Deployment Strategy Research Articles

Current Patient Management for Bacterial Meningitis Simultaneously Affected by Stroke.

Bacterial meningitis (BM) is a critical central nervous system infection characterized by increased risks of complications and potentially fatal outcomes. The chances of full recovery are significantly reduced in the presence of concomitant neurovascular complications such as ischemic and hemorrhagic strokes, intracerebral hemorrhage, and cerebral sinus thrombosis. Effective treatment of BM requires a targeted approach that simultaneously addresses the causative pathogen and manages the neurologically related complications. However, clinicians continue to face challenges in determining optimal pharmacotherapy for these patients.The presence of neurological complications is pivotal in determining patient outcomes, contributing to high disability and mortality rates. Early medical management is crucial and begins with essential stabilization followed by rapid diagnostic testing and the administration of empirical broad-spectrum antimicrobial therapy. The use of targeted antibiotics based on culture results is standard, with adjunct therapies such as dexamethasone and, in some cases, anticoagulants playing supportive roles. Despite the deployment of such comprehensive therapeutic strategies, the variability in treatment response and the high incidence of adverse outcomes necessitate ongoing research. This includes exploring novel therapeutic approaches and enhancing current clinical practices through retrospective studies and clinical trials to mitigate the high morbidity and mortality rates associated with BM and its complications. Strategic management is crucial for patient recovery, considering the substantial risk, a broad spectrum of complications, and fatal outcomes from this meningeal infection and stroke. The use of antimicrobial therapy with intravenous adjunct dexamethasone isthe current standard of care for patients with cerebrovascular complications and acute BM. In addition, other options that can provide benefits in complicated cases include anticoagulants and neurosurgery. Further investigation into treatment algorithms for patients with meningeal infection complicated by stroke and/or increased intracranial pressure is still needed.

Occurrence and Distribution of Major Cassava Pests and Diseases in Cultivated Cassava Varieties in Western Kenya.

Cassava is an important food crop in western Kenya, yet its production is challenged by pests and diseases that require routine monitoring to guide development and deployment of control strategies. Field surveys were conducted in 2022 and 2023 to determine the prevalence, incidence and severity of cassava mosaic disease (CMD) and cassava brown streak disease (CBSD), whitefly numbers and incidence of cassava green mite (CGM) in six counties of western Kenya. Details of the encountered cassava varieties were carefully recorded to determine the adoption of improved varieties. A total of 29 varieties were recorded, out of which 13 were improved, although the improved varieties were predominant in 60% of fields and the most widely grown variety was MM96/4271. The CMD incidence was higher in 2022 (26.4%) compared to 2023 (10.1%), although the proportion of CMD attributable to whitefly infection was greater (50.6%) in 2023 than in 2022 (18.0%). The CBSD incidence in 2022 was 6.4%, while in 2023 it was 4.1%. The CMD incidence was significantly lower (5.9%) for the improved varieties than it was for the local varieties (35.9%), although the CBSD incidence did not differ significantly between the improved (2.3%) and local varieties (9.7%). Cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV) were both detected. Most infections were single CBSV infections (82.9%), followed by single UCBSV (34.3%) and coinfection with both viruses (16.7%). Whiteflies were more abundant in 2023, in which 28% of the fields had super-abundant populations of >100/plant, compared to 5% in 2022. KASP SNP genotyping designated 92.8% of the specimens as SSA-ECA for 2022, while it was 94.4% for 2023. The cassava green mite incidence was 65.4% in 2022 compared to 79.9% in 2023. This study demonstrates that cassava viruses, whiteflies and cassava green mites continue to be important constraints to cassava production in western Kenya, although the widespread cultivation of improved varieties is reducing the impact of cassava viruses. The more widespread application of high-quality seed delivery mechanisms could further enhance the management of these pests/diseases, coupled with wider application of IPM measures for whiteflies and mites.

