Improved spatio-temporal alignment measurement method for hull deformation

In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time.The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatiotemporal aligned hull deformation measurement model.In addition,two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation.The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.

Assessment of public perspectives and barriers towards dengue preventive practices using the Health Belief Model in Puducherry,India:A cross-sectional study

Objective:To assess the perspectives and barriers towards dengue preventive practices among the residents of Puducherry,India.Methods:A cross-sectional survey was conducted in 300 households in Puducherry,using a population-proportionate(7:3)distribution from urban and rural areas by grid sampling.One adult interview per household was conducted and the participants were selected using a KISH grid.A semi-structured questionnaire based on the Health Belief Model(HBM)with additional questions on knowledge assessment was used.Knowledge was assessed based on the correctness of answers and the HBM scores were calculated on a 5-point Likert scale.Participants were categorized based on the median score under each domain.Logistic regression was used for adjusted analysis and models were built to predict the performances in each domain.Results:Four percent of the participants lacked basic knowledge regarding dengue transmission.While 208(69.3%)participants did not consider themselves at risk of contracting dengue within the next year,majority perceived dengue as a disease with low severity.Around 49.3%(148)were skeptical about the benefit of time and money spent on dengue prevention.Inadequate government efforts were stated as the major barrier(47.0%)and frequent reminders(142,47.3%)as the major cue to action.Age above 50 years(aOR 1.78,95%CI 1.04-3.06,P=0.037)and rural locality(aOR 2.68,95%CI 1.52-4.71,P=0.001)were found to be significantly associated with poor knowledge scores.Urban participants had a significantly higher chance to perceive low susceptibility as compared to the rural counterparts(aOR 1.74,95%CI 1.05-2.9,P=0.03).Participants with less than a high school education had low perceived benefits(aOR 2.46,95%CI 1.52-3.96,P<0.001)and low self-efficacy scores(aOR 2.66,95%CI 1.61-4.39,P<0.001).Conclusions:This study identifies key gaps in dengue prevention,including low perceived susceptibility,mild disease perception,limited knowledge of breeding sites,and overreliance on government efforts.Tailoring interventions to community needs,stratified to factors influencing the community perspectives can significantly improve dengue prevention efforts.

Epidemic Characteristics and Spatio-Temporal Patterns of HFRS in Qingdao City,China,2010-2022

Objective This study investigated the epidemic characteristics and spatio-temporal dynamics of hemorrhagic fever with renal syndrome(HFRS)in Qingdao City,China.Methods Information was collected on HFRS cases in Qingdao City from 2010 to 2022.Descriptive epidemiologic,seasonal decomposition,spatial autocorrelation,and spatio-temporal cluster analyses were performed.Results A total of 2,220 patients with HFRS were reported over the study period,with an average annual incidence of 1.89/100,000 and a case fatality rate of 2.52%.The male:female ratio was 2.8:1.75.3%of patients were aged between 16 and 60 years old,75.3%of patients were farmers,and 11.6%had both“three red”and“three pain”symptoms.The HFRS epidemic showed two-peak seasonality:the primary fall-winter peak and the minor spring peak.The HFRS epidemic presented highly spatially heterogeneous,street/township-level hot spots that were mostly distributed in Huangdao,Pingdu,and Jiaozhou.The spatio-temporal cluster analysis revealed three cluster areas in Qingdao City that were located in the south of Huangdao District during the fall-winter peak.Conclusion The distribution of HFRS in Qingdao exhibited periodic,seasonal,and regional characteristics,with high spatial clustering heterogeneity.The typical symptoms of“three red”and“three pain”in patients with HFRS were not obvious.

Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting

Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.

