Coal is an important resource that is closely related to people’s lives and plays an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. Therefore, this paper proposes a coal mine environmental safety early warning model to detect abnormalities and ensure worker safety in a timely manner by assessing the underground climate environment. In this paper, support vector machine (SVM) parameters are optimized using an improved artificial hummingbird algorithm (IAHA), and its safety level is classified by combining various environmental parameters. To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm during iterations, a strategy incorporating Tent chaos mapping and backward learning is used to initialize the population, a Levy flight strategy is introduced to improve the search capability during the guided foraging phase, and a simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model is established using the improved algorithm to classify and predict the safety of the coal mine environment as one of four classes. Finally, the performance of the IAHA algorithm and the IAHA-SVM model are simulated separately. The simulation results show that the convergence speed and the search accuracy of the IAHA algorithm are improved and that the performance of the IAHA-SVM model is significantly improved. Coal, as a precious resource on Earth, has played a significant role in the economic development of many countries, leading to increased surface and underground mining activities in countries with abundant coal reserves . HoweverWith the development of computer technology, machine learning has gradually become integrated into our daily lives , and itsIntelligent optimization algorithms are an important research area within machine learning. These algorithms based on heuristic principles can overcome the uncertainty and complexity of problems during the optimization process, making them suitable for various types of optimization problems . Some weC) and the penalty factor (g), which determine the performance of SVM. However, these parameters are often selected manually, and choosing inappropriate values can lead to overfitting or underfitting. To ensure the proper selection of the kernel function parameter (C) and the penalty factor (g), researchers frequently employ intelligent optimization algorithms to optimize these parameters ; and n is the population size.jth food source is currently visited by the ith hummingbird.The food source access table is initialized as follows:ith candidate food source after ith candidate food source in the tth iteration, ith hummingbird to visit the target food source, and a is a bootstrap factor obeying a normal distribution . The food source locations are updated as follows:ith iteration of the food source, and When b is a region factor that follows a normal distribution .When If Support vector machine (SVM) is a powerful and widely used machine learning algorithm for classification and regression problems. It offers numerous advantages, including the use of a small number of training samples, a short training time, and a simple structure . The funb is the threshold determined after training the data. C represents the penalty factor, which controls the degree of punishment for misclassified samples. It balances the tradeoff between maximizing the margin and minimizing the classification errors. When the value of C is high, it indicates low tolerance for errors and may lead to overfitting issues. Conversely, when the value of C is low, it may result in underfitting problems. If the value of C is too large or too small, it can lead to a decrease in the model’s generalization ability. g is the kernel function parameter, and the value of g affects the speed and results of SVM model training and prediction.The core idea of SVM is to address nonlinear problems by mapping the samples to a high-dimensional space and finding a linear hyperplane in that space. To tackle this problem, the concept of Lagrange multipliers is introduced to solve the quadratic optimization problem subject to inequality constraints. By solving this problem, a classification hyperplane can be obtained that maximizes the margin between different classes. Finally, the classification hyperplane is mapped back to the original input space, achieving the goal of nonlinear classification in the original input space. The classification function can be expressed as:In this section, we propose an improved version of the artificial hummingbird algorithm (AHA) called IAHA, which incorporates multiple enhancement strategies. We then compare IAHA with four basic algorithms, three single-stage improved AHA algorithms, and two existing improved artificial hummingbird algorithms. The comparison is conducted using eight benchmark test functions and simulation experiments and evaluated using the Wilcoxon rank-sum test.The experimental results demonstrate that IAHA outperforms the four basic algorithms, three single-stage improved AHA algorithms, and two existing improved artificial hummingbird algorithms. IAHA exhibits a faster convergence speed, stronger global optimization capability, and better overall algorithm performance.d dimension in the ith individual corresponding to The standard initialization strategy of AHA adopts a random initialization approach, which often leads to an uneven distribution of individuals in the population and insufficient population diversity. Previous research has shown that an effective population initialization strategy plays a crucial role in improving the optimization accuracy and convergence speed of the algorithm . ChaoticLevy flight is a random flight simulated using a heavy-tailed distribution, where the individual takes small steps for a long time and occasionally takes large steps . IntroduEquation  is the ss), which obeys the Levy distribution, is calculated as follows:v are normally distributed and are defined as:Its step size  on the basis of the best point (g), the second-best point (b), and the lowest point (w). The basic operation of the simplex method is as follows:The aim of the simplex method is to determine the location of the center point ((1) Reflection operation:The reflection point is (2) Expansion operation:e) is used instead of the closest point (w); otherwise, the reflection point (r) is used instead of the closest point (w).When (3) Compression operationt) is used instead of the closest point (w). When s) is used instead of the closest point (w); otherwise, the reflection point (r) is used instead of the closest point (w).When In this paper, by using the simplex method, the algorithm optimizes the worst-quality individuals in the process of each iteration so as to improve the overall quality of the population, which can effectively solve the problem of the algorithm falling into a local optimum and improve the local search and optimality-seeking abilities of the algorithm.Algorithm 1 IAHA implementation steps.Step 1: Parameters such as population size, dimensionality, number of iterations, and upper and lower limits of the search space are set;Step 2: The food source locations are initialized using a fused Tent chaos mapping and direction-learning strategy, and the corresponding fitness values are calculated. The access table is also initialized;Step 3: Flight skills are randomly selected;Step 4: The phase of guided foraging or area foraging begins based on the Levy flight strategy with a 50% probability of each of the two foraging behaviors. The visit table is updated after foraging behavior;Step 5: When migratory foraging conditions are met, hummingbirds perform migratory foraging, randomly replacing the worst food source location. The access table is updated after foraging behavior;Step 6: The location of poor food sources is optimized using the simplex method;Step 7: The algorithm is terminated when the algorithm termination condition is met; otherwise, return to step 3.After AHA is improved by the above three improvement strategies, the flow-specific implementation process of IAHA is shown in Algorithm 1.The process of finding a solution in this study was conducted on an Intel Core i5 CPU with a clock speed of 2.50 GHz and 8 GB of memory. The experiments were performed in a Windows 10 64-bit testing environment using MATLAB R2022a software. The proposed IAHA algorithm was validated using eight classical test functions, as shown in (1) Simulation resultsTo validate the effectiveness and feasibility of the algorithm proposed in this study, a comparative analysis is performed between the proposed IAHA algorithm and other algorithms, including PSO, WOA, GWO, and AHA. The analysis is conducted using the eight aforementioned test functions. The average values and standard deviations obtained by each algorithm are calculated and presented in Based on the analysis presented For multimodal functions In conclusion, the IAHA algorithm demonstrates strong search capability and high stability in both unimodal and multimodal functions. It exhibits adaptability to different types of functions.(2) Comparative analysis of convergence curvesFrom In summary, compared to the other four basic algorithms, IAHA demonstrates excellent performance in terms of convergence speed and optimization accuracy. The convergence curves of IAHA consistently remain below those of the other algorithms, indicating higher optimization accuracy and faster convergence speed.