Cross-Cultural Variations in Visual Search: Exploring Attention Deployment Strategies and Novel Priming on Search Asymmetry

Cross-Cultural Variations in Visual Search: Exploring Attention Deployment Strategies and Novel Priming on Search Asymmetry

Optimising Drone-Assisted Logistics for Urban Last-Mile Delivery: An Overview of Applications, Methodologies, and Emerging Trends

The rapid evolution of drone-assisted logistics for urban last-mile (ULM) delivery has garnered significant interest from both academia and industry. This article presents a comprehensive review of the state-of-the-art research and practical implementations of ULM systems, focusing on the use of unmanned aerial vehicles (UAVs) for the final stage of goods and parcel delivery in urban environments. The applicability of UAV-based logistics across various contexts, including urban and rural areas, is examined, with real-world case studies highlighted to demonstrate practical uses. Key methodologies and models employed in optimising UAV routing and operations are discussed, particularly those that enhance the efficiency and reliability of ULM. The critical advantages and limitations of drone-assisted last-mile logistics are analysed, providing insights into the operational, regulatory, and technological challenges. The discussion is further expanded by addressing emerging trends in UAV technology, as well as innovations in drone deployment strategies and the evolving regulatory landscape. In conclusion, potential theoretical advancements and future applications of ULM systems are outlined, with an emphasis on integrating drones into broader logistics networks and smart city frameworks. The insights offered aim to guide future research and practical developments in this rapidly advancing field.

Incorporating Radioactive Decay Batteries into the USA's Energy Grid: Solutions for Winter Power Challenges

This paper explores the integration of radioactive decay batteries into the USA's energy grid as a strategic solution for addressing winter power challenges. Radioactive decay batteries, leveraging isotopes such as tritium or americium-241, offer significant advantages over traditional fission and fusion-based nuclear batteries. Their design inherently ensures enhanced durability, providing long-lasting energy outputs that can span several decades with minimal maintenance. Unlike conventional nuclear reactors, these batteries operate safely outside a reactor environment, significantly reducing the risks associated with nuclear power generation. This operational safety is further augmented by the relatively low radiation levels they emit, making them viable for diverse applications, including residential, commercial, and remote off-grid systems. The implementation of these batteries promises a stable and reliable energy supply during winter months when energy demands peak, mitigating the risk of power outages and enhancing the resilience of the energy grid. This study examines the potential deployment strategies, technical feasibility, and regulatory considerations necessary to incorporate radioactive decay batteries effectively into the USA's energy infrastructure, positioning them as a transformative technology for sustainable and reliable winter energy solutions.

A Coupled Human and Natural Systems (CHANS) framework integrated with reinforcement learning for urban flood mitigation

To address the challenge of escalating urban flood risk and the deficiency in effective flood emergency management, this study introduces a novel Coupled Human and Natural Systems (CHANS) modelling framework that employs hierarchical reinforcement learning to optimise mobile pump scheduling and placement for urban flood risk mitigation. The CHANS framework integrates hydrodynamic and agent-based models within a multi-GPU computing environment for high-resolution, real-time flood inundation modelling and risk assessment to enrich Reinforcement Learning (RL) training. In the application to Ninh Kieu District in Can Tho City, Vietnam, the new RL-enabled modelling framework is used to evaluate optimal mobile pumping strategies for concurrent pluvial flooding and post-flooding events against the traditional deployment approaches. Results demonstrate that RL-based strategies can significantly enhance flood risk reduction, outperforming traditional methods by achieving 2× and 4× improvements in the concurrent and post-flooding periods/events, respectively. Incorporating human factors and adapting to local conditions, the RL agent provides valuable insights into mobile pump scheduling and deployment strategies. Sensitivity analysis confirms the robustness of the CHANS modelling framework and underscores the role of RL in optimising mobile pump scheduling and placement where traditional rule-based strategies are challenging.

A Retrospective and Prospective Analysis of Cloud Computing: Historical Context, Deployment Strategies, and Service Models

The network concerned usually discusses the use of the Internet for various services, including data storage, software, security, etc. These services are commonly referred to as cloud-based information services. Examples of such services include Gmail, YouTube, Amazon, and Dropbox. The paper covers the evolution of cloud computing from its early stages to the present day and explores the different services offered by cloud computing in both personal and business computing scenarios. It provides an overview of the history, deployment, and service models that led to the development of cloud computing.

Value of Combined Optical Coherence Tomography and Optical Flow Ratio Measurements After Percutaneous Coronary Intervention