Recent advances and perspectives of zinc metal-free anodes for zinc ion batteries

Zinc-ion batteries(ZIBs) are recognized as potential energy storage devices due to their advantages of low cost, high energy density, and environmental friendliness. However, zinc anodes are subject to unavoidable zinc dendrites, passivation, corrosion, and hydrogen evolution reactions during the charging and discharging of batteries, becoming obstacles to the practical application of ZIBs. Appropriate zinc metal-free anodes provide a higher working potential than metallic zinc anodes, effectively solving the problems of zinc dendrites, hydrogen evolution, and side reactions during the operation of metallic zinc anodes. The improvement in the safety and cycle life of batteries creates conditions for further commercialization of ZIBs. Therefore, this work systematically introduces the research progress of zinc metal-free anodes in “rocking chair” ZIBs. Zinc metal-free anodes are mainly discussed in four categories: transition metal oxides,transition metal sulfides, MXene(two dimensional transition metal carbide) composites, and organic compounds, with discussions on their properties and zinc storage mechanisms. Finally, the outlook for the development of zinc metal-free anodes is proposed. This paper is expected to provide a reference for the further promotion of commercial rechargeable ZIBs.

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.

Warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography

High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy,high resolution and high efficiency.However,it faces challenge in dense objects tracking and 3D trajectories reconstruction due to the characteristics of small size and dense distribution of fragment swarm.To address these challenges,this work presents a warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography.Firstly,background difference algorithm is utilized to extract the center and area of each fragment in the image sequence.Subsequently,a multi-object tracking(MOT)algorithm using Kalman filtering and Hungarian optimal assignment is developed to realize real-time and robust trajectories tracking of fragment swarm.To reconstruct 3D motion trajectories,a global stereo trajectories matching strategy is presented,which takes advantages of epipolar constraint and continuity constraint to correctly retrieve stereo correspondence followed by 3D trajectories refinement using polynomial fitting.Finally,the simulation and experimental results demonstrate that the proposed method can accurately track the motion trajectories and reconstruct the spatio-temporal distribution of 1.0×10^(3)fragments in a field of view(FOV)of 3.2 m×2.5 m,and the accuracy of the velocity estimation can achieve 98.6%.

A cloud model target damage effectiveness assessment algorithm based on spatio-temporal sequence finite multilayer fragments dispersion

To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.

Self-Attention Spatio-Temporal Deep Collaborative Network for Robust FDIA Detection in Smart Grids

False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal selfattention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.

Surgeon Perspectives on Anastomotic Leaks and FluidAI’s StreamTM Platform: Integrating Qualitative Insights for Technological Innovation

Purpose: This article investigates the critical importance of integrating surgeons’ direct input into the development of innovative technologies that address gaps in surgical care, including those aimed at reducing anastomotic leaks (AL), a major complication in gastrointestinal surgery. While traditional quantitative research methods are prevalent, they often overlook the invaluable insights of the surgeons who manage these complications firsthand. Subjects and Methods: This study employs a qualitative approach, utilizing semi-structured interviews with 40 surgeons from various specialties, including general, bariatric, colorectal, trauma, hepato-biliary, and thoracic surgery. The interviews were designed to probe the needs of surgeons, challenges currently faced, and gaps in clinical practice, research, and technology for detection and/or management of AL. The data were analyzed using thematic analysis, which revealed significant gaps in current technologies for early detection and prevention of leaks. Results: Surgeons expressed strong interest in FluidAI’s Stream™ Platform, a non-invasive medical device designed to monitor postoperative drainage fluid in real-time, providing continuous data on AL risk. The ability of this platform to offer early prediction through pH and electrical conductivity analysis was particularly appealing to participants, who emphasized the importance of timely interventions in improving patient outcomes. The study’s findings highlight not only the clinical challenges but also the emotional toll that AL takes on surgeons, underlining the need for innovations that are both data-driven and humanistic. Conclusion: By centering surgeons’ perspectives, this research advocates for a human-centered approach to technological advancement, ensuring that new tools are both clinically effective and aligned with the real-world needs of surgical practitioners.