(1) Simulation resultsTo verify the effectiveness of the different improvement strategies proposed in this paper, a comparison is conducted between IAHA, the original AHA algorithm, and the AHA algorithms with single-strategy improvements. These single-strategy improved algorithms, including TAHA, which incorporates the fusion of Tent chaotic mapping and a reverse learning strategy; LAHA, which solely incorporates the Levy flight strategy; and DAHA, which solely incorporates the simplex method. The comparison is conducted on the eight test functions presented in From the analysis in (2) Comparative analysis of convergence curves(1) Simulation resultsTo further demonstrate the performance of the IAHA algorithm, we conduct comparative experiments with two recent improvement algorithms: CLAHA proposed by Wang et al.  and AOAHFrom (2) Comparative analysis of convergence curvesTo further elucidate the performance differences between the IAHA algorithm proposed in this study and other algorithms, such as PSO, WOA, GWO, AHA, TAHA, LAHA, BAHA, CLAHA, and AOAHA, the Wilcoxon rank-sum test is conducted. The experimental parameters are kept consistent with the previous settings, and the 30 independent runs are subjected to the rank-sum test. The results of the test are presented in a. b. p-value is less than the significance level  and the kernel function parameter (g) of the SVM model, resulting in the development of the IAHA-SVM coal mine environmental safety level warning model. Based on the safety regulations of coal mines, the model classifies the safety levels into four categories. We then apply the model to classify coal mine safety data that we collected. By comparing the classification accuracy of the IAHA-SVM model with that of the AHA-SVM model, we aim to demonstrate the advantages of the improved algorithm integrated with the SVM model in this study.In this section, we utilize the IAHA algorithm to optimize the penalty factor  and the kernel function parameter (g) have a significant impact on the prediction accuracy of the model. Usually, these parameters are determined empirically and greatly affect the prediction results. Therefore, in this study, we employ the IAHA algorithm to optimize these parameters and select the values that lead to the best performance of the model. A flow chart of the proposed model is shown in Algorithm 2 IAHA-SVM Execution Steps.Step 1: The collected coal mine safety-related data are divided into training and test sets and normalized;Step 2: The SVM penalty-term coefficients (C); kernel function parameters (g); and IAHA-related parameters, including population size, maximum number of iterations, etc., are initialized;Step 3: The food source locations are initialized using a fused Tent chaos mapping and direction-learning strategy, and the training-set samples are classified, with the SVM coal mine environmental safety classification accuracy as the individual fitness value;Step 4: Flight skills are randomly selected;Step 5: The phase of guided foraging or area foraging based on the Levy flight strategy begins, with a 50% probability of each of the two foraging behaviors. The visit table is updated after the foraging behavior;Step 6: When migratory foraging conditions are met, hummingbirds perform migratory foraging, randomly replacing the worst food source location. The access table is updated after the foraging behavior;Step 7: The location of poor food sources is optimized using the simplex method;Step 8: The algorithm is terminated if the IAHA algorithm termination condition is met and the optimal C and g parameter values are output; otherwise, return to step 4;Step 9: The IAHA-SVM coal mine environmental safety warning model is established.In the SVM model, the penalty factor  and kernel function parameter (g) to achieve the best classification performance based on the accuracy of the training data levels. The parameter range is set from 0 to 1000. The experimental results are shown in In this section, a total of 4120 data samples are selected for the simulation experiment. Among them, 4000 samples are used for training, with 1000 samples for each warning level. The remaining 120 samples are used for testing, with 30 samples for each warning level. The purpose of the experiment is to find the optimal penalty factor , we employ a strategy that combines Tent chaotic mapping with reverse learning to initialize the population. In the foraging phase, the Levy flight strategy is introduced to enhance the search ability. Additionally, the Simplex method is incorporated at the end of each iteration to replace the worst solution. Comparative experiments are conducted to demonstrate the effectiveness of the IAHA algorithm. Next, we combine the improved IAHA algorithm with support vector machine (SVM) to optimize the SVM model’s penalty factor and kernel function parameters. The IAHA algorithm is used to search for the optimal parameters, and a coal mine environmental safety level warning model is established. The effectiveness of the proposed model is validated using a dataset generated from actual measurements in a coal mine in Ningdong Town, Ningxia. Compared to the SVM model optimized by the basic AHA algorithm, the IAHA-SVM model shows superior performance.Integrating intelligent optimization algorithms with real-world application problems can enhance work efficiency. The IAHA-SVM model proposed in this paper can also be applied to classification problems in various other domains. In our future work, we will continue to explore the application of intelligent optimization algorithms in different aspects of coal mine safety. Some potential directions include optimizing the deployment of wireless sensors underground, addressing underground positioning problems, and solving three-dimensional path planning for underground drones.


As the key safety-critical component of a bridge support system, bridge bearings are extensively used to accommodate, balance, and transfer differential displacements and loads between the superstructure and substructure of a bridge during operations. Several studies have been conducted to obtain dynamic modal parameters of traditional bridge bearings only in perfectly dry environments. However, in extreme weather conditions , water can ingress and change the bearings’ properties. In this study, novel 3D-printed porous bridge bearings (3DPPBBs) have been fabricated by Fused Deposition Modeling (FDM) with thermoplastic polyurethane (TPU) filaments. This study is the first to determine the influences of flood conditions on their dynamic properties, which has never been done before. An idealised single degree of freedom (ISDOF) for these novel bearings is considered for the non-destructive field-testing technique of the critical bridge component. A series of experimental tests have been performed under several conditions of flooding levels. The new results unprecedentedly indicate that relatively higher dynamic damping ratios can be found with the increasing flood levels. In contrast, the natural frequencies and dynamic stiffness decrease with the same conditions. Novel insights are essential for bridge engineers to assess and monitor bridge vibrations exposed to extreme weather conditions. Bridge bearings play an important role in a bridge system. Their function is to transfer/facilitate loads/displacements between the superstructure and substructure of a bridge, when the bearings are subject to either static loading or dynamic loading during operation. Another function of bearings is to support the weight of the bridge superstructure. Typical bridge bearings well-known as elastomeric ones are a combination of elastomeric layers and with/without any reinforcement. For example, bridge bearings without reinforcement referred to as plain elastomeric bridge bearings (PEBBs) are required for tiny bridge systems experiencing small horizontal displacements, whilst bridge bearings with reinforcement called steel/fibre-reinforced elastomeric bridge bearings (S/FREBBs) are used to address high horizontal and rotational displacements for relatively large bridges.In our critical review , during As mentioned previously, the failures and inadequate structure designs of these typical bridge bearings under extremely various loading ,19, espeAnother research study from the authors has been conducted on the identification of mechanical properties and energy-absorption capability of a 3D-printed triply periodic minimal surface (TPMS) sandwich lattice bridge bearing model without a thickness under combined compression-shear loading. The results have clearly shown that the proposed lattice model with at least six-unit cells under the combined compression-shear load can reach the mechanical responses and specific energy absorption of an elastomeric block unit cell. Moreover, the shear force and the specific energy absorption of the lattice model are relatively higher under combined compression-shear loading and uniaxial compression loading, respectively. Its failure modes for bridge bearing application observed through the stress-stain curve of the lattice model under both the same conditions are identified as elastic-plastic and hysteretic behaviours. It is evident that there are three main factors  which dominate these two behaviours of the TPMS lattice model .Since the benefits of using these developed structures for bridge bearing applications, they are likely to be used in bearings’ structure under any seismic action, potentially offering better physical and mechanical properties, including crashworthiness, better energy absorption, vibration attenuation, lightweight, eco-friendly and recycling materials, and so on. According to the behaviours of these developed bridge bearings have been investigated under static loading with only dry conditions. It is necessary to understand their behaviour affected by floods, which has never been investigated. The effect of floods on railway structure could result in the change of dynamic material properties . To inveTo best of our knowledge following a critical review of the available literature, there are no current works focusing on the influence of the flood conditions on the dynamic modal paraments of 3D-printed