Bluetooth Low-Energy (BLE) beacons-based indoor positioning is a promising method for indoor positioning, especially in applications of position-based services (PbS). It has low deployment cost and it is suitable for a wide range of mobile devices. Existing BLE beacon-based positioning methods can be categorized as range-based methods and fingerprinting-based methods. For range-based methods, the positions of the beacons should be known before positioning. For fingerprinting-based methods, a pre-requisite is the reference fingerprinting map (RFM). Many existing methods focus on how to perform the positioning assuming the beacon positions or RFM are known. However, in practical applications, determining the beacon positions or RFM in the indoor environment is normally a difficult task. This paper proposed an efficient and graph optimization-based way for estimating the beacon positions and the RFM, which combines the range-based method and the fingerprinting-based method. The method exists without need for any dedicated surveying instruments. A user equipped with a BLE-enabled mobile device walks in the region collecting inertial readings and BLE received signal strength indication (RSSI) readings. The inertial measurements are processed through the pedestrian dead reckoning (PDR) method to generate the constraints at adjacent poses. In addition, the BLE fingerprints are adopted to generate constraints between poses (with similar fingerprints) and the RSSIs are adopted to generate distance constraints between the poses and the beacon positions (according to a pre-defined path-loss model). The constraints are then adopted to form a cost function with a least square structure. By minimizing the cost function, the optimal user poses at different times and the beacon positions are estimated. In addition, the RFM can be generated through the pose estimations. Experiments are carried out, which validates that the proposed method for estimating the pre-requisites (including beacon positions and the RFM). These estimated pre-requisites are of sufficient quality for both range-based and fingerprinting-based positioning. The positioning process is independent of extra hardware. After the BLE beacons have been installed, the only equipment needed is a BLE-enabled smart phone, while foot-mounted or waist-mounted inertial pedestrian positioning ,8 needs It is suitable for positioning adopting a wide range of mobile devices, including both Android devices and IOS devices. While Wi-Fi-based positioning is only suitable for Android devices, because the API of IOS does not provide the Wi-Fi scanning results.The deployment cost for BLE beacons is low. Once the beacons are deployed, it can continuously work for a long duration  using their inner batteries, because the beacon nodes have low power consumption.Location has become increasingly important for many position-based services (PbS). However, although the global navigation satellite system (GNSS) can satisfy the needs for most outdoor situations, indoor positioning still remains a challenge. The abundant sensors embedded in today’s mobile devices have greatly enhanced their ability to sense the indoor environment, thus providing possibilities for indoor positioning. Many indoor positioning methods rely on pre-installed infrastructures and can provide reasonable accuracy, such as UWB ranging anchor-based ,2, Wi-FiThe least square estimation (LSE) method . By miniThe three-border method. By establishing the equations which represent the distance between the user and the beacons, the user’s position can be found by solving the equations.The centroid method. A polygon is firstly defined according to the vertexes defined by the intersecting points from the distance arcs. Then the centroid is regarded as the user’s position.The BLE beacon-based positioning methods consist of mainly two types: range-based and fingerprinting-based. Range-based methods adopt a pre-defined radio frequency (RF) path-loss model to estimate the distance between the receivers (users) and the beacons. Assuming at least three received signal strength indication (RSSI) measurements are available, the user’s position can be solved by trilateration according to the distances estimated accordingly. The authors in  proposedIn , a more The fingerprinting-based method consists of two phases: the offline phase and the online phase. In the offline phase, the fingerprints (a vector of RSSIs from different beacon nodes collected at a coordinate point) at different places are collected to create a reference fingerprint map (RFM). In the online phase, a fingerprint collected at an unknown place is compared with the fingerprints in the RFM, to solve for the position of the user. The online phase is rather simple and a commonly seen method for the phase is the k-nearest neighbor (kNN) method. However, as fingerprints needs to be collected at different places at the offline phase, the workload is huge. Moreover, the positions where the fingerprints are collected needs to be known, which is challenging. Normally, the positions need to be measured through dedicated surveying methods. For example, in , the finAs mentioned, the range-based BLE beacon positioning methods need to know the positions of the beacons to start with. Similarly, a pre-requisite for fingerprinting-based methods is the RFM. Existing methods mainly focus on the positioning methods and assumes the beacon positions and the RFM is known before positioning. However, in practical applications, determining the beacon positions or fingerprinting collection positions, especially for the indoor environment with a relatively large area, is an energy intensive and error prone task.Unlike the mentioned methods, the proposed method in the paper provides an efficient way for estimating the pre-requisites for BLE-based positioning. The proposed method can estimate both the beacon positions and the RFM using graph-based optimization. The priority advantage of the proposed method is that it is free of any dedicated surveying process for the beacon positions and RFM. A user equipped with a BLE-enabled mobile device walks in the region. As shown in A few related works are given in The RSSI can change very dramatically with even a very small spatial change. As each of the broadcasting channels are narrow banded, the BLE signals have fast fading nature. Also, the indoor environment is very complex with many surfaces , where the BLE signals can reflect. This can further cause multipath fades and add instability for the RSSI. In an experiment performed in , the noiThe RSSI can be reported for multiple times or not reported at all during a single scan. As the advertisement is repeated on three channels and if a scan is longer than a broadcasting interval, multiple reporting on the same beacon can be seen. Also, if the RSSI from all the three channels is below the environment noise due to fading effect, no reporting of the beacon is available for the scan. This can cause inconsistency for adjacent scans in time.BLE beacons operate in the same 2.4 GHz band as the Wi-Fi transceivers. Normally, in positioning, the beacons are in advertising mode, broadcasting short and flexible messages at flexible update rates ,13. ApplThe mentioned features can cause huge noises in both RSSI (first feature) and beacon availability (second feature). To minimize the unfavorable effects, some smoothing processes should be added for the RSSI. In this paper, we adopt the median value of nearby RSSI (within a time window size of 1 s) as the current RSSI. This has been validated by an experiment in , which fL is the stride length estimation and The PDR algorithm can update the pedestrian’s positions adopting the inertial measurements. Normally, the position update rate relies on the step rate of the pedestrian, i.e., the positions are updated each time a new step is taken by the pedestrian (in Equation ).  The details of the PDR algorithm differ with the body part where the inertial measurement unit (IMU) is mounted. If the IMU is mounted on the foot, the zero-velocity update (ZVU) method is adopted. In this method, a filtering process (assuming zero velocity) is added to the normal inertial calculation when the foot of the pedestrian touches the ground (zero-velocity phase) ,17. If tIn the front-end tracking procedure, sensors on the robots are adopted to find constraints between the poses at different times. For example, features in adjacent image frames are adopted to find position and attitude constraints between poses  . These cThe back-end optimization procedure is generally establishing a cost function and then minimize it. The previously mentioned odometry observations and all other available observations are adopted to generate constraints between pose variables. Each constraint denotes a term in the cost function, which represents the errors between the sensor observation and the observation derived from pose variables. The cost function has a least square form which is in essence representing how the observations match the estimation. By finding the best match (minimizing the cost function), the optimal poses (trajectory) and maps can be found. The establishment of constraints and optimization process in our case is explained in detail in the next section.Graph-based optimization is currently widely adopted in robotics for solving the simultaneously localization and mapping (SLAM) problem . It aimsAs shown in The problem of user poses and beacon position estimation in our method is transformed to a least square optimization problem. In this section, a general least square optimization framework for user pose estimation is given at first. Then the framework is elaborated by adding least square terms according to the mentioned constraints to the cost function. After optimization using the Levenberg-Marquardt algorithm , the optThe model for general pose estimation is formed at first. Assuming the noises are Gaussian, the maximum likelihood estimation of the poses is equivalent to optimize the formed cost function. Some fundamentals for the optimization process are given here.In a general pedestrian positioning problem, the