Mistreatment during Childbirth: Impact on Maternal Outcomes and Importance of Provider Perspectives

Background: Dying in childbirth is one of the most common causes of death for women. While maternal mortality rates, defined as deaths per 100,000 live births, have been steadily dropping in most countries worldwide, maternal mortality rates have doubled in the United States in the last twenty years. This commentary examines the various contributing factors to this trend. Methods: A literature review was performed using the keywords: maternal mortality, United States, disrespectful maternity care, obstetric violence, provider perspectives, and disparities. Maternal mortality statistics were obtained from the World Health Organization website. Results: Medical factors associated with maternal mortality include increased maternal age and cardiovascular conditions. Social factors include barriers to healthcare access, delays in receiving medical care, reduction in reproductive health services in some states, and non-obstetrical deaths such as accidents, domestic violence, and suicide. Racial inequities and disparities of care are reflected in higher maternal mortality rates for minorities and people of color. Disrespectful maternity care or obstetric violence has been reported worldwide as a factor in delay of lifesaving obstetrical care and reluctance by a pregnant person to access the healthcare system. About one in five US women has reported experiencing mistreatment, varying from verbal abuse to lack of privacy, from coerced procedures to neglect during childbirth. Conclusion: This commentary highlights the importance of inclusion of providers in research on respectful maternity care. Provider burnout, moral distress, limited time, and burden of clinical responsibilities are known challenges to respectful and comprehensive medical care. The association of disrespectful care with poor maternal outcomes needs to be studied. Exploring root causes of disrespectful childbirth care can empower nurses, midwives, and physicians to improve their environment and find solutions to reduce a potential cause of maternal mortality.

Research on Early Warning of Banking Crises from the Perspective of Credit Structures

The relationship between credit expansion and banking crises is complex and cannot be fully explained by total credit alone.A systematic analysis of the relationship between the amount and structure of total credit and banking crises is important for an objective prediction of the influence of potential financial risks.This paper,drawing on data from 15 selected countries,delves into the power of credit indicators in the early warning of banking crises from the perspectives of industrial structure,sector structure,and term structure of credit.Various machine learning methods were used,including Logistic Regression,Random Forest,Decision Tree,Support Vector Machine(SVM),Bagging,and Boosting models.The empirical findings indicate that credit expansion plays a crucial role in triggering banking crises.However,total credit is better suited for the early warning of short-term banking crises,whereas credit structure is more useful for the early warning of long-term banking crises.Moreover,in an early warning system,identifying key early warning indicators is more meaningful than merely increasing the number of indicators.Machine learning can somewhat enhance the early warning power,but it may not always be robust.Therefore,more attention should be paid to potential systemic banking crises resulting from an imbalance in credit structure while regulating the total credit threshold.

Moral Perspective as Depicted in the Archetypes on Divine-Human Perspective of Dianshizhai Pictorial

This article examines the archetypes related to the divine-human perspective in the Dianshizhai Pictorial and uses the basic theory of“Myth-Archetype Criticism”to analyze the concepts of moral perspective and the view of cause and effect reflected in the text.It summarizes the interactive modes and various archetypes of cause and effect in three contexts:gods to humans,humans to gods,and humans to humans,explores the social reality and the relevant views of moral perspective and causality reflected in the illustrated magazine,and reveals the archetype foundation of the narrative of moral perspective retribution in Dianshizhai Pictorial in the three dimensions of psychology,literature,and society.

Current perspectives of viral hepatitis

Viral hepatitis represents a major danger to public health,and is a globally leading cause of death.The five liver-specific viruses:Hepatitis A virus,hepatitis B virus,hepatitis C virus,hepatitis D virus,and hepatitis E virus,each have their own unique epidemiology,structural biology,transmission,endemic patterns,risk of liver complications,and response to antiviral therapies.There remain few options for treatment,in spite of the increasing prevalence of viral-hepatitiscaused liver disease.Furthermore,chronic viral hepatitis is a leading worldwide cause of both liver-related morbidity and mortality,even though effective treatments are available that could reduce or prevent most patients’complications.In 2016,the World Health Organization released its plan to eliminate viral hepatitis as a public health threat by the year 2030,along with a discussion of current gaps and prospects for both regional and global eradication of viral hepatitis.Today,treatment is sufficiently able to prevent the disease from reaching advanced phases.However,future therapies must be extremely safe,and should ideally limit the period of treatment necessary.A better understanding of pathogenesis will prove beneficial in the development of potential treatment strategies targeting infections by viral hepatitis.This review aims to summarize the current state of knowledge on each type of viral hepatitis,together with major innovations.

