Walking Strategy Research Articles

An integrated strategy involving high-throughput sequencing to characterize an unknown GM wheat event in Canada.

Glyphosate-resistant wheat plants were discovered in southern Alberta in 2017, representing an unauthorized GM release in Canada. The Canadian Food Inspection Agency undertook a series of experiments to characterize and identify this unknown GM wheat, as well as to develop and validate construct-specific and event-specific qPCR assays. Results of PCR-based assays and Sanger sequencing indicated the presence of CaMV 35S promoter (p35S), Rice Actin 1 intron (RactInt1), CP4-EPSPS gene and nopaline synthase terminator (tNOS) elements in the unknown GM wheat. Genome walking and bead capture strategies, combined with high-throughput sequencing, were used to identify the 5' and 3' wheat junctions and the subsequent mapping of the insert to chromosome 3B of the wheat genome. A probable transformation vector, pMON25497, was recognized, and further testing identified the unknown GM wheat as MON71200 event, one of two events obtained with the pMON25497 vector. The two construct-specific assays targeted the junctions of the RactInt1 and the CP4-EPSPS elements and the CP4-EPSPS and tNOS elements, while the event-specific assay was located at the 3' junction into the wheat genome. Both construct-specific and event-specific assays had limits of detection of 0.10% of MON71200 in a seed pool. As expected, the two construct-specific assays cross-reacted with other wheat and corn events containing the same elements in the same order. No cross-reactivity was observed for the event-specific assay. The integrated strategy employed in this study can serve as a model for other cases when facing similar challenges involving unknown GM events.

Path Planning of Unmanned Surface Vehicle Based on Improved Sparrow Search Algorithm

In order to solve the problem of many constraints and a complex navigation environment in the path planning of unmanned surface vehicles (USV), an improved sparrow search algorithm combining cubic chaotic map and Gaussian random walk strategy was proposed to plan it. Firstly, in the population initialisation stage, cubic chaotic map was used to replace the random generation method of the traditional sparrow search algorithm to optimise the uneven initial distribution of the population and improve the global search ability of the population. Secondly, in the late iteration of the algorithm, the standard deviation of fitness is introduced to determine whether the population is trapped in the local optimum. If true, the Gaussian random walk strategy is used to perturb the optimal individual and assist the algorithm to escape the local optimum. Thirdly, the chosen water environment is modelled, and the navigation information of the original inland electronic navigation chart (ENC) is preprocessed, gridised, and the obstacle swelling is processed. Finally, the path planning experiments of USV are carried out in an inland ENC grid environment. The experimental results show that, compared with the traditional sparrow search algorithm, the average fitness value of the path planned by improved sparrow search algorithm is reduced by 14.8% and the variance is reduced by 49.9%. The path planned by the algorithm is of good quality and high stability and, combined with ENC, it can provide a reliable path for USV.

Improving Writing Skill in Narrative Text of The First Grader of MA Al-Mahrusiyah Lirboyo by Using Gallery Walk Strategy

Penelitian ini bertujauan untuk mendeskripsikan peningkatan hasil belajar siswa Kelas X MA Al Mahrusiyah Lirboyo Kediri dalam menulis teks naratif menggunakan metode Gallery Walk Strategy. Dalam penelitian ini, peneliti menggunakan Penelitian Tindakan Kelas. Penelitian tindakan ini dilakukan dalam satu siklus. Prosedur penelitian terdiri dari studi pendahuluan, perencanaan, pelaksanaan, observasi, dan refleksi. Dalam pengumpulan data, peneliti menggunakan lembar observasi, catatan lapangan, tes tulis dan rubrik penilaian sebagai instrumen. Prosedur yang digunakan dalam menerapkan Gallery Walk Strategy ke dalam tulisan terdiri dari mengamati, mengasosiasi, mendiskusikan, menyusun, merevisi, dan menerbitkan. Hasil penelitian menunjukkan bahwa Gallery Walk Strategy dapat meningkatkan keterampilan menulis siswa. Minat siswa dalam menulis meningkat setelah diterapkannya strategi gallery walk oleh interpreter persentase pencapaian siswa dalam menulis teks naratif. Persentase siswa yang memperoleh nilai ≥75 sebanyak 46,4% (15 siswa) dengan nilai rata-rata 73,9. Namun meningkat pada post test (siklus) siswa yang mendapat nilai ≥75 sebanyak 100% (28 siswa) dengan nilai rata-rata siswa 81,5. Berdasarkan hasil penelitian, peneliti menyimpulkan bahwa kemampuan menulis siswa dapat ditingkatkan melalui Gallery Walk Strategy. Oleh karena itu, disarankan agar guru menerapkan Gallery Walk Strategy dalam mengajar menulis.