poses of the pedestrian and the observations satisfy:t is denoted by t is denoted by Here, the pose at time Equation ). NotingIn the formation, as the noises are assumed to be Gaussian, the maximum likelihood estimation of the poses is equivalent to finding the optimal poses which minimize the cost function with a least square form in Equation . The detThe error Equation , we can A factor graph can be adopted to better represent the cost function and relationships of the variables in it. In Equation . Here arEquation . ot denoEquation . (.) cank, if we assume there are n variables in the factor graph, then it can be written as Equation ;The general square optimization (g2o) framework is proposed in  and is aExpand the ration):ek(x˘k+Δ)Equation  and we cEquation ) with a Calculate the derivation of Equation  over the can getHΔ=-bHer can getHΔ=-bHer can getHΔ=-bHerribed in . Here weContinue step 1 and step 2 until the increment norm Then we adopt the Levenberg-Marquardt algorithm for finding the variable values which minimize the cost function iteratively. Assuming that in the previous iteration, the variable values can be written as The dimension of Equation .The graph optimization method for a general least square cost function is described in the previous subsection. Here we focus on forming the cost function for BLE beacon implementation using the constraints from PDR, beacon-based range constraints and fingerprint-based constraints.The PDR constraints. Similar to The beacon position constraints. New variables representing beacon positions are added to the factor graph in The fingerprints matching constraints. These types of constraints are between two pose variables whose collected fingerprints are with vicinity. For example, Again, a factor graph is adopted to represent the cost function in Based on the factor graph, we elaborate the square error terms of the cost function based on the different types of constraints.As mentioned before, the pose variable te it asepdr exist in the cost function, the optimal poses should be the initial poses. In such a situation, the cost function is zero.As mentioned before, the RSSI readings from BLE beacons have fast fading nature. Therefore, in our implementation, a pre-processing procedure is added to smooth the RSSI. Here the median value of the RSSI from a beacon within a time window (with duration 1 s) is taken as the current RSSI.A is the absolute RSSI value represented by dBm at 1 m away from the beacon; n is a parameter related to the signal propagation environment and d is the distance from the beacon. In our implementation, we use the method proposed in [To estimate the distance from a beacon, a path-loss model is needed. Here we adopt the path-loss model in ,(18)RSSposed in  to estimThe path-loss model with pre-defined parameter in our implementation isAn issue worth noting is that the model will be inaccurate when readings are higher than 0 dBm. However, we have checked our measurements and we have found out that none of RSSI readings are higher than 0 dBm. In our implementation, the BLE beacons adopted have the largest emitting power of 4 dBm and it is set to 4 dBm here. The reason maybe that, in the experiments, the beacons are installed on the walls and the person does not go too near to the walls.Here we only use the RSSI if it is larger than −100 dBm. A simple experiment is performed to explain the reasons. In an indoor corridor, the user holding the receiver starts from a place far away from the beacon, walks near the beacon, and then leaves. The RSSI readings are shown in Assuming a RSSI reading for the Equation . Then thAnother importance thing worth noting is that the initial values for the beacon position variables is needed to start the optimization. Here we consider no heuristic information about the beacons are available including the number of beacons and their positions. To start the optimization with initial guess of the beacon positions, we assume the initial beacon positions are at the poses from the PDR results when the beacons’ RSSI are available for the first time.For fingerprinting-based positioning using BLE beacon, an RFM should be established in the offline phase. The RFM consists of many fingerprints collected at different known positions. In our implementation, the user walks in the area collecting fingerprints. The optimal poses can provide the position estimations in the RFM.N is the number of the beacons. Two RSSI vectors may have readings from different beacons. In this case, we add default readings to the RSSI vector such that both vectors have readings from the same beacon set. Here the beacon set denotes the union set of the two original beacon sets. An example is shown in In fingerprinting-based BLE positioning method, the distances between fingerprints can indicate the correlations for the positions. If two fingerprints are similar , then the positions where the fingerprints are collected should be also with small distance (high correlation). We also use the mentioned feature for generating constraints through fingerprint matching. For such purposes, the distance metric is defined firstly according to ,(22)d are stored on the phone for offline processing. The BLE beacons adopted in our implementation are the Smart Beacons from the Bright Beacon company . In our As shown in As mentioned before, there are two types of beacon-based positioning method: range-based and fingerprinting-based. For range-based method, the positions of the beacons need to be known for positioning. For fingerprinting-based method, an RFM should be known for positioning. The proposed method in this paper gives an efficient way to estimate the beacon positions and to establish an RFM. To verify that the quality of the estimation for the beacon positions and the RFM is sufficient to support beacon-based positioning, another experiment is carried out. The person walked into the site again collecting BLE scanning RSSI readings. These RSSI readings are regarded as test data for showing the accuracy of beacon-based positioning. Here as the beacon-based positioning normally has many outliers, we adopt a simple extended Kalman filter (EKF)-based positioning method to fuse the beacon positioning results and the position update data from PDR. Noting that all the results including the results presented in For measuring the ground truth of the persons, we have put some stickers to the ground and labeled the stickers with numbers on it to distinguish them. The stickers can be regarded as landmarks and the positions of landmarks are also measured through a total station prior to the positioning experiments. During the positioning experiments, the person can record the time and the landmark label during walking. This is done through pressing buttons on the APP designed for collecting data. In this way, the ground truth positions of the person when he/she walks to the landmarks are known. The positioning errors can then be determined through the differences between the estimated positions and the ground truth positions. In fact, we have set 50 landmarks roughly even distributed in the area. Since the positions of the landmarks should not affect the final results much, we did not show the positions of the landmarks.Here a classical type of range-based positioning is selected to test the positioning performance: the LSE method. By minimizing the square sums of the distance errors to the beacon positions, an optimal position can be found. Noting that here, the beacon positions should be known before positioning. Our method can provide an RFM adopting the estimated positions of the person and the collected BLE RSSI readings. The RFM can be adopted for fingerprinting-based positioning. The positions of the collected fingerprints are firstly estimated adopting the proposed method. Then we partition the area to small grids with the size of 0.5 m × 0.5 m. The RSSI fingerprints at the centers of the grids are predicted through a simple Gaussian regression process adopting nearby fingerprints. These predicted fingerprints are then adopted to form the RFM in our implementation. Here the weighted kNN method is chosen for beacon-based positioning (in our case k = 3). The CDFs of positioning errors is shown in Fingerprinting-based positioning normally outperforms range-based positioning. This is also true in our experiments. The fingerprinting-based results and range-based results should be compared under the same condition. In fact, the fingerprinting-based results using the estimated Reference Fingerprint Map (RFM) should be compared to the range-based results using estimated beacon positions. We directly take the CDFs from our experiments and compare them in The proposed method provide an efficient way for estimating the beacon positions and the RFM without dedicated and time costly surveying process at the price of loss of positioning accuracy. However, considering the time and energy saved from dedicated surveying , the method is meaningful.A comparison to the method in  is preseMost BLE beacon-based indoor positioning methods need some pre-requisites prior to positioning. For range-based methods, the beacons’ positions are needed. For fingerprinting-based methods, the RFM are needed. Surveying the beacon positions or the RFM is normally an energy intensive task. This paper proposes an easy way to estimate the pre-requisites for BLE beacon-based indoor positioning using graph optimization. On the hardware perspective, only the BLE beacons and a BLE-enabled mobile device is needed. The mean errors of the beacon positions are 1.27 m in dense beacon situation and 2.26 m in sparse beacon situation. Using the estimated pre-requisites, the BLE beacon positioning performance is considered enough for many indoor positioning applications, although it is worse than methods adopting accurate beacon positions and RFM.In the future, we plan a deeper and more complete research into the proposed method on a wide range of affecting factors, including the number of beacons needed, the effect of walking persons in the environment, the effect of more sophisticated path-loss model, the upper bound error, and so on.


