Vector Map Research Articles

The Effect of the Chemical Composition on Mechanical Properties of CMAS Diopside Glass Ceramics

Molecular dynamics simulations were performed on CaO-MgO-Al2O3-SiO2 (CMAS) diopside glass ceramics (GCs) to study the effect of nanocrystal on glass and the effect of chemical composition on mechanical properties. Under tensile loading, the GCs demonstrated that the strength lay between its glass and ceramic counterparts and maintained considerable ductility. Moreover, high Mg/Ca ion ratios are conductive to both the strength and ductility of GCs. In addition, Al ions should be avoided as far as possible since they would promote fracture. After analyzing the shear strain and displacement vector map for ion structures, we found that the presence of nanocrystal in glass changes the original deformation pattern and led to the deformation concentration surrounding the nanocrystal. A high Mg/Ca ion ratio would make the deformation more homogeneous, while a high Ca/Mg ion ratio would aggregate the deformation in the glass region near the nanocrystal. The existence of Al ions near the interface between glass and crystal would promote the formation of voids.

Species and population density of malaria vector Anopheles in Sichuan Province from 2011 to 2021

To investigate the distribution of malaria vector Anopheles in Sichuan Province from 2011 to 2021, so as to provide the scientific evidence for improving the surveillance of malaria vector Anopheles and preventing re-establishment of imported malaria in Sichuan Province. The density and species of Anopheles mosquitoes were investigated using human-bait trapping and light trapping techniques in malaria vector surveillance sites of Sichuan Province from 2011 to 2021. The number, population and density of captured Anopheles mosquitoes were collected and descriptively analyzed, and the geographical distribution map of malaria vectors was plotted using the software ArcGIS 10.7 in Sichuan Province. A total of 152 243 Anopheles mosquitoes were captured in malaria vector surveillance sites of Sichuan Province from 2011 to 2021, including 150 987 An. sinensis (99.18%) and 1 256 An. anthropophagus (0.82%), and no other Anopheles species were captured. The annual densities of An. sinensis and An. anthropophagus were 0.64 to 1.27 mosquitoes/(person-hour) and 0 to 0.07 mosquitoes/(person-hour) by the human-bait trapping technique, and 6.46 to 26.50 mosquitoes/(light-night) and 0 to 0.82 mosquitoes/(light-night) by the light trapping technique in malaria vector surveillance sites of Sichuan Province from 2011 to 2021. A relatively higher density of An. anthropophagus was seen in Renshou County, Jianyang City, Weiyuan County and Mabian Yi Autonomous County [> 0.40 mosquitoes/(person-hour)] by the human-bait trapping technique, and in Cuiping District and Gaoxian County in Yibin City [> 1.00 mosquito/(light-night)] by the light trapping technique in Sichuan Province from 2011 to 2018, with no An. anthropophagus captured from 2019 to 2021, and a relatively higher density of An. sinensis was detected in Emeishan City, Lushan County, Luojiang District, Tongchuan District and Zhaohua District [> 4.00 mosquitoes/(person-hour)] by the human-bait trapping technique, and in Huili County, Yuexi County, Dechang County, Langzhong City, Pingchang County and Xuanhan County [> 40.00 mosquitoes/(light-night)] by the light trapping technique in Sichuan Province from 2011 to 2021. Malaria vectors were still widespread in Sichuan Province from 2011 to 2021, and An. sinensis was the dominant species of malaria vectors. There is still a risk of local re-establishment of imported malaria in Sichuan Province, and it is needed to continue to improve the surveillance of imported malaria cases and malaria vectors.