porous bridge bearing (3DPPBBs) using gyroid unit cells under vibration with various flood conditions. In this paper, we aim to determine the dynamic modal paraments of the 3DPPBBs affected by flood conditions, and also to develop a numerically vibrating model from these dynamic modal parameters for practical use under various flood conditions. Moreover, this model can potentially be a promising alternative for 3D-printed porous bridge bearings with a high-performance to density ratio under bridge operations with severe environmental conditions. Materials and methods, including the process of manufacturing specimens of this gyroid structure, will be detailed in the following section. The impact hammer excitation method is used in the paper due to the ease of setup and the possibility of complete tests, when considering the size and weight of specimens as well as their range of frequency excitation. Subsequently, the experimental measurements and the discussion of the experimental and numerical analysis will be presented. Finally, the proposed gyroid bridge bearing model subjected to vibration with various flood conditions is generated using the dynamic modal parameters from the experiment data, in order to predict its behaviour in practical use.The specimen materials are stainless steel and thermoplastic polyurethane (TPU) considered as the mass and spring, respectively  . In termIn this study, we aim to determine the dynamic modal parameter of a single-degree-of-freedom mass-spring system supported by a novel TPMS bridge bearing under various flood conditions. Furthermore, the proposed model is established and developed by using these modal parameters. Impact hammer testing is a well-known non-destructive approach based on instrumented hammer impact excitation and a signal-processing analysis to measure the vibration response of the structure to impact excitation. In order to obtain the frequency response function (FRF), the Fourier transformation is used to convert the vibration signals to the FRF. Then, it is employed to extract the dynamic modal parameters and the dynamic properties .An impact hammer force (tap force) and accelerometer records are proper equipment for modal analysis, if the dynamic properties are found in a range between middle and high frequency domains. The record data for analyses in a short time is performed using an acquisition device connected to a laptop. It is important to mention that the idealised concept for lightweight TPMS bridge bearing testing is considered as a lumped mass model . This can potentially enable the test setup to obtain the direct vicinity of the resonant peak. Regarding the SDOF idealisation of the system, the use of an accelerometer is conducted to measure the response at the middle point with at least 3 hit times and then a range of FRFs obtained are extracted to acquire the natural frequencies and the mode shapes using modal analysis software (DATs).The flood simulation is presented to consider the effect of flood on the dynamic modal parameters of the lumped mass using the proposed TPMS bridge bearing as a spring on various flood conditions. The dynamic behaviours are measured at every level (increase every 25 per cent from 0 to 100 per cent), as shown in In reality, most of physical structures are continuous, their behaviour can generally be represented by a discrete parameter model as illustrated in In addition to the SDOF system, it is fundamental for the investigation of MDOF systems, as numerous vibration systems employed in today’s engineering are able to be simplified to an SDOF idealisation. Thus, idealised outcomes can be acquired using this theory, and the structural SDOF system can be represented as a single input-single output system ,38,39, wThe idealised components of the physical system can be described by the formula of motion presented in Equation (2).The solution of a vibration system, This selection is made because the sine function describes oscillation. Where the constant A is the amplitude  of the displacement; the angular natural frequency (Substitution of Equations (3)–(5) into 2 without damping and excitation force yieldsDividing by In case of an underdamped system , can be integrated to determine the impulse. The impulse of the force f(t) is determined by the integral, expressed byRecalling the response of an underdamped single-degree-of-freedom system with zero initial displacement Substitution of The experimental measurements derived from the modal testing of a thick steel plate over the proposed 3D-printed porous TPU bridge bearing are indicated in With the increasing water level, the acceleration reduces as shown in It is obvious that the FRFs are affected by the water level as seen in As such the natural frequency decreases when the flood level increases, and the stiffness also decreases as presented in In short, as presented in According to the experimental results, in this section the development of a 3D-printed gyroid bridge bearing has been proposed and validated against the experimental data in the following section. The numerical prediction resulting from the modal analysis of a thick steel plate over the proposed 3D-printed porous TPU bridge bearing under dry condition is conducted in Abaqus software, as indicated in It is important to note that in this paper, to enable the modal testing, an idealised single-degree-of-freedom (SDOF) is considered for the proposed bearing’s vibration system due to the extremely lightweight porous bridge bearing specimen. In other words, the specimen can easily move in any direction if there is no large mass on the top of the specimen. Thus it is likely to behave under a free-free condition, resulting in a multiple-degrees-of-freedom (MDOF) system. Thus, the concept method of SDOF is justified to correctly obtain the dynamic modal parameters of the proposed novel 3D-printed porous TPU bridge bearing. These dynamic modal parameters will be useful for bridge engineers to model and develop 3D-printed porous bridge bearings with composite materials under vibration with various environmental conditions. Further work would be conducted on the determination of dynamic modal parameters of these bearings using the principle of a single-degree-of-freedom system shown in this paper to obtain their modal parameters and to comprehend how the bearings will behave under various vibrations with flooding conditions.Typical bridge systems have extensively adopted elastomeric bridge bearings, which are one of the most important components in the bridge systems and offer good performance during operations. Their functions are not only to support the weight of a bridge superstructure, but also to accommodate/transfer horizontal and rotational displacements/vertical and horizontal forces to the substructure. However, their performance could be better if using a porous structure with a high performance to weight ratio stated in the introduction section. As a resonance phenomenon can occur in a bridge system under vibration, its dynamic modal parameters of dry conditions are determined in order to safely design and monitor the vibration performance of these bridge bearings before the resonance failure. On the other hand, dynamic modal parameters of a flood porous bridge bearing condition have never been examined before. According to a review in , rubber In terms of manufacturing 3DPPBBs, they are produced by the fused deposition modeling (FDM) method with thermoplastic polyurethane (TPU) filaments, which have rubber-like properties for bearing conditions. To enable the manufacturing process, the features of a University of Birmingham’s Ultimaker 3D printer are required. Non-destructive field-testing approach involving the impact hammer excitation is considered for investigating the influence of flood conditions on dynamic characteristics of novel 3D-printed porous bridge bearings. Moreover, their vibration system is considered as an idealised single degree of freedom (SDOF), due to their complicated geometry, and extremely lightweight. A range of experimental tests has been conducted under several conditions of flooding levels.This research is the first to focus on such critical flood conditions. Experiments and numerical analysis are performed to handle such a pressing problem. The experiment results have discovered an unprecedented insight into the dynamic properties of the flooded porous bridge bearings. With the increase in water level, the damping of the bridge bearing system rises, while both the natural frequency and stiffness of the system reduce. It is evident that the dynamic properties of the 3DPPBBs can change considerably under flooding conditions compared to the initial state (dry condition). Unlike flood conditions, the damped natural frequency shifts are relatively small as presented on the FRF curves, the damping increases with the rising water level, but the trend of the system’s stiffness continues to decrease as the natural frequency is proportional to the stiffness. In short, this experiment is the world’s first examination of the porous bridge bearing behaviour in flood conditions. The numerical model using these dynamic modal parameters has been established to visualise the experimental vibration behaviour for flooding conditions. Moreover, these insights into modal parameters in flood conditions would offer a new benchmark for the numerical 3D-printed porous bridge bearing modelling in reality. Future study will focus on the determination of dynamic modal parameters of porous bearings with 3D-printed composite materials under various vibrations (referring to a multiple-degree-of-freedom vibration isolation system for bridge bearing applications) with flooding conditions to obtain their parameters used for the numerical modeling of complex porous structures.