The authors wish to make the following corrections to this paper :We have found one inadvertent error in our paper published in this journal : At the beginning of Section 4, “Sensor Measurement Results”, we missed a paragraph as follows:The prototype of the Micro-electromechanical Systems (MEMS) and Integrated Circuit (IC) composite structure requires a long lifecycle and a high cost. The Charge Coupled Device (CCD) chip has a charge coupling and amplification function, so the electrostatic imaging principle was verified by using the modified CCD chip. By removing the filter on the CCD surface, a prototype of a fingerprint sensor based on electrostatic imaging was built. In a dull environment, the modified CCD chip generates an induced charge on the electrostatic field through the surface pixel and uses the charge amplifying circuit to perform induced charge amplification to realize the electrostatic imaging function. Compared with the sensor designed in the third section of the thesis, the modified CCD chip has fewer shield electrodes and can only achieve single-shot electrostatic imaging; however, it can verify the electrostatic imaging principle and design scheme proposed in the paper.This change has no material impact on the conclusions of our paper. We apologize to our readers.


Narrow gap welding is a prevalent technique used to decrease the volume of molten metal and heat required to fill a joint. Consequently, deleterious effects such as distortion and residual stresses may be reduced. One of the fields where narrow groove welding is most employed is pipeline welding where misalignment, productivity and mechanical properties are critical to a successful final assemblage of pipes. This work reports the feasibility of joining pipe sections with 4 mm-wide narrow gaps machined from API X80 linepipe using cold wire gas metal arc welding. Joints were manufactured using the standard gas metal arc welding and the cold wire gas metal arc welding processes, where high speed imaging, and voltage and current monitoring were used to study the arc dynamic features. Standard metallographic procedures were used to study sidewall penetration, and the evolution of the heat affected zone during welding. It was found that cold wire injection stabilizes the arc wandering, decreasing sidewall penetration while almost doubling deposition. However, this also decreases penetration, and incomplete penetration was found in the cold wire specimens as a drawback. However, adjusting the groove geometry or changing the welding parameters would resolve this penetration issue. Narrow gap welding (NGW) is a technique used to weld thick joints with the aim of reducing the molten metal deposited volume, ultimately decreasing distortion and residual stresses caused by thermal stresses developed during welding. One of the drawbacks associated with this technique is sidewall penetration caused by the arc wandering during welding. This problem is so critical in heavy duty welding that models to predict it are found in the literature .Various NGW variants were developed to overcome such drawbacks: rotating arc, swing arc and wave-shaped wire systems. Another reason to use these modified systems in narrow gap welding is to improve the wettability of the sidewall by the welding pool and to better distribute the heat across the weld to mitigate distortions. A common setback for these systems is their expense and the use of highly trained personnel in their operation, which considerably increases the costs of manufacturing.Not only are arc-modified processes employed in NGW, but also increased-deposition processed are successfully used, e.g., tandem gas metal arc welding (T-GMAW)  and twinOne alternative to these sophisticated welding processes is the cold wire gas metal arc welding (CW-GMAW), which consists of the standard gas metal arc welding (GMAW) with an extra cold wire (non-energized) fed into the arc-welding pool system. This process increases the deposition of the welds while maintaining the nominal heat input for the same welding parameters in standard GMAW. Previous research  has showPrevious research  demonstrThe present work reports the further development on the work of prior research , and repNGW were fabricated using both GMAW and the cold wire gas metal arc welding (CW-GMAW) process. To manufacture the welds, ER100S-G in the diameter of 1.2 mm was used as electrode, while the cold wire had a diameter of 1.0 mm. API X80 [9] was selected as base metal. The nominal compositions of the electrodes and of the base metal are given in Cold wire feed rates are expressed here as a fraction of the electrode mass feed rate, due to the fact that two wires of different diameters were used for the electrode and cold wire, respectively. To quantify the cold wire feed rate as function of the electrode wire, the mass percentage as a fraction of the electrode was used. The mass feed rate was calculated from the wire density and cross-sectional area.To evaluate the welding process, a NGW joint design used in heavy welding applications such as pipeline welding was considered. This configuration was chosen to demonstrate an immediate application of the process, which is critical to welding of thick structures, since it decreases the heat-induced detrimental effects of welding passes.During welding, the current and voltage signals were acquired at the sampling frequency of 20 kHz for 2 s with synchronized high speed imaging at 5000 fps with shutter speed of 25 ms, an aperture of f/22, and a narrow band pass filter of 900 ± 10 nm. The videography was performed with the camera in parallel to the groove longitudinal line to record the metal transfer inside the groove. The high speed images shown in this work were selected to adequately represent the arc dynamics and metal transfer, when in a stable condition. The welding parameters used are reported in quations :(1)UaverThe arc arc stability was also assessed through cyclogrammes which are voltage versus current plots, and are used to study the events occurring in the arc electric. They are useful since they show the amount of short-circuits and account for the general arc stability of the process . For a mOnce the experiments were completed, the specimens were subjected to standard metallographic procedures and etched with 5% Nital to reveal the macrostructure. The cross-sections showing the passes sequence were taken from the start, middle, and end of the joints. The cross-sections showing the complete joint were taken from the middle of the joints. The hardness map was performed with a 200 g load and 10 s dwell time. The distance between indentations was 0.3 mm and the distance between lines was 0.3 mm.On the other hand, one can notice that this repetitive behavior did not occur in the fill and cap passes c,d, likeAs reflected by oscillograms, the fill pass was more stable, with some short-circuits and high voltage points, indicating large variations in arc length see b. In theThe erosion of the sidewall was caused by the arc self regulation dynamics, which tried to establish the shortest path to electron flow, thereby increasing the melting rate. However, as the arc moved through the groove, the attachment point was continually changing, melting multiple points across the groove walls. This multiple erosion points might have detrimental effects on the mechanical properties of the joint.The presence of inclusions points to the need for more careful grinding after the root to clean the silicates formed during welding pool solidification. Another feature that differs between the cross-sections is the quantity of passes to fill them out. Standard GMAW required three passes and CW-GMAW only two. Ultimately, the overall heat input in CW-GMAW was lower due this difference in number of passes.To mitigate incomplete fusion, two alternatives might be used: increase the current by means of wire feed speed to increase penetration, or decrease the root face height to facilitate higher penetration.The HAZ width of the standard GMAW and CW-GMAW specimens were compared. The intercritical heat affected zone (ICHAZ), which is basically formed between 800 and 500 One can see that the ICHAZ is slightly narrower in CW-GMAW than in the standard GMAW. This is consistent with previous research. This seems to indicate that CW-GMAW specimens had a shorter cooling time between 800 and 500 To characterize the strength of the of the joint via cross-sections, maps of the Vickers hardness values are reported here. One can see that the Vickers hardness in the weld for the CW-GMAW is higher, which indicates that the cooling rate of the weld metal in CW-GMAW was faster than the standard GMAW. For some conditions, the melting efficiency of CW-GMAW was higher than in conventional GMAW. The melting efficiency is the amount of heat that actually transfers to the melting pool over the welding total power, and the difference in this value might explain slower cooling rates.The electrical oscillograms in This phenomenon in constant voltage (CV) leads ultimately to the melting of the contact tip and interruption of the welding processes. However, it was noted that, in constant current (CC), this process did not occur since the reduction in arc length, induced by the arc attachment to the sidewall, caused a small reduction in arc length in comparison the reduction in current, thus the power source reached a new equilibrium point.On the contrary, in CW-GMA welding, the current path was shortest to the cold wire, which caused the arc to climb to it. This phenomenon prevented the arc attachment to the sidewalls, and consequently prevented sidewall erosion. Moreover, one observes that the arc was much more stable in CW-GMA welding  than in Regarding the cap pass, one can observe that, in conventional GMAW, the arc tended to climb towards the contact, as indicated by the higher values of voltage (increase in arc length) and current in The macros, taken from the middle of joints , show thThe welds using GMAW process presented serious sidewall penetration that might compromise their integrity.The welds fabricated using CW-GMAW did not present sidewall erosion during root pass, conversely the increase in deposition compromised the penetration in the root region.The amount of inclusions found in the the cold wire welds might be linked to the grade of the welding wires used.Narrow groove welds in API X80 were fabricated using the standard GMAW and the CW-GMAW to assess the process feasibility using CW-GMAW for a joint with 4 mm gap. Taking into account the results discussed, the following conclusions can be drawn:


