method is used to improve the effectiveness and accuracy of decision. Experimental results illustrate that the decision results of NLDDPG algorithm proposed in this article can effectively reduce system overhead. At present, we only consider the multi-task decision problem within the coverage of a single MEC server, so the decision results still have a certain degree of locality. In the next step, we will study the task offloading decision problem in a multi-MEC server multi-task request environment and use the collaboration mechanism to construct a hierarchical hybrid decision strategy.


Soft polymers such as the investigated polyurethane, characterized by low Young’s moduli and prone to high shear deflection, are frequently applied in pneumatic cylinders. Their performance and lifetime without external lubrication are highly determined by the friction between seal and shaft and the wear rate. FEM simulation has established itself as a tool in seal design processes but requires input values for friction and wear depending on material, load, and velocity. This paper presents a tribological test configuration for long stroke, reciprocating movement, allowing the generation of data which meet the requirements of input parameters for FEM simulations without the geometrical influences of specific seal profiles. A numerical parameter study, performed with an FEM model, revealed the most eligible sample geometry as a flat, disc-shaped sample of the polymer glued on a stiff sample holder. At the same time, the study illustrates that the sensitivity of the contact pressure distribution to Poisson’s ratio and CoF can be minimized by the developed and verified setup. It ensures robust, reliable, and repeatable experimental results with uniform contact pressures and constant contact areas to be used in databases and FEM simulations of seals, enabling upscaling from generically shaped samples to complex seal profiles. Polymers are widely used in tribological applications such as seals due to their exceptional mechanical and tribological properties, chemical resistance, and cost efficiency and their demand is continuously increasing. Pneumatic cylinders commonly work with so-called soft polymers, a group of polymers with low elastic or viscoelastic properties; examples are rubbers and polyurethane. For optimized systems, in order to streamline the design process for engineers and identify best choice of polymer compound, the finite element method (FEM) is the most efficient procedure today . For the®) also reveals the gap between the states of the art of both disciplines. The tribological models only consist of the Coulomb friction model and the Archard wear model, lacking specific parameters for individual materials [There is a lack of standardization in tribological tests in general and for soft polymers in particular because there is a high variance of test results under seemingly identical conditions in tribological tests . This coaterials .Various methods of characterizing the tribological properties of a system of polymers against a metal counter surface can be found in the literature. Laboratory tests with point contacts include the crossed-cylinder contact configuration ,8, and lIn this work, the different methods to characterize soft polymers tribologically are assessed via numerical simulations. This basis allows us to understand key influence parameters on the tribological contact and optimize the test setup in order to obtain material specific friction and wear coefficients for soft polymers, such as thermoplastic polyurethane sliding against a steel counter surface under technically dry conditions.The polymer investigated is tested in a flat-on-flat configuration to ensure a constant contact pressure that is independent of wear occurring during the test over a wide range of applied normal pressures and sliding velocities. Experimental data on friction and wear rate can only be used for prediction of the efficiency and lifetime of materials on the component level if tribological test procedures are carefully chosen. It is essential to standardize all test conditions and parameters, and meticulously record all metadata, such as counter body topography. The values obtained from the experimental test have to be independent of the specimen geometry in order for them to be usable in the upscaling of the material performance in a numerical model. Upscaling refers to the process of simulating a component  embedded in a system, such as a pneumatic cylinder, based on input values obtained in a short-time, simple tribological test configuration, here referred to as model test; then, both CoF and wear rate are used as material specific parameters within an FEM simulation that characterizes the tribological interaction of the system for given surface topographies and environmental conditions .The investigations of local effects such as stick-slip and surface topography are not included, since the finite element simulation will determine the frictional and wear system behavior based on macroscopic frictional and wear models. The surface topography will be measured and saved as metadata , since tThe material investigated in this study is a thermoplastic polyurethane with the commercial name HPU premium supplied by Trygonal Group GmbH . This poTypically, thermoplastic polyurethane is modelled with hyperelastic material models ,23. For meter D1 .(1)W=G2aterials ,27 (ν<0)Tribological testing conditions are especially challenging for soft polymers operating in reciprocating motion as high material deflections result in non-uniform contact pressures with the nominal contact area; both of which change at each turning point. This chapter describes the development of a model test to ensure a testing method capable of generating frictional and wear data to directly formulate models usable in FEM simulations. A model test is characterized by its simple sample configuration and comparably short testing times. It has the aim to eliminate geometrical effects, e.g., from seal profiles, on the results and enable standardized testing procedures.While the stresses in a uniaxial tensile test are constant over the cross-sectional area of the specimen The output values of a tribological test usually are given by:the normal load the frictional force the relative velocity the loss of volume due to wear A tribological test setup needs to be found that provides the ability to derive directly the local CoF and the specific wear rate with the measured values given. This is especially critical for soft polymers as thermoplastic polyurethanes show a strong dependence on the measured CoF and the contact pressure. Direct determination of the CoF from the frictional