Dataset from Singapore Police Force. For more information, visit https://data.gov.sg/datasets/d_89b44df21fccc4f51390eaff16aa1fe8/view

Singapore Police Force NPC Boundary

Abbreviation: Ref. = Standard GTV reference.Results of inter-modality DSC analysis of semi-automatic methods.

The AI NPC Generator market is experiencing rapid growth, fueled by the increasing demand for immersive and realistic gaming experiences across PC, console, and mobile platforms. The market's expansion is driven by advancements in artificial intelligence, particularly in natural language processing and machine learning, enabling the creation of more sophisticated and believable non-player characters (NPCs). This allows game developers to significantly reduce development time and costs while enhancing player engagement through dynamic and unpredictable interactions. The market is segmented by application (PC and Console Games, Mobile Games) and type (Fully AI NPC, Speech AI NPC), with the fully AI NPC segment currently holding a larger market share due to its ability to create more complex and independent characters. However, the speech AI NPC segment is projected to witness significant growth, driven by the increasing adoption of voice-based interactions in games. Geographic regions like North America and Europe currently dominate the market due to the established gaming industry and higher adoption rates of new technologies. However, the Asia-Pacific region is expected to show robust growth in the coming years, fueled by the increasing popularity of mobile gaming and rising disposable incomes. Market restraints include the high cost of development and implementation of AI NPC generators, along with the need for powerful computing resources. Despite these challenges, the overall market outlook remains positive, with a projected CAGR of 25% from 2025 to 2033. The competitive landscape is currently dominated by a few key players like Inworld AI and Baichuan AI, but the market is expected to attract more entrants in the coming years. This influx of new players will likely lead to increased innovation and competition, resulting in more affordable and accessible AI NPC generator solutions. The market will see further fragmentation based on specialized AI capabilities, potentially including emotional AI, behavioral AI, and AI driven narrative generation integrated with NPC functionalities. The focus will shift towards creating highly personalized and adaptive NPC experiences, pushing the boundaries of what's possible in interactive entertainment and potentially spilling over into other industries like virtual training and simulations. Future growth will depend heavily on the continued advancements in AI technology and its wider adoption across the gaming industry.

Demonstrability—the extent to which group members can recognize a correct solution to a problem—has a significant effect on group performance. However, the interplay between group size, demonstrability and performance is not well understood. This paper addresses these gaps by studying the joint effect of two factors—the difficulty of solving a problem and the difficulty of verifying the correctness of a solution—on the ability of groups of varying sizes to converge to correct solutions. Our empirical investigations use problem instances from different computational complexity classes, NP-Complete (NPC) and PSPACE-complete (PSC), that exhibit similar solution difficulty but differ in verification difficulty. Our study focuses on nominal groups to isolate the effect of problem complexity on performance. We show that NPC problems have higher demonstrability than PSC problems: participants were significantly more likely to recognize correct and incorrect solutions for NPC problems than for PSC problems. We further show that increasing the group size can actually decrease group performance for some problems of low demonstrability. We analytically derive the boundary that distinguishes these problems from others for which group performance monotonically improves with group size. These findings increase our understanding of the mechanisms that underlie group problem-solving processes, and can inform the design of systems and processes that would better facilitate collective decision-making.

The global Game AI NPC market is experiencing robust growth, driven by the increasing demand for more immersive and realistic gaming experiences. The integration of AI-powered Non-Player Characters (NPCs) significantly enhances gameplay by providing more dynamic interactions, adaptive behaviors, and believable personalities. While precise market sizing data wasn't provided, considering the rapid advancements in AI and the significant investment in game development, a reasonable estimate for the 2025 market size could be around $500 million. This figure reflects the growing adoption of AI NPCs across various game genres, including PC and console games, and particularly within the booming mobile gaming sector. The Compound Annual Growth Rate (CAGR) is expected to be strong, potentially reaching 25% over the forecast period (2025-2033), fueled by technological advancements in natural language processing (NLP) and machine learning (ML), enabling more sophisticated NPC interactions and behaviors. The market is segmented by application (PC & Console Games, Mobile Games) and type (Fully AI NPC, Speech AI NPC). Mobile gaming is projected to dominate due to its wider accessibility and larger player base. Fully AI NPCs, capable of independent decision-making and complex interactions, are gaining traction, although Speech AI NPCs offering voice-enabled interaction represent a significant and rapidly expanding portion of the market. Key players like Inworld AI and Baichuan AI are at the forefront of innovation, constantly pushing the boundaries of NPC capabilities. Geographic expansion will also significantly contribute to market growth. North America and Europe are expected to remain major markets, but the Asia-Pacific region, particularly China and India, presents substantial growth potential, driven by the increasing popularity of gaming and the rising adoption of smartphones. However, challenges such as the high cost of development, the need for advanced computing power, and potential ethical concerns related to AI-driven NPC behavior remain restraints to wider adoption. Overcoming these challenges through strategic collaborations, technological improvements, and the establishment of ethical guidelines will be crucial for ensuring sustainable and responsible growth within the Game AI NPC market.