In human bipedal walking, the plantar surface of the foot is in contact with the floor surface, so that a vertical free moment (VFM), a torque about a vertical axis acting at the centre-of-pressure due to friction between the foot and the ground, is generated and applied to the foot. The present study investigated the functional significance of the VFM in the mechanics and evolution of human bipedal walking by analysing kinematics and kinetics of human walking when the VFM is selectively eliminated using point-contact shoes. When the VFM was selectively eliminated during walking, the thorax and pelvis axially rotated in-phase, as opposed to normal out-of-phase rotation. The amplitudes of the axial rotation also significantly increased, indicating that the VFM greatly contributes to stable and efficient bipedal walking. However, such changes in the trunk movement occurred only when arm swing was restricted, suggesting that the VFM is critical only when arm swing is restrained. Therefore, the human plantigrade foot capable of generating large VFM is possibly adaptive for bipedal walking with carrying food, corroborating with the so-called provisioning hypothesis that food carrying in the early hominins is a selective pressure for the evolution of human bipedalism. Numerous studies have been published elucidating GRF during human walking to explain the biomechanics and motor control of human bipedal locomotion10. However, the VFM was often neglected in these studies, and studies focusing on the VFM have been limited until recently8, despite its potential importance for regulation of rotational stability of bipedal walking in humans. These previous studies have identified that (1) the VFM is acting in the direction of external and internal rotations during the first and second half of the stance phase during walking7; (2) the magnitude of the VFM is greater in the second half of the stance phase7; (3) the VFM is strongly affected by arm swing and the peak magnitudes of the VFM increase when arm swing is restrained, resulting in higher energy expenditure in locomotion8; and (4) the VFM is also affected by gait speed, and the peak magnitudes of the external and internal rotations decrease and increase, respectively, as gait speed increases7. However, to date, no studies have attempted to identify the true biomechanical impact of the VFM on bipedal walking in humans by “knocking out” or selectively eliminating VFM that is applied to the foot while walking. Therefore, it remains largely unclear how and to what extent does the VFM contribute to the generation of stable and efficient human bipedal walking.In human bipedal walking, the plantar surface of the foot is in surface contact with the floor, so that the ground reaction forces (GRFs) are applied to the foot in a distributed manner, generating a vertical free moment (VFM), i.e., the torque about a vertical axis acting at the centre of pressure due to friction between the foot and the ground11, predator avoidance12, thermoregulation13, locomotor efficiency14, and foraging in a wetlands habitat15 or on flexible branches16. However, why the adoption of upright bipedal locomotion can lead to improved reproductive success of our ancestors in the course of human evolution still remains largely obscure. One of the possible selective advantages of bipedal locomotion is that it releases the arms so that they can be used to carry things, such as food items and infants. However, carrying food items or infants certainly restricts arm swing during walking, possibly requiring generation of larger magnitude of VFM. Under such circumstances, the derived plantigrade foot of humans19 may facilitate generation of larger VFM because of the increased moment arms of the horizontal GRFs with respect to the centre-of-pressure (COP). Therefore, it could be anticipated that the human plantigrade foot might have evolved as an adaptation to facilitate generation of rotationally stable bipedal walking while carrying things, such as food items and infants, by counterbalancing the vertical moment of the body.Understanding the contribution of the VFM in the regulation of the body’s rotational movement around the vertical axis during human walking is also important in the context of the evolution of human bipedalism. The ultimate and proximate causes for the evolution of habitual bipedalism remain a key question in palaeoanthropology. A number of hypotheses have been proposed for the positive selective pressure for human habitual bipedal locomotion, including the feeding strategy8, arm swing was restrained by asking the participants to fold their arms while conducting the walking experiment. Therefore, the present study primarily compared bipedal walking using the PC and NL shoes without arm swing  to test our null hypothesis that the selective elimination would not affect the kinematics and kinetics of human bipedal walking. In addition, to investigate the possible contribution of arm swing when the VFM is selectively eliminated, we also investigated bipedal walking with the PC shoes and arm swing (PCwAS) for comparisons, and tested whether kinematics and kinetics of bipedal walking in the above three conditions are equal to one another.The present study kinematically and kinetically investigated the functional significance of VFM in human bipedal walking by analysing human walking when the VFM is selectively eliminated. For this purpose, a pair of ‘point-contact (PC) shoes’ with a metal spherical segment  attached to each sole of an ordinary athletic shoe was fabricated Fig. . As the The mean gait cycle duration , stride length , and speed  were essentially identical among the three conditions, PCwoAS, NLwoAS, and PCwAS, respectively.Three components of the GRF profiles exhibiting typical waveforms of bipedal walking were very similar for all three conditions. Although there were statistically significant differences in the peak magnitudes of the propulsive, lateral, and the first and second vertical GRFs among the three conditions Fig. , these dThe ranges of axial rotation of the head, thorax, and pelvis segments relative to the global coordinate system were significantly larger in PCwoAS than in NLwoAS   around the anterior and vertical axes (Lx and Lz) during walking was significantly larger  and smaller  in PCwoAS than in NLwoAS  was the largest in the magnitude during double-support phase in both PCwoAS and NLwoAS . However, the external moment due to GRF was nearly zero during the single-support phase. The amplitude of the VFM (τz) was much smaller than the moment due to GRF, but the VFM was generated during the single-support phase when the external moment due to GRF was nearly zero.The net external moment  during walking was significantly smaller while walking with the PC shoes than while walking with the NL shoes Fig. . This isThe present study also observed that even when the VFM was eliminated during walking with the PC shoes, if the arm swing was not restrained, the thorax and pelvis could rotate out-of-phase and the ranges of rotation were much reduced as observed in walking with the NL shoes Fig. c, even t19. In addition, the human foot is unique in having comparatively short toes than non-human primates25. Therefore, the area of the plantar surface of the human foot is comparatively much larger in the anteroposterior direction than those of the other non-human primates. Furthermore, the human foot lost opposable hallucis27, indicating that the plantar surface of the foot (thenar eminence) is mediolaterally wider in humans compared to other non-human primates due to the presence of the first ray next to the second ray in parallel . The large contact area between the plantar surface of the foot and the ground may provide a larger moment arm of the horizontal GRFs with respect to the COP. Nevertheless, the human foot uniquely possesses plantar arch that reduces the surface contact area, but what is important here is the large contact area distant from the COP. Therefore, these unique features of the human foot possibly contributed to the generation of a large VFM. In fact, plantigrade feet were reportedly capable of producing larger VFM to the ground than digitigrade feet in human participants resisting against vertically oriented external torsional moment applied to their body28. In addition, the human foot is known to possess a unique capacity to generate a VFM during axial loading30 due to the kinematic coupling of the calcaneus and tibia, the so-called tibio-calcaneal coupling33. Owing to the innate morphology of the human foot, axial loading of the human foot resulted in eversion of the calcaneus and internal rotation of the talus and tibia34, which in turn resulted in generation of ground reaction moment around the vertical axis of the ground (VFM)35. However, the coupling motion of the calcaneus and tibia and the resultant VFM were shown to be much smaller in the chimpanzee foot30. Therefore, the human foot might have evolved to produce a large VFM, possibly to facilitate generation of stable and efficient bipedal walking particularly when arm swing is restrained. Recent studies have suggested that evolutionary changes in the compliance of the foot have been the key for the evolution of human bipedal walking and running38, but selection for the capacity of the foot to produce large VFM may also have been a critical factor for the evolution of the derived features of the human foot anatomy.The human foot uniquely possesses an enlarged, robust calcaneus, the tuberosity of which points inferoposteriorly, allowing prominent heel strike during walking41. This hypothesis has become more accepted particularly after the discovery of Ardipithecus demonstrating only a low degree of sexual dimorphism42. In this hypothesis, positive selective pressure was considered to be imposed on pair-bonded male individuals, provisioning dependent females, and offspring by walking on two legs and carrying food items in the freed arms. Such provisioning likely leads to increase in their survival fitness and reproductive success. In addition, walking on two legs allows female individuals to carry infants who were probably born relatively immature in early hominins44, which could further contribute to increased reproductive success. If the arms are used for carrying food items or an infant, the arm motions should be very much restricted, and the arms cannot be used for regulation of the WBAM during walking. Therefore, the present results show that the ability to generate VFM is critical for generating stable and efficient bipedal walking when arm swing is restrained, thereby supporting the provisioning hypothesis. It should be noted that Ardipithecus had an opposable hallux, indicating that the capacity to generate VFM was probably more restricted as it is in chimpanzees30. Therefore, the foot of early hominins was not as adapted to habitual bipedal walking as that of modern humans. However, the morphology of the foot in early hominins should have been gradually derived to be more human-like up to the time of Australopithecus and early Homo through the course of human evolution possibly due to continuous selective pressure on male