Commutative encryption and watermarking algorithm based on compound chaotic systems and zero-watermarking for vector map

Commutative encryption and watermarking (CEW) is an efficient and safe protection technology combining cryptography and digital watermarking. It features the dual functions of secure transmission and copyright safeguarding. In traditional CEW algorithms for vector map, the watermark is embedded by modifying the host data, which cannot meet the needs of high precision vector map, especially for vector maps applied in military and navigation positioning fields. To fill this gap, a CEW algorithm for vector map based on zero-watermarking and permutation encryption is proposed. Firstly, compound chaotic systems are applied to permutation encryption to encrypt the storage order and position of the vertex coordinates respectively. The key is generated by the SHA-256 hash method, which has higher security. Secondly, the number of vertex features is regarded as the feature invariants to establish the watermark information and watermark index. After that, the zero-watermarking image is constructed through the XOR operation of the feature matrix and the copyright information matrix. Experiments show that the combination of permutation encryption and zero watermark can better meet the needs of secure transmission and copyright protection of high-precision vector graphics. The security, robustness and watermark capacity of the algorithm have been improved, making it more practical.

Low-temperature grapho-epitaxial La-substituted BiFeO3 on metallic perovskite

Bismuth ferrite has garnered considerable attention as a promising candidate for magnetoelectric spin-orbit coupled logic-in-memory. As model systems, epitaxial BiFeO3 thin films have typically been deposited at relatively high temperatures (650–800 °C), higher than allowed for direct integration with silicon-CMOS platforms. Here, we circumvent this problem by growing lanthanum-substituted BiFeO3 at 450 °C (which is reasonably compatible with silicon-CMOS integration) on epitaxial BaPb0.75Bi0.25O3 electrodes. Notwithstanding the large lattice mismatch between the La-BiFeO3, BaPb0.75Bi0.25O3, and SrTiO3 (001) substrates, all the layers in the heterostructures are well ordered with a [001] texture. Polarization mapping using atomic resolution STEM imaging and vector mapping established the short-range polarization ordering in the low temperature grown La-BiFeO3. Current-voltage, pulsed-switching, fatigue, and retention measurements follow the characteristic behavior of high-temperature grown La-BiFeO3, where SrRuO3 typically serves as the metallic electrode. These results provide a possible route for realizing epitaxial multiferroics on complex-oxide buffer layers at low temperatures and opens the door for potential silicon-CMOS integration.

Web-based visualization and rendering of aerial LiDAR point cloud for urban flood simulation

Purpose In an era overshadowed by the alarming consequences of climate change and the escalating peril of recurring floods for communities worldwide, the significance of proficient disaster risk management has reached unprecedented levels. The successful implementation of disaster risk management necessitates the ability to make informed decisions. To this end, the utilization of three-dimensional (3D) visualization and Web-based rendering offers decision-makers the opportunity to engage with interactive data representations. This study aims to focus on Thiruvananthapuram, India, where the analysis of flooding caused by the Karamana River aims to furnish valuable insights for facilitating well-informed decision-making in the realm of disaster management. Design/methodology/approach This work introduces a systematic procedure for evaluating the influence of flooding on 3D building models through the utilization of Web-based visualization and rendering techniques. To ensure precision, aerial light detection and ranging (LiDAR) data is used to generate accurate 3D building models in CityGML format, adhering to the standards set by the Open Geospatial Consortium. By using one-meter digital elevation models derived from LiDAR data, flood simulations are conducted to analyze flow patterns at different discharge levels. The integration of 3D building maps with geographic information system (GIS)-based vector maps and a flood risk map enables the assessment of the extent of inundation. To facilitate visualization and querying tasks, a Web-based graphical user interface (GUI) is developed. Findings The efficiency of comprehensive 3D building maps in evaluating flood consequences in Thiruvananthapuram has been established by the research. By merging with GIS-based vector maps and a flood risk map, it becomes possible to scrutinize the extent of inundation and the affected structures. Furthermore, the Web-based GUI facilitates interactive data exploration, visualization and querying, thereby assisting in decision-making. Originality/value The study introduces an innovative approach that merges LiDAR data, 3D building mapping, flood simulation and Web-based visualization, which can be advantageous for decision-makers in disaster risk management and may have practical use in various regions and urban areas.

Vector Field Heterogeneity for the Assessment of Locally Disorganised Cardiac Electrical Propagation Wavefronts From High-Density Multielectrodes.