forces is only possible for the test setups that ensure a nearly constant contact pressure over the contact area. Additionally, the stresses at the edges should be minimal to guarantee reliable test conditions. Only then is the CoF constant over most of the contact area and is determinable from the measured forces. Naturally, soft polymers are very prone to exhibit stick-slip effects during reciprocating sliding experiments. Due to the high deformability in the elastic regime, full cylinders usually show pronounced stick-slip even over strokes of several centimeters. Therefore, deriving a CoF for sliding as a basis for simulations, such as full-scale seal FEM models of the whole pneumatic cylinder system, is utterly impossible.The test setup should provide a high stiffness to minimize stick-slip effects that can lead to a difference in the relative velocity between contact body of the polymer sample and the counter body and the actual sliding velocity in the contact. Finally, the contact area should not change with wear. The volume loss due to wear is determined over the difference in mass of the sample before and after testing. Consequently, the specific wear rate is determined in post-processing based on the normal force given and the total sliding distance for a given contact pressure and a relative velocity. Inconsistent test conditions, e.g., a reduction of the contact pressure over time due to increasing contact area caused by wear renders the determination of the correct wear behavior very difficult. The wear coefficient includes all surface effects, such as plastic deformation as well as the removal of wear particles. Therefore, the plastic deformation is not included separately in the FEM simulation.To evaluate possible test setups, three configurations were numerically investigated in a pre-study see . All conAdditionally, the contact between the lateral surface of the polymer pin and steel holder in the pin-on-plate configuration see a was conx-axis) between the four test cases shows clear differences  leads to an additional improvement of the contact pressure distribution. A sample geometry with 8 mm in diameter and 1 mm in height was chosen to characterize the HPU polymer material. According to the relationship derived between geometry and contact area see a, this rThe CoF of the selected test conditions is determined by averaging the CoF measured from 20% to 80% displacement of the stroke, respectively, from −20 to 20 mm for a stroke of 50 mm. The static CoF is determined using the maximal values of the CoF of each forward and backwards movement and averaging these values over all cycles. All these values are determined within the steady-state condition of the experiment, where stable conditions have been reached .The frictional behavior of the polymer material is modelled with a combined empirical equation, which is able to model the velocity and contact pressure influences on the CoF. The velocity dependence of the CoF allquist  in EquatHallquist refers to The accumulated strain rate, which also considers the incremental energy dissipation, can be equalized with the coefficient The current study focuses on friction and dissipation effects acting when the polymer slides over the asperities. Naturally, the degree of indentation changes with the contact pressure tionship :(5)μpc =a et al.  found vaIn order to provide an equation describing a pressure and velocity dependance, the current study combined Equations (4) and (5). As the coefficient Note, here, that the CoF approaches infinity as the contact pressure approaches zero; the resulting frictional stresses still become zero with behavior .The empirical friction and wear models used here do not explicitly take roughness into account; therefore, roughness is included through the model parameter. These will change with different surface topographies for both the friction and the wear model. On a laboratory scale, the running-in of soft polymers is considered not to influence the tribological behavior in the steady state of a test significantly. Therefore, the running-in is not considered in the models applied.For polymers with no viscoelastic properties and contact surface effects, e.g., adhesion, the static CoF and the dynamic CoF equal each other. In Equation (6), the exponential relationship with the sliding velocity vanishes and the CoF is henceforth only dependent on the contact pressure.To verify the derived frictional model against experimental data, cycle 16,200 of a model test with a nominal pressure of 1 MPa and a sliding velocity of 100 mm/s was selected as representative for the experimental tests. This test was then replicated by a numerical FEM setup and the resulting nominal CoF of the simulation and the experiment were compared to provide some insight into the accuracy of the frictional model and the testing procedure. For a proper representation of the reciprocal movement of the counter surface, the data points measured by the linear encoder during the experimental test were used to choose a suitable approach. A multilinear fit as well as the first three terms of the Fourier series were used to replicate the displacement see a. Both aThe data input table for the multilinear fit can be found in The first three terms of the Fourier series for triangular wave function are given by:Comparing the results of the nominal CoF over time and displacement for the experimental test, the simulation shows good agreement with respect to the fluctuations of the signals of the experiments measured see . The sliThe agreement between the experimental data of the model test and the simulation proves that the testing conditions provide trusted data from which to directly derive a frictional model as a function of contact pressure and sliding velocity useful for finite element simulations to evaluate the tribological performance of a system.This new approach of characterizing soft polymers by gluing polymer discs with optimized diameter to thickness ratios on a much harder substrate shows a significant improved contact pressure distribution. This way, it is possible to use a flat-on-flat configuration to characterize soft polymers and use the generated data to directly derive tribological models useful for FEM simulations.The pre-study investigating of a full pin flat-on-flat configuration and a crossed-cylinder configuration shows that a flat-on-flat configuration can provide a constant contact pressure over a large area of the contact, which makes it possible to directly retrieve a CoF for a given nominal contact pressure. The full-cylinder configuration displays the maximum contact pressure at the leading edge, which is a multiple of the nominal contact pressure and