PurposeAccurate segmentation of gross target volume (GTV) from computed tomography (CT) images is a prerequisite in radiotherapy for nasopharyngeal carcinoma (NPC). However, this task is very challenging due to the low contrast at the boundary of the tumor and the great variety of sizes and morphologies of tumors between different stages. Meanwhile, the data source also seriously affect the results of segmentation. In this paper, we propose a novel three-dimensional (3D) automatic segmentation algorithm that adopts cascaded multiscale local enhancement of convolutional neural networks (CNNs) and conduct experiments on multi-institutional datasets to address the above problems.Materials and MethodsIn this study, we retrospectively collected CT images of 257 NPC patients to test the performance of the proposed automatic segmentation model, and conducted experiments on two additional multi-institutional datasets. Our novel segmentation framework consists of three parts. First, the segmentation framework is based on a 3D Res-UNet backbone model that has excellent segmentation performance. Then, we adopt a multiscale dilated convolution block to enhance the receptive field and focus on the target area and boundary for segmentation improvement. Finally, a central localization cascade model for local enhancement is designed to concentrate on the GTV region for fine segmentation to improve the robustness. The Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), average symmetric surface distance (ASSD) and 95% Hausdorff distance (HD95) are utilized as qualitative evaluation criteria to estimate the performance of our automated segmentation algorithm.ResultsThe experimental results show that compared with other state-of-the-art methods, our modified version 3D Res-UNet backbone has excellent performance and achieves the best results in terms of the quantitative metrics DSC, PPR, ASSD and HD95, which reached 74.49 ± 7.81%, 79.97 ± 13.90%, 1.49 ± 0.65 mm and 5.06 ± 3.30 mm, respectively. It should be noted that the receptive field enhancement mechanism and cascade architecture can have a great impact on the stable output of automatic segmentation results with high accuracy, which is critical for an algorithm. The final DSC, SEN, ASSD and HD95 values can be increased to 76.23 ± 6.45%, 79.14 ± 12.48%, 1.39 ± 5.44mm, 4.72 ± 3.04mm. In addition, the outcomes of multi-institution experiments demonstrate that our model is robust and generalizable and can achieve good performance through transfer learning.ConclusionsThe proposed algorithm could accurately segment NPC in CT images from multi-institutional datasets and thereby may improve and facilitate clinical applications.

H,I, Immunostaining for HIF-1α (red) and DAPI (blue) in control (H, H', H'') and Gpr124KO (I,I',I'') cortices. Panels H',H'',I' and I'' are magnifications of the boxed areas in panels I and J, showing HIF-1α signal alone (H',I') or together with DAPI (H'',I''). Asterisks indicate autofluorescent blood cells. The dashed line indicates the basal boundary of the VZ. Scale bar = 100 µm. Full, dotted and dashed lines indicate basal and apical boundaries of the cortex or boundaries of the cortical zones, respectively. CP, cortical plate; IZ, intermediate zone; SVZ, subventricular zone; VZ, ventricular zone. Scale bar: 100 µm. List of tagged entities: Hif1a (uniprot:Q61221), nucleus (go:GO:0005634), blood cell (cl:CL:0000081), Adgra2 (ncbigene:78560), DAPI,immunofluorescent labeling (bao:BAO_0002426)

Population data by administrative level 1 and 2 based on 2006 Census conducted by National Population Commission (NPC) of Nigeria .

These tables are suitable for database or GIS linkage to the Nigeria - Administrative Boundaries (levels 0 - 3) and senatorial districts administrative level 0 to 2 and senatorial district shapefiles