High-density multielectrode catheters are becoming increasingly popular in cardiac electrophysiology for advanced characterisation of the cardiac tissue, due to their potential to identify impaired sites. These are often characterised by abnormal electrical conduction, which may cause locally disorganised propagation wavefronts. To quantify it, a novel heterogeneity parameter based on vector field analysis is proposed, utilising finite differences to measure direction changes between adjacent cliques. The proposed Vector Field Heterogeneity metric has been evaluated on a set of simulations with controlled levels of organisation in vector maps, and a variety of grid sizes. Furthermore, it has been tested on animal experimental models of isolated Langendorff-perfused rabbit hearts. The proposed parameter exhibited superior capturing ability of heterogeneous propagation wavefronts compared to the classical Spatial Inhomogeneity Index, and simulations proved that the metric effectively captures gradual increments in disorganisation in propagation patterns. Notably, it yielded robust and consistent outcomes for [Formula: see text] grid sizes, underscoring its suitability for the latest generation of orientation-independent cardiac catheters.

New directions for malaria vector control using geography and geospatial analysis.

As we strive towards the ambitious goal of malaria elimination, we must embrace integrated strategies and interventions. Like many diseases, malaria is heterogeneously distributed. This inherent spatial component means that geography and geospatial data is likely to have an important role in malaria control strategies. For instance, focussing interventions in areas where malaria risk is highest is likely to provide more cost-effective malaria control programmes. Equally, many malaria vector control strategies, particularly interventions like larval source management, would benefit from accurate maps of malaria vector habitats - sources of water that are used for malarial mosquito oviposition and larval development. In many landscapes, particularly in rural areas, the formation and persistence of these habitats is controlled by geographical factors, notably those related to hydrology. This is especially true for malaria vector species like Anopheles funestsus that show a preference for more permanent, often naturally occurring water sources like small rivers and spring-fed ponds. Previous work has embraced geographical concepts, techniques, and geospatial data for studying malaria risk and vector habitats. But there is much to be learnt if we are to fully exploit what the broader geographical discipline can offer in terms of operational malaria control, particularly in the face of a changing climate. This chapter outlines potential new directions related to several geographical concepts, data sources and analytical approaches, including terrain analysis, satellite imagery, drone technology and field-based observations. These directions are discussed within the context of designing new protocols and procedures that could be readily deployed within malaria control programmes, particularly those within sub-Saharan Africa, with a particular focus on experiences in the Kilombero Valley and the Zanzibar Archipelago, United Republic of Tanzania.

Few-shot bearing fault detection based on multi-dimensional convolution and attention mechanism.

Bearings are critical components of industrial equipment and have a significant impact on the safety of industrial physical systems. Their failure may lead to equipment shutdown and accidents, posing a significant risk to production safety. However, it is difficult to obtain a large amount of bearing fault data in practice, which makes the problem of small sample size a major challenge for bearing fault detection. In addition, some methods may overlook important features in bearing vibration signals, leading to insufficient detection capabilities. To address the challenges in bearing fault detection, this paper proposed a few sample learning methods based on the multidimensional convolution and attention mechanism. First, a multichannel preprocessing method was designed to more effectively utilize the information in the bearing vibration signal. Second, by extracting multidimensional features and enhancing the attention to important features through multidimensional convolution operations and attention mechanisms, the feature extraction ability of the network was improved. Furthermore, nonlinear mapping of feature vectors into the metric space to calculate distance can better measure the similarity between samples, thereby improving the accuracy of bearing fault detection and providing important guarantees for the safe operation of industrial systems. Extensive experiments have shown that the proposed method has good fault detection performance under small sample conditions, which is beneficial for reducing machine downtime and economic losses.