could lead to severe wear during tests and instable test conditions see . The croWhile the geometrical dimensions of the sample can be altered for an improved test procedure, parameters such as the mechanical material properties are given by the selected compound materials and required pressure ranges are determined by the application. The tribological properties are inherently the result of all latter parameters as well as of the choice of the counter body surface and type of lubrication. As the current work is dedicated to dry contacts between soft polymers against much harder counter surfaces, no external lubrication is applied in the model tests nor simulated explicitly in the FEM model. These types of polymers are designed to operate in pneumatic actuators operating with air or other gases such as nitrogen. A parameter study investigating the various model-influencing parameters on the tribological test setup shows a highly non-uniform contact pressure distribution between the polymer test samples and the counter body for high CoFs and Poisson’s ratios above 0.4. Both properties are typically found in soft polymers, e.g., TPU, with a Poisson’s ratio ranging from 0.48 to 0.5 ,31, whicA linear relationship was found between the sample geometry parameters, height and diameter, and contact pressure distribution see a. A consA second linear relationship was found that describes the reduction of the average contact pressure due to the elastic deformation of the pin. For very high loads, the contact pressure based on the initial cross-sectional area of the pin should be considered to derive the correct relationship between pressure and the corresponding tribological properties.The presented workflow for testing soft polymers demonstrates new guidelines for a sample design for a flat-on-flat contact against much harder materials, designed to ensure a nearly constant contact pressure over the contacting surface that shows little influence of the material properties and wear throughout the experimental tribological test. Providing all necessary metadata of the test generates highly reproducible test data to ensure comparability between test results, while also providing data that can be used as input for numerical simulations.A common procedure of testing a normed standard material under the same test conditions is to express the tribological properties relative to this standard material in order to compare the material performance of a different polymer on a different experimental setup. With the better understanding of the influence parameters presented here in this work, this additional testing of the standard material will arguably be obsolete, leading to a much faster testing process. This procedure was, among other things, developed within the testbed project i-Tribomat  to follow the FAIR  principles  and geneA well-known challenge in tribology is that little changes in the test setup can alter the result of the testing procedure completely. In particular, reciprocal tests with soft polymers sliding against hard counter surfaces result in high distortions caused by high coefficients of friction and the experiment can descend into unstable conditions quickly. Typically, the choice for such tests falls to point or line contact realized with a ball-on-plate, cylinder-on-plate, or crossed-cylinder configuration. These are often appreciated for their comparably easy setup. However, the contact area and contact pressure of these tests are highly dependent on the mechanical properties of the polymers. The used sample geometries, the load, and the occurring wear, which increases the contact surface, lowers the contact pressure and usually results in a change of the CoF and wear rate.A polymer disc with a height to diameter ratio of less than 1/6 was demonstrated to be most eligible for robust test procedures and uniform contact pressures. This ensures that occurring wear does not change contact pressure;The Young’s modulus, which is typically low for soft polyurethane, has little influence on the contact pressure distribution of the newly developed disc-on-plate configuration as long as the ratio of the nominal contact pressure and Young´s modus is below 0.1;Poisson’s ratios of the polymer above 0.4 significantly reduce the uniformity of the contact pressure distribution and lead to a maximum in contact pressure in the center of the polymer pin;The CoF mainly influences the contact pressure at the leading edge; the higher the CoF, the higher the maximal contact pressure at the leading edge. This effect could be minimized successfully in the disc setup presented in this paper.The only way to perform a test that does not change its contact conditions is a flat-on-flat configuration. Numerical investigations of different setups such as the pin-on-plate and crossed-cylinder configuration showed significant differences in the contact pressure over the tribological contact area during sliding. The pin-on-plate configuration shows a highly asymmetric contact pressure distribution across the contact area with contact pressures significantly increased at the leading edge. The crossed-cylinder configuration leads to a nearly symmetric contact pressure distribution and much lower maximal contact pressure in the center of the polymer sample compared to, e.g., the pin-on-plate setup. For robust test conditions, the maximal contact pressure should not be significantly higher than the nominal contact pressure to prevent uneven wear in the contact area. A new developed setup with a thin polymer disc, glued onto a specifically designed aluminum adapter, shows a significant reduction of the contact pressure at the leading edge compared to the pin-on-disc test. A numerical parameter study was performed to investigate the influences of the polymer disc parameters on the contact pressure distribution. The study has shown that:The Poisson’s ratio and the CoF have a strong influence on the contact pressure distribution and their high values are assumed to have a critical influence on the reliability of the test conditions. Both values are typically high for polymers, which renders the tribological testing of polymers much more difficult. Based on the observations of the parameter study, a soft polyurethane was tested and characterized with a disc of 8 mm in diameter and 1 mm in height as sample geometry. From the generated data, a friction model was derived and implemented in an FEM model to replicate the experimental tests. Comparing the frictional output of one cycle of the experiments to the simulation showed good agreement, which verifies the accuracy of the model and the generation of reliable and robust data from the experimental testing procedure.