Rewilding is emerging as a promising restoration strategy to enhance the conservation status of biodiversity and promote self-regulating ecosystems while re-engaging people with nature. Overcoming the challenges in monitoring and reporting rewilding projects would improve its practical implementation and maximize its conservation and restoration outcomes. Here, we present a novel approach for measuring and monitoring progress in rewilding that focuses on the ecological attributes of rewilding. We devised a bi-dimensional framework for assessing the recovery of processes and their natural dynamics through (i) decreasing human forcing on ecological processes and (ii) increasing ecological integrity of ecosystems. The rewilding assessment framework incorporates the reduction of material inputs and outputs associated with human management, as well as the restoration of natural stochasticity and disturbance regimes, landscape connectivity and trophic complexity. Furthermore, we provide a list of potential activities for increasing the ecological integrity after reviewing the evidence for the effectiveness of common restoration actions. For illustration purposes, we apply the framework to three flagship restoration projects in the Netherlands, Switzerland and Argentina. This approach has the potential to broaden the scope of rewilding projects, facilitate sound decision-making and connect the science and practice of rewilding.This article is part of the theme issue ‘Trophic rewilding: consequences for ecosystems under global change’. This Recent studies describe rewilding as a nature restoration action that emphasizes the dynamic character of ecosystems and that explicitly acknowledges the role of reducing human forcing of the system ,15, i.e.https://www.iucn.org/content/ecological-restoration-protected-areas-principles-guidelines-and-best-practices) [https://www.ser.org/page/SERStandards) [Approaches to monitor restoration progress and success rely on the quantification of indices of recovery progress ,20, recoactices) , whereasandards) . HoweverWithin this restoration context, rewilding is aligned with newer visions of restoration  decreasing direct human inputs and outputs of materials into the system and (ii) restoring the ecological integrity of ecosystems ,32. This2.(a)We assume that the condition of ecosystems is a function of the intensity of human forcing over natural processes and of the system's ecological integrity . We defiH) captures the pressures of direct human forcing on the ecosystem at the time of measurement; thus, changes in management regimes will immediately be captured by changes in the rewilding score on this axis. This metric of human control can be considered an application of the ‘cultural energy’ framework in [E) is affected by human legacy effects on ecological composition, structure and functions. Hence, there will be temporal lags—from days to even centuries—between the implementation of restoration actions and the resulting increase in the integrity of the system [In this framework, both axes capture changes in the natural condition of the system at different temporal scales. The axis of human inputs and outputs  and o to material outputs . While some indicators combine both inputs and outputs , we do not quantify inputs and outputs separately. Importantly, this axis also captures impacts from management activities (e.g. removing deadwood for pest control or wildlife population control) and, in some cases, conservation management activities with a direct influence on the system dynamics, such as population reinforcements that are expected to have a limited duration. That said, certain rewilding projects might require an initial level of active restoration to overcome constraints that prevent full restoration of natural processes that eventually will translate into an increase of ecological integrity.The human forcing on natural processes and ecosystem dynamics is defined here as a function of the direct human inputs and outputs of material into the systems that are linked to today's management:et al., under review): namely to (i) allow for natural stochasticity and disturbances influencing ecological processes [While most approaches to monitoring restoration progress focus on the composition, structure and function of ecosystem ,36, we crocesses , (ii) inrocesses  and The rewilding assessment framework was developed combining expert knowledge, analysis of data and feedback from stakeholders including conservation and rewilding practitioners, in an attempt to balance between the reliable recording of ecological changes  while ensuring real-world applicability (i.e. the degree to which the approach could be routinely used with the best available knowledge or data). To select experts for each case study, we first identified the type of expertise required to monitor rewilding progress including an understanding of the complexity of different ecosystem components and familiarity with spatial information. Then, we selected experts with demonstrated background on the study system in the field (according to the extent and duration of the rewilding project), and the professional connection to conservation or restoration agencies and organizations .Our set of pressure and state variables and indicators allows measuring the rewilding progress on a particular site, namely the rewilding project. This focal unit may be defined at any spatial and temporal extent. Nevertheless, we recognize that human infrastructure and activities beyond the spatial boundaries of the rewilding area might interfere with the recovery of its naturalness, in particular through their impact on connectivity and dispersal. In addition, because of the slow speed of expected ecosystem recovery and the long-term nature of rewilding projects, 5-year or longer monitoring cycles are recommended .To select variables and indicators, we drew up a list of the major human inputs and outputs into ecosystems, and of potential indicators that could be used to describe the naturalness of disturbance regimes, landscape connectivity and composition, and trophic processes. We then revised the indicators to ensure that they were conceptually independent and that they were implementable by practitioners without specialist knowledge . In addition, following best practices, indicators should ideally be (i) feasible to monitor; (ii) useful at multiple spatial and temporal scales; (iii) practical to implement, without prohibitive technical or financial requirements; (iv) respond predictably to human impact; and (v) represent a causal impact on the desired outcome –46. It wWe assembled a suite of 18 indicators tied to particular restoration actions, from passive or non-intervention to active management (S) to each variable. The scores are described on a continuous 0–1 scale, in which 1 represents the maximum intensity of human forcing  or the maximum ecological integrity , and 0 represents an area without human inputs or outputs into the system (Hmin) or with minimum influence of human legacy effects on ecosystem composition, structure and functions (Emin), respectively. Reference values for each indicator are proposed in In the bi-dimensional rewilding space , it is pN axis is calculated as the geometric mean of the scores for the naturalness of disturbance regimes, the landscape connectivity and the trophic complexity. This integration based on the geometric mean emphasizes the critical role of the interactions among the three ecosystem components in rewilding.The score for each of the components of the framework is calculated as the arithmetic average standardized scores of the variables within such component. Thus, a normalized score on a continuous 0–1 scale is obtained for the human inputs and outputs into the system :R values range from 0 to 1. For a particular site, a higher positive change in R means a higher success of rewilding, i.e. a reduction in human forcing over natural processes and/or increase in ecological integrity. Given that they are based on a standardized set of indicators, H, E and R can be compared across diverse rewilding projects. However, a complementary set of additional indicators could be tailored specifically for any given rewilding project to capture the local nuances. In this case, however, the general and components scores may no longer be comparable between systems.Finally, the values for the (c)www.conservationevidence.com) [Rewilding initiatives need to move beyond anecdote, personal experience, expert criteria and conventional wisdom, towards a more systematic appraisal of evidence collected by practitioners tackling a given restoration action. Here, together with the list of pressure and state variables and indicators, we provide a list of management activities for rewilding based upon the review of evidence inspired by the Conservation Evidence approach (nce.com) . Thus, fWe gathered evidence on the effectiveness of 16 restoration actions by reviewing 137 primary studies from key scientific journals for each action. We used Web of Science and Google Scholar to identify and review primary studies evaluating the evidence for each action where available. When no reviews were identified, we searched for studies on each topic published since 2014 and used these publications to identify further relevant studies for evaluating each action. Next, we reviewed the collated evidence and added further key studies when required. We summarize the evidence for each restoration action in electronic supplementary material, table S2, and scored these activities from 0 to 4 according to the effectiveness of the intervention . We conceive these evidence syntheses as a key first step towards systematic revision of evidence for the effectiveness of each restoration action in the context of rewilding projects.