Automated Large-Scale Tornado Treefall Detection and Directional Analysis Using Machine Learning

Abstract In many regions of the world, tornadoes travel through forested areas with low population densities, making downed trees the only observable damage indicator. Current methods in the EF scale for analyzing tree damage may not reflect the true intensity of some tornadoes. However, new methods have been developed that use the number of trees downed or treefall directions from high-resolution aerial imagery to provide an estimate of maximum wind speed. Treefall Identification and Direction Analysis (TrIDA) maps are used to identify areas of treefall damage and treefall directions along the damage path. Currently, TrIDA maps are generated manually, but this is labor-intensive, often taking several days or weeks. To solve this, this paper describes a machine learning– and image-processing-based model that automatically extracts fallen trees from large-scale aerial imagery, assesses their fall directions, and produces an area-averaged treefall vector map with minimal initial human interaction. The automated model achieves a median tree direction difference of 13.3° when compared to the manual tree directions from the Alonsa, Manitoba, tornado, demonstrating the viability of the automated model compared to manual assessment. Overall, the automated production of treefall vector maps from large-scale aerial imagery significantly speeds up and reduces the labor required to create a Treefall Identification and Direction Analysis map from a matter of days or weeks to a matter of hours. Significance Statement The automation of treefall detection and direction is significant to the analyses of tornado paths and intensities. Previously, it would have taken a researcher multiple days to weeks to manually count and assess the directions of fallen trees in large-scale aerial photography of tornado damage. Through automation, analysis takes a matter of hours, with minimal initial human interaction. Tornado researchers will be able to use this automated process to help analyze and assess tornadoes and their enhanced Fujita–scale rating around the world.

Updating Road Maps at City Scale With Remote Sensed Images and Existing Vector Maps

Updating Road Maps at City Scale With Remote Sensed Images and Existing Vector Maps

A hybrid domain-based watermarking for vector maps utilizing a complementary advantage of discrete fourier transform and singular value decomposition

Digital watermarking plays a crucial role in the copyright protecting of vector maps. Due to its solid theoretical foundation, Discrete Fourier transform (DFT) is frequently used in the construction of watermarking scheme for diverse electronic data. However, when applied to vector maps, DFT is particularly vulnerable to local changes in coordinate points, posing challenges in surviving coordinate point attacks and limiting its practicality. Furthermore, its resistance against geometric attacks is quite weak. To address these issues, we propose an algorithmic complementary strategy that enhances the performance of DFT-based vector map watermarking scheme. The proposed scheme is founded upon a hybrid transform domain consisting of DFT and singular value decomposition (SVD). The first step is to extract feature points using the Douglas Peucker algorithm, with the distance threshold set to a relative value to ensure synchronization of feature points throughout scaling. The feature points are then subjected to DFT to obtain magnitude coefficients, which are then further transformed using SVD. By combining the geometric invariance of magnitude coefficients with the singular vectors of SVD, an invariant with rotation, scaling, and translation invariance can be generated. For watermark embedding, the invariant serves as an embedding domain and are multiplied to minimize the impact on the host vector maps. The experimental results reveal that the proposed vector map watermarking has very low disturbance, substantially lower than the comparing algorithms, deriving in excellent invisibility. The scheme can extract watermark images with a normalized correlation value of 1 under a variety of attacks, including rotation, scaling, translation, vertex addition, simplification, and map cropping, and it can even extract identifiable copyright information under a 10% vertex deletion, demonstrating very comprehensive robustness.