BackgroundOptical flow ratio (OFR) is a novel computational fractional flow reserve derived from optical coherence tomography (OCT). However, the impact of combining post-stenting morphology (OCT) and physiology (OFR) remains largely unknown. MethodsOCT and OFR were analysed at an independent core laboratory. Target lesion failure (TLF) was defined as the composite of cardiac death, target lesion myocardial infarction, and target lesion revascularisation. Suboptimal stent deployment was identified with at least 1 TLF-related OCT or OFR characteristic. ResultsA total of 448 patients with acute coronary syndrome (459 vessels) were assessed. Stent expansion < 80%, minimal stent area < 4.5 mm2, stent edge lipid-rich plaque and OFR < 0.90 were independent predictors of TLF (all P < 0.001). Patients with OCT-suboptimal (adjusted hazard ratio [aHR] 7.88, 95% CI 2.73-22.72,-P < 0.001) or OFR-suboptimal (aHR 5.78, 95% CI 2.54-13.14; P < 0.001) stent deployment showed significantly higher risk of TLF compared with those with optimal stent deployment, with a significant interaction (Pinteraction < 0.001). OCT and OFR both–suboptimal stent deployment was confirmed as an independent predictor of TLF (aHR 9.39, 95% CI 4.25-20.76; P < 0.001). ConclusionsCombined OCT and OFR conferred an optimal reclassification of stent deployment, which may aid in decision making regarding a tailored PCI strategy for optimal stent deployment.

TAMU TRACER: Targeted Mobile Measurements to Isolate the Impacts of Aerosols and Meteorology on Deep Convection

Abstract Difficulty in using observations to isolate the impacts of aerosols from meteorology on deep convection often stems from the inability to resolve the spatiotemporal variations in the environment serving as the storm’s inflow region. During the U.S. Department of Energy (DOE) Tracking Aerosol Convection interactions Experiment (TRACER) in June–September 2022, a Texas A&amp;M University (TAMU) team conducted a mobile field campaign to characterize the meteorological and aerosol variability in air masses that serve as inflow to convection across the ubiquitous mesoscale boundaries associated with the sea and bay breezes in the Houston, Texas, region. These boundaries propagate inland over the fixed DOE Atmospheric Radiation Measurement (ARM) sites. However, convection occurs on either or both the continental or maritime sides or along the boundary. The maritime and continental air masses serving as convection inflow may be quite distinct, with different meteorological and aerosol characteristics that fixed-site measurements cannot simultaneously sample. Thus, a primary objective of TAMU TRACER was to provide mobile measurements similar to those at the fixed sites, but in the opposite air mass across these moving mesoscale boundaries. TAMU TRACER collected radiosonde, lidar, aerosol, cloud condensation nuclei (CCN), and ice nucleating particle (INP) measurements on 29 enhanced operations days covering a variety of maritime, continental, outflow, and prefrontal air masses. This paper summarizes the TAMU TRACER deployment and measurement strategy, instruments, and available datasets and provides sample cases highlighting differences between these mobile measurements and those made at the ARM sites. We also highlight the exceptional TAMU TRACER undergraduate student participation in high-impact learning activities through forecasting and field deployment opportunities.

Enhancing network lifetime in wireless sensor networks through coverage-aware optimized sensor activation

Wireless sensor networks (WSNs) are a pivotal technology in the modern era, enabling the monitoring and sensing of environmental conditions across vast areas with unprecedented precision and flexibility. At the heart of WSNs lie crucial challenges such as optimizing coverage, extending network lifetime, and strategizing node deployment to ensure efficient operation while conserving energy. This paper introduces the coverage-aware optimized sensor activation and deployment (CAOSAD) Strategy, a novel methodology designed to address these challenges. By integrating advanced node placement algorithms and scheduling techniques, the EcoNet lifespan maximization (ELM) strategy significantly enhances area and target coverage, minimizes energy consumption, and thereby prolongs the network’s operational lifespan. We present a comprehensive framework that dynamically adjusts node activity based on a predictive model, ensuring robust coverage and connectivity with minimal energy expenditure. Through a series of simulations, the ELM strategy demonstrates a substantial improvement in network sustainability compared to existing methodologies, offering a promising approach for the development of future WSNs. By focusing on the synergy between coverage optimization, energy-efficient node deployment, and innovative scheduling algorithms, this paper contributes a ground-breaking perspective to the research and application of WSNs, setting a new benchmark for the design of eco-friendly and durable sensing infrastructures.

Evolution of radiology staff perspectives during artificial intelligence (AI) implementation for expedited lung cancer triage

Evolution of radiology staff perspectives during artificial intelligence (AI) implementation for expedited lung cancer triage

Prescribed-Time Network-Based Deployment of Nonlinear Multiagent Systems: A Discrete-Space PDE Method.