Considering all sessions, the experimental group showed significantly higher progress, lower average lap time, and more consistent records (Mann-Whitney U test at 95% confidence level) than the control group. This study demonstrated that SmartSwim can assist coaching by quantitatively assessing swimmers’ performance, leading to more efficient training.Swimming coaches provide regular timed and technical feedback to swimmers and guide them efficiently in training sessions. Due to the complexity of swimmers’ performance, which is not visible in qualitative observation, quantitative and objective performance evaluation can better assist the coach in this regard. Inertial measurement units (IMUs) are used in swimming for objective performance evaluation. In this study, we propose a new performance evaluation feedback (SmartSwim) using IMU and investigate its effects on the swimmer’s weekly progress. Measurements were conducted each week with 15 competitive swimmers for 10 weeks using a Sacrum IMU. The SmartSwim report included a comprehensive representation of performance based on goal metrics of each phase extracted from the IMU signals. The swimmers were divided into two groups: the experimental and control groups. The SmartSwim report for each swimmer in the experimental group was given to the coach, who used it to adjust the training accordingly. The results showed that the experimental group outperformed the control group when comparing each swimmer, each session and the whole sessions. At the level of each individual, more members of the experimental group showed significant downward trend of average lap time . While comparing the sessions, the experimental group showed significantly lower lap time than the control group from the sixth session onwards ( Swimming can be classified as a complex task because it cannot be mastered in a single session and has multiple degrees of freedom . LearninAs in any other sport, swimming coaches rely mainly on their observations and coaching experience to monitor and evaluate swimmers’ performance. However, such subjective and qualitative analysis is not accurate enough to provide precise information about a swimmer’s strengths and weaknesses . The comIn one of the oldest studies of feedback in swimming, Chollet et al. converted hydrodynamic pressure applied to the swimmer’s palm into auditory information. The swimmer was able to maintain stroke velocity, and improve motion stability and control through real-time sonification . Visual Many studies have extracted kinematic variables from IMUs and shown them to be a powerful tool for swimming analysis ,17,18, bUsing the signals of a single sacrum-worn IMU, we developed a new approach in a previous study to segment a swimming lap into push, glide, strokes preparation, and swim phases . Then, a® IV, GaitUp, Lausanne, Switzerland) was attached to the swimmer’s sacrum with a waterproof tape  and recorded 3D angular velocity (±2000°/s) and 3D accelerometer (±16 g) at a sampling rate of 500 Hz. To make the sensor data independent of sensor placement on the swimmer’s sacrum, a functional calibration with simple movements (standing upright and squats) was performed before starting the test out of the water [Fifteen swimmers  of a competitive team participated in this study. They had similar performance levels (50 m front crawl record: 28.60 ± 2.04 s) and were placed on the same team by the swimming club. The swimmers had similar training experiences and regularly trained together six days per week under the supervision and guidance of the same coach. A single IMU  so that we could analyze their performance within each phase . Lap timThe swimmers were divided into two groups, an experimental and a control group. Since performance is assumed to be related to lap time as a key metric, the lap times of the first test session were considered as the baseline and used to select the swimmers of the two groups. The two groups were selected to have similar performance levels (as measured by lap time), similar age range, similar physical characteristics (body mass and height), and similar gender. The characteristics of the two groups are shown in The SmartSwim solution proposed in this study consists of two parts. In the first part, we performed a phase segmentation of each lap using our previously validated algorithms , and thePush phase: push maximum velocity.Glide phase: glide end velocity.Strokes preparation phase: strokes preparation average velocity.Total Swim phase: swim phase average velocity.Swim phase strokes: average velocity per stroke of the swim phase.Whole lap: lap average velocity.Following our previous study evaluating swimming performance with a sacrum-worn IMU, each lap was segmented into the push, glide, strokes preparation, and swim phases . The folThe errors attributed to lap segmentation and goal metrics estimation are explained in the corresponding papers. Although this analysis was performed for both the experimental and control groups, only the reports of the experimental group members were given to the coach.For the experimental group, three types of feedback were given to the coach: (i) individual performance per session, (ii) individual multi-session performance, and (iii) comparison of swimmers per session. The reporting format was visually tailored to the coach’s needs to facilitate understanding and make it more efficient.An example of the individual feedback provided after each session is shown in The individual multi-session result is the second type of feedback, including the swimmer’s average performance graphs during all previous sessions , left. TThe third type of feedback per session is to compare the swimmers by plotting the average performance of each swimmer on the same radar chart . The coaWe shared the report of the experimental group’s performance with the coach. He considered the reports for each swimmer and adjusted the training sessions accordingly. We asked the coach to explain his observations and findings from the feedback and then write down the training changes planned for the next week for each swimmer. The charts for single-session and multi-session feedback were also explained and shared with the swimmers so they could self-monitor during the training sessions.Because lap time is considered as the relevant measure of swimming performance, we evaluated the lap times of both groups for performance comparison. The two groups were compared at three levels: (i) per person, (ii) per session, and (iii) all sessions.S value, which is the number of positive minus the number of negative differences when comparing all observations (Equation (1)).ith and jth sessions, respectively, and S to find out whether the trend is significant or not. The trend significance for the swimmers with absences was analyzed for the existing number of records. A significance level of 95% is used for this analysis.At the person-level, the trend of average lap times during the ten sessions for each swimmer was analyzed using the nonparametric Mann-Kendall trend test ,27. The p-value <0.05 as significant. The second analysis at this level is the comparison of the standard deviation of lap times in each session. For this analysis, the standard deviation of the five lap times for each swimmer was calculated, averaged across the group, and then compared to the other group. Because the sample size for this analysis is small (eight versus seven swimmers), the Mann-Whitney U test, a non-parametric method [In the level of per session, we compared the lap times of the two groups in each session to determine if the experimental group significantly