(d)To illustrate and test the framework, we applied the assessment to three flagship restoration projects with very different characteristics: the rewilding area of Millingerwaard in a highly urbanized landscape 10 km outside Nijmegen (the Netherlands); the Iberá Project (Argentina), which is one of the largest naturalized inland wetland systems in South America ; and the3.Our proposed monitoring framework was applicable to measure rewilding progress across the three different restoration contexts. The resultant scores exhibit clear trends resulting from the set of restoration strategies used in the three case studies , althougCapra ibex) reintroduction programme occurring in the reserve's early years and accompanied by artificial feeding initiatives  are more likely to occur in the mid- to long-term.While we contend that our approach reasonably captures rewilding progress, we acknowledge a set of limitations to be addressed in future work. Firstly, caution should be taken when comparing the progress of initiatives occurring over considerably different spatial or temporal scales. For example, the Millingerwaard project scored more positively on the connectivity indicator than the Swiss National Park project, despite the latter containing a far greater extent of continuous habitats owing to the project covering an area 20 times larger. Furthermore, the changes in ecological integrity in the Swiss National Park have occurred over the past century, in contrast with 28 and 19 years associated with the Millingerwaard and the Iberá projects, respectively. Comparisons of the absolute magnitude of the changes in Secondly, some of the indicators are more sensitive to changes than others, meaning that differential amounts of effort are required to induce changes in the various indicators. For instance, reducing agricultural production or removing a large dam requires more effort than ceasing deadwood removal. Future iterations of the framework might weight the different indicator contributions to the overall score relative to the sensitivity of those indicators ; this woAs for other types of restoration ,24, the The precise shape of the mathematical functions integrating the different sub-components and the components themselves into the rewilding score is also an area for further research. For instance, in order to capture that low human interventions can be acceptable in our framework, a nonlinear response for the human forcing could be used to convert the sum of the indicator scores into the aggregate score, instead of the arithmetic average we propose. Work with stakeholders could further elaborate on the shapes that better capture the expert assessment of rewilding progress in a range of scenarios.We stress that achieving the highest score should not be considered as the default objective or ambition, but that gradual increases in the natural condition of ecosystems at lower and intermediate scores can constitute a sensitive restoration target in many situations where it is critical to balance the socio-economic consequences. In these cases, the rewilding assessment framework should be used in conjunction with other socio-economic management objectives to optimize the trade-off between maximizing ecosystem integrity and delivering sustainable socio-economic value to communities and users . For insAs a concluding remark, the rewilding assessment framework presented here responds to calls to better integrate the science and practice of rewilding ,67. Alth


At present times, healthcare systems are updated with advanced capabilities like machine learning (ML), data mining and artificial intelligence to offer human with more intelligent and expert healthcare services. This paper introduces an intelligent prediction and classification system for healthcare, namely Density based Feature Selection (DFS) with Ant Colony based Optimization (D-ACO) algorithm for chronic kidney disease (CKD). The proposed intelligent system eliminates irrelevant or redundant features by DFS in prior to the ACO based classifier construction. The proposed D-ACO framework three phases namely preprocessing, Feature Selection (FS) and classification. Furthermore, the D-ACO algorithm is tested using benchmark CKD dataset and the performance are investigated based on different evaluation factors. Comparing the D-ACO algorithm with existing methods, the presented intelligent system outperformed the other methodologies with a significant improvisation in classification accuracy using fewer features. A person with CKD has a higher chance of developing heart disease3. The earlier stage of CKD does not show any major symptoms and it very hard to identify it without some tests like urine and blood test. When the CKD is detected at the initial stages, preventive actions and better treatment can be given to control the chances of dialysis or transplantation. A study reported that the earlier detection of CKD could reduce the growth of disease even by the nurses in the specialization of nephrology and primary care doctors4. Generally, imaging techniques are employed to identify the presence of CKD. But, because of a large number of patients, it is impossible to test each person, and people with a higher possibility of having CKD will be recommended to undergo extensive testing. At present, the preservation of clinical database becomes a difficult process in the healthcare industry. The patient’s data holds different features and diagnosis related to disease needs to be provided with extreme importance to attain high quality service. Since the data archived in the hospital database may have missing as well as unnecessary data, it becomes burdensome to mine the patient data. So, better data processing and data reduction approaches are needed prior to the application of data mining techniques. Then, the identification of CKD becomes simpler and faster when the available data is accurate and reliable.In the last decade, the recent developments in information technology include mobile communication system, big data, Internet of Things (IoT), and wearable computing are employed in the field of healthcare. In particular, various intelligent healthcare systems are modeled with the help of big data and mobile computing devices to offer intellectual and expert services. Also, the increase in medical data leads to different issues for managing, storing and processing data. Persistent, low-grade inflammation is now treated as an important characteristic of chronic kidney disease (CKD). Though considerable enhancements have been made in the healthcare domain, CKD still being a critical health problem which affects 10–15% of the population, and its pervasiveness is continuously increasing. Because of its subtle nature, CKD is not often identified in premature stages6 utilizes the training data to create a model and is applied for test data to analyze the prediction performance. Recently, artificial intelligence (AI) methodologies can be used to improve the available classification model. At the same time, the existence of several features in the high-dimensional medical data resulted in different problems such as overfitting, high computation complexity and low interoperability of the finishing model8. The easiest method to resolve the problem is to decrease the number of features using Feature selection (FS) approach. This procedure intends to select a feature subset by the elimination of redundant or irrelevant features. It is based on the idea of extracting the maximum possible information using a reduced number of features to save computation time9. The selected feature subset finds helpful in representing a classification function that has a serious impact on learning time, classification accuracy and cost involved with the features10. FS methods are applied in diverse applications such as data mining, ML and pattern recognition, to lessen the number of features for improving the prediction results11. Concerning the validation parameters, FS methods are classified into the wrapper, embedded and filter based approaches12. Filter approaches validate the feature subset by the use of fixed measures instead of learners and selected features14.Identification of CKD from the patient’s data can be treated as a data classification issue. The classification task is generally a supervised learning process that deduces a connection between features and class labels. A classification and prediction technique15. Embedded approaches have less complexity