HYDRODYNAMIC MODEL OF SOLOTVYN ROCK SALT DEPOSIT

Rock salt development brings risks of harmful impact on water resources, in particular the Tysa River, posing a threat of cross-border spread of saline pollution on the border between Ukraine and Romania (Solotvyn rock salt deposit, Transcarpathia, western Ukraine). The impact of technogenic load (underground mining of rock salt) within the deposit led to deformation of the structure and nature of water exchange, intensification of suffusion and karst processes, ground surface deformations, catastrophic inflows of groundwater into mines, and, as a result, to the cessation of development of this deposit in 2010. However, the suspension of salt mining did not stop the development of the above-mentioned hazardous geological processes caused by both natural and man-made factors. In order to justify measures for prevention pollution of the Tysa River basin, a hydrogeological model of the Solotvyna rock salt deposit and surrounding areas was created, which allowed to predict the direction and flow rate of fresh and saline groundwater. The model was developed using new data on the geological structure and hydrogeological conditions of the study area (groundwater monitoring data). The modernised hydrodynamic model includes five layers (main aquifers and confining layers). As a result of modelling, maps of the velocity vectors and head contours for two aquifers (Quaternary and Tortonian) were obtained. Based on the results of solving a number of inverse problems, the functional correspondence of the model to natural and anthropogenic conditions was proved. According to the preliminary calculations of the actual groundwater filtration velocity and the path lines of the inert pollutant spreading, it was found that the time of its migration from the salt contour to the Tysa River is approximately 2–3 years. The developed groundwater flow model will be used to substantiate the network of hydrological and hydrogeological observation points in order to optimise the monitoring of water pollution processes.

Multi-guiding spark fireworks algorithm: Solving multimodal functions by multiple guiding sparks in fireworks algorithm

Many real-world problems can be abstracted as multimodal global optimization, which is one of the main challenges for optimization algorithms due to its complexity. The fireworks algorithm (FWA) is a swarm intelligence optimization algorithm that has been widely studied and applied by virtue of the synergistic property among fireworks. Current FWA variants have poor exploitation capability to handle some locally complex multimodal functions, which greatly limits the application of the FWA to practical problems. To solve the above problems, in this paper, we propose the multi-guiding spark fireworks algorithm (MGFWA) to solve multimodal functions by enhancing FWA exploitation capabilities. Three different strategies which are boosted guiding vector, multi-guiding sparks, and population-based random mapping are designed to boost the guiding vector, enrich the guiding spark diversity, and fix the mapping function, separately. The validity and parameter setting of MGFWA are theoretically analyzed. Experimentally, the results on the CEC2013 and CEC2017 single objective optimization benchmarks illustrate the remarkable performance of the MGFWA compared to other typical optimization algorithms and FWA variants. Moreover, the ablation study shows each of the three parts plays an important role in the algorithm and the efficiency experiment shows the MGFWA can improve the efficiency of guiding sparks by 10%. We believe that the MGFWA can be considered the SOTA variant of the FWA.

Mapping of dental caries incidence based on geographical information system (gis) in elementary school

Background: Dental caries in Central Java, Indonesia is still important problem, around 43.45% even prevalence of caries in early childhood is still high around 82%. As an effort to control and prevent increasing that case, a risk analysis is needed to determine the risk factors supporting caries. Geographical information system (GIS) as a delivery of information on the spread of disease to support planning efforts. Aim of this study is to determine mapping of distribution dental caries incidence based on GIS concerned to analysis of caries risk factors at Elementary School in Semarang. Methods: Using cross sectional analytic survey with spatial vector map data of 31 public elementary schools at Banyumanik district, Central Java and attribute data on distribution of dental caries. Mapping distribution of caries incidence based on secondary data from City Health Office and Community Health Centre using (Quantum GIS QGIS) and Web GIS software. Results: Elementary schools with caries risk factor environment were good (n=16, 52%) and bad (n=15, 48%), otherwise those with health services were good (n=22, 71%) and bad (n=9, 29%). There was a relationship between caries risk factors and dental caries incidence (p value=0.004) and OR 19.83 (environment) and p value=0.027 and OR 7.917 (health services). Conclusion: Mapping distribution of caries incidence based on GIS was in a very high category influenced by bad environmental factors and poor health services.