This article attempts to design the prescribed-time time-varying deployment schemes for first-order and second-order nonlinear multiagent systems (MASs). We assume that all agents can obtain the information of their current and final relative positions with their neighbors, and the final absolute velocities (as well as their current and final relative velocities, the final absolute accelerations for the second-order MASs) through a communication network, whereas two boundary agents are able to obtain their current and final absolute positions (as well as their current and final absolute velocities for the second-order MASs). The neighbor relationship of all agents is described by a spatial variable and two static-feedback controllers are introduced, which can be expressed as a second-order space difference of the spatial variable. Then, the deployment of MASs can be transformed into the stabilization of discrete-space partial differential equation (PDE) systems. Three virtual agents are introduced to constitute the Dirchlet and Neumann boundary conditions. Several algebraic inequality criteria are derived to guarantee that the prescribed-time time-varying deployment can be achieved within a prescribed time under the Dirchlet and mixed boundary conditions. Unlike the published results, our results are derived based on the discrete-space PDE systems instead of continuous-space PDE systems, which is consistent with the discrete spatial distribution of agents. Finally, two numerical examples are given to illustrate the effectiveness of our results.

Challenges in the Quality of Malaria Case Management in Sub-Saharan Africa: A Narrative Review

Malaria continues to be a parasitic disease that poses a persistent public health concern in countries of the Southern region. Despite the deployment of strategies aimed at its elimination by 2030, significant gaps still impede the achievement of this goal. A critical gap is the quality of case management. This article presents a narrative review specifically focusing on the quality of malaria case management in SubSaharan African countries. It delves into the complex challenges associated with managing this disease, encompassing aspects such as healthcare service organization, diagnosis, treatment, and patient follow-up. The article highlights underlying factors that hinder progress, including insufficient resources, inadequate skills, limited monitoring and evaluation capacities, lack of motivation and commitment among stakeholders, and challenges related to implementation and governance. It explores various initiatives to strengthen and enhance the quality of care, including continuous training programs, epidemiological and operational research endeavors, fostering a culture of quality, and promoting collaboration among stakeholders. Furthermore, the article emphasizes the importance of sustainable approaches at the country level to contribute to effective malaria elimination. It underscores the need for further exploration and implementation of strategies to achieve this crucial goal.

IoTDeploy: Deployment of IoT Smart Applications over the Computing Continuum

IoT Smart Applications have created a demand for architectures, infrastructure, platforms, orchestration, and service deployment strategies. They are deployed from sensors to the cloud over a geographical and computing continuum, which is challenging for service orchestration and DevOps strategies. The distributed infrastructure to implement the end-to-end data path may vary, even for similar applications, concerning the services deployed over the mist, fog, edge, and cloud stages. This paper proposes and evaluates IoTDeploy, a solution for streamlining and scaling static and dynamic IoT service deployment over the continuum. IoTDeploy implements a CI/CD tool plugin for deploying applications running on the continuum and supports dynamic service migration. We evaluated the service migration from cloud to fog and fog to cloud with a case study on smart irrigation in agriculture. The experiments reveal that deployment in IoT-distributed environments is reliable and resilient, enabling migration without interrupting the application and losing data.

Decoding the influence of servitization on green transformation in manufacturing firms: The moderating effect of artificial intelligence

This research addresses three crucial dimensions in operations management: the servitization of manufacturing, the utilization of artificial intelligence (AI) platforms, and green transformation. Employing the by-production method, we construct a metric for green transformation applicable to listed firms. Our comprehensive analytical framework integrates the resource-based view and information asymmetry theories, enabling systematic investigation into the influence of manufacturing servitization on firms' green transformation. In addition, we examine the moderating effect of AI platforms on the execution of servitization strategies. The empirical foundation of our study is an annually updated dataset of 554 manufacturing firms listed on China's A-share market. Our findings reveal a strong positive correlation between the deployment of servitization strategies and the green transformation of firms. This association withstands multiple robustness tests, including core variable substitution, outlier removal, and adjustments in clustering standard errors. Our research uncovers notable nuances. The effect of servitization on green total factor productivity is more visible for eastern and central China firms. Also, state-owned enterprises demonstrate a more conspicuous influence from servitization strategies. However, we observe a slight diminishing of this effect in firms audited by the Big Four. An essential contribution of our study is the illumination of the role AI platforms play in enhancing the efficacy of servitization. These AI platforms facilitate the creation of tailored solutions that curtail resource wastage, thus amplifying the positive effect of servitization strategies on green transformation.

Risk of selection and timelines for the continued spread of artemisinin and partner drug resistance in Africa.