outperformed the control group. For this comparison, all lap time values for both groups in each session  were compared. Since there is enough data for parametric test, first, the normality of the data distribution was checked using the Kolmogorov–Smirnov normality test  and thenc method ,31 with Finally, to compare the groups across all test sessions, the mean and standard deviation of the five lap times were estimated for each swimmer and then averaged across all swimmers in each group. The Mann-Whitney U test with a 95% confidence level was used to compare between groups across the ten sessions. Comparison of the means allows us to understand whether the experimental group was faster than the control group, and comparison of the means of the standard deviations evaluates the regularity of the swimmers between laps as a factor of efficient swimming. To test whether or not the overall progress of the swimmers with feedback was higher, we compared the average progress (average change in lap time compared to the first session) of the swimmers across all sessions using the Mann-Whitney U test.Four swimmers in the control group missed seven sessions due to swimmer unavailability, while swimmers in the experimental group participated in all test sessions. The average lap time of all swimmers during the ten sessions is shown in p-value < 0.001 for both groups). For a qualitative comparison, the graphs for goal metrics and lap times of the first and tenth sessions are shown in Comparing the first and the last session of all swimmers, the lap times of the last session are significantly lower than those of the first session, based on t-test results  was observed for swimmers in the experimental group.The results of the applied Mann–Kendall trend test for the person-level comparison  show a dAt the session level comparison, after confirming the normality of the data by Kolmogorov–Smirnov test, an independent t-test was performed for comparing the groups. The two groups showed a significant difference from the sixth session onward . The stastatU = 18, n1 = n2 = 10, p-value < 0.05, two-tailed). The standard deviation of the experimental group is also significantly lower than that of the control group . By taking the first sessions as the baseline, we quantified the progress of each swimmer as the difference between the lap time of each session and the baseline. According to the result of the Mann–Whitney test, the average progress of the members of the experimental group was significantly higher than that of the control group . Considering all swimmers in each group, the swimmers in the experimental group and the control group achieved an average progress of 0.65 s (4.4%) and 0.35 s (2.3%), respectively.Finally, the average lap times of the groups in all 10 test sessions are compared. Although both groups showed a significant decreasing trend in lap time  when they received advice based on performance-related goal metrics.Based on the evaluations of the coaches’ reports, he quickly understood how to use SmartSwim feedback. Unlike traditional methods, he could easily identify and focus on the phase with a higher chance of progress, while it takes more time to determine a swimmer’s potential through subjective observation. He also observed the interaction between swimming phases and examined how well the swimmer managed to improve all phases together. Finding the optimal training for each swimmer requires trial and error , which iIn this study, the SmartSwim feedback report functioned as an assistant to the coach. The involvement of the coach is essential because the final decision to optimize training depends on the coach’s judgment and the swimmer’s profile. The coach usually relies on his or her personal experience, based on which he or she can usually make a qualitative assessment of the training sessions . Our resStarting with the person-level comparison, the results of Mann–Kendall trend test with 95% confidence level showed that the decreasing trend in lap time during the ten training sessions is significant for seven out of eight swimmers in the experimental group, while only two swimmers in the control group showed such a significant trend. According to the logic of the Mann–Kendall trend test, a significant trend exists when the lap time decreases continuously from week to week. This confirms that although the swimmers in both groups achieved a lap time at the end of the ten weeks that was significantly lower than the baseline time, the swimmers in the experimental group made continuous progress during the measurements, which is a crucial factor in efficient training . In addip-value > 0.05). During the training sessions, the coach trained the experimental group based on feedback, while for the control group he relied only on his own coaching experience. Consequently, the experimental group’s progress in the remaining sessions is influenced by feedback-based training. The results show that the average lap time and standard deviation of the experimental group are significantly lower than those of the control group from the sixth session . This shows that the swimmers in the experimental group performed not only faster, but also more consistent and systematic [For the session-level assessment, the two groups had a similar lap time average and standard deviation in the first session . This suggests that the effect of feedback can be observed not only for each swimmer and session, but also in the overall picture of long-term training. Considering the importance of seasonal evaluations of swimmers [Finally, swimmers in the experimental group show lower lap time and standard deviation and higher progress when all test sessions are considered together (Mann-Whitney U test, swimmers , this leswimmers .We tried to keep all effective factors the same for the experimental and control groups, but we cannot claim that the superiority of the experimental group over the control group is solely due to feedback. The effects of factors such as psychology , nutritiThe main disadvantage of wearables is the increased water drag on the swimmer’s body . The useIn this study, we examined the effects of coaching with SmartSwim, a new phase-based performance evaluation feedback, on swimmers’ performance during 10 weeks of training. The coach used a comprehensive report of phase-based goal metrics from IMU as an assistant for eight swimmers in the experimental group and adjusted their training accordingly, while he guided seven swimmers in the control group based only on his observations and coaching experience. The results showed that the experimental group outperformed the control group when considering the performance of each swimmer, the performance of the group in sessions, and the group performance in all training sessions. Most of the swimmers in the experimental group showed a significant downward trend in their average lap times in 10 test sessions. The experimental group significantly outperformed the control group in terms of lap times from the sixth session onward. In addition, the swimmers in the experimental group showed more consistent results than those in the control group. Finally, considering all 10 sessions, the swimmers in the experimental group showed significantly higher progress, lower average lap times, and more consistent records than the control group. The coach found the feedback reports very helpful in “diagnosing” the swimmers’ weaknesses and monitoring their progress more efficiently during the training sessions. This study has helped meet the needs of the coaching community and promote objective coaching in swimming.