The Embodiment of Digital Art Elements in Traditional Cultural and Creative Product Designs

Abstract In order to extract the image path of traditional cultural and creative items, we set the binary image target of such products to be black and the backdrop to be white in this research. We followed the use of image vectorization processing technology. In order to address the issue of edge jaggedness in the traditional cultural and creative product image path extraction process, polygonal contour fitting is utilized to optimize the image paths of these products, resulting in the creation of a vector map of these products. The PCA point cloud alignment technique is used, based on the vector map of traditional cultural and creative products, to identify the attributes of these items and build a model of their traditional cultural and creative product design that incorporates digital art. After choosing dependent and independent variables, traditional cultural and creative product design, including digital art aspects, is studied and analyzed using linear quantile regression. The findings demonstrate that all of the value-added ratio’s coefficients are significant and bigger at the 0.2 and 0.95 quantile points, suggesting that raising the ratio can aid in the merger of digital art and more established creative and cultural sectors. The medium (M=3.586) and small standard deviation of the traditional cultural and creative items incorporating digital art suggest that the use function and cost-effectiveness of these products have a stronger influence on purchase motivation. This research encourages the fusion of digital art components with conventional cultural and artistic output in order to give traditional culture a fresh lease of life in the information era.

LIDAR MATCHING STRATEGIES FOR HD POINT CLOUD MAP GENERATION IN URBAN AREA

Abstract. Autonomous driving relies on high accuracy point vector map, which was generated by the point cloud map, and pre-provides the vehicle preliminary road environment information. Lidar Odometry and Mapping (LOAM) has always been a promising research topic in the field of robotics, environment sensing, and currently autonomous driving. However, in certain urban environments like basement parking lot, tunnels, highways, or other similar settings, the geometric features are not clearly discernible. As a result, algorithms resembling the LOAM framework may encounter difficulties in accurately mapping these areas. The paper utilized relative low-cost LiDAR expecting to propose a state-of-the-art point cloud mapping/update scheme. We compared the GNSS-challenge area with straight line and loop area separately, simultaneously considered the DG, ICP, NDT matching algorithm for the low-cost mapping/update strategy. With the realistic experiment conduction, our result evaluated by point to point corresponding mean error and standard error. For the straight line environment, ICP has the fastest convergence in empirical cumulative distribution under 0.4 meters. For the loop scenario, point-to-point ICP still has the fastest convergence in empirical cumulative distribution under 0.22 meters. Yet both of them still suffer from the fault matching.

Detection of Changes in Buildings in Remote Sensing Images via Self-Supervised Contrastive Pre-Training and Historical Geographic Information System Vector Maps

The detection of building changes (hereafter ‘building change detection’, BCD) is a critical issue in remote sensing analysis. Accurate BCD faces challenges, such as complex scenes, radiometric differences between bi-temporal images, and a shortage of labelled samples. Traditional supervised deep learning requires abundant labelled data, which is expensive to obtain for BCD. By contrast, there is ample unlabelled remote sensing imagery available. Self-supervised learning (SSL) offers a solution, allowing learning from unlabelled data without explicit labels. Inspired by SSL, we employed the SimSiam algorithm to acquire domain-specific knowledge from remote sensing data. Then, these well-initialised weight parameters were transferred to BCD tasks, achieving optimal accuracy. A novel framework for BCD was developed using self-supervised contrastive pre-training and historical geographic information system (GIS) vector maps (HGVMs). We introduced the improved MS-ResUNet network for the extraction of buildings from new temporal satellite images, incorporating multi-scale pyramid image inputs and multi-layer attention modules. In addition, we pioneered a novel spatial analysis rule for detecting changes in building vectors in bi-temporal images. This rule enabled automatic BCD by harnessing domain knowledge from HGVMs and building upon the spatial analysis of building vectors in bi-temporal images. We applied this method to two extensive datasets in Liuzhou, China, to assess its effectiveness in both urban and suburban areas. The experimental results demonstrated that our proposed approach offers a competitive quantitative and qualitative performance, surpassing existing state-of-the-art methods. Combining HGVMs and high-resolution remote sensing imagery from the corresponding years is useful for building updates.

