Terrain Changes Research Articles

(In) visibility of health and illness: Instagram as an unregulated public health platform

This paper examines the changing terrain of accessing health and illness guidance and information through the lens of social media, specifically It argues how Instagram, has increasingly become an unregulated ­public health platform in today’s digital society. Drawing on extensive empirical interview data from two research projects before and during the COVID-19 pandemic, which explored how users showcased and performed health and illness on Instagram, this paper demonstrates how Instagram has became an important forum from which to perform proactive health practices, as well as to legitimate ill health through making healthy behaviours visible, as well as invisible illnesses like disease, COVID-19, and mental health conditions. Over time and through continuous sharing of this content may contribute to increased understanding or even a de-stigmatisation of such illnesses or chronic conditions. With the social media market currently valued at 49.9 billion, and influencer spending around 4.9 billion, the value, reach and direct impact of this ‘attention economy’ on public health should not be underestimated. Instagram serves as a forum for these practices and helps legitimize ill health by making both healthy behaviours and ­invisible illnesses visible. This visibility can contribute to increased understanding and de-stigmatization of chronic conditions and mental health issues. The paper argues that Instagram has evolved into an informal, unregulated public health platform.

An adaptive low‐rank group sparse model based on edge‐preserving for eliminating mixed noise in SRTM

AbstractThe Shuttle Radar Topography Mission (SRTM) is a digital representation of the terrain surface morphology that contains rich terrain information and is widely used in environmental analyses. However, SRTM is adversely affected by mixed noise, which typically include random and stripe noise. Mixed noise results in the significant loss of topographic information, which reduce the validity of related research. To eliminate mixed noise in SRTM data, we propose an adaptive low‐rank group sparse model based on edge preservation (ALGS_EP) to remove mixed noise from datasets. The method relies on a low‐rank group sparse model that considers the gradient features of the terrain. It calculates a terrain factor to adapt the noise elimination model to terrain changes. Additionally, it integrates with the edge structure of elevation data and applies a double‐gradient constraint to preserve the structural details of the elevation data. The proposed model, built upon the alternating direction multiplier method framework, enhances the traditional weighted kernel paradigm minimization algorithm by introducing variable weights that adjust according to the gradient of elevation data during iterations. Additionally, it incorporates the correlation between strip noise and residual data blocks when computing the iteration count, ensuring an iterative solution approach that converges to the optimal solution. We used ALGS_EP to process global SRTM 1 data and published a higher‐quality and higher‐precision elevation dataset. The elevation data noise before and after noise elimination were statistically analyzed. Simulated and empirical results show that the model is highly robust and more effective than existing methods in both visual and quantitative evaluations. The noise elimination rate was 97.6%, compared to the original data. Therefore, this research was valuable for applications that use digital elevation model as an important data layer.

Study on Wind Farm Flow Field Characteristics Based on Boundary Condition Optimization of Complex Mountain Numerical Simulation

The accurate prediction of the flow field characteristics of complex mountains is of great practical significance for the development and construction of wind farms, but it is not yet fully understood. The main purpose of this study is to propose a method for the study of flow field characteristics under complex mountain conditions, which can optimize the boundary conditions required for numerical simulation through the wind acceleration ratio and, at the same time, couple the numerical simulation and wind measurement data to reflect the real mountain flow field distribution. The results show that the proposed method has good applicability in complex mountain wind farms, can reproduce the real flow field distribution, and has a certain practical value. Wind speed distribution and turbulence intensity are greatly affected by boundary conditions such as wind speed and wind direction and are also affected by the shielding effect brought by terrain changes. The contrast between 120° and 150° wind direction is more obvious. When the incoming wind moves to the top of a mountain or the ridgeline, it will form a low-speed wake area behind it, resulting in reduced wind speed, increased turbulence intensity, and an unstable flow field.

Rendering Frankenstein's monsters? The emergence and impacts of human digital twins

AbstractHuman digital twins (HDTs) are set to revolutionise human–system integration and improve human capacities, productivity, and well‐being by including human characteristics in system design. Reflecting real‐life behaviours and emotional reactions, HDTs render virtual data‐driven models of people. Their presence in many fields—including office work, education, and healthcare—showcases their ability to advance diagnoses, workforce training, and individualised learning. However, the general HDTs acceptance hypothesis calls for a critical analysis of their ethical ramifications and social influence. Emphasising the need of social adaptations to maintain stability, structural functionalism provides a suited framework to examine the roles of HDTs inside social systems. Despite their obvious advantages, HDTs raise ethical questions. While addressing concerns about user acceptance and ethical norms, engineers, designers and the legal regulators, but also the users—by means of ‘owners’—must consider the substantial consequences of HDTs and their customisation. HDTs' duality, like a double‐edged sword, calls for deliberate usage and considerable thought to negotiate the changing terrain of human–machine interaction.

From Debt to Sustainability: Advancing Wastewater Projects in Developing Countries through Innovative Financing Mechanisms—The Role of Debt-for-Climate Swaps

Developing countries, including Small Island Developing States (SIDSs) and Least Developed Countries (LDCs), are exceptionally vulnerable to climate change due to their distinct geographical and environmental characteristics. Escalating sea levels and heightened salinity levels imperil freshwater reserves, while warmer ocean temperatures and acidification disrupt water demand, tourism, health services, and fisheries. Concurrently, these countries bear the brunt of water shortages, flooding, and declining water quality. However, significant barriers such as limited financing capacities to fund water security initiatives, exacerbated by a growing debt crisis marked by escalating interest rates and inflation, hinder developmental progress and investments in climate adaptation and mitigation endeavors. Consequently, there arises a critical necessity to harness innovative financial mechanisms to transform these debts into opportunities that support effective climate action. This paper explores the potential of debt-for-climate swaps as a catalyst for advancing transformative wastewater projects, focusing on their strategic deployment to underpin critical initiatives. Through case studies and empirical evidence, the paper elucidates how debt-for-climate swaps can enhance sustainable wastewater management systems in developing countries and delineates best practices for leveraging these mechanisms and the roles and responsibilities of key stakeholders, including governments, policymakers, the private sector, communities, and climate financial institutions. Combining theoretical insights with tangible examples, this paper furnishes a comprehensive framework for harnessing debt-for-climate swaps to enhance water security and resilience in developing countries. It offers actionable strategies for policymakers, practitioners, and stakeholders to navigate the complex terrain of climate change and engender sustainable development.

Molecule Design for Non-Aqueous Wide-Temperature Electrolytes via the Intelligentized Screening Method.

Operating a lithium-ion battery (LIB) in a wide temperature range is essential for ensuring a stable electricity supply amidst fluctuating temperatures caused by climate or terrain changes. Electrolyte plays a pivotal role in determining the temperature durability of batteries. However, specialized electrolytes designed for either low or high temperatures typically possess distinct features. Therefore, wide-temperature electrolytes (WTEs) are necessary as they encompass a combination of diverse properties, which complicates the clear instruction of WTE design. Here we represent an artificial intelligence (Al)-assisted workflow of WTE design through stepwise parameterizations and calculations. Linear mono-nitriles are identified as ideal wide-liquidus-range solvents that can "softly" solvate lithium ions by weak interactions. In addition, the explainable modules revealed the halogenoid similarity of cyanide as fluorine on the electrolyte properties (e.g. boiling point and dielectric constant). With the further introduction of an ether bond, 3-methoxypropionitrile (MPN) has been eventually determined as a main electrolyte solvent, enabling the battery operation from -60 to 120 °C. Particularly, a LiCoO2/Li cell using the proposed WTE can realize stable cycling with capacity retention reaching 72.3 % after 50 cycles under a high temperature of 100 °C.

Molecule Design for Non‐Aqueous Wide‐Temperature Electrolytes via the Intelligentized Screening Method

AbstractOperating a lithium‐ion battery (LIB) in a wide temperature range is essential for ensuring a stable electricity supply amidst fluctuating temperatures caused by climate or terrain changes. Electrolyte plays a pivotal role in determining the temperature durability of batteries. However, specialized electrolytes designed for either low or high temperatures typically possess distinct features. Therefore, wide‐temperature electrolytes (WTEs) are necessary as they encompass a combination of diverse properties, which complicates the clear instruction of WTE design. Here we represent an artificial intelligence (Al)‐assisted workflow of WTE design through stepwise parameterizations and calculations. Linear mono‐nitriles are identified as ideal wide‐liquidus‐range solvents that can “softly” solvate lithium ions by weak interactions. In addition, the explainable modules revealed the halogenoid similarity of cyanide as fluorine on the electrolyte properties (e.g. boiling point and dielectric constant). With the further introduction of an ether bond, 3‐methoxypropionitrile (MPN) has been eventually determined as a main electrolyte solvent, enabling the battery operation from −60 to 120 °C. Particularly, a LiCoO2/Li cell using the proposed WTE can realize stable cycling with capacity retention reaching 72.3 % after 50 cycles under a high temperature of 100 °C.

GLOBAL MOBILITY: BIBLIOMETRIC APPROACH

The research seeks to understand the complex interactions between migration, population relocation, and climate change in today's dynamic global terrain. It explores changing trends, drivers, and repercussions of modern migration in the context of environmental changes using extensive data analysis. Notably, the research investigates new cyber migration tendencies, emphasizing the link between human movement and technology. It also examines how international student mobility affects global knowledge networks and talent migration. In addition, the study looks at both forced and voluntary migration, considering factors including environmental change, war, and socioeconomic differences.

Ecological Vulnerability Assessment of the Three Rivers Source Area Based on the Analytic Hierarchy Process

The Three Rivers Source, known as the “Water Tower of China”, is the birthplace of the Yangtze River, Yellow River, and Lancang River. The carrying capacity and environmental capacity of its ecological environment affect the ecological balance and resource utilization in the surrounding areas of the Three Rivers Source region, and are even closely related to the country’s ecology and economy. Taking the Three Rivers Source region as the research object, this paper selects six indicator layers and their corresponding 23 indicator items that affect the ecological vulnerability of the region. Nine professors in the field of ecological environment were invited to score and screen the indicators. Finally, a judgment matrix was established, and the Analytic Hierarchy Process (AHP) was used to comprehensively evaluate the ecological vulnerability of the Three Rivers Source region. The research results show that the most significant factor affecting the ecological vulnerability of the Three Rivers Source region is the reduction in ecological area, with a weight of up to 23.4%. This factor is followed by changes in terrain, growth and development environment, ground trampling effects, changes in vegetation structure, and potential changes in the integrity of natural vegetation. Their weights are 19.8%, 18.5%, 15.4%, 13.8%, and 12.3%, respectively. The weight of reduction in area is approximately 1.6 times higher than the potential impact on the integrity of natural vegetation. Changes in soil environment and terrain are the primary factors affecting ecological vulnerability. The lower the potential integrity of vegetation, the more fragile the ecosystem. Among the 20 research sites in the Three Rivers Source region, one has an extremely fragile ecological environment, and only six sites do not require excessive intervention. By analyzing and evaluating the different influencing factors of ecological vulnerability in the Three Rivers Source region, this study formulates corresponding ecological strategies to ensure the ecological restoration and sustainable development of the Three Rivers Source region. It provides a theoretical basis for the protection, management, and ecological construction of the Three Rivers Source region.

The Role of Remote Sensing Data in Assessing Human Impact on the Environment

The article focuses on the application of remote sensing methods to assess the impact of human activities on the environment. Emphasis is placed on the need to accurately monitor anthropogenic impacts, such as deforestation and water pollution, to maintain environmental balance. The Materials and Methods section describes passive and active sensing methods, including infrared imaging, radar sensing, and laser scanning. Passive sensing analyzes the spectral characteristics of objects, which is useful for monitoring vegetation changes, while radar sensing can detect oil spills even in adverse weather conditions. Laser sensing is used for precise measurement of terrain changes. The Results and Discussion section provides examples of practical application of these methods: analysis of deforestation in the Kirov region using Landsat 8 and 9 satellite data, and investigation of an oil spill in Novorossiysk using radar images from the Sentinel-1 satellite. These examples demonstrate the effectiveness of remote sensing for monitoring changes and identifying the negative consequences of human activities. The conclusion highlights the importance of remote sensing as a powerful tool for environmental research and monitoring.

Effects of Terrain on the Landing of Locusta migratoria migratoria (Orthoptera: Acrididae) at the China–Kazakhstan Border1

Abstract We conducted a case study on the severe damage caused by the transboundary migration of Locusta migratoria (L.) (Orthoptera: Acrididae) in the Tacheng Prefecture and the Altay Prefecture at the China–Kazakhstan border by examining the effects of terrain on their landing behaviors. Based on the time and location of the locusts’ mass landings, we used mesoscale weather research and forecasting models to test the sensitivity (horizontal wind, vertical wind, temperature, and rainfall) of the terrain and simulate the effects of different terrain heights (terrain leveling, half terrain height, and actual terrain height) on landing. The results showed that changes in the regional terrain of the Tacheng and Altay prefectures did not enhance the vertical airflow over the landing area. With an elevation of the terrain, the precipitation intensity in the landing area of locusts also did not increase. We did detect a direct impact of wind direction and changes in temperature in the regional terrain on the landing process of L. m. migratoria. With an increase in terrain altitude, the wind direction over the landing area changed and the temperature decreased, resulting in large-scale forced landing. The results of this study have important value in predicting and forecasting the arrival of migratory locusts.

Climate Change: An Obvious Phenomenon or Myth, in the African Context

Climate change is a global phenomenon of unprecedented consequence, engendering substantial scientific, political, and societal discourse. This literature review article offers an exploration of climate change perceptions and realities in the African context. It begins by establishing the unmistakable scientific consensus, with the Intergovernmental Panel on Climate Change (IPCC) serving as a primary authority. The review highlights robust evidence, including temperature trends, extreme weather events, and ecological impacts across the African continent, affirming climate change as an undeniable scientific phenomenon. However, the discourse surrounding climate change in Africa is not homogeneous. Dissenting arguments challenging climate change as a myth emphasize the variability in public perception, data limitations, and the complexities of attribution. These counterarguments reflect diverse perspectives, economic interests, and political inclinations, contributing to the multifaceted character of the climate change discussion. The implications of these findings are substantial, especially for a region highly susceptible to climate change impacts. Proactive measures for climate mitigation and adaptation are imperative to address the vulnerabilities experienced by communities, ecosystems, and economies. A multidisciplinary approach is underscored, recognizing climate change as an intricate challenge interwoven with economics, politics, and human behaviour. The diversity of voices in this discourse emphasizes the need for ongoing research, collaborative efforts, and an inclusive approach to policy formulation. In navigating the complex terrain of climate change, unwavering commitment to scientific rigor, transparency, and sustainable solutions is paramount. This literature review contributes to the ongoing dialogue, providing a basis for further inquiry and informed decision-making. It has the potential to guide future investigations, shape public policy, and elevate awareness regarding the critical significance of addressing climate change in the African context and, by extension, the entire world. The findings presented here are a testament to the indispensable role of rigorous scholarship in comprehending and confronting one of the most pressing challenges of our time

Validation of beyond visual-line-of-sight drone photogrammetry for terrain and canopy height applications

Aerial drones enable detailed three-dimensional terrain and vegetation canopy reconstructions supporting a diverse set of environmental monitoring applications. Small drone-derived photogrammetric datasets (<5 km2) often extend field-scale observations and enable calibration and validation for satellite-based mapping. Applications usually require data over much larger areas or in remote areas such as the Arctic, yet large-area drone datasets are rare due to aviation regulations usually stipulating that missions adhere to within-visual-line-of-sight (VLOS) conditions. Terrain and vegetation canopy reconstruction based on longer-range drone missions requires evaluation to justify future beyond-visual-line-of-sight (BVLOS) data initiatives. This work analyzed BVLOS drone photogrammetric datasets for terrain and vegetation height applications along a forest-tundra ecotone (FTE) spanning 396 km in northern Canada. Specifically, we: 1) performed validation assessments of derived terrain models and canopy height models, and 2) investigated remote sensing applications supported by BVLOS data in a FTE experiencing the effects of climate change. The BVLOS data covered in excess of 550 km2, increasing conventional drone data coverage by two orders of magnitude. Elevation validation indicated that strong agreement with GNSS benchmarks, Airborne Laser Scanning (ALS) and VLOS drone data (0.15–0.19 m RMSE) is possible, and that vertical RMSEs <2x the image spatial resolution is achievable. To validate vegetation canopy height modelling capabilities, comparisons with NASA's Land, Vegetation, and Ice Sensor (LVIS) full-waveform ALS observations indicated satisfactory agreement for height metrics (R2 = 0.7 to 0.8, RMSEs <1.0 m). Canopies were reconstructed at fine spatial scales, which in turn supported a unique comparison of vegetation height distributions between six land-cover classes and ecoregions along the FTE, and identified discrepancies in how national and international canopy height models represented the FTE. Detected terrain subsidence, including retrogressive thaw slumping and ice-wedge degradation, illustrated how climate-driven permafrost thaw is modifying landscapes and placing infrastructure at increased risk, which requires intensive monitoring. These applications demonstrated how BVLOS-derived data enables broader scale assessments of regional vegetation structure and terrain changes across larger spatial extents than previously possible with VLOS drones. Mapping opportunities will arise in the environmental remote sensing discipline as drone systems are increasingly operated beyond visual range.

Nodes for modes: Nodal honeycomb metamaterial enables a soft robot with multimodal locomotion

Soft-bodied animals, such as worms and snakes, use many muscles in different ways to traverse unstructured environments and inspire tools for accessing confined spaces. They demonstrate versatility of locomotion which is essential for adaptation to changing terrain conditions. However, replicating such versatility in untethered soft-bodied robots with multimodal locomotion capabilities have been challenging due to complex fabrication processes and limitations of soft body structures to accommodate hardware such as actuators, batteries and circuit boards. Here, we present MetaCrawler, a 3D printed metamaterial soft robot designed for multimodal and omnidirectional locomotion. Our design approach facilitated an easy fabrication process through a discrete assembly of a modular nodal honeycomb lattice with soft and hard components. A crucial benefit of the nodal honeycomb architecture is the ability of its hard components, nodes, to accommodate a distributed actuation system, comprising servomotors, control circuits, and batteries. Enabled by this distributed actuation, MetaCrawler achieves five locomotion modes: peristalsis, sidewinding, sideways translation, turn-in-place, and anguilliform. Demonstrations showcase MetaCrawler’s adaptability in confined channel navigation, vertical traversing, and maze exploration. This soft robotic system holds the potential to offer easy-to-fabricate and accessible solutions for multimodal locomotion in applications such as search and rescue, pipeline inspection, and space missions.

Variation in Debris-Flow-Prone Areas with Ecosystem Stability: A Case Study of the Qipan Catchment in the Wenchuan Earthquake Region

The spatial distribution of vegetation in a basin has a far-reaching influence on the potential for sediment separation and transport capacity. However, many landslides induced by strong earthquakes have greatly changed the existing pattern, which further increases the probability of debris flow in a basin during heavy rainfall and has a significant impact on the stability of the basin. Thus, this study selected the debris flow basin in the Qipan catchment of the Wenchuan earthquake area as the research object. Multisource and high-precision remote sensing images were used to analyze the land use changes in the basin, and the index of connectivity (IC) was introduced to analyze the evolution of sediment transport capacity. An ecosystem stability assessment method suitable for post-earthquake debris flow basins was proposed. Through quantitative assessment of the ecosystem stability of the basin after the Wenchuan earthquake in 2008 and the two debris flow events after the earthquake, the dynamic relationship between the debris-flow-prone area and the ecosystem stability of the basin was revealed. The results showed that the stability of the ecosystem in the Qipan catchment increased annually, indicating a stable and substable state. The spatial distribution characteristics were lower in the north and south and greater in the middle. By comparing the evaluation results with the actual terrain change trend, the accuracy and feasibility of the evaluation method are verified. The results of this study provide a scientific basis for the formulation of regional disaster prevention strategies and help to accelerate the improvement of regional stability in debris-flow-prone areas.

Utilizing Aerial Photography to Trace the Development of Urbanization in the Chennai District

Chennai, India is growing more urbanized by the day, hence studying its development is essential. We may see the world from a bird's-eye viewpoint thanks to aerial photography, a method for taking pictures of the Earth's surface from an elevated position—typically from an aircraft or drone. Scientific research, land surveying, urban planning, environmental monitoring, and mapping are just a few of the applications for these photos. Using remote sensing techniques, we are able to examine and record changes in Chennai's physical terrain over time, allowing us to trace the city's evolution through these aerial photos. ArcGIS, ERDAS, ENVI, Pix4D, Global Mapper, and QGIS were among the programs we used to process and examine the aerial photo data. Through the help of these software programs, users can extract insightful data and useful information from photos taken by satellites, drones, or airplanes. When compared to satellite images, aerial photography frequently produces imagery with a better spatial resolution, enabling more accurate and thorough study of ground-based objects. Through a comparative analysis of maps produced using aerial photography data throughout time, we have discovered that Chennai is losing open land, green spaces, and water bodies because of its growing urbanization. By closely analysing this trend and taking into account various factors, we can forecast the continued growth of Chennai.

An On-Site InSAR Terrain Imaging Method with Unmanned Aerial Vehicles.

An on-site InSAR imaging method carried out with unmanned aerial vehicles (UAVs) is proposed to monitor terrain changes with high spatial resolution, short revisit time, and high flexibility. To survey and explore a specific area of interest in real time, a combination of a least-square phase unwrapping technique and a mean filter for removing speckles is effective in reconstructing the terrain profile. The proposed method is validated by simulations on three scenarios scaled down from the high-resolution digital elevation models of the US geological survey (USGS) 3D elevation program (3DEP) datasets. The efficacy of the proposed method and the efficiency in CPU time are validated by comparing with several state-of-the-art techniques.

Multisource elevations strategy obtaining robust seed points and reference surfaces for ground points extraction in complex terrain area

The appearance of unmanned aerial vehicle photogrammetry and airborne lidar makes it possible to obtain measurement data for complex terrains such as gullies and mountainous regions. However, extracting ground points from these abundant and massive measurement datasets is challenging. In traditional extractions, their essence is to determine the surfaces that can describe the terrain from the seed points in the grid and use them as the basis for separating non-ground points. For effective extraction, this study proposes a multisource elevations strategy (MES) obtaining robust seed points and reference surfaces. First, two-level extended grids were constructed as the basic units. Then, to select more robust values between measurement and interpolation elevations, an elevation-determination rule was established for seed points. After, based fitting and interpolation elevations of grid nodes, the correction range is determined and the elevation is corrected for reference surfaces. In two representative complex terrain areas, when non-ground points were marked as seed points, the MES effectively reduced the phenomenon of seed points moving away from the ground. Reference surfaces can also accurately represent the global change trend and local elevation of the ground in areas where the terrain changes rapidly. This strategy provides a new thinking for ground point extraction from point cloud.

Forecasting some climate parameters of Türkiye using the SSP3-7.0 scenario for the years 2040–2059

This study employs the Coupled Model Inter-comparison Projects (CMIPs) and the Sixth phase of CMIPs (CMIP6) to unravel the multifaceted impacts of global climate change on climate of Türkiye. The CMIP6 data, fundamental to the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports, forms the basis for projecting future climate scenarios, specifically under the medium-high reference scenario SSP3-7. Utilizing a suite of global climate models, including the innovative Multi-Model Ensemble (MME) approach, this study combines predictions to enhance the precision climate projections of Türkiye. Historical data spanning from 1951 to 2020 were subjected to rigorous statistical analysis, including descriptive statistics and regression analysis. The findings reveal an unequivocal upward trajectory in Türkiye’s annual mean temperature, with an accelerated pace in recent decades. Despite a lack of a significant long-term trend in annual precipitation from 1951 to 2020, the rate of change in precipitation is accelerating, indicating potential future challenges. Projections for 2040-2059 under the SSP3-7.0 scenario indicate a non-uniform increase in mean temperature across Türkiye, with the southern and western regions facing the most significant impact. This warming trend poses imminent threats to agriculture, altering crop yields and increasing the risk of heat stress for livestock. Additionally, the projected decrease in precipitation, alongside a surge in hot days and tropical nights, underscores the urgency for adaptive measures. As Türkiye navigates the complex terrain of climate change, this study provides valuable insights, emphasizing the significance of robust climate modeling for informed decision-making. The results underscore the imminent challenges Türkiye faces and emphasize the critical importance of proactive climate action on both national and global fronts.

Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation

Insects are good examples of ground locomotion because they can adapt their gait pattern to propel them in any direction, over uneven terrain, in a stable manner. Nevertheless, replicating such locomotion skills to a legged robot is not a straightforward task. Different approaches have been proposed to synthesize the gait patterns for these robots; each approach exhibits different restrictions, advantages, and priorities. For the purpose of this document, we have classified gait pattern generators for multi-legged robots into three categories: precomputed, heuristic, and bio-inspired approaches. Precomputed approaches rely on a set of precalculated motion patterns obtained from geometric and/or kinematic models that are performed repeatedly whenever necessary and that cannot be modified on-the-fly to adapt to the terrain changes. On the other hand, heuristic and bio-inspired approaches offer on-line adaptability, but parameter-tuning and heading control can be difficult. In this document, we present the K3P algorithm, a real-time kinematic gait pattern generator conceived to command a legged robot. In contrast to other approaches, K3P enables the robot to adapt its gait to follow an arbitrary trajectory, at an arbitrary speed, over uneven terrain. No precomputed motions for the legs are required; instead, K3P modifies the motion of all mechanical joints to propel the body of the robot in the desired direction, maintaining a tripod stability at all times. In this paper, all the specific details of the aforementioned algorithm are presented, as well as different simulation results that validate its characteristics.