Malaria Prevalence and Associated Risk Factors among Batwa Indigenous People of Kanungu District in Southwestern Uganda: Does “Place” Matter?

Introduction: Malaria is an important disease, causing high morbidity and mortality, especially in Sub-Saharan Africa. Measuring malaria prevalence using malaria rapid diagnostic tests (mRDTs), particularly among a vulnerable population sub-group, is a vital public health step in discovering effective methods of prevention and control. This study set out to examine the association between “place” and other risk factors with malaria prevalence among Batwa Indigenous People (IP). Methods: An analytical cross-sectional study design was implemented. Two surveys January 2014 (n = 572) and April 2014 (n = 541) involving interviews and testing for malaria using mRDTs were conducted in 10 Batwa settlements where a total of 1113 Batwa of all ages were surveyed and tested. The data were first compiled in MS Excel and then imported and analyzed using STATA ver.14. Descriptive statistics, were generated, followed by bivariable and multivariable regression model analysis to establish associations between the predictor and outcome variables with p ≤ 0.05 considered statistically significant. Results: Overall prevalence was 13.94% (n = 146). There is a significant relationship between settlement (place) and malaria prevalence AOR 11.7, 95% CI (1.38 - 98.93), p-value = 0.02. More males 16.97% (n = 84) tested positive compared to females 11.23% (n = 62) but there was no statistically significant association between gender and mRDT (p-value > 0.005). Children less than 5 years registered high prevalence and there was a significant relationship between age and mRDT (p-value ≤ 0.005). Wealth proxy indicators showed no association with prevalence p-value = 0.390. Season had no association with prevalence (p-value = 0.80). However, the proportion of the day spent in the forest/woodlands was significantly associated with malaria prevalence COR 12.83, 95% CI (1.14 - 143.73) p-value = 0.04. Low elevation was significantly associated with malaria prevalence COR 2.42, 95% CI (1.32 - 4.41), p = 0.004 but sleeping under a net and level of education did not show any association with malaria prevalence. Conclusion: This study highlights the importance of place in predicting malaria prevalence among Batwa Indigenous People a marginalized and remortely located sub-population. This study has shown that place matters in determining malaria prevalence. However, other factors like age, elevation and gender also contribute to malaria prevalence. Batwa have higher prevalence than the national and even non-indigenous populations in the same district. We recommend targeting hotspots intervention approach since it has proven reasonable impact on reducing malaria prevalence.

Indigenous Breeding Practices in Guinea Fowl (Numida meleagris) as Influenced by Plumage Features in Northern Cameroon

The objective of this work was to draw up an inventory of environmental and anthropogenic factors linked to indigenous breeding practices of guinea fowl (Numida meleagris) influenced by plumage characteristics. The information was collected using the snowball technique and using a formal questionnaire developed (AU-IBAR, 2015). The dominant plumage is pearl gray with a frequency of 38.89% followed by black (13.85%). In total, 154 adult animals were lost by operators with a frequency of 22.95%. According to plumage, white comes first with a frequency of 51.61% followed by Lavender plumage (36.58%). Predation is the major constraint with a frequency of 35.72%. The guinea fowl most targeted by predators are the white guinea fowl with a frequency of 56.25%, while predation is low for black plumage (25%), royal purple (33.34) and pearl gray (34.69). The pearl gray guinea fowl and the royal purple guinea fowl appear to have a more developed wild instinct, which explains the frequency of recorded escapes. White plumage is perceived as being more docile during breeding and tends to exhibit better resistance to heat stress compared to other phenotypes, making it more valued for traditional rituals. Dark plumage seems more sought after in breeding for its supposed prolificacy, its resistance to infections and its weight. According to breeders, the dominance relationship mainly depends on the number of individuals of the same plumage. Dark plumage in significant numbers in most flocks seems to have dominance in terms of food and choice of sexual partners, which would explain their large numbers.

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

Perception of Local Populations of Bouba-Ndjidda National Park on Indigenous Knowledge and the Impact of Climate Change on Biodiversity Conservation

This study deals with the perceptions of indigenous knowledge in the field of conservation and the impact of climate change on biodiversity in the peripheral villages of the Bouba-Ndjidda National Park (PNBN), Department of Mayo-Rey, Cameroon. To this end, 23 out of 70 villages in the study area were selected, for a sample of 368 households surveyed through a questionnaire addressed to the heads of households. Data collection took place from August 27 to December 22, 2022. The results reveal that 70.7% of the population living near the PNBN practice agriculture as their main income-generating activity, followed by livestock (23.3%) and trade (6%). Knowledge in the field of the conservation of natural resources is rooted in occultism through rituals and sacrifices (99.50%) offered to the geniuses of nature and prohibitions on the felling of trees and sacred animals (13.00%). The populations perceive climate change through the variation of the seasons (97.60%), the increase in temperature (84.80%), the reduction in the duration of the rainy seasons (54.60%), the drop in agricultural yield (84.80%) and floods (74.70%). The consequence of these changes on wildlife is the disappearance of animal species such as the Rhinoceros, the Wild Dog, the Cheetah and the Ostrich. In order to cope with climate change, the natives have developed strategies such as the adoption of early crop varieties (maize, millet, soybeans, etc.), the reorganization of the agricultural calendar, the practice of reforestation, extension of field surfaces, the cessation of excessive deforestation and uncontrolled bush fires, the construction of fired brick houses, the practice of irrigation, seasonal migration and occultism. This indigenous knowledge is a perfect adaptation to climate change and makes it possible to better take into account the vision of local populations in the conservation of biodiversity.