Polarized Communities Search via Co-guided Random Walk in Attributed Signed Networks

Polarized communities search aims at locating query-dependent communities, in which mostly nodes within each community form intensive positive connections, while mostly nodes across two communities are connected by negative links. Current approaches towards polarized communities search typically model the network topology, while the key factor of node, i.e., the attributes, are largely ignored. Existing studies have shown that community formation is strongly influenced by node attributes and the formation of communities are determined by both network topology and node attributes simultaneously. However, it is nontrivial to incorporate node attributes for polarized communities search. Firstly, it is hard to handle the heterogeneous information from node attributes. Secondly, it is difficult to model the complex relations between network topology and node attributes in identifying polarized communities. To address the above challenges, we propose a novel method Co-guided Random Walk in Attributed signed networks (CoRWA) for polarized communities search by equipping with reasonable attribute setting. For the first challenge, we devise an attribute-based signed network to model the auxiliary relation between nodes and a weight assignment mechanism is designed to measure the reliability of the edges in the signed network. As to the second challenge, a co-guided random walk scheme in two signed networks is designed to explicitly model the relations between topology-based signed network and attribute-based signed network so as to enhance the search result of each other. Finally, we can identify polarized communities by a well-designed Rayleigh quotient in the signed network. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed CoRWA. Further analysis reveals the significance of node attributes for polarized communities search.

Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction

Wind power forecasting can effectively improve the energy utilization efficiency of a power system and ensure its stable operation. In this study, a novel hybrid multistep prediction model, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), the kernel principal component analysis (KPCA), an enhanced arithmetic optimization algorithm (ENAOA), a bidirectional long short-term memory (BILSTM) neural network, and error correction, was designed for short-term wind power forecasting. First, the collected original wind power data were decomposed into multiple intrinsic mode functions (IMFs) through a secondary decomposition composed of the CEEMDAN and VMD, which eliminated the interactions between different components to achieve denoising. Second, the KPCA was adopted to reduce the dimensionality of the multiple IMFs by extracting the principal components, effectively reducing the complexity of the multidimensional IMF data and improving the forecasting efficiency of the proposed prediction model. Subsequently, an ENAOA was proposed based on the Sobol sequence, adaptive T-distribution, and random walk strategy to optimize the BILSTM parameters. Finally, multiple preprocessed components were predicted by the optimized BILSTM, after which error correction was performed to obtain the final prediction results, which could further reduce the forecast error of the designed prediction model. Based on two sets of data collected from a wind farm in northwest China, the simulation results of 1-step, 4-step, 7-step, and 10-step forecasting revealed that compared with other incomplete models, the various algorithms adopted in the hybrid forecasting model reduced the prediction errors to different degrees, significantly enhanced the wind power prediction performance, and validated the effectiveness and feasibility of the proposed model.