compared to wrapper approaches. However, the chosen feature subset is based on the learning methodology16. Naturally, the embedded method integrates the filter and wrapper method and eliminates the limitations of them. Though the filter methods have low computation complexity, the chosen feature subset showed inadequate reliability for classification. Contrastingly, the wrapper methods attain higher classification performance with high computational complexity. These three methods have enhanced the features’ discrimination for classification. Furthermore, the process of FS has not improved the classifier but improved the features. In addition, the wrapper method and hybrid methods attained high classification performance but with the cost of high computational complexity.In contrast, the wrapper method makes use of the learning technique as a sub-process of evaluation for assessing the betterment of the chosen feature set. Even though wrapper methods are commonly employed, it facts few difficulties such as high computation complexity, identifying user-defined parameter of the learner, and inbuilt learner constraintsTo overcome these issues, we propose a novel wrapper approach for CKD identification by incorporating density-based FS (DFS) with Ant Colony based Optimization (ACO) named as D-ACO algorithm. The DFS approach is a heuristic method to evaluate the worthiness of a feature. The inclusion of DFS removes the unnecessary features and assists to increase the accuracy of the ACO based classifier and thereby improvise the final classifier results of D-ACO algorithm. The D-ACO algorithm is employed to a benchmark CKD dataset from UCI repository. For comparison purposes, ACO, genetic algorithm (GA) and particle swarm optimization (PSO) based classification algorithms are employed. The experimental outcome depicted that the presented D-ACO approach achieves effective classification performance over the compared approaches.The upcoming sections of the study are planned as follows: Section 2 briefs the approaches relevant to the presented model. Section 3 discusses the presented D-ACO model, and section 4 investigates the results obtained by the D-ACO algorithm against CKD dataset. And, the concluding remarks are made in section 5.17. Initially, the presented model is designed to resolve the issues that exist in the local search based learning methods. The CS algorithm helps to optimally select the input weight vector of the NN to train data properly. The classifier results of the proposed algorithm showed that it attains better performance. A modified version of NN-CS (NN-MCS) algorithm18 is developed to overcome the problem of local optima of the NN-CS algorithm. As the initial weights of the neuron connection control the NNs performance, the proposed method uses employs MCS algorithm to decrease the root mean square error (RMSE) value employed in the training process of NN. The simulation results reported that NN-MCS algorithm attained better performance than NN-CS method.Different techniques have been proposed for effective prediction of CKD by the exploitation of patient’s medical data. A Cuckoo Search trained neural network (NN-CS) method is presented for the identification of CKD19, two fuzzy classifiers are known as fuzzy rule-building expert system (FuRES) and fuzzy optimal Associative Memory (FOAM) are presented for the identification of CKD. FuRES generates a classification tree which comprises a minimal NN. It creates the classification rules to determine the weight vector with the least fuzzy entropy. The two fuzzy classifiers are employed for the identification of 386 CKD patients. Also, FuRES is better compared to FOAM especially in situations where the training, as well as the prediction process, contain a similar intensity of noise. FuRES and FOAM attained better performance in the identification of CKD; at the same time, FuRES is proficient than FOAM. In20, another fuzzy-based method is presented to identify the CKD. The author designed an Improved Hybrid Fuzzy C-Means (IHFCM), an improved version of FCM with Euclidean distance for the detection of CKD. This study revealed that the probability based methods are unsuitable for CKD prediction because of the necessity of proper output. Statistical methods, Bayesian classification or association rule based prediction methods are infeasible to use as it leads to inaccurate results. So, IHFCM is developed for the identification of CKD. At the initial stage, IHFCM removes the frequent records as a preprocessing step. Then, it computes the diffuse score for each value in the particular table of contents of the query. The higher fuzzy score represents the clusters of higher risk and lower fuzzy score indicates lower or no risk at all.Inet al.21 devised a novel method namely Weighted Average Ensemble Learning Imputation (WAELI). The missing values in the dataset reduce the precision level of CKD. As the existing methods use of data preprocessing technique, the data cleaning process is needed to fill up the missing values and to remove the inaccurate values. A recalculation procedure is present in different CKD stages where the missing values are computed and placed in their respective positions. Although the existing methods are effective, it needs an expert in healthcare dataset to ensure the values for CKD.In the year 2017, Dilli Arasu 22, a wrapper method is presented by the hybridization of GA with support vector machine (SVM) called GA-SVM method to properly select the feature subset. The reduction in the redundant features of the proposed method improves the classification performance which is validated using five different disease dataset.FS process acts as a significant part in the area of data classification, employed to find out a smaller set of rules from the training dataset with fixed goals. Different methodologies like AI techniques, bio-inspired algorithms are used for FS. Inet al.23 also presented a wrapper method for CKD identification by following three steps: (1) a framework is generated from data mining, (2) Wrapper subset attribute evaluator and best first search approach are employed to select attributes and (3) Classification algorithms are employed. The experimental observations revealed that the accuracy is improved for reduced dataset compared to the original dataset24. developed a framework for enhancing the quality of CKD. This framework involves three processes include FS, ensemble learning and classification. The integration of Correlation-based FS (CFS) and k-nearest neighbor (kNN) classifier results in high classification accuracy25. developed another CKD identification method by the use of filter as well as wrapper approaches. The simulation outcome depicted that the decrease in a number of features does not ensure effective classification performance.Naganna Chetty The outline of the D-ACO algorithm is illustrated in Fig. 26.To provide effective performance with low cost for data mining processes, the quality of the data should be good. The values missing in the database should be filled in the whole CKD dataset. In some situations, when continuous features exist, the methods can be synchronized to build discrete traits. They contain some noisy and missing values in each instance. For improving the behavior of medical data, the original data is preprocessedFor the selection of optimal features, the proposed algorithm uses the following processes. Once the preprocessing of CKD dataset is completed, the next step is to organize the input data into groups. Here, DFS is used which selects a group of features in every iteration. A subset of the optimal features from the raw dataset is considered as the most significant feature for the classification process.27. The former method assumes that the data follows Gaussian distribution and therefore the density estimation task is just to decide proper values for mean as well as the variance of the distribution. Contrastingly, non-parametric approaches have no assumptions regarding the shape of the density function; instead, it computes the density straightly from the instances. It is noted that many of the pattern recognition applications have no fixed format to estimate the density of the primary data. On the other hand, non-parametric methods can be utilized with random distributions with no consideration that the form of the fundamental densities are known28. Hence, the proposed method uses the parametric approach and is equated asf, in class cl is computed using Eq.  of every feature in every class individually. And, the next step is the ranking procedure of the features based on the overlapping area. The common approaches to compute PDF can be divided into parametric and non-parametric approaches29. In general, the discovered knowledge in the classification process is defined in Eq.  holds a set of conditions which is connected by a logical conjunction operator (AND). Then, the rule subsequent (THEN part) indicates the predicted classes for cases whose predictor features fulfill each term represented in the rule antecedent.Structural schemaGeneration of rulesHeuristic functionPruning of rulesPheromone updateUtilizing discovered rulesThe application of ACO algorithm in the classification task of CKD involves the following processes:fm and Vmn is the discrete value belongs to the feature, where j denotes the series number of the value present in the feature. The end of the features is the class and the class values are written as Ck, where k is the sequence value in the class. As shown in figure, the ant starts its traversal from the source and picks a value for the class and consumes