Cooperative UAV search strategy based on DMPC-AACO algorithm in restricted communication scenarios

Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference(EMI)has become a contentious research problem.In certain mission environments,due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations,the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages.Furthermore,the data interaction between unmanned aerial vehicles(UAVs)can only be performed in specific communication-available stages.Traditional cooperative search algorithms cannot handle such situations well.To solve this problem,this study constructed a distributed model predictive control(DMPC)architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor.An attention mechanism ant-colony optimization(AACO)algorithm is proposed for UAV search-control decision planning.The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information,a priori information,and emergent information of the mission to satisfy different search expectations to the maximum extent.Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.

Towards Inclusive Development: Situating the Socio-Economic Wellbeing and Environmental Issues of an Indigenous Cultural Community in the Philippines

The Municipality of Carranglan is a rural community where agriculture is the main source of livelihood. The Kalanguya Ancestral Domain (KAD) is also located in the area, and it provides a home for the Kalanguya indigenous cultural community. They have fled to the area for a variety of reasons. Despite its rural setting, Carranglan is regarded as a first-class municipality and the largest municipality in Nueva Ecija. This study described and documented the socio-economic profile, and existing issues and problems the Kalanguya Indigenous Peoples (IPs) experienced in their milieu, especially in terms of environmental protection. Recognizing and valuing their perspectives is vital for inclusive development. This study also situated the current status and conditions of the IPs, who generally live below the poverty line and face a range of plights and challenges. As part of safeguarding indigenous rights and interests, this study was undertaken after series of consultations with community elders and leaders. Key informant interviews, household surveys, semi-structured interviews, focused-group discussion (FGD), and direct observations were used to elicit information. Many of the respondents are elementary undergraduates which show that the community had lacked access to education. The following are the primary socioeconomic issues confronting IPs: insufficient access to potable water, suboptimal agricultural productivity, meager monthly income, and a dearth of supplementary and alternative livelihood opportunities. Forest fires, landslides, encroachment of illegal loggers, and concerns about wildlife resources are some of the current environmental issues they have encountered. The local population was supportive of plans and actions targeted at improving the environment. This reveals that the IPs possess unique indigenous forest expertise that enables them to properly manage natural resources.

Socio-Psychological Factors of Rising Trend of Suicidal Ideation among Indigenous Workforce: Evidence from Himalayan Range

Apart from socio-economic disparities,indigenous people of the Himalayan range in Asia face an increasing trend of suicides.The tragic suicidal events usually go unaddressed,and no strategies are presently in place to mitigate suicides in the future.This study aims to explain the prevailing causes of suicidal ideation to come up with some policy recommendations.Through a preliminary survey,we identified social stigma,social isolation,lack of healthcare facilities,and domestic violence as the potential reasons for suicidal ideation.We identified individuals with suicide ideation for further data collection to test the relationships between the identified variables and suicidal ideation.A quantitative,explanatory and cross-sectional design was used to test the hypotheses.Inferential statistics were applied to the data containing 314 responses,and the results revealed that all the predictive variables influence the formation of suicidal ideation.The study uncovered some new dimensions of social stigma and social isolation that are common among the indigenous people.The results also indicate that some domestic violence is socially acceptable and considered a normal familial matter.Thefindings have theoretical and practical implications.We have identified new dimensions of social stigma,social isolation,and domestic violence that are new additions to the existing bank of literature.It provides insights to encourage public authorities,healthcare providers,and NGO managers to play their role in the right direction to mitigate the problem.This study opens further venues for researchers to investigate the complex issue of suicide more deeply.