Weighted ensemble clustering with multivariate randomness and random walk strategy

Ensemble clustering algorithms have made significant progress in recent years due to their excellent performance. However, most of these algorithms face two challenges: one is to focus on the selection of subspaces since there is limited discussion on how to construct a potential metric space, the other is to treat basic clustering equally without fully considering the local connection between clusters when constructing the cooperative association matrix. To solve these issues, we propose a weighted ensemble clustering algorithm with multiple randomness and random walk strategy. We define the free exponential similarity kernel to create a diverse set of random metric spaces coupled with random subspaces and use spectral clustering to generate base clustering. Moreover, we use random walk strategy to discover the local connection between clusters and weight the collaborative association matrix. Finally, the collaborative association matrix uses consensus functions based on hierarchical clustering and meta clustering to obtain clustering results. On this basis, two specific ensemble clustering algorithms WECMR-HC and WECMR-MC are proposed. Theoretical analysis and experimental results demonstrate that our proposed algorithm outperforms existing ensemble algorithms.

Gait and simulation analysis of quadruped crawling robot based on metamorphic structure

PurposeThis study aims to present a type of metamorphic mechanism-based quadruped crawling robot. The trunk design of the robot has a metamorphic mechanism, which endows it with excellent crawling capability and adaptability in challenging environments.Design/methodology/approachThe robot consists of a metamorphic trunk and four series-connected three-joint legs. First, the walking and steering strategy is planned through the stability and mechanics analysis. Then, the walking and steering performance is examined using virtual prototype technology, as well as the efficacy of the walking and turning strategy.FindingsThe metamorphic quadruped crawling robot has wider application due to its variable trunk configuration and excellent leg motion space. The robot can move in two modes (constant trunk and trunk configuration transformation, respectively, while walking and rotating), which exhibits outstanding stability and adaptability in the examination and verification of prototypes.Originality/valueThe design can enhance the capacity of the quadruped crawling robot to move across a complex environment. The virtual prototype technology verifies that the proposed walking and steering strategy has good maneuverability and stability, which considerably expands the application opportunity in the fields of complicated scene identification and investigation.

Synchronization of multiple mobile reservoir computing oscillators in complex networks

We investigate collective behavior of multiply moving reservoir computing oscillators. Each moving oscillator is fitted to a same dynamical system and then wanders a network navigating by random walk strategy. Interestingly, we find that the moving oscillators gradually present a coherent rhythmic behavior when their number is large enough. In this situation, temporal and spatial characteristics of each moving oscillator are in excellent agreement with that of their learned dynamical system under consideration. Remarkably, we show that these reservoir computing oscillators can exhibit significantly distinct collective behaviors that resemble bifurcation phenomenon when changing a critical reservoir parameter. Furthermore, we find that when studying a continuous chaotic system, intermittent synchronization emerges among these reservoir computing oscillators. But there is no evidence of enhanced correlation in the associated laminar length sequence. Our work provides a feasible framework for studying synchronization phenomena of mobile agents in nature when merely observed information is available.

Single and multi-objectives based on an improved golden jackal optimization algorithm for simultaneous integration of multiple capacitors and multi-type DGs in distribution systems

This paper proposes a novel placement technique based on the improved golden jackal optimization (IGJO) algorithm for multiple capacitor banks (CBs) and multi-type DGs in a distribution network considering single and multi-objective problems. The proposed algorithm incorporates memory-based equations and random walk strategy to enhance the performance of the recent golden jackal optimization in terms of accuracy and convergence speed. The optimization problem is formulated as a weighted multi-objective that seeks to enhance the voltage profiles, boost stability, and minimize the total active power loss. An index named reactive loss sensitivity (QLSI) is also employed with the developed IGJO to identify the candidate nodes for the DGs and CBs installation to reduce the search space of the optimization algorithm. The robustness of the developed IGJO algorithm is evaluated through the CEC 2020 benchmark functions, and a comparison study is conducted with the original GJO and the other nine fresh competitors using various statistical tests to confirm its dominance and superiority. Then, the proposed IGJO is implemented in single and multi-objectives for the optimal deployment of multiple CBs individually and simultaneously with multiple DGs with different operating modes to enhance the performance of the IEEE 69-bus radial distribution system (RDS). The fetched outcomes are compared with the original GJO, weevil optimizer algorithm (WeevilOA), skill optimization algorithm (SOA), and Tasmanian devil optimization (TDO) to further measure its efficacy using different statistical tests. The IGJO algorithm is also applied to deploy multiple DGs for the IEEE 118-bus RDS with the aim of minimizing active loss. The simulation findings affirmed that the proposed IGJO technique beats the other rivals in all investigated situations, qualifying for the optimal inclusion of DGs in the presence of generation and demand uncertainties. Specifically, the integration of three units of CBs synchronously with three DGs Type-I and DG Type-III reduces the active power loss to 4.2664 kW and 3.4178 kW, respectively. The lowest power loss, approximately 2.7989 kW, is achieved with the simultaneous integration of three DG Type-I and DG Type-III using the developed IGJO algorithm.