Multi-Scale Location Attention Model for Spatio-Temporal Prediction of Disease Incidence

Due to the increasingly severe challenges brought by various epidemic diseases,people urgently need intelligent outbreak trend prediction.Predicting disease onset is very important to assist decision-making.Most of the exist-ing work fails to make full use of the temporal and spatial characteristics of epidemics,and also relies on multi-variate data for prediction.In this paper,we propose a Multi-Scale Location Attention Graph Neural Networks(MSLAGNN)based on a large number of Centers for Disease Control and Prevention(CDC)patient electronic medical records research sequence source data sets.In order to understand the geography and timeliness of infec-tious diseases,specific neural networks are used to extract the geography and timeliness of infectious diseases.In the model framework,the features of different periods are extracted by a multi-scale convolution module.At the same time,the propagation effects between regions are simulated by graph convolution and attention mechan-isms.We compare the proposed method with the most advanced statistical methods and deep learning models.Meanwhile,we conduct comparative experiments on data sets with different time lengths to observe the predic-tion performance of the model in the face of different degrees of data collection.We conduct extensive experi-ments on real-world epidemic-related data sets.The method has strong prediction performance and can be readily used for epidemic prediction.

Adsorption behavior of CO_(2)/H_(2)S mixtures in calcite slit nanopores for CO_(2) storage:An insight from molecular perspective

It is acknowledged that injecting CO_(2) into oil reservoirs and saline aquifers for storage is a practical and affordable method for CO_(2) sequestration.Most CO_(2) produced from industrial exhaust contains impurity gases such as H_(2)S that might impact CO_(2) sequestration due to competitive adsorption.This study makes a commendable effort to explore the adsorption behavior of CO_(2)/H_(2)S mixtures in calcite slit nanopores.Grand Canonical Monte Carlo(GCMC)simulation is employed to reveal the adsorption of CO_(2),H_(2)S as well as their binary mixtures in calcite nanopores.Results show that the increase in pressure and temperature can promote and inhibit the adsorption capacity of CO_(2) and H_(2)S in calcite nanopores,respectively.CO_(2)exhibits stronger adsorption on calcite surface than H_(2)S.Electrostatic energy plays the dominating role in the adsorption behavior.Electrostatic energy accounts for 97.11%of the CO_(2)-calcite interaction energy and 56.33%of the H_(2)S-calcite interaction energy at 10 MPa and 323.15 K.The presence of H_(2)S inhibits the CO_(2) adsorption in calcite nanopores due to competitive adsorption,and a higher mole fraction of H_(2)S leads to less CO_(2) adsorption.The quantity of CO_(2) adsorbed is lessened by approximately 33%when the mole fraction of H_(2)S reaches 0.25.CO_(2) molecules preferentially occupy the regions near the po re wall and H_(2)S molecules tend to reside at the center of nanopore even when the molar ratio of CO_(2) is low,indicating that CO_(2) has an adsorption priority on the calcite surface over H_(2)S.In addition,moisture can weaken the adsorption of both CO_(2) and H_(2)S,while CO_(2) is more affected.More interestingly,we find that pure CO_(2) is more suitable to be sequestrated in the shallower formations,i.e.,500-1500 m,whereas CO_(2)with H_(2)S impurity should be settled in the deeper reservoirs.

Linking Perceived Risk of Public Health Emergency to Psychological Distress among Chinese College Students: The Chain Mediation Role of Balanced Time Perspective and Negative Coping Styles