The introduction of artemisinin combination therapies (ACTs) has significantly reduced the burden of Plasmodium falciparum malaria, yet the emergence of artemisinin partial resistance (ART-R) as well as partner drug resistance threatens these gains. Recent confirmations of prevalent de novo ART-R mutations in Africa, in particular in Rwanda, Uganda and Ethiopia, underscore the urgency of addressing this issue in Africa. Our objective is to characterise this evolving resistance landscape in Africa and understand the speed with which ART-R will continue to spread. We produce estimates of both ART-R and partner drug resistance by bringing together WHO, WWARN and MalariaGen Pf7k data on antimalarial resistance in combination with a literature review. We integrate these estimates within a mathematical modelling approach, aincorporating to estimate parameters known to impact the selection of ART-R for each malaria-endemic country and explore scenarios of ART-R spread and establishment. We identify 16 malaria-endemic countries in Africa to prioritise for surveillance and future deployment of alternative antimalarial strategies, based on ART-R reaching greater than 10% prevalence by 2040 under current malaria burden and effective-treatment coverage. If resistance continues to spread at current rates with no change in drug policy, we predict that partner drug resistance will emerge and the mean percentage of treatment failure across Africa will reach 30.74% by 2060 (parameter uncertainty range: 24.98% - 34.54%). This translates to an alarming number of treatment failures, with 52,980,600 absolute cases of treatment failure predicted in 2060 in Africa (parameter uncertainty range: 26,374,200 - 93,672,400) based on current effective treatment coverage. Our results provide a refined and updated prediction model for the emergence of ART-R to help guide antimalarial policy and prioritise future surveillance efforts and innovation in Africa. These results put into stark context the speed with which antimalarial resistance may spread in Africa if left unchecked, confirming the need for swift and decisive action in formulating antimalarial treatment policies focused on furthering malaria control and containing antimalarial resistance in Africa. The rise of artemisinin partial resistance (ART-R) and increasing partner drug tolerance by Plasmodium falciparum malaria in Africa threatens to undo malaria control efforts. Recent confirmations of de novo ART-R markers in Rwanda, Uganda, and Ethiopia highlight the urgent need to address this threat in Africa, where the vast majority of cases and deaths occur. This study characterises the resistance landscape and predicts the spread of antimalarial resistance across Africa. We estimate and map the current levels of resistance markers related to artemisinin and its partner drugs using WHO, WWARN, and MalariaGen Pf7k data. We combine these estimates with current malaria transmission and treatment data and use an established individual-based model of malaria resistance to simulate future resistance spread. We identify 16 African countries at highest risk of ART-R for prioritisation of enhanced surveillance and alternative antimalarial strategies. We project that, without policy changes, ART-R will exceed 10% in these regions by 2040. By 2060, if resistance spreads unchecked, we predict mean treatment failure rates will reach 30.74% (parameter uncertainty range: 24.98% - 34.54%) across Africa. This alarming spread of resistance is predicted to cause 52.98 million treatment failures (uncertainty range: 26.37 million - 93.67 million) in 2060. The impact of antimalarial resistance in Africa, if left unchecked, would hugely damage efforts to reduce malaria burden. Our results underscore the critical need for swift policy action to contain resistance and guide future surveillance and intervention efforts.

Considering traffic characteristics: Roadside unit deployment optimization algorithm based on dynamic division of road network subareas

AbstractGiven that the overall coverage deployment method fails to meet information needs in important areas, there are redundancies and deficiencies in the information provided. To enhance communication stability for roadside units (RSUs), improve information coverage at critical intersections and optimize algorithm efficiency. Here, a method for deploying RSUs is proposed that aims to optimize revenue in road network subareas. The road network is divided into several subareas based on critical intersections, node similarity, road segment correlations, and characteristics of RSU information transmission. Then, a roadway accessibility algorithm is developed that accounts for channel fading. Considering the robustness of wire network deployment, an improved traveling salesman problem (TSP) problem is proposed that includes candidate locations and constructs a model for optimal RSU deployment that maximizes consolidated revenue. Finally, using the Sioux Falls network as an example, the RSU deployment strategy is evaluated for the overall network and the road network after being subdivided. The results indicate that subdividing the road network improves the efficiency of the optimization solution, the information coverage of critical intersections increases by 1.8 times. The deployment optimization scheme of RSUs is directly influenced by various parameters such as bandwidth capacity and cost coefficient. When deploying RSUs in road network subareas, variations in total demand have minimal impact on RSU deployment, ensuring a stable deployment scheme.