Breaking boundaries: TINTO in POKY for computer vision-based NMR walking strategies.

Nuclear magnetic resonance is a crucial technique for studying biological complexes, as it provides precise structural and dynamic information at the atomic level. However, the process of assigning resonances can be time-consuming and challenging, particularly in cases where peaks overlap, or the data quality is poor. In this paper, we present TINTO (Two and three-dimensional Imaging for NMR sTrip Operation via CV/ML), an advanced semiautomatic toolset for NMR resonance assignment. TINTO comprises two separate tools, each tailored for either two-dimensional or three-dimensional imaging. The toolset utilizes a computer-vision approach and a machine learning approach, specifically structural similarity index and principal components analysis, to perform visual similarity searches of resonances and quickly locate similar strips, and in that way overcome the challenges associated with peak overlap without requiring peak picking. Our tool offers a user-friendly interface and has the potential to enhance the efficiency and accuracy of NMR resonance assignment, particularly in complex cases. This advancement holds promising implications for furthering our understanding of biological systems at the molecular level. TINTO is pre-installed in the POKY suite, which is available at https://poky.clas.ucdenver.edu .

S7-3 The contribution of walking policy in Ireland to the global sustainable development agenda

Abstract Purpose Increasing population levels of walking holds benefits for public and planetary health. While individual level interventions to promote walking have been shown to be efficacious, upstream interventions such as policies harness the greatest potential for impact at the population level. However, little is known about the nature and presence of walking policy in Ireland and the extent to which it aligns to national and global goals. This research provides an overview of a policy analysis of walking policy in Ireland. Methods This study used multiple methods to provide a critical overview of walking policy. Firstly, a six-phase process was employed to conduct a content analysis of local and national walking policy in Ireland. Secondly, conceptual linkage exercises were conducted to assess the contribution of walking policy in Ireland to Ireland’s National Strategic Outcomes and the United Nations Sustainable Development Goals. Results Overall, half (n = 13) of the counties in the Republic of Ireland were found to have no local level walking policies. Walking was identified to hold the potential to contribute to over half (n = 7) of the United Nations Sustainable Development Goals. Ireland’s only national level walking specific policy, the Get Ireland Walking Strategy and Action Plan 2017-2020, was identified to potentially contribute to four of Ireland’s National Strategic Outcomes and three United Nations Sustainable Development Goals. There were no actions within national walking policy in Ireland which were identified as contributing to SDG 13 Climate Action. Conclusions Multidisciplinary action is required to update walking-related policy with embedded evaluation and governance mechanisms in all local walking systems. Furthermore, analysing walking policy in Ireland through the lens of the Sustainable Development Goals has informed the direction of future walking policy in Ireland. As of January 2023, the results of this research have shaped the next iteration of Get Ireland Walking’s national policy to ensure alignment with global sustainable development goals. Support/Funding Source This research was co-funded by South East Technological University and Get Ireland Walking.

