Fetal DNA concentration (FF) is a critical indicator affecting the accuracy and stability of non-invasive prenatal testing (NIPT). An excessively low FF may lead to test failure or false negatives. To address the lack of quantitative prediction for the earliest safe testing time at which clinical thresholds (FF ≥4%) are met, this study constructed multivariate linear regression and Logistic regression models based on publicly available sample data. The models analyzed the influence of gestational age and maternal body mass index (BMI) on FF levels and the probability of meeting the threshold, and introduced a gestational age quadratic term to establish a nonlinear model. The results demonstrated that BMI was a significant negative predictor of both FF and the probability of meeting the threshold. Further, the study derived the earliest gestational age at which the probability of meeting the threshold ≥95% for different BMI stratifications and validated model stability using the Bootstrap method. This research provides a quantitative basis for individualized NIPT testing timing.

This study thoroughly investigates the influence mechanism of n-hexane insolubles (INS) and their blending ratio on product yields during the co-pyrolysis of biomass and coal. First, Spearman's correlation coefficient analysis revealed intrinsic relationships between INS content and yields of tar, water, and char. Results indicate significant correlations between INS and tar/char yields, while its effect on water yield is negligible. Subsequently, to further elucidate yield variation patterns under complex operating conditions, variance analysis was employed to investigate the interactive effects of INS and blending ratios on product yields. Findings revealed that different INS-blend combinations significantly altered tar growth rates and coke residue decline trends. Building upon these findings, a nonlinear optimization model was constructed to maximize energy conversion efficiency and product utilization. The Sequential Quadratic Programming (SLSQP) algorithm was employed to identify optimal blending ratios and INS dosage under specified cost and technical constraints. The research outcomes provide scientific theoretical foundations and operational guidance for optimizing biomass energy utilization, enhancing energy efficiency, and achieving sustainable energy production.

Non-invasive prenatal testing (NIPT) is a widely applied clinical technique for detecting fetal chromosomal abnormalities by analyzing cell-free fetal DNA fragments in maternal plasma. The accuracy and reliability of NIPT are affected by multiple factors, including gestational age and maternal body mass index (BMI). This paper investigates the correlation between Y chromosome concentration, gestational weeks, and BMI in male fetuses, as well as the feasibility of predicting female fetal abnormalities in the absence of Y chromosome signals. To address these issues, this article establish a comprehensive framework combining interaction regression analysis, gradient boosting decision trees (GBDT), rule-based grouping, random forests, and logistic regression models. Based on historical data, optimal NIPT time points are estimated for different BMI groups by calculating the gestational weeks at which 95% of pregnant[1] women achieve the required fetal DNA concentration, with additional safety margins introduced to enhance robustness. Experimental results show that the optimal testing window for male fetuses is approximately 11 ± 3 weeks, while the proposed model achieves a precision of 0.8178 and a recall of 0.8621 for female fetal abnormality detection. The findings provide practical clinical recommendations to minimize potential testing risks, improve detection success rates across maternal BMI groups, and offer effective judgment methods for female fetuses lacking Y chromosome markers.

As a core basis for clinical diagnosis, medical imaging holds crucial information for disease screening, classification, and treatment efficacy evaluation. However, traditional manual image interpretation faces three major bottlenecks: low efficiency, limited capability, and uneven resource distribution. With the popularization of precision medicine and exponential growth of imaging data, intelligent medical imaging diagnosis has become vital to optimizing the diagnostic process. For example, the ViT-VGG16 hybrid architecture enables collaborative modeling of "local details-global correlations" in small-sample scenarios; BoTNet balances multi-scale complex lesion modeling with hardware compatibility; and the CNN-dictionary representation hybrid builds a "global-local" dual feature system to improve diagnostic accuracy. Due to new diseases and more complex medical needs, this field is evolving, with intelligent technologies addressing challenges like small-sample learning, data privacy, and workflow disconnection. This review compiles relevant literature with two key goals: first, systematically sort out and analyze mainstream intelligent technologies, their principles, and performance; second, examine technical value, significance, dataset characteristics, and propose clinical implementation solutions. These analyses not only guide researchers in technological innovation but also provide evidence-based references for clinicians to promote intelligent technology in frontline practice, while serving as an empirical basis for governments to formulate medical AI development strategies.

The maze problem is a classic research topic in artificial intelligence, algorithm design, and path planning. Its core lies in exploring how to find an efficient path from the starting point to the target location within a maze, possessing both theoretical research value and practical application significance. This article provides a comprehensive review of multiple maze-solving algorithms. These include wall-following algorithm, backtracking algorithm, M-DFS, A* algorithm, as well as Maze-solving robot and computer mouse. This paper first provides detailed explanations of the principles for each method. Subsequently, this paper uses tables to clearly compare the Application Scenarios, Time and Space Complexity, and Disadvantages of each approach. Furthermore, this paper analyzes the shortcomings of each method and discuss the future development prospects of maze-solving techniques in light of current technological advancements. Overall, this paper offers readers in-depth explanations of several algorithms, enabling them to better understand the current state of the maze field and its future outlook.

In today’s highly competitive markets-characterized by abundant substitutes and consumer choices