The cornerstone of nanomaterial-based sensing systems is the synthesis of nanoparticles with appropriate surface functionalization that ensures their stability and determines their reactivity with organic or inorganic analytes. To accomplish these requirements, various compounds are used as additives or growth factors to regulate the properties of the synthesized nanoparticles and their reactivity with the target analytes. A different rationale is to use the target analytes as additives or growth agents to control the formation and properties of nanoparticles. The main difference is that the analyte recognition event occurs before or during the formation of nanoparticles and it is based on the reactivity of the analytes with the precursor materials of the nanoparticles . The transition from the ionic (or molecular) state of the precursor materials to ordered nanostructured assemblies is used for sensing and signal transduction for the qualitative detection and the quantitative determination of the target analytes, respectively. This review focuses on assays that are based on analyte-mediated regulation of nanoparticles’ formation and differentiate them from standard nanoparticle-based assays which rely on pre-synthesized nanoparticles. Firstly, the principles of analyte-mediated nanomaterial sensors are described and then they are discussed with emphasis on the sensing strategies, the signal transduction mechanisms, and their applications. Finally, the main advantages, as well as the limitations of this approach, are discussed and compared with assays that rely on pre-synthesized nanoparticles in order to highlight the major advances accomplished with this type of nano-sensors and elucidate challenges and opportunities for further evolving new nano-sensing strategies. Nanomaterials have unique attributes, rendering them excellent scaffolds for chemical and biological sensing. Their most salient properties are their high surface-to-volume ratio, the tunable morphology , and the versatility of their composition  that imbues nanoparticles with different reactivity and solvation properties in aqueous and organic solvents. An external stimuli event, which can modify these properties, induces changes in their physicochemical, thermal, and optoelectronic properties such as the plasmon resonance absorption, conductivity, redox behavior, thermal capacity, and catalytic activity, which are associated both qualitatively and quantitatively to the external stimulus.In Analytical Chemistry, such external stimulus is related to the direct or indirect interaction of nanomaterials with a target analyte that may be an organic molecule, an inorganic ion, or a macromolecule . The interaction may occur either directly on the nanoparticle surface or with appropriate receptor molecules anchored on the nanomaterial surface. Following this interaction, nanomaterials undergo various physiochemical transitions that modify their properties such as a change in their aggregation state, the transformation of their morphology, or a variation in their composition . These changes and their intensity are used to detect the presence of (macro)molecules or ions and determine their concentration in a sample. Based on this strategy, enormous research effort has been devoted to the development of new analytical probes, sensors, and biosensors for the determination of a vast variety of analytes of environmental, biological, food, and industrial interest  –3. The cA different and less represented approach is to exploit the interaction of the analytes with the precursor constituents of the nanomaterials before their assembly into organized nanostructures or during their growth into larger structures . In thisIn this review, we emphasize on nanomaterial-based analytical methods that rely on analyte-modulated regulation and control of nanoparticles. We provide an outline of research advances focusing on the sensing mechanisms and the concurrent detection strategies and evaluate the main characteristics and analytical merits of these methods. Not least, we try to elucidate the advantages and disadvantages of these methods and highlight fields for future research.The principles of sensing methods that rely on analyte-mediated control of nanomaterials are similar to those governing (a) the synthesis of nanomaterials using various additives as regulators or (b) the growth of nanomaterials using nanoparticle seeds as nucleation and autocatalytic centers.111] facet without affecting their size . L-. L-21].  Au ions . A novel2+ to activate the ALP and increase its stability against autolysis, the Si CNPs were produced providing a fluorimetric and colorimetric signal which increased linearly with PSA concentration from 0.02 to 20 ng mL−1 and exhibited a detection limit of 0.0096 ng mL−1 [Another classic enzymatic reaction is the alkaline phosphatase (ALP)-catalyzed hydrolysis of 4-aminophenol phosphate (APP) to the reducing agent p-aminophenol which was used to trigger the in situ formation of silicon nanoparticles (Si CNPs) from N-[3-(trimethoxysilyl)propyl]ethylenediamine (DAMO), as a silicon precursor . The Si  ng mL−1 .2S which generates fluorescent CdS in the presence of Cd2+ ions. The AHCY assay uses the same sensing strategy, but it is precedent by the enzymatic hydrolysis of S-adenosyl-L-homocysteine (AdoHcy) to HCy which is then decomposed by MGL  were necessary in order to obtain the fluorescence signal, which necessitates careful handling due to its high toxicity.Pavlov and co-workers used an unconventional route for the in situ synthesis of fluorescent CdS quantum dots (QDs) in order to develop assays for specialized analytes such as methionine γ-lyase (MGL) and S-adenosyl-L-homocysteine hydrolase (AHCY) . The priMGL Fig. B. Based ivatives . An advan-electron reductant hydroxyl groups of antioxidants, which means that it was proportional to the total number of —OH groups . I. I58]. I+ ions on AuNP seeds with kojic acid as a reducing agent. In the presence of TYRase, kojic acid could complex with the di-copper active