A powered simple walking model explains the decline in propulsive force and hip flexion torque compensation in human gait

Excessive hip flexion torque to prioritize leg swings in the elderly is likely to be a factor that reduces their propulsive force and gait stability, but the mechanism is not clear. To understand the mechanism, we investigated how propulsive force, hip flexion torque, and margin of stability (MoS) change when only the hip spring stiffness is increased without changing the walking speed in the simple walking model, and verified whether the relationship holds in human walking. The results showed that at walking speeds between 0.50 and 1.75 m/s, increasing hip spring stiffness increased hip flexion torque and decreased the propulsive force and MoS in both the model and human walking. Furthermore, it was found that the increase in hip flexion torque was explained by the increase in spring stiffness, and the decreases in the propulsive force and MoS were explained by the increase in step frequency associated with the increase in spring stiffness. Therefore, the increase in hip flexion torque likely decreased the propulsive force and MoS, and this mechanism was explained by the intervening hip spring stiffness. Our findings may help in the control design of walking assistance devices, and in improving our understanding of elderly walking strategies.

Structure Learning Via Meta-Hyperedge for Dynamic Rumor Detection

Online social networks have greatly facilitated our lives but have also propagated the spreading of rumours. Traditional works mostly find rumors from content, but content can be strategically manipulated to evade such detection, making these methods brittle. To improve the accuracy and robustness of rumor detection, we propose to integrate and exploit the content, propagation structure, and temporal relations because information in the networks always spreads dynamically with significant structures. In this paper, we propose a novel rumor detection framework in online temporal networks via structure learning. Specifically, to exploit the propagation structure, we propose a novel hyperedge walking strategy on a meta-hyperedge graph to learn the representations of sub-structures in the networks. Then a hyperedge expansion method is proposed to generate more global structural features. The expanded hyperedges are more hierarchical, making the learned structural embeddings more expressive. To make full use of content, we design a hypergraph learning model using hyperedge expansion to fuse node content with structural features and generate comprehensive representations for the entire graph. To exploit temporal relations, we design a masked temporal attention unit for learning the evolving patterns of the network. Extensive evaluations with six state-of-the-art baselines on two real-world datasets demonstrate the superiority of our solution.

An Approach to Semantic-Aware Heterogeneous Network Embedding for Recommender Systems.

Recent studies on heterogeneous information network (HIN) embedding-based recommendations have encountered challenges. These challenges are related to the data heterogeneity of the associated unstructured attribute or content (e.g., text-based summary/description) of users and items in the context of HIN. In order to address these challenges, in this article, we propose a novel approach of semantic-aware HIN embedding-based recommendation, called SemHE4Rec. In our proposed SemHE4Rec model, we define two embedding techniques for efficiently learning the representations of both users and items in the context of HIN. These rich-structural user and item representations are then used to facilitate the matrix factorization (MF) process. The first embedding technique is a traditional co-occurrence representation learning (CoRL) approach which aims to learn the co-occurrence of structural features of users and items. These structural features are represented for their interconnections in terms of meta-paths. In order to do that, we adopt the well-known meta-path-based random walk strategy and heterogeneous Skip-gram architecture. The second embedding approach is a semantic-aware representation learning (SRL) method. The SRL embedding technique is designed to focus on capturing the unstructured semantic relations between users and item content for the recommendation task. Finally, all the learned representations of users and items are then jointly combined and optimized while integrating with the extended MF for the recommendation task. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed SemHE4Rec in comparison with the recent state-of-the-art HIN embedding-based recommendation techniques, and reveal that the joint text-based and co-occurrence-based representation learning can help to improve the recommendation performance.

Model optimization of a high-power commercial PEMFC system via an improved grey wolf optimization method