provisioning and bipedal walking. Therefore, the fact that the derived characteristics of human foot facilitated production of larger VFM30 further advocates that food provisioning of dependent females and offspring by males and infant carrying by females in early hominins are selective pressures for the evolution of human bipedalism as well as the corresponding anatomy of the human foot.Currently, one of few hypotheses concerning the origin of the evolutionary transition to bipedalism accompanied by substantial paleontological contexts is the so-called provisioning hypothesisThe present study has several limitations. First, this study did not attempt to directly quantify the difference in the energetic cost of locomotion during bipedal walking with the PC and NL shoes. A future study should directly confirm whether the in-phase and larger axial rotation of the thorax and pelvis observed when the VFM was selectively eliminated during walking actually reduces the energetic efficiency of locomotion by using expiratory gas analysis. Second, the present study analysed bipedal walking when arm swing was restrained by asking the participants to fold their arms, but not asking them to carry actual food items or a mannequin of an infant, because what to carry  was difficult to choose, and normalisation of the object size, shape, and weight could also be troublesome. However, a future study should also confirm whether bipedal walking when the VFM was selectively eliminated is actually deteriorated in terms of stability and energetic efficiency by carrying objects, such as food items and an infant mannequin. Third, only adult male individuals were recruited for the experiment. Although absolute differences of bipedal walking between sexes are small, the current findings should also be confirmed by female participants in the future.A pair of point-contact (PC) shoes with a metal spherical segment  bonded to each sole of an ordinary athletic shoe  was fabricated to eliminate VFM during walking Fig. . It was Ten adult male individuals without any history of orthopaedic or neuromuscular impairments  participated in the study. Informed consent was obtained from each participant. This study was reviewed and approved by the Office for Life Science Research Ethics and Safety at the University of Tokyo. All methods were carried out following the relevant guidelines and regulations.45, and the positions of the markers were captured at 100 Hz. The GRF signals were simultaneously recorded at 200 Hz using a universal recorder . The participants were asked to walk with restricted arm swing by folding their arms. In the experiment, the participants firstly walked with the NL shoes and, then, with PC shoes . To adapt to the walking with the PC shoes, the participants were allowed to initially walk back and forth along the walkway for 5 min and, then, on a 1.8-m long treadmill  set at 4 km/h for 10 min as a practice session. We also asked the participants to walk with the PCwAS for comparisons. We did not randomise the order of conditions to eliminate possible after-effects in walking patterns following adaptation to the PC shoes. The participants were instructed to step on the three force plates with their left, right, and left feet, respectively. Two pairs of photoelectric cells were placed 4 m apart across the force plates to instantaneously measure walking speed of each trial. Trials with walking speed within 5% of the target speed (1.1 m/s) were selected as successful trials, and five successful trials were recorded for each condition.The participants walked across three 600 mm × 400 mm force plates  set in a wooden walkway (8.2-m long) with the NL and PC shoes of appropriate size Fig. 45, and t46. No lowpass filtering was applied to the GRF signals. To quantify the three-dimensional (3D) body kinematics, a segment-fixed coordinate system was defined for each of the 13 body segments  using the attached markers , and speed were calculated using the motion-captured and force plate data. The gait cycle was defined as the time interval between two successive right heel-contacts. The marker data were low-pass filtered at 7 Hz using a zero-phase shift lowpass filterL, was calculated as the sum of the individual segment angular momenta as in the study by Herr and Popovic47:mi is the mass, Ii is the inertia tensor of the segment’s COM, ci is the COM position vector, and ci), with respect to the whole-body COM, of the i-th segment. ωi is the angular velocity vector of the i-th segment, all represented in the laboratory coordinate system. The segmental masses, inertia tensors, and COM positions of each participant were estimated based on the measured marker positions and the anthropometric parameters  as presented in the studies by de Leva48 and Zatsiorsky49. The WBAM was normalised by the product of the body mass, COM height, and walking velocity47.A 15-segment whole-body model, consisting of the above 13 segments and hands defined as point masses, was used to calculate the WBAM regarding the body’s COM. The WBAM concerning the body’s COM, M, can be calculated as follows:47.The net external moments of the body’s COM due to GRFs and VFMs, p < 0.05. All statistical analyses were performed using R, version 4.1.2 50.To test for significant differences in the mean peak values of the measured quantities  among three conditions , one-way repeated measures of analysis of variance with post hoc Bonferroni tests were performed. If the normality or homogeneity was violated using the Shapiro–Wilk normality test, Friedman tests and nonparametric Bonferroni tests were used. The statistical significance level was set at Supplementary Figures.


Denoising remote sensing images is crucial in the application and research of remote sensing imagery. Noise in remote sensing images originates from sensor characteristics, signal transmission, and environmental conditions, among which Gaussian noise is the most common type. In this paper, we proposed a multiple-optimization bilateral filtering (MOBF) algorithm based on edge detection and differential evolution (DE) methods. The proposed algorithm optimizes the spatial domain filtering kernel and the spatial domain Gaussian kernel by using the standard deviation and width of the edge response. By employing the DE algorithm, the individuals in the population based on the standard deviation of the gray value domain are subjected to iterative mutation, crossover, and selection operations to refine the latent solution vectors and determine the optimal color space for optimizing the standard deviation of the pixel range domain kernel. As a result, the MOBF algorithm, which does not require any parameter input, is realized. To verify the feasibility and effectiveness of the proposed algorithm, denoising experiments were conducted on remote sensing images by using evaluation metrics such as the mean squared error, peak signal-to-noise ratio, and structural similarity index. The experimental results revealed that the MOBF algorithm outperforms traditional algorithms for all three evaluation metrics. Remote sensing imagery is widely used in various fields, such as geological exploration, urban planning, and environmental monitoring. However, various noise sources, such as circuit noise and dark current noise , can intDenoising methods for remote sensing images typically employ linear or nonlinear filtering techniques, such as median filtering , GaussiaBilateral filtering represents an extended form of the Gaussian filtering methodology. This technique incorporates two key factors during image processing: the spatial coordinates of pixels and the disparities in pixel intensities. This amalgamation enables the enhanced preservation of edge data . In contIn this paper, we proposed a remote sensing image denoising method based on multi-objective optimized bilateral filtering. The proposed method employs several techniques to enhance the bilateral filtering parameters. It combines edge detection operators and the differential evolution (DE) algorithm with traditional bilateral filtering. The adjusted parameters include the convolution kernel size, the spatial standard deviation of pixel positions, and the color space standard deviation of pixel values. These modifications greatly improve the noise reduction capabilities of the proposed method. Furthermore, the implementation process and experimental results of the proposed method are discussed in this paper. The results demonstrated a substantial enhancement in denoising performance while preserving the edges, thus indicating that the proposed method is suitable for subsequent processing and applications of remote sensing images.Bilateral filtering is a powerful nonlinear filtering method that eliminates noise and preserves edge details in an image. It is based on a weighted average of the image’s spatial domain and grayscale value domain. The weights are determined based on the product of a spatial domain Gaussian kernel and a grayscale value domain Gaussian kernel ,16. By cThe Canny edge detection algorithm is a multistage edge detection algorithm proposed by John F. Canny in 1986. This algorithm processes single-channel grayscale images by detecting the first and second derivatives of pixel intensities and determines whether a pixel is located on an edge ,18.x, y) denote the coordinates of the filter and σ denotes the standard deviation of the Gaussian function.The algorithm involves the following steps. Gaussian filtering is applied to the input grayscale image to effectively remove noise:Next, the gradient magnitude and direction are computed for each pixel in the image:Subsequently, non-maximum suppression is applied to the gradient magnitude to reduce the width of the detected edges.The gradient magnitude is categorized into three groups by using a dual threshold algorithm: strong edges, weak edges, and non-edge pixels. Strong edges have gradient magnitudes surpassing the high threshold, weak edges have gradient magnitudes falling between the low and high thresholds, and non-edge pixels have gradient magnitudes below the low threshold. Furthermore, edge connectivity processing is employed for the weak edges to detect weak edge pixels adjacent to strong edges and upgrade their classification to strong edges. The integration process establishes connections among the weak edges, resulting in a comprehensive and refined edge detection outcome.To determine the upper and lower thresholds for the Canny algorithm, an automated technique known as the Otsu method is applied. This method serves to establish the binarization threshold of the image, a crucial step in the process of image binarization segmentation. The Otsu method autonomously identifies a suitable threshold for binarization, thereby enabling the clear extraction of target object contours ,20. The The image’s histogram is computed, tabulating the occurrences of grayscale levels for each pixel.t”. The between-class variance t” according to the formula:The within-class variance is calculated by categorizing pixels into two groups, namely the background and the foreground, based on each possible threshold value “k” signifies the two categories 0 and 1; “ω” denotes