site of TYRase; hence, the concentration of free kojic acid in the solution decreased. As a result, the in situ metallization of Ag+ on AuNPs was inhibited, and the color of the solution gradually shifted from yellow (in the absence of TYRase) to pink (in the presence of TYRase). The detection limit of the method was as low as 0.019 U mL−1, but common biomolecules and inorganic electrolytes were found to interfere with the quantification, dictating appropriate sample pre-treatment steps  before analysis.Similarly, the determination of tyrosinase (TYRase) concentration in blood serum samples by monitoring the formation of core–shell Au@Ag bimetallic nanoparticles from AuNP seeds was reported . In the 4− as a growth factor and NH2OH as a reducing agent of Au ions . A. A2+ andn of CdS , 70. The4− ions to AuNPs, yielding a red to purple coloration [34− ions. Both of these methods achieved the determination of melamine at the low nanomolar concentration levels.Except for enzymatic methods, analyte-mediated, nanoparticle inhibition assays were developed for the determination of melamine by exploiting its ability to form hydrogen bonds with the carboxyl and -OH groups of reducing compounds. The sensing mechanism was based on the use of ellagic acid or 3,5-dihydroxybenzoic acid as reducing agents of AuClloration , 72. Whe4 ions to AuNPs in the presence of a mild reducing agent (such as the amino acid threonine). When biothiols were present in the sample, the formation of AuNPs was obstructed. In this manner, the red color of the AuNP solution gradually faded, becoming colorless at high biothiol concentrations [The inhibitory effect of analytes on the photochemically assisted reduction of noble metals is another process that was utilized for the development of nanoparticle inhibition assays. In contrast to silver-analyte complexes, which are favorably reduced to AgNPs under the influence of UV light, as discussed above –42, the trations . ConsequBeyond the inhibition in the photochemically assisted formation of AuNPs, Kostara et al.  observed3 and NaCl and grow in size with increasing chloride concentrations due to Ostwald’s and coalescence ripening [+ and inhibited the formation and growth of AgCl crystals. Consequently, the photosensitivity of the AgCl suspension was reduced due to the lower amount of AgCl in the suspension, and the color of the UV-irradiated AgCl suspension became brighter with increasing biothiol concentration. The difference in the incident light absorbed by the AgCl suspension in the presence and absence of thiols was related to the concentration of biothiols in the sample solution [The inhibitory effect of biothiols in the formation of AgCl (nano)crystals and the concomitant influence on the photosensitivity of the suspension is another example of a non-enzymatic method where the analyte could inhibit the formation and growth of nanomaterials. In the absence of biothiols, AgCl crystals could rapidly form upon mixing AgNOripening . The AgCsolution . This apThe interaction of analytes with the precursor materials or nanoparticle seeds may not only affect the formation and growth of nanoparticles but also their properties and morphology. In these cases, the analytical signal is related both to the change in the properties of the nanoparticles and their formation or growth.The morphological transitions induced by thiolated molecules in AuNRs were used for the development of nerve gas biosensors. Thiocholine, produced from the hydrolysis of acetylthiocholine by AChE, was used to modulate AuNR growth towards the formation of various Au nanostructures that varied from nanorods to cubes and finally to spheres, with increasing thiocholine concentration . AChE in44− and a mild reducing agent). At low concentrations, biothiols inhibited the growth of AuNPs by decreasing the catalytic activity of AuNP seeds, while at higher concentrations, they could cause the aggregation of AuNPs through the formation of COO-NH bonds 2 and Na2S. To this system, p-nitrophenyl phosphate was added as a substrate which was hydrolyzed by ALP to produce inorganic phosphate that stabilized CdS QDs and enhanced their fluorescence [−1. Other researchers used adenosine triphosphate (ATP) to stabilize CdS QDs, through interaction with its phosphate groups and enhance their fluorescence. ALP, which could hydrolyze ATP molecules to produce adenosine and phosphoric acid through enzymatic dephosphorylation, induced the precipitation of CdS QDs because neither of the products of the enzymatic hydrolysis reaction could act as a stabilizer [−1 with a LOD of 0.17 nU mL−1 which is lower than many previous methods.Pavlov and co-workers discovered that phosphate can stabilize CdS QDs and trigger their fluorescence, while no fluorescence was observed in the absence of phosphate ions. . This firescence . The senabilizer . In this2+ and S2− were added to the solution, the produced GSH bound to the surface of the growing CdS crystals through the mercapto group, stabilizing CdS crystals and leading to the generation of fluorescence. By keeping constant the concentrations of either component, namely NADH, GSSG, or GR, either species could be determined interchangeably with very high sensitivity  that surpassed many previous colorimetric and fluorescence assays.Aminothiols could also act as stabilizers of CdS QDs during their formation and growth and enhance their fluorescence . In thatTo elucidate the advantages and disadvantages of analyte-mediated nanoparticle assays, compared to nanoparticle assays that rely on pre-synthesized nanomaterials, a non-exhaustive comparison of the analytical merits of each approach was performed. Table From an analytical standpoint, analyte-mediated assays usually offer improved sensitivity as compared to those relying on pre-formed nanoparticles Table . An explOne of the most salient features of analyte-mediated nanoparticle assays is that they rely on the affinity of the target analytes for the precursor constituents of the nanomaterials. This feature offers two significant