Proton exchange membrane fuel cell (PEMFC) models are conventionally established with a set of parameters identified under steady-state operating conditions. However, such an approach is insufficient to accurately capture the dynamic characteristics of multi-parameter changes in real-world scenarios. This paper develops a semi-empirical model for a 110-kW commercial PEMFC system based on its dynamic operation data to remedy the defects. To improve the fitting accuracy of the semi-empirical PEMFC model, an improved grey wolf optimization (IGWO) algorithm is proposed for model parameter identification. The IGWO algorithm adopts chaotic mapping to optimize the initial population distribution, and a random walk strategy is incorporated to boost the local search ability of the traditional grey wolf optimization (GWO) algorithm. The effectiveness of this IGWO algorithm in optimizing the semi-empirical model is experimentally verified on the 110-kW PEMFC system under highly dynamic operating conditions. Results show that the proposed IGWO algorithm can effectively identify the semi-empirical model’s parameters, establishing a stable and robust model that outperforms those based on traditional metaheuristic algorithms such as GWO, particle swarm optimization, and genetic algorithm. The demonstrated improvement renders it as better suited for optimizing PEMFC semi-empirical models under real-world operating conditions.

THE EFFECT OF GALLERY WALK STRATEGY ON THE STUDENTS’ ABILITY IN WRITING DESCRIPTIVE PARAGRAPH AT SMA NEGERI 2 BIREM BAYEUN ACEH TIMUR

<span id="docs-internal-guid-89d98319-7fff-a1f8-9b27-8ee4af79d903"><span>This study was aimed to obtain empirical evidence about the effect of the gallery walk strategy on students’ ability in writing descriptive paragraphs in the tenth grade of SMA Birem Bayeun Aceh in 2020/2021 academic year. This research used quantitative method and the research design used is a quasi-experimental design. The population of this study were all tenth grade students of SMA Negeri 2 Birem Bayeun Aceh Timur. The sample technique used total sampling that consist of 47 students. The sample are divided into two groups: class X MIA</span><span><span>1 </span></span><span>as an experimental class consist of 24 students and X MIA</span><span><span>2 </span></span><span>as a control class consist of 23 students. The instrument of this research was written test. The results and the effect size of this study were obtained from data calculations using t-test with a significance level of (α) =0,05. The results showed that the post-tests’ mean score in the experimental class was 78,45 while in the control class it was 60,26. thus means the post-tests’ mean score in the experimental class is higher than the control class (78,45 > 60,26). Therefore, the statistical hypothesis test showed that the ρ-value of the post-test and the gained score is 0.000 < sig α 0,05. The effect size of this result is 1,53. It can be conclude that the alternatives hypothesis (Ha) is accepted and the null hypothesis (H0) is rejected. It means that the gallery walk strategy was significant effect on the students’ ability in writing descriptive paragraph at SMA Negeri 2 Birem Bayeun Aceh Timur in academic year.</span></span>

Effects of muscular and mental fatigue on spatiotemporal gait parameters in dual task walking in young, non-frail and frail older adults.

Dual-task (DT) walking is of great interest in clinical evaluation to evaluate frailty or cognitive declines in older adults. Frail older adults are known to adopt different walking strategy to overcome fatigue. However, no studies evaluated the effect of muscular or mental fatigue on dual-task walking strategy and the difference between frail and non-frail older adults. Evaluate the effect of mental and muscular fatigue on spatio-temporal parameters in dual-task walking in young, non-frail and frail older adults. 59 participants divided into 20 young (Y) (24.9 ± 3years old), 20 non-frail (NF) (75.8 ± 4.9years old) and 19 frail older adults (F) (81 ± 4.7years old) performed single-task (ST) walking, single-task cognitive (serial subtraction of 3), and dual-task (subtraction + walking) for 1min at their fast pace. Gait speed, step length, step length variability, stance and swing phase time, single and double support time, cadence, gait speed variability were recorded in single- and dual-task walking. The dual-task effect (DTE) was calculated as ((DT-ST)/ST)×100). Generalized linear mixed models (GLMM) were used to compare the effects of mental and muscular fatigue on gait and cognitive variables between the groups. The DTE walking parameters were worse in F compared to NF or Y but no significant effect of fatigue were highlighted except for swing time and single support time DTEs. The results were mitigated but a clear difference in dual-task spatio-temporal parameters was found between F and NF which brings hope into the capacity of DT to better reveal frailty.