Seismic Behaviors of Prefabricated Reinforced Concrete Shear Walls Assembled with a Cast-in-Place Vertical Joint

The seismic performance of prefabricated reinforced concrete shear walls is a key point in the safety of the whole assembly structure under earthquake actions. In this study, six specimens of reinforced concrete shear walls were assembled with a cast-in-place vertical joint with a straight, L, or convex shape. The specimens were tested using a low cyclic loading test under an axial compression ratio of 0.2 or 0.3. The stress process, failure pattern, and hysteretic curve of each specimen were measured. Combined with a numerical analysis using the finite element method, the variations in the bearing capacity, stiffness degradation, ductility, and energy dissipation capacity of the tested specimens were analyzed. Results showed that all specimens failed in a shear pattern without an obvious failure phenomenon along the vertical joint. The hysteresis curves exhibited an obvious pinch phenomenon and good deformation ability. The seismic behavior decreased in sequence for the shear walls assembled with a cast-in-place vertical joint with a straight, L, or convex shape, while a higher axial compression ratio improved the bearing capacity of the shear walls. The shear wall with an L-shaped vertical joint had similar seismic behavior to that with a straight vertical joint, but the shear wall with a convex vertical joint exhibited a decrease of 8.5% and 10.9% in bearing capacity, 18.2% and 1.2% in ductility, and 13.1% and 20.6% in energy dissipation, respectively, under an axial compression ratio of 0.2 and 0.3. The bearing mechanisms of shear walls with different vertical joints are explained with the numerical analysis of the stress vector maps of concrete and the stress cloud maps of reinforcements at different stress levels.

Data Management and Application System of Geological Gisaster Monitoring Data

The geological disaster monitoring data management and application system adopts B/S technology structure, based on data pre-processing technology and comprehensive analysis technology such as statistic calculation of the monitoring data, displacement vector map and comprehensive analysis map of displacement and rainfall, etc., this paper expounds the management and application of geological disaster monitoring data, and solves the problems of the lack of data, the poor quality of data and the lack of analytical models in geological disaster monitoring work. The ability of comprehensive processing and scientific analysis of geological disaster monitoring data has been further improved.

Advancing medical affair capabilities and insight generation through machine learning techniques

BackgroundPharmaceutical companies are increasingly leveraging machine learning techniques to optimize healthcare research, drug development, and medical affairs activities. AI (artificial intelligence) tools such as chatbots, virtual digital assistants, and research tools have been explored to varying degrees of maturity in industries such as consumer goods or software technology. However, there continues to be untapped opportunities within the pharmaceutical industry to employ these technologies for enhanced engagement and education with healthcare professionals (HCPs). Pharmacists, situated at the crossroads of clinical sciences and innovation, have the potential to elevate their role and significance within the pharmaceutical industry by developing and leveraging such technologies.MethodsTo address this, the python-coded tool, Medical Information (MI) Data Uses For AI Semantic Analysis (MUFASA), utilizes state-of-the-art Sentence Transformer library, clustering, and visualization techniques. MUFASA harnesses unsolicited MI data with AI technology, improving efficiency and providing actionable medical affairs intelligence for targeted content delivery to HCPs.ResultsMUFASA optimizes medical affairs activities through its distinctive features: semantic search, cluster analysis, and visualization. Its proficiency in understanding inquiries, as demonstrated through 3D vector mapping and clustering tests, enhances the efficiency of MI and Medical Science Liaison (MSL) case handling. It proves invaluable in training new staff, bolstering response uniformity, and mitigating compliance risks. Leveraging the HDBSCAN algorithm, MUFASA's cluster analysis uncovers deep insights and discerns actionable themes from large inquiry data sets. The visualization graphs, generated from semantic searches, support evidence-based decisions by tracking the effectiveness of initiatives and monitoring trend shifts. Collectively, MUFASA enriches strategic decision-making, cultivates actionable insights, and bolsters healthcare professional engagement.ConclusionThere are numerous opportunities for innovation within the intersection of healthcare and data science. Pharmaceutical manufacturers, with one of their medical affairs responsibilities being the collection of unsolicited inquiries, particularly from HCPs, stand poised to leverage machine learning capabilities to optimize its processes. The abundance of data generated by the growing effort to use it in meaningful ways presents an opportunity for pharmaceutical companies to harness machine learning techniques.