the proportion of pixels with grayscale values less than or equal to “t” in the “k”-th class; and t” in the “k”-th class.Here, “The global inter-class variance The optimal threshold is identified by selecting from all feasible thresholds the one that maximizes the inter-class variance g(t). By undergoing this computation, the Otsu algorithm automatically pinpoints the most appropriate binarization threshold, thereby enhancing the distinction between the foreground and the background and effectively facilitating object extraction from the image. In this study, the high and low thresholds for the Canny algorithm utilize binarization thresholds determined by the Otsu algorithm, scaled by 0.5 and 1.5 times.The DE algorithm is an optimization algorithm primarily used to solve multidimensional continuous optimization problems. It was proposed by Storn and Price in 1997 and has since become a widely used global optimization algorithm ,22,23. TNP (population size); D ; and First, an initial population, where each individual represents a potential solution, is randomly generated:NP].The mutation operation randomly selects three distinct individuals and updates the current individual’s position by using the difference vectors between them, resulting in a new candidate solution:During the crossover operation, the mutation vector is combined with the original individual to create a new individual. The target vector The selection operation involves comparing the trial individuals with the original individuals and selecting the superior individuals as members of the next-generation population:The termination criteria for the DE algorithm are met when either the maximum predetermined number of generations is reached or when the objective function has converged to a considerable extent. At this stage, the algorithm concludes and outputs the optimal solution.The parameters of the bilateral filter, such as the spatial domain filter kernel and the standard deviation of the spatial domain Gaussian kernel, are adjusted using the Canny operator. First, the Otsu thresholding technique is used to adjust the high and low thresholds  is defined to assess the fitness of each individual by determining the PSNR value of the filtered image corresponding to each individual:A population of 20 individuals is randomly generated within the range of [Individuals with a better fitness are selected from the population, and genetic operators such as crossover and mutation operations are applied to them to generate new individuals. The population is then updated by replacing the individuals with the newly generated ones. This is constantly iterated, ultimately yielding the optimal parameters and their respective PSNR values.The flowchart of the multiscale-optimized bilateral filtering algorithm is illustrated in Edge preservation and smoothness control: The standard deviation of the Canny operator indicates the intensity variation in the edges of the image. By adjusting the size of the spatial domain Gaussian kernel based on the standard deviation of the Canny operator, the smoothness of the filter can be controlled. When the standard deviation of the Canny operator is larger, increasing the size of the spatial domain Gaussian kernel enhances the smoothing effect. In contrast, when the standard deviation of the Canny operator is smaller, reducing the size of the spatial domain Gaussian kernel helps preserve more details during the filtering process.Noise suppression: The convolution kernel size (D) of bilateral filtering can be used to control the degree of noise suppression. A larger convolution kernel can effectively average the neighboring pixel values and reduce the effect of noise. By dynamically adjusting the convolution kernel size based on the standard deviation and edge response width of the Canny operator, an optimal kernel size that aligns with the intensity of the noise can be adaptively selected, enabling superior noise suppression.High adaptability: The DE algorithm, which incorporates differential and mutation operations, enables a global search and the automatic selection of the optimal standard deviation for the pixel value color space based on the characteristics of the image. By considering the characteristics of the image, the DE algorithm selects the optimal standard deviation for the pixel value color space to adjust the pixel range domain kernel. This is achieved by combining it with the spatial domain Gaussian kernel and kernel size, enabling adaptive filtering.Multiscale-optimized bilateral filtering offers several advantages. It adjusts the spatial domain Gaussian kernel and spatial domain filter kernel (D is the convolution kernel size) of the bilateral filtering based on the standard deviation and the edge response width of the Canny operator; moreover, it employs the DE algorithm to optimize the pixel range domain kernel. The advantages are listed as follows:MSE) is a commonly used metric for evaluating the denoising performance of image-processing algorithms. It is the average of the squared differences between the image pixel values and their corresponding true values. A smaller MSE indicates a more effective denoising result [I and J represent images of the same size with a width W and a height H, and The mean squared error (g result :(22)MSE=PSNR is a metric used for evaluating image quality [PSNR is commonly used to compare the disparity in quality between an image and its post-processed version, such as after compression or denoising. The calculation formula for the PSNR is as follows:The  quality . It quanSSIM) is a standardized metric used for measuring the degree of similarity between two images. Unlike conventional metrics such as the MSE and PSNR, the SSIM better corresponds to the human visual perception of image quality [SSIM compares the mean, variance, and covariance of the pixel values in the two images, and these metrics are then weighted and aggregated to obtain the final similarity score. SSIM values range from −1 to 1, with a higher value indicating a stronger similarity between two images. An SSIM value of 1 means that the two images are perfectly identical.x and y, respectively; The structural similarity index  algorithm and several other denoising techniques, namely anisotropic filtering (AF), adaptive median filtering (AMF), original bilateral filtering (BF), wavelet denoising (WD), and non-local means (NLM), was performed. The denoised images were evaluated using objective metrics such as the PSNR, the MSE, and the SSIM. Furthermore, an analysis of the noise power spectral density was conducted to determine the level of information preservation across different frequency domains, thus providing valuable insights into the effectiveness of the denoising techniques.In order to find the optimal parameters of the DE algorithm, this paper conducts experiments under the conditions of a Gaussian noise concentration of 1% or 3%, and changes the iterative parameters of the DE algorithm to find the optimal parameters. The parameter of the iteration with the largest noise ratio (PSNR) is obtained through experiments. The optimal number of iterations is 400. Therefore, the number of iterations of the DE algorithm in the remote sensing simulation experiment in this paper is set to 400. The figure below shows the process of parameter optimization. The experimental results are shown in To validate the effectiveness of the proposed denoising algorithm, an experiment was conducted using GF-1 imagery for a remote sensing image simulation. A higher PSNR value indicated superior denoising results, reflecting a closer resemblance between the processed and original images. A smaller MSE value indicated a higher level of similarity in pixel values between the processed and original images, indicating minimal distortion. A higher SSIM value indicated an improved performance in terms of human perception, encompassing factors such as the brightness, contrast, and structural fidelity. The experimental results are presented in As can be seen from Similarly, a simulation experiment was conducted on unmanned aerial vehicle (UAV) remote sensing images to assess the denoising effectiveness of different algorithms in terms of three metrics: the PSNR, the MSE, and the SSIM. The experiment’s results are presented in As can be seen in As can be observed from the experimental results summarized in In the power spectral density (PSD) plot of an image, the low-frequency region corresponds to low-frequency information, the overall brightness, and the color; the mid-frequency region represents texture information, details, textures, and other intricate patterns within the image; and the high-frequency region reveals edge information, including the edges, contours, and other distinct features within the image.In this study, a comparison of noise power spectral density was performed between the proposed algorithm and three filtering algorithms . The power spectral density plots of the images after applying different processing methods are shown in In the noise power spectral density plot, a high energy in the high-frequency region indicates the existence of high-frequency noise in the image, a high energy in the mid-frequency region indicates a considerable amount of detail and a lower level of image smoothness, and a higher energy in the low-frequency region is indicative of increased brightness and contrast in the image, leading to an overall brighter appearance. As can be observed from In this paper, the MOBF algorithm was proposed to improve upon traditional bilateral filtering in terms of denoising effectiveness and edge texture preservation by utilizing edge detection operators and the DE evolutionary algorithm for multiple optimizations. The optimizations are made at the spatial domain filter kernel, spatial domain Gaussian kernel, and pixel range domain kernel. The proposed MOBF algorithm exhibited satisfactory performance, even in the absence of input parameters for the bilateral filtering algorithm.The experiments detailed in this paper focus on varying levels of Gaussian noise intensity. The denoising experimental results for remote sensing images demonstrated the superior performance of the MOBF algorithm. Evaluation metrics such as the PSNR, the MSE, and the SSIM revealed the superiority of the MOBF algorithm over traditional approaches. Based on the outcomes of the experiments, the algorithm introduced in this study demonstrates enhancements in comparison to the optimal outcomes achieved by the comparative algorithms. Notably, there was a noteworthy 2.79% enhancement in the PSNR, a substantial 6.25% decrease in the MSE, and a discernible 1.05% improvement in the SSIM. The images processed using the MOBF algorithm exhibited remarkable denoising and edge preservation effects, resulting in enhanced image clarity.These results validate the feasibility and effectiveness of the MOBF algorithm for denoising remote sensing images. Future research can further explore the algorithm’s applicability in other remote sensing scenarios and focus on enhancing its computational efficiency and practicality to drive advancements in the field of remote sensing image processing.