the artificial food. When the traversal process is completed, a value will be chosen for every class. To discover the rules, sufficient number of ants trail an identical path as discussed below. In this case, as shown in Fig., the discovered path is indicated by solid line: Start-Val1,2-Val2,1-Val3,3-C3-End.The structural schema of the presented classification model is represented in Fig. mn) that Vij is chosen as value of fm (fm = Vmn) where fm is the mth feature and Vij is the nth value of the feature.ij and As explained earlier, the ants begin from the artificial nest and selects a value for every feature for rule generation. This task is performed using the probability function as provided in Eq. . It provi.The number of cases should be lesser than the fixed value can be placed to the rule, named as minimum_cases_per_rule.ii.mn) for selecting a feature value for rule generation as represented in Eq.  is present in the range of 0 ≤ d in Eq. .5\docume32. Due to the positive feedbacking procedure, the errors in the heuristic measure could be corrected and results to enhanced classifier performance. The ants apply this process to determine better classifier rules. At the beginning, every trail is provided with equal quantity of pheromone as represented in Eq. , false negative rate (FNR), sensitivity, specificity, accuracy, F-score and kappa value. In prior to explaining the evaluation parameters, the idea of a confusion matrix is discussed.Confusion matrix is essential in the assessment of classification performance of any classifiers. It is a 2 × 2 matrix that provides the data about the actual and predicted classifications. The confusion matrix contains four elements: TP, TN, FP and FN. Using these four elements, the classification measures can be defined as given in Fig. th iteration.Table Table Figure The improved results of D-ACO algorithm are due to the addition of DFS which removes the undesirable features to enhance the classifier results. Comparative results of diverse classification models concerning various metrics under different measures are given in Fig. The most important measure of the classification algorithm is accuracy, and the comparative results based on accuracy is given in Fig. Finally, the results of different classification algorithms on CKD dataset interms of various performance measures revealed that the proposed D-ACO algorithm is found to be efficient on the classification of CKD dataset. This is due to the advantage of DFS as well as the nature of wrapper method which continuously execute the DFS and ACO algorithms consecutively.This paper has presented an intelligent prediction and classification system for healthcare, namely DFS with ACO algorithm called D-ACO algorithm is proposed for the classification of CKD dataset. The proposed D-ACO framework, however, jointly performs FS, ACO based learning and removes irrelevant features. Using a benchmark CKD dataset, the efficiency of the D-ACO algorithm is evaluated, and a comparison is also made with the existing methods. On comparing with the existing methods, the proposed D-ACO algorithm outperformed the other methods with improved classification performance in various aspects. In overall, the proposed D-ACO algorithm is found to be an appropriate classifier for the identification of the CKD.


Balance tests are commonly used in clinical practice with applicability in injury prevention and return to sport decisions. While most sports injuries occur in a changing environment where reacting to a non-planned stimulus is of great importance, these balance tests only evaluate pre-planned movements without taking these dynamics environmental aspects into account. Therefore, the goal of this paper was to develop a clinician-friendly test that respects these contextual interactions and to describe the test protocol of an adapted Y-balance test that includes environmental perception and decision-making.TM was chosen for the environmental perception and decision-making component of the test.Within the theoretical construct of balance and adaptability, balance errors were selected as outcome measures for balance ability and, visuomotor reaction time and accuracy are selected as outcome measures for adaptability. A reactive balance task was developed and described using the Y-balance test for the balance component, while the FitLight training systemThis paper describes the test protocol of a reactive balance test as an adapted Y-balance test. The LED-lights of the FitLight training systemTM are placed at 80% of the maximal reach distance for each axis along the Y-Balance test kitTM. To induce cognitive load within the visuomotor task, colours were fixed to a corresponding axis, and both the order of the visual stimuli as the interstimulus time were randomised to integrate environmental perception and decision-making.The reactive balance test is a functional test that allows clinicians to score balance ability and athlete adaptability easily. Recently, several systematic reviews and clinical commentaries emerged regarding the clinimetric value of clinician-friendly lower extremity functional performance tests, and their associations with injury –4. AlthoThe adequate and immediate motor reaction to a changing visual stimulus can be seen as a hallmark of an athlete’s adaptability. In this perspective, balance and visuomotor reaction time are two very important and strongly related components of sports performance, and are also correlated to (recurrent) injury risk in a sports context , 16–18. Therefore, the goal of this paper was to describe the development of a test protocol for a reactive balance test incorporating environmental perception, decision-making and visuomotor reaction time.The methodology of Kazman et al. (2016)  was usedDespite the widespread use of the term balance, no unanimous consensus exists on its definition. It is often associated or misinterpreted with concepts like stability, equilibrium or postural control . We demaAdaptability was defined as the ability to effectively modify responses under a broad spectrum of conditions . A subjeThe visuomotor reaction time on a behavioural level considers the time needed from start to end-point of the VMT. In detail, this pathway includes visual perception, visual processing, visuomotor transformation and motor execution time . In cognIn conclusion, these insights showed that the concepts of balance and adaptability are compatible and reinforcing in a real-world context. A functional test that provides objective measures within both concepts would be beneficial to aid clinicians to map the rehabilitation progress and substantiate criterion based return to sport decisions.The selection of an appropriate balance testIntegrate adaptability – designing the VMTProvide an objective measure of balance abilityProvide an objective measure of adaptabilityThe development of a functional test designed to assess a subject’s ability to maintain balance and adequately react to environmental stimuli should go through different phases in order to provide an added value for clinicians and researchers:FunctionalMovement.com, Danville, VA).. Moreover, the YBT has been previously used and adapted by Batson in 2010 in order to build a more sport specific balance test for dancers, with continuing research on its applicability by clinicians by Wilson and Batson (2014) [The validity and reliability of the star excursion balance test (SEBT) and Y-balance test (YBT) have been thoroughly investigated as a measure of (dynamic) balance both in healthy and musculoskeletal injured populations . Both are used in clinical practice to monitor the rehabilitation progress or to identify high risk athletes via screening , 29–33. n (2014) , 35.The YBT was selected as the base of our functional test given its predicate as a clinician-friendly performance test, its moderate to good reliability as a balance test , 31, 33,Given the aforementioned definition of adaptability , the tesTM) were used for providing the visual stimuli. The order of the stimuli was randomized as well as the interstimulus time. This avoided the athlete anticipating both the timing of the next visual stimulus and the direction of the targeted motor response. To obtain an objective measure of visuomotor reaction time data corresponding to each axis, the number of reaches per axis needed to be maximized. This would have prejudiced the ecological validity of the test, so the choice was made to set the number of stimuli per axis to as much as possible without the duration of the test exceeding two minutes. The cognitive decision-making process could be intensified by reducing interstimulus time or using more than 36 stimuli to put a greater strain on focus and attention, as well as on balance errors since the participant would get less time to correct perturbances of balance. Reducing the interstimulus time to less than 1.5 s, however, would not allow the subject to return to the starting position of the test after extinguishing the preceding sequence. To ensure the ecological validity of the RBT, the decision was made to use the minimal number of stimuli needed and to focus the load of the cognitive process on randomization of both the order of the stimuli as the interstimulus time, with the interstimulus time being as low as possible ranging between 1.5 and 2.5 s.The cognitive process was designed using an initial visual stimulus in the colour red, blue or green whereby each of the three axes of the YBT corresponded to a specific colour of the visual stimulus. The colour blue was assigned to the anterior axis, the colour red to the right posterior axis and the