Cloning and characterization of the promoters of the key genes CPT, SRPP and REF involved in Periploca sepium rubber biosynthesis

Hevea brasiliensis is the main source of natural rubber. Restricted by its tropical climate conditions, the planting area in China is limited, resulted in a low self-sufficiency. Periploca sepium which can produce natural rubber is a potential substitute plant. cis-prenyltransferase (CPT), small rubber particle protein (SRPP) and rubber elongation factor (REF) are key enzymes involved in the biosynthesis of cis-1, 4-polyisoprene, the main component of natural rubber. In this study, we cloned the promoter sequences of CPT, SRPP and REF through chromosome walking strategy. The spatial expression patterns of the three promoters were analyzed using GUS (β-glucuronidase) as a reporter gene driven by the promoters through Agrobacterium-mediated genetic transformation. The results showed that GUS driven by CPT, SRPP or REF promoter was expressed in leaves and stems, especially in the leaf vein and vascular bundle. The GUS activity in stems was higher than that in leaf. This study provided a basis for analyzing the biosynthesis mechanism of natural rubber and breeding new varieties of high yield natural rubber.

Simulated Increase in Monoarticular Hip Muscle Strength Reduces the First Peak of Knee Compression Forces During Walking.

Reducing compressive knee contact forces (KCF) during walking could slow the progression and reduce symptoms of knee osteoarthritis. A previous study has shown that compensating for the hip flexion/extension moment could reduce the KCF peak occurring during early stance (KCFp1). Therefore, this study aimed to identify if monoarticular hip muscle could allow this compensation while considering different walking strategies. Gait trials from 24 healthy participants were used to make musculoskeletal models, and five load-cases were examined: (I) Normal, (II) with an applied external moment compensating for 100% of the hip flexion/extension moment, and (III-V) three conditions with isolated/combined 30% increase of peak isometric strength of gluteus medius and maximus. Knee contact forces, hip muscle forces, and joint moments were computed. A cluster analysis of the Normal condition was performed with hip and knee flexion/extension moment during KCFp1 as input to examine the influence of different walking strategies. The cluster analysis revealed two groups having significantly different hip and knee moments in early-stance (p < 0.01). The reduction in KCFp1 from the Normal condition, although present in both groups, was greater for the group with the highest hip and lowest knee flexion/extension moments for all conditions tested (II: -21.82 ± 8.71% versus -6.03 ± 6.68%, III: -3.21 ± 1.09% versus -1.59 ± 0.96%, IV: -3.00 ± 0.89% versus -1.76 ± 1.04%, V: -6.12 ± 1.69 versus -3.09 ± 1.95%). This reduction in KCFp1 occurred through a shift in force developed by the hamstrings during walking (biarticular) to the gluteus medius and maximus (monoarticular), whose isometric strength was increased. The differences between the groups suggest that this reduction depends on the walking strategy.

Traffic planning for the protection of vulnerable groups of persons exemplified by the City of Munich

Pedestrian and bicycle traffic make asignificant contribution to the traffic turnaround. Measures to improve the road safety of pedestrians and cyclists are insofar an important building block to asuccessful sustainable city and traffic planning. The mobility strategy 2035 of the City of Munich includes partial strategies for walking and cycling as well as for road safety, which was confirmed through former city council resolutions on Vision Zero. The most important measures for the comfort and safety of pedestrians are aspeed limit of 30 km/h, wide and free sidewalks (without obstacles) and crossing assistance with good visibility conditions. Depending on the local situation, sidewalk extensions, road islands, pedestrian crossings (zebra crossings) and traffic lights with attractive circuit conditions for pedestrians make crossing easier. The comfort and safety of cyclists can be improved with broad cycling paths along the main streets. They should allow the overtaking of cyclists in both directions. At least on side streets, acomprehensive speed limit of 30 km/h is an important issue. One-way streets should be open for oncoming cyclists against the one-way direction. At road crossings and road junctions the visibility of cyclists should be improved with road markings and widened bike lanes and there should be aconflict-free circuit of traffic lights, especially where there is ahigh percentage of commercial vehicles.