Hazard Analysis and Critical Control Points (HACCP) systems are a risk based preventive approach developed to increase levels of food safety assurance. This is part 1 of a pilot study on the development, implementation and evaluation of a HACCP-based approach for the control of good udder health in dairy cows. The paper describes the use of a novel approach based on a deconstruction of the infectious process in mastitis to identify Critical Control Points (CCPs) and develop a HACCP-based system to prevent and control mastitis in dairy herds. The approach involved the creation of an Infectious Process Flow Diagram, which was then cross-referenced to two production process flow diagrams of the milking process and cow management cycle. The HACCP plan developed, may be suitable for customisation and implementation on dairy farms. This is a logical, systematic approach to the development of a mastitis control programme that could be used as a template for the development of control programmes for other infectious diseases in the dairy herd. Hazard Analysis and Critical Control Points (HACCP) is a preventive risk management approach that has been extensively used by food industries to increase product safety and protect public health . HACCP hAs yet, agreement has not been reached on robust and practical systems relevant to food safety during primary livestock production. Several papers have examined the potential application of HACCP methods to livestock production -9. HowevIn many European countries, practicing veterinarians have introduced herd health programmes, specifically focusing on animal health and fertility management. Widespread adoption of herd health programmes has been problematic. In a recent United Kingdom (UK) report on veterinary expertise in food animal production, there was a perception among farmers that veterinary input was too focussed on individual animal care, did not 'add value' and was not targeted on profitability, one of the key objectives of optimal herd health implementation . Lievaarper se. Furthermore, several authors have highlighted specific problems relating to the effective implementation of mastitis control programmes, including time constraints and insufficient direct economic benefits [Milk quality is one of the most important issues facing the dairy industry internationally . Somaticbenefits ,20 and pbenefits .As mentioned previously, concerns have been raised about the feasibility of implementing HACCP systems during primary livestock production. However, there has been limited work examining the application of HACCP systems to milk quality. Therefore, the objective of the present pilot study was to develop, implement and evaluate a HACCP-based approach for the control of mastitis on six Irish dairy farms. This paper (part 1 of the study) will describe the development of a template, based on HACCP principles, suitable for the control of mastitis on dairy farms. The desired output of part 1 of this study was to develop a HACCP-based control programme for mastitis that could be readily customised for use on individual farms.An innovative, HACCP-based approach was used to create a mastitis control programme that could be applied as a disease management tool on individual Irish dairy farms, whilst also enabling external verification.HACCP principles, as developed by the Codex Alimentarius Commission  and furtThe selection of critical control points was carried out initially using a series of flow diagrams developed as part of the study with consideration of the definitions and the decision tree developed previously by the Codex Alimentarius Commission  and thosIn the first instance, an interdisciplinary team comprising the five authors, with expertise in veterinary epidemiology, bovine health management and food safety, was assembled. In order to create a HACCP-based system for mastitis control, it was necessary to create two process flow diagrams describing the milking process and the annual cow management cycle, respectively. To identify the potential hazards associated with the key stages in the mastitis infection process, it was also considered necessary to generate a flow diagram representing a deconstruction of the infectious process in mastitis. These flow diagrams were used to identify CCPs by cross-referencing the risk factors for each of the hazards identified in the infectious process flow diagram to the two production process flow diagrams using a colour-coded table (data not shown). Identification of the CCPs was then finalised by cross-referencing these steps with the infection process using the Codex Alimentarius decision tree adapted by Noordhuizen et al. . SubsequTwo production process decomposition flow diagrams, the Milking Process Flow Diagram and the Cow Management Cycle Flow Diagram, were developed to identify the relevant management activities in the context of mastitis that might occur on an Irish dairy farm. The team considered the potential risks associated with each activity after considering relevant published literature -31.A Milking Process Flow Diagram Figure  was deveThe Cow Management Cycle Flow Diagram Figure  illustraThis conceptual flow diagram Figure  represenThe critical steps for the establishment of infection are as follows:I. Presence of mastitis pathogens on the teat orifice.II. An open teat orifice.III. Physical introduction of mastitis pathogens.IV. Establishment of infection.via an open teat orifice. When the teat orifice is closed, physical introduction of pathogens is unlikely to take place. The pathway described is consistent for either environmental or contagious mastitis. In both cases, bacteria have to enter the teat canal, and subsequently the parenchyma of the mammary gland, to cause mastitis.Before the establishment of infection can occur, mastitis pathogens have to be present on the teat orifice and subsequently introduced to the mammary gland A slight modification of the Infectious Process Flow Diagram takes account of infection due to bacterial contamination of milk that enters/re-enters the teat canal, e.g. in the parlour as follows:Ia. Bacterial contamination of milk, e.g. due to the presence of mastitis pathogens on the teat orifice.II. An open teat orifice.IIIa. Bacterial contamination of milk and physical introduction of mastitis pathogens.IV Establishment of infection.A separate pathway was identified for those rare cases in which mastitis develops as a result of haematogenous infection, but was not considered further .When a cow develops mastitis by the mechanisms described, the disease may be clinically detectable or subclinical. The Infectious Process Flow Diagram also represents the shift between clinical and subclinical infection and acute and chronic disease, as well as the various ways in which mastitis can be cured. Cure can take place during lactation or in the dry period, and it can occur spontaneously or as a response to treatment .The next step in the HACCP-based approach involved the identification of CCPs (see above). To generate these critical control points within the present study, the Infectious Process Flow Diagram Figure  was crosThereafter, the HACCP team decided when exposure to the various stages of infection could occur. In order to identify CCPs, it was important to ascertain whether stages I, II and III of infection, controlling teat contamination and physical introduction of bacteria could be prevented. When the team decided that exposure at a particular stage could be prevented, the event was highlighted and risk factors for each step(s) were identified based on team expertise and published literature. Six CCPs were identified based on the key principle of preventing physical introduction of mastitis pathogens into the udder and the subsequent establishment of infection, as follows: udder preparation, cluster attachment, post-milking teat disinfection, milking machine monitoring, drying off process, and the calving period.The determination of hazards, control measures, monitoring strategies, verification methods, corrective actions and critical limits or targets for every identified CCP is fundamental to the HACCP system. After these were determined, all the various components were represented in a table for each CCP Table . HazardsTable HACCP systems have mainly been applied in the food industry , while rA HACCP case study on calf rearing  reportedIn the present study, an attempt was made to create a mastitis-focused, streamlined, user-friendly system that would be practical to implement on farm, would minimise on-farm documentation and record keeping but would also facilitate verification. It was also envisaged that the system would also lend itself to further streamlining of monitoring and documentation procedures following implementation at farm level.Willock et al.  and ValeThe objective of the present study was to ascertain if a HACCP-based approach could be created for potential use as a mastitis control programme. For optimal implementation of the HACCP-based approach developed at farm level, specific tailoring based on an understanding of the mastitis problem on an individual farm will be critical, allowing the opportunity to concentrate on relevant CCPs for each specific farm situation or to specific control measures within CCPs, making the system easier to apply and more adaptable to individual farms. The results of the present study have allowed the creation of a HACCP-based handbook, which can be customised at farm level and facilitate verification Table . The HACWhile European legislation has not yet made HACCP mandatory for primary production, member states have been prompted to adopt 'HACCP-like' plans to meet the issues of food safety, public health and animal health and welfare . The HACA novel approach based on a deconstruction of the infectious process, the milking process and the cow management cycle was used to develop a HACCP-based system to prevent and control mastitis in dairy herds. The study was the basis for the creation of a HACCP-based handbook, which can be readily modified for specific dairy farms and implemented in collaboration with the veterinary practitioner and farmer. The HACCP-based approach is designed to be user friendly in its implementation, and to lend itself to independent verification with minimal documentation and administrative requirements. The approach adopted may provide a template for developing a HACCP-based control programme for other infectious diseases of significance to the dairy herd.The authors declare that they have no competing interests.LB-G conducted the literature review and co-ordinated the overall approach to the study. PW provided specific expertise in relation to HACCP principles. LOG, SM and MD provided expertise on bovine health management and epidemiology as it applies to mastitis control. All authors read and approved the final manuscript.