A Study on the Spatiotemporal Distribution and Usage Pattern of Dockless Shared Bicycles—The Case of Nanjing

Determining the spatiotemporal deployment strategy for dockless shared bicycles in urban blocks has always been a focal point for city managers and planners. Extensive research has delved into the usage patterns in terms of time and space, deduced travel purposes, and scrutinized the relationship between trips and the built environment. The elements of the built environment are significantly correlated with the starting and ending points of dockless shared bicycle trips, leading to a scarcity of shared bicycles in areas that are more frequently used as starting points and an abundance of idle bicycles in areas that serve as endpoints. This paper posits that the idle state of shared bicycles is as important as their usage. Utilizing a case study of Xinjiekou Central District in Nanjing, China, we propose a framework for analyzing the temporal and spatial usage and idleness of shared bicycles. We also discuss the impact of various factors, such as proximity to transit stations, land use, and road accessibility, on the different usage and idle states of dockless shared bicycles. The findings reveal that the public transportation system has a similar influence on both the utilization and idleness of dockless shared bicycles, indicating that areas with a dense concentration of transportation services experience greater demand for shared bicycles as both origins and destinations. The influence of other factors on the usage and idleness of dockless shared bicycles varies significantly, resulting in either a shortage or surplus of these bicycles. Consequently, based on the findings regarding the use and idleness of dockless shared bicycles, we formulate a redistribution and zone-based management strategy for shared bicycles. This paper offers new insights into the spatiotemporal distribution and utilization of shared bicycles under the influence of different built environments, contributing to the further optimization of dockless shared bicycle resource allocation.

Utilizing ubiquitous learning to foster sustainable development in rural areas: Insights from 6G technology

Rural education frequently grapples with demanding situations such as isolation, confined assets, and a virtual divide. The emergence of the sixth generation (6G) era, characterized by its speedy connectivity, minimal latency, and robust reliability, stands out as a beacon of desire, promising to revolutionize these educational landscapes by adopting ubiquitous Learning (U-Learning) environments. This research delves into the transformative skills of the 6G generation in rural schooling through U-Learning. It highlights 6G's position in ensuring identical entry to advanced instructional materials, fostering immersive and tailored learning stories, and permitting immediate interplay within virtual school rooms. Employing a combined-methods framework, this look intertwines an in-depth literature assessment with empirical inquiries, such as case studies, surveys, and dialogues with critical rural schooling stakeholders. It scrutinizes the prevailing situations of pastoral training, 6G's technological advancements, and the conditions for deploying green U-Learning ecosystems. The findings indicated significant improvements in educational resource access, student engagement, and learning outcomes due to implementing 6G technology. The key benefits observed were high-speed connectivity, reduced latency, and enhanced reliability, facilitating more interactive and immersive learning experiences. 6G emerges as a pivotal pressure in reshaping rural education, addressing essential problems of accessibility, pupil engagement, and academic excellence. Achieving this vision needs concerted efforts in infrastructure improvement, instructional programming tailored to 6G, and collaborative endeavors amongst all stakeholders. Future research should focus on developing specific 6G deployment strategies and educational programs tailored to the unique needs of rural populations.

HAIGEN: Towards Human-AI Collaboration for Facilitating Creativity and Style Generation in Fashion Design

The process of fashion design usually involves sketching, refining, and coloring, with designers drawing inspiration from various images to fuel their creative endeavors. However, conventional image search methods often yield irrelevant results, impeding the design process. Moreover, creating and coloring sketches can be time-consuming and demanding, acting as a bottleneck in the design workflow. In this work, we introduce HAIGEN (Human-AI Collaboration for GENeration), an efficient fashion design system for Human-AI collaboration developed to aid designers. Specifically, HAIGEN consists of four modules. T2IM, located in the cloud, generates reference inspiration images directly from text prompts. With three other modules situated locally, the I2SM batch generates the image material library into a certain designer-style sketch material library. The SRM recommends similar sketches in the generated library to designers for further refinement, and the STM colors the refined sketch according to the styles of inspiration images. Through our system, any designer can perform local personalized fine-tuning and leverage the powerful generation capabilities of large models in the cloud, streamlining the entire design development process. Given that our approach integrates both cloud and local model deployment schemes, it effectively safeguards design privacy by avoiding the need to upload personalized data from local designers. We validated the effectiveness of each module through extensive qualitative and quantitative experiments. User surveys also confirmed that HAIGEN offers significant advantages in design efficiency, positioning it as a new generation of aid-tool for designers.