colour green to the left posterior axis. Colours were fixed to spatial orientation because this eliminated corresponding cognitive errors. The initial visual stimulus needs to be processed to determine the direction of the correct motor response. LED-lights . The inter-stimulus time varied between 1.5, 2, or 2.5 s to eliminate anticipatory timing effects, provide enough challenge for the test subject and give enough time to recover the standard position when a balance or decision error was made. Furthermore, the starting point of the colour sequence was randomised for every performed RBT, so participants could not memorize the colour sequence, nor the inter-stimulus times when performing the test multiple times .The test incorporated the Y Balance Test KitTM in combination with Fitlight-training systemsee Fig. . InstrucIn summary, the outcome measures of the RBT are visuomotor reaction time, accuracy and balance errors. A detailed description of all outcome measures of the RBT is listed in Table This test protocol described a reactive balance test that added environmental perception, decision-making and variable motor responses as additional dynamic components to the construct of balance. Given the importance of adaptability in sports performance, this functional test can be of added value in the functional test repertoire to objectify the progress throughout rehabilitation or support return to sport decisions. The development of this test was a first step in incorporating the insights of adaptability into functional testing, but is still a long way removed from a real sports environment. Yet, this test could easily be used in clinical practice during the rehabilitation process as one of the first objective indicators of an athlete’s ability to maintain balance in a changing environment. The RBT involved different levels of uncertainty and tests the athlete’s capacity regarding the combined components of decision-making, balance and visuomotor reaction time. The multi-layered approach of this test allowed for multiple outcome measures to be described, based on its construct validity. Visuomotor reaction time and accuracy were frequently used outcome measures in research towards neurocognitive functioning, often related to a non-specific task –43. In cThis paper described the theoretical construct and validity of a RBT. It was only a first step to introduce adaptability and a corresponding clinician-friendly functional test into the functional testing repertoire of clinicians and researchers and needs further research. To support the use of this functional test, validity and reliability of test outcomes need to be researched in the future. Consequently, the added value of this test within the emerging research can grow with possible indications throughout the clinical decision-making and the sports injury prevention research domain. It is important to note that significant learning can be expected in balance tests, including the YBT and RBT. Given the matter that reliability and measurement error of the YBT is still under debate, no recommendation can be given towards the number of practice trials for the RBT. In addition, reliability, standard error of measurement and number of practice trials needed to eliminate learning effects in both YBT and RBT need to be studied in future research in order give proper recommendations. Future research should also investigate both the test properties of the YBT when drawn or taped as well as the test properties when using the YBT test kit, since an important difference in performance can arise due to the different materials used to build the test. Another consideration that we have to keep in mind is possible colour-blindness of the patient or participant. Therefore, the clinician or researcher administering such a test should always check for colour vision deficiency prior to administering such a test and change the colours of the LED-lights accordingly.This paper described a test protocol and construct validation for a reactive balance test within the theoretical constructs of balance and adaptability. This clinician-friendly test can be applied in clinical practice and in future research. The RBT used the YBT protocol and implemented adaptability using a VMT. Outcome measure of the test are balance errors, visuomotor reaction time and accuracy. This was a first step within the functional testing repertoire to score an athlete’s adaptability during a balance test.


Under this transitional range, cracks are always formed in the cutting zone, but the stress states cannot guarantee their propagation. The cutting mode is brittle when the cracks can propagate and otherwise ductile mode cutting happens. Plunge cutting experiment is conducted to produce a taper groove on a 6H SiC wafer. There is a transitional zone between the brittle-cut and ductile-cut regions, which has a mostly smooth surface with a few brittle craters on it. This study contributes to the understanding of the detailed process of brittle-ductile cutting mode transition (BDCMT) as it shows that a transitional range can occur even for single crystals without internal defects and provides guidance for the determination of t It is well known that brittle materials undergo a transition of cutting mode from brittle to ductile when the machining scale decreases to be small enough, which is referred to as brittle-ductile cutting mode transition (BDCMT) ,2,3. Thituc) ,13. Whentuc) . A numbetuc) , plunge tuc) , and nantuc) .While the concept of critical undeformed chip thickness has been widely acknowledged and applied to guide the ultra-precision machining of brittle materials ,19, the Molecular dynamics (MD) simulation is a prospective alternative for investigating micro scale processes ,25, but This study investigated the transitional process of cutting mode in micro cutting of single crystal 6H SiC by large-scale MD simulations with up to 19 million atoms. SiC was chosen as the workpiece due to its important applications in many industries ,28,29, aDue to the extreme hardness of SiC, its ultra-precision machining mainly relies on abrasive processes like ultra-precision grinding and lapping . The griThe MD codes used for the simulation were the same as those in , which wThe results of MD simulations are presented in the form of snapshots of atom distributions, stress distributions, and dislocation structures, since the major objective of this study is to visualize the atomistic scale details of the brittle-ductile cutting mode transition process. The codes for mechanical stress visualization and atom distribution visualization were also adopted from . The disThe chip formation modes for the six It needs to be pointed that In order to identify the mechanism of the unstable behavior of crack formation and propagation in the transitional range, the stress distribution was calculated. Since the observed cracks were roughly parallel to the x axis, the stress component in the y direction was analyzed. The distribution of Two reasons are possible for the instability of stress states in the transitional range. One is that there is a competition between the effect of e stress  and the e stress . When thsoftware . The chiA plunge cutting experiment was conducted to verify the MD modelling results. The experimental setup is described in ref . The worThe plunge cutting produced a taper groove as shown in The cross-sectional profiles of the taper groove at the start and end points of the transitional range are shown in The transitional range of undeformed chip thickness explains why scattered tiny craters existed between the ductile-cut and brittle-cut regions in many studies ,20,21. IIt needs to be mentioned that the experimental cutting speed of 3 mm/s was much lower than that in the MD simulations, i.e., 100 m/s. Two factors need to be considered regarding the effect of cutting speed on the results. One is the heat effect which might affect the mechanical properties of the workpiece material. Though the cutting speed in the MD simulation was quite high, the total time of simulation was very short, and the heat accumulation would not be significant. Thereby it is supposed that the high cutting speed in MD simulation would not bring much heat softening of the workpiece material. Another factor is the shock wave which might be induced by the high cutting speed. It was reported that the shock wave speed in cubic SiC was ~11.5 km/s, and amorphization would occur when the impact speed exceeded 4.91 km/s . The cutThe mechanism of unstable material removal modes in micro cutting of 6H SiC was investigated by large scale MD simulations and plunge cutting experiment. It is found that the brittle and ductile cutting modes were not simply separated by a critical undeformed chip thickness. Instead, a transitional range of undeformed chip thickness with uncertain cutting modes was observed, though there are no internal defects in the single crystal. The MD simulations showed that cracks were always formed in the cutting zone under this transitional range of undeformed chip thickness. However, the stress states in the cutting zone were not always sufficient to propagate the cracks. The cutting mode was in brittle when the cracks did propagate and otherwise in ductile. This provides guidance for interpreting the surface morphologies produced by various experimental techniques for studying the BDCMT behaviors, and helps optimizing the machining parameters for ultra-precision grinding or cutting of brittle materials, e.g., increase undeformed chip thickness to the end of the transitional range if the cracks do not extend below the machined surface.