Reconciling Local and Global Agendas in Sustainable Development: Participatory Research with Indigenous Andean Communities

This paper discusses participatory research in the Andes and presents a case study in Cotacachi, Ecuador, where sustainability scientists and indigenous people seek common ground in their respective but drastically different research and social agendas. Participatory research based on Andean experiences pre-dated and inspired much of the later international movement in agriculture, health, and conservation. Andean communities have a long history in demanding that outsiders address the needs of the community as a condition for carrying out scientific or applied activities. What an Andean community, however, sees as relevant may or may not be within the rubric of ‘participatory research’ as it is practiced throughout much of the world. In fact, overzealous participatory researchers are just as bothersome as their predecessors bearing long questionnaires. More important to Andean people is an equitable relationship with researchers and developers in which exchanges of value are made. A distinction between ‘enriching’ and ‘extractive’ research is drawn. In the case of the SANREM project in Cotacachi, Ecuador, scientists carried out enriching research activities of interest to local people as a wayto generate social capital for conducting basic research which does not have an obvious, immediate local benefit. The requested research did not have a conventional participatory methodology but provided valuable products (educational opportunity, germplasm, community visualization tools, and information) to the indigenous community in exchange for time and resources to conduct research on more basic natural resource questions. We argue that in the Andean context the key to reconciling the needs of scientists and of local needs is seeking new forms of equitable collaboration which reach beyond the present and now somewhat tired discourse of ‘participation’.

Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

Enhanced Differentiable Architecture Search Based on Asymptotic Regularization

In differentiable search architecture search methods,a more efficient search space design can significantly improve the performance of the searched architecture,thus requiring people to carefully define the search space with different complexity according to various operations.Meanwhile rationalizing the search strategies to explore the well-defined search space will further improve the speed and efficiency of architecture search.With this in mind,we propose a faster and more efficient differentiable architecture search method,AllegroNAS.Firstly,we introduce a more efficient search space enriched by the introduction of two redefined convolution modules.Secondly,we utilize a more efficient architectural parameter regularization method,mitigating the overfitting problem during the search process and reducing the error brought about by gradient approximation.Meanwhile,we introduce a natural exponential cosine annealing method to make the learning rate of the neural network training process more suitable for the search procedure.Moreover,group convolution and data augmentation are employed to reduce the computational cost.Finally,through extensive experiments on several public datasets,we demonstrate that our method can more swiftly search for better-performing neural network architectures in a more efficient search space,thus validating the effectiveness of our approach.

Bridge Bidding via Deep Reinforcement Learning and Belief Monte Carlo Search

Contract Bridge,a four-player imperfect information game,comprises two phases:bidding and playing.While computer programs excel at playing,bidding presents a challenging aspect due to the need for information exchange with partners and interference with communication of opponents.In this work,we introduce a Bridge bidding agent that combines supervised learning,deep reinforcement learning via self-play,and a test-time search approach.Our experiments demonstrate that our agent outperforms WBridge5,a highly regarded computer Bridge software that has won multiple world championships,by a performance of 0.98 IMPs(international match points)per deal over 10000 deals,with a much cost-effective approach.The performance significantly surpasses previous state-of-the-art(0.85 IMPs per deal).Note 0.1 IMPs per deal is a significant improvement in Bridge bidding.

A Lightweight, Searchable, and Controllable EMR Sharing Scheme

Electronic medical records (EMR) facilitate the sharing of medical data, but existing sharing schemes suffer fromprivacy leakage and inefficiency. This article proposes a lightweight, searchable, and controllable EMR sharingscheme, which employs a large attribute domain and a linear secret sharing structure (LSSS), the computationaloverhead of encryption and decryption reaches a lightweight constant level, and supports keyword search andpolicy hiding, which improves the high efficiency of medical data sharing. The dynamic accumulator technologyis utilized to enable data owners to flexibly authorize or revoke the access rights of data visitors to the datato achieve controllability of the data. Meanwhile, the data is re-encrypted by Intel Software Guard Extensions(SGX) technology to realize resistance to offline dictionary guessing attacks. In addition, blockchain technology isutilized to achieve credible accountability for abnormal behaviors in the sharing process. The experiments reflectthe obvious advantages of the scheme in terms of encryption and decryption computation overhead and storageoverhead, and theoretically prove the security and controllability in the sharing process, providing a feasible solutionfor the safe and efficient sharing of EMR.

Privacy-Preserving Multi-Keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so on.Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers.Servers on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure state.The issue of privacy data protection has become an important obstacle to data sharing and usage.How to query outsourcing graph data safely and effectively has become the focus of research.Adjacency query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same time.This work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a solution.In our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud server.Our proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental analysis.The research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.