benefits; the first is that it minimizes non-specific interactions with non-target analytes affording improved selectivity, compared to assays that use pre-synthesized nanoparticles, which are more reactive against matrix components due to the high surface area and the chemical surface functionalization that is usually necessary to ensure their stability. Another benefit is the exploitation of reaction and sensing mechanisms which are not easily feasible with pre-formed nanomaterials. Multi-analyte assays, based on a single reaction pathway , 73, 79,Although many advantages stem from the direct interaction of the analytes with the precursor materials, such interactions are not feasible or straightforward for all analytes. This restriction limits the applications of analyte-mediated nanoparticle assays as compared to sensing strategies that use pre-formed nanomaterials and can be tailored to the needs of the analysis . Another disadvantage of analyte-mediated nanoparticle assays is that they usually involve several steps to accomplish a specific sequence of reactions that lead to signal emission. Each reaction needs a different time to be completed which does not facilitate the unattended operation of the assays. For the same reason, the analysis time is often longer than methods that use pre-synthesized nanoparticles Table . TherefoThe analyte-mediated formation and enlargement of nanoparticles is a versatile platform for chemical and biochemical sensing. The sensing motif for analyte recognition is based on the particular chemistry and affinity of the precursor materials of the nanoparticles, while signal transduction is performed by exploiting the unique properties of the nanoparticles that are formed in situ during analysis. Although the pre-requisite for analyte interaction with one of the precursor materials brings some limitations , a large plethora of assays have been developed for the determination of organic and inorganic compounds and macromolecules of biological, environmental, and food interest. Automation of experimental procedures and miniaturization to reduce reagent consumption in this approach are currently underdeveloped, but they could contribute to further avail its opportunities in portable analytical systems. Such systems are at the forefront of analytical research, and analyte-mediated nanoparticle assays offer a unique sensing medium with high sensitivity and exceptional selectivity. The immobilization of reagents onto surfaces and solid supports could assist both in automation and miniaturization and open new fields for research and development. Not least, the resolution of the misconception that this sensing strategy is similar to standard nanoparticle-based assays can instigate discoveries in chemical and biological sensing.


It can also be used by molecular simulation package developers to run representative test systems during development, increasing code correctness. The theoretical foundation of the physical validation tests were established by physical_validation package was first mentioned.Molecular simulations such as molecular dynamics (MD) and Monte Carlo (MC) simulations are powerful tools allowing the prediction of experimental observables in the study of systems such as proteins, membranes, and polymeric materials. The quality of predictions based on molecular simulations depend on the validity of the underlying physical assumptions.  For most of the history of molecular simulation-based research in chemistry, biophysics, physics and engineering, most users of molecular simulation packages were experts that contributed to the code bases themselves or were very familiar with the methodology used. Increased popularity of molecular simulation methods has led to a significantly increased user base and to an explosion of available methods. The simulation packages are faster and more powerful than ever, and even more than before require expertise to avoid using combinations of methods and parameters that could violate physical assumptions or affect reproducibility. Unphysical artifacts have frequently been reported to significantly affect physical observables such as the folding of proteins or DNA, the properties of lipid bilayers, the dynamics of peptides and polymers, or properties of simple liquids (see physical_validation tackles the problem of robustness in molecular simulations at two levels. The first level is the end-user level. physical_validation allows users to test their simulation results for a number of deviations from physical assumptions such as the distribution of the kinetic energy, the equipartition of kinetic energy throughout the system, the sampling of the correct ensemble in the configurational quantities, and the precision of the integrator. The combination of these tests allow to cover a wide range of potential unphysical simulation conditions (physical_validation also returns its conclusions in machine-readable form, it can be included in pipelines allowing results to be tested for physical validity without user interaction. The second level of usage is by code developers. Unphysical behavior might not only result from poor or incompatible parameters and models, it might also stem from coding errors in the simulation programs. physical_validation can therefore be used to regularly run representative simulations as end-to-end tests in a continuous integration setup, ensuring that code changes do not introduce bugs that lead to unphysical results. GROMACS (physical_validation since 2019 to test every release version with a comprehensive set of simulations covering all major code paths.nditions . This in GROMACS , one of physical_validation package. github.com/shirtsgroup/checkensemble. This script was used as a base for the ensemble validation tests in physical_validation. physical_validation and its use in the validation of GROMACS releases.μVT ensembles, covering the full set of ensembles described by In the three years since the publication, the software has matured into a stable release. The ensemble tests now also support