Many calssifiers which are constructed with chosen gene markers have been proposed to forecast the prognosis of patients who suffer from breast cancer. However, few of them has been applied in clinical practice because of the bad generalization, which results from the situation that markers selected by one method are very different from those obtained by anohter mothod, and thus such markers always lack discriminative capability in the other data sets.In this work, a new ensemble classifier, on the basis of context specific miRNA regulation modules, has been proposed to forecast the metastasis risk of cancer sufferers. First, we defined all of the miRNAs which regulate the same context as a module that contains miRNAs and their regulating context, and applied the CoMi (Context-specific miRNA activity) score in order to illustrate a miRNA's effect which happened in a particular background; then the miRNA regulation modules with distinguising abilities were detected and each of them was responsible for building a weak classifier separately; at last, by using majority voting strategy, we integrated all weak classifiers to establish an ensembled one that was applied to forecast the prognosis of patients who suffer from cancer.After comparing, the results on the cohorts containing over 1,000 samples showed that the proposed ensemble classifier is superior to other three classifiers based on miRNA expression profiles, mRNA expression profiles and CoMi activity patterns respectively. Significantly, our method outperforms the representative works. Moreover, the detected modules from different data sets show great stability . For investigating the biological significance of those selected modules, case studies have been done by us and the results suggested that our method do help to reveal latent mechanism in metastasis of breast cancer.One context specific miRNA regulation module can uncover one critical biological process and its involved miRNAs that are related to the cancer outcome, and several modules together can help to study the biological mechanism in cancer metastasis, thus the classifer based on ensembling multiple classifers which were built with different context specific miRNA regulation modules has showed promising performances in terms with both prediction accuracy and generalization. For breast cancer, many classifiers based on gene signatures were built to predict the prognosis of cancer patients -4, with Context-specific miRNA activity (CoMi activity) estimate method, was proposed to estimate a miRNA's activity in a given background (function gene set) in our earlier works [As we all know that the expression levels of a miRNA is not equal to its activity , thus a er works ,12. The er works .We thought that multiple miRNAs may affect the prognosis of cancer sufferers by regulating a certain biological process, and several biological processes may co-affect on the patient's prognosis, thus we stepped forward in our recent work . SeveralTo evaluate the methods, we downloaded five normalized breast cancer date sets from NCBI GEO: GSE2034 , GSE6532Using the similar strategy in , we diviThe main frame of our work is as follow  Obtaining the union set of all miRNA targets  from two tools: TargetScan -21 and R(2) Dividing the genes in the given gene list into different groups based on their GOBP term annotations. One group consists of genes related to the same GOBP term.(3) Dividing the genes within a GOBP into targets and non-targets of a miRNA by the intersection of the two sets (GOBP genes and miRNA target genes). Thus we can obtain a lot of miRNA-GOBP pairs; each of them represents the miRNA and the term of GOBP Figure  regulaten and m are the sizes of two sets respectively (targets and non-targets).(4) According to our previous work, for each miRNA-GOBP pair, the CoMi activity is calculated by t-test Figure . The t-tBased on these steps, the samples' CoMi activity profiles can be got by calculating all the miRNA-GOBP pairs' activity scores from their gene expression profiles. The profiles are described by a two-dimensional array, in which each column stands for a patient, each row represents a miRNA-GOBP. In addition, an element in the array is the miRNA activity on the GOBP Figure .In order to reduce noises, a total of 10% rows in the matrix (the process is based on CoMi activity profiles in GSE2034) with the smallest square deviations were discarded. Moreover, if the elenments in two rows were the same, the first row would be retained with the purpose of removing the redundancy which may be caused by the prediction tools.One biological process may describe one aspect in cancer prognosis, thus we regard the miRNAs acting on the same GOBP as an entire module Figure . In the Now that multiple miRNAs may regulate a given biological process together, and have an effect on the prognosis of patients, some modules may be discriminative in the various risk groups of the cancer sufferers. Therefore, for each module, a classifier was built based on it to classify the patients into two groups, and the ones with classification capability (AUC ≥ 0.6) were considered Figure .We adopted the centroid classifier in our work. The first reason is that the centroid classifier is suit for microarray data, which has the character of large size of features and few samples . The secSuppose there are N breast cancer patients Let If GSE2034 was used to select the distinguishing modules. For each module, we constructed a centroid classifier and used five-fold cross validation to evaluate the classification capability. And we chose the distinguishing modules by using the AUC (AUC ≥ 0.6). As described above, one module could depict a feature that influence the metastasis of tumor sufferers, thus after combining the whole discriminative modules we may reveal a more overall biological mechanism in tumor outcome. Consequently, the weak classifiers were integrated to a combined classifier by majority voting rule Figure .For the purpose of comparison, we also built three classifiers from TCGA breast cancer data set with miRNAs, mRNAs and CoMs as features respectively, by using the same centroid classification model. The original data contains the profiles to express both mRNA and miRNA, and we can compute the CoMi activity patterns by using our previous method .In order to get the optimal classifiers, we first ranked the features by the weight vector described as above, then we constructed the centroid classifiers based on the features ranking the top, and then we evaluated the classifiers by the use of five-fold cross validation. We varied the feature number from 1 to 200 when building the classifiers, and adopted the classifier having the best performance (AUC as the measure index) for the comparison purpose.In order to evaluate our method, three typical methods using in outcome forecasting of breast cancer were adopted to compare with our method on the same data sets.The fisrt one was the most famous gene marker classifier in this filed . They taet al. [The second one was proposed by Wang et al. . In thiset al. ,12. At lThe last two methods used the gene set statistics as features . We gathWe adopted the resampling method to test the specificity of those selected modules. First, the identical number of modules was randomly selected from all the generated modules. Second, the randomly selected modules were applied to establish weak classifiers and all these classifiers were combined to an ensemble classifier on GSE2034, which is then evaluated on the merged set of the other four NCBI data sets. The process is repeated 10,000 times, and all the performances (AUC) of the random ensemble classifiers were used as null distribution, based on which, we can calculate the significance p-value, which can be used to assess the specificity of these selected modules.As the severe unbalance between the two different risk groups . Many measures indexes, such as sensitivity (SN), specificity (SP), and accuracy (ACC), are not efficient enough to character the performance of the classifiers.In this work, the AUC (area under the receiver operating characteristic curve) and MCC (Matthews Correlation Coefficient) are applied as the two main measures to evaluate our classifiers.A ROC (operating characteristic curve) is created by plotting the sensitivity versus one minus the specificity at various threshold settings, and the AUC is the area under the ROC, which is widely used to illustrate the performance of a binary classifier.MCC is also used as the major standard to evaluate the performances of the classfiers in our study, for MCC is a measure method which can provide us with the most information when the samples in the dataset are seriously unbalanced . The MCCAfter using the CoMi activity estimate method, 14,122 CoMi activity socres was got for each sample. And then 347 miRNA regulation modules were acquired. As a result, a total of 55 distinguishing modules were chosen to establish the combined classifier . But when the modules were put together, there are actually obvious distinguishing abilities. This situation may illustrate that our method concentrated on choosing features which are related to particular biology process when put together, instead of those which are prognosis-related respectively, resulting in the situation that the chosen modules were more likely being the "drivers" rather than the "passengers". Consequently, the established classifier is greatly robust across various data sets.Now that a few miRNAs which regulate a biological process can work together to affect the prognosis of tumor sufferer, and a couple of biology processes may participate in the prognosis for cancer, we offered to find out the markers which contain the miRNAs and the GOBP regulation by the miRNAs so as to establish a combined classifier as a way to predict cancer prognosis. From the train data set, fifty five modules were chosen as distinguishing ones. Every chosen module was utilized in establishing a weak classifier separately, all of which have been utilized in constructing one integrated classifier with the rule of majority voting. The results of experiment show that, compared with other methods, the ensemble classifier has a better performance. Furthermore, the chosen modules has a high specificity and a stability across various data sets, which can lead to the conclusion that our method performs both well and robust. The biological anylisis also proves that the chosen modules are able to reveal hidden metastasis mechanism in breast cancer.The authors declare that they have no competing interests.XZ, JL and JX developed the methodology. XZ executed the experiments, XZ and J L wrote this paper. XZ, JL, XY, and WW revised the manuscript. All authors read and approved the final manuscript.Data sets and detailed performance of the classifiers. This file contains the data sets used in our work (S.table 1), performances of the weak classifiers constructed by the selected CoMi modules (S.table 2), the detailed performance of the ensemble classifier on NCBI data set (S.table 3), and results of the representative classifiers on NCBI data sets (S.tables 4 - S.tables 7).Click here for fileCoMi features of the selected modules. This file describes the detailed CoMi features of the 55 selected modules. Features with the same GO term are regarded as a module.Click here for file