Spatial search weighting information contained in cell velocity distribution

Cell migration plays a significant role in physiological and pathological processes.Understanding the characteristics of cell movement is crucial for comprehending biological processes such as cell functionality,cell migration,and cell–cell interactions.One of the fundamental characteristics of cell movement is the specific distribution of cell speed,containing valuable information that still requires comprehensive understanding.This article investigates the distribution of mean velocities along cell trajectories,with a focus on optimizing the efficiency of cell food search in the context of the entire colony.We confirm that the specific velocity distribution in the experiments corresponds to an optimal search efficiency when spatial weighting is considered.The simulation results indicate that the distribution of average velocity does not align with the optimal search efficiency when employing average spatial weighting.However,when considering the distribution of central spatial weighting,the specific velocity distribution in the experiment is shown to correspond to the optimal search efficiency.Our simulations reveal that for any given distribution of average velocity,a specific central spatial weighting can be identified among the possible central spatial weighting that aligns with the optimal search strategy.Additionally,our work presents a method for determining the spatial weights embedded in the velocity distribution of cell movement.Our results have provided new avenues for further investigation of significant topics,such as relationship between cell behavior and environmental conditions throughout their evolutionary history,and how cells achieve collective cooperation through cell-cell communication.

Study on Joint Method of 3D Acoustic Emission Source Localization Simplex and Grid Search Scanning

Acoustic emission(AE)source localization is a fundamental element of rock fracture damage imaging.To improve the efficiency and accuracy of AE source localization,this paper proposes a joint method comprising a three-dimensional(3D)AE source localization simplex method and grid search scanning.Using the concept of the geometry of simplexes,tetrahedral iterations were first conducted to narrow down the suspected source region.This is followed by a process of meshing the region and node searching to scan for optimal solutions,until the source location is determined.The resulting algorithm was tested using the artificial excitation source localization and uniaxial compression tests,after which the localization results were compared with the simplex and exhaustive methods.The results revealed that the localization obtained using the proposed method is more stable and can be effectively avoided compared with the simplex localization method.Furthermore,compared with the global scanning method,the proposed method is more efficient,with an average time of 10%–20%of the global scanning localization algorithm.Thus,the proposed algorithm is of great significance for laboratory research focused on locating rupture damages sustained by large-sized rock masses or test blocks.

Even Search in a Promising Region for Constrained Multi-Objective Optimization

In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties.First, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

Database Search Behaviors: Insight from a Survey of Information Retrieval Practices

This study examines the database search behaviors of individuals, focusing on gender differences and the impact of planning habits on information retrieval. Data were collected from a survey of 198 respondents, categorized by their discipline, schooling background, internet usage, and information retrieval preferences. Key findings indicate that females are more likely to plan their searches in advance and prefer structured methods of information retrieval, such as using library portals and leading university websites. Males, however, tend to use web search engines and self-archiving methods more frequently. This analysis provides valuable insights for educational institutions and libraries to optimize their resources and services based on user behavior patterns.

Research on Evacuation Path Planning Based on Improved Sparrow Search Algorithm

Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale Algorithm,Gray Wolf Algorithm,Improved Gray Wolf Algorithm,Sparrow Search Algorithm,and MSSA Algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential.

Research on intelligent search-and-secure technology in accelerator hazardous areas based on machine vision

Prompt radiation emitted during accelerator operation poses a significant health risk,necessitating a thorough search and securing of hazardous areas prior to initiation.Currently,manual sweep methods are employed.However,the limitations of manual sweeps have become increasingly evident with the implementation of large-scale accelerators.By leveraging advancements in machine vision technology,the automatic identification of stranded personnel in controlled areas through camera imagery presents a viable solution for efficient search and security.Given the criticality of personal safety for stranded individuals,search and security processes must be sufficiently reliable.To ensure comprehensive coverage,180°camera groups were strategically positioned on both sides of the accelerator tunnel to eliminate blind spots within the monitoring range.The YOLOV8 network model was modified to enable the detection of small targets,such as hands and feet,as well as larger targets formed by individuals near the cameras.Furthermore,the system incorporates a pedestrian recognition model that detects human body parts,and an information fusion strategy is used to integrate the detected head,hands,and feet with the identified pedestrians as a cohesive unit.This strategy enhanced the capability of the model to identify pedestrians obstructed by equipment,resulting in a notable improvement in the recall rate.Specifically,recall rates of 0.915 and 0.82were obtained for Datasets 1 and 2,respectively.Although there was a slight decrease in accuracy,it aligned with the intended purpose of the search-and-secure software design.Experimental tests conducted within an accelerator tunnel demonstrated the effectiveness of this approach in achieving reliable recognition outcomes.

基于ElasticSearch的医疗数据检索系统的设计与实现

随着医疗技术的发展,医疗业务场景越加复杂,由此产生的医疗数据也越来越多,其来源复杂,结构多变,信息冗余,数据不完整。这些特性使得系统在进行检索时,无法快速、有效、精确地查询数据。为了解决这个问题,设计并实现了基于ElasticSearch的医疗数据检索系统。该系统将医疗数据进行标准化,填补缺失值,选取合适的分词算法进行分词,将处理后的数据存入ElasticSearch中,同时使用SpringBoot构建系统应用,消耗多个医疗基础业务系统产生的数据,最终形成统一的医疗数据检索系统,给用户提供便捷、精确的查询服务。