Background:With public health emergencies(PHE)worldwide increasing,the perceived risk of PHE has been one of the critical factors influencing college students’psychological distress.However,the mechanisms by which the perceived risk of PHE affects college students’psychological distress are not clear.The study’s purpose was to investigate the mediation roles of deviation from a balanced time perspective(DBTP)and negative coping styles between the perceived risk of PHE and psychological distress.Methods:A convenience sampling method was used to survey 1054 Chinese college students with self-reporting.Data was collected using the Public Risk Perception Scale(PRPS),the Zimbardo Time Perspective Inventory(ZTPI),the Simplified Coping Style Questionnaire(SCSQ),the PHE Anxiety Scale,and the Chinese version of the Patient Health Questionnaire(PHQ).The associations between the perceived risk of PHE,DBTP,negative coping styles,and psychological distress were clarified using the correlation analysis.Additionally,the mediating roles of DBTP and negative coping styles between the perceived risk of PHE and psychological distress were investigated using a structural equation model.Results:The findings revealed low to moderate correlations between the variables studied.Students’perceived risk of PHE was a positive predictor of their psychological distress(b=0.219,p<0.01).DBTP and negative coping styles played chain mediation roles between them with the effect being 0.009 and a 95%Boot CI of[0.003,0.023].This chain mediation model had an excellent fit index(χ^(2)/df=4.732,CFI=0.973,TLI=0.930,RMSEA=0.048,SRMR=0.047).Conclusion:These findings showed how the perceived risk of PHE affected college students’psychological distress.Specifically,these results suggested that improving students’mental ability to switch effectively among different time perspectives depending on task features and situational considerations and reducing their negative coping styles might be effective ways to promote their mental health.

Operation optimization strategy of existing residential building energy-saving renovation market:From the perspective of subject behavior

The energy-saving renovation of existing residential buildings is a crucial measure to achieve the strategic goal of energy conservation and emission reduction in China and build ecologically livable cities.This article focuses on the perspective of subject behavior,starting from analyzing the current situation and difficulties of the operation of the energy-saving renovation market for existing residential buildings in China,drawing on the practical experience of the operation of the existing residential building energy-saving renovation market abroad.Based on principles such as systematicity,humanization,feasibility,and sustainability,the article constructs an operation optimization system of the existing residential building energy-saving renovation market from the perspective of subject behavior.In order to provide a reference for the healthy and orderly operation of the existing residential building energy-saving renovation market,this paper proposes implementation strategies for optimizing the operation of the existing residential building energy-saving renovation market.Suggestions are proposed from four aspects:optimizing the market environment,innovating the financing model,building the information sharing platform,and utilizing the synergies of the main subjects.

Access to Financial Services——An Analysis from the Human Rights Perspective

There is a broad connection between finance and human rights,with finance having both positive and negative impacts on human rights.Everyone has a need for access to financial services.Documents in both the international human rights and international finance fields address the relationship between financial services and human rights.Among financial services,microcredit and inclusive finance have the closest connection to human rights and potentially the greatest impact on human rights.Access to financial services promotes economic,social,and cultural rights as well as the rights of specific groups.The conditions for access to financial services to promote human rights require the state to assume obligations to recognize,respect,protect,and fulfill the need for individuals to access financial services,and to ensure the availability,accessibility,acceptability,and adaptability of basic financial services.Access to financial services has played a significant role in China’s comprehensive victory in the battle of poverty alleviation,providing valuable experience for the international community in poverty eradication,achieving sustainable development goals,and protecting and promoting human rights.

Spatio-Temporal Change of Dispersal Areas of Greater Kudu (Tragelaphus strepsiceros) in Lake Bogoria Landscape, Kenya

Decline in wildlife populations is manifest globally, regionally and locally. A wildlife decline of 68% has been reported in Kenya’s rangelands with Baringo County experiencing more than 85% wildlife loss in the last four decades. Greater Kudu (Tragelaphus strepsiceros) is endemic to Lake Bogoria landscape in Baringo County and constitutes a major tourist attraction for the region necessitating use of its photo on the County’s logo and thus a flagship species. Tourism plays a central role in Baringo County’s economy and is a major source of potential growth and employment creation. The study was carried out to assess spatio-temporal change of dispersal areas of Greater Kudu (GK) in Lake Bogoria landscape in the last four years for enhanced adaptive management and improved livelihoods. GK population distribution primary data collected in December 2022 and secondary data acquired from Lake Bogoria National Game Reserve (LBNGR) for 2019 and 2020 were digitized using in a Geographic Information System (GIS). Measures of dispersion and point pattern analysis (PPA) were used to analyze dispersal of GK population using GIS. Spatio-temporal change of GK dispersal in LBNR was evident thus the null hypothesis was rejected. It is recommended that anthropogenic activities contributing to GK’s habitat degradation be curbed by providing alternative livelihood sources and promoting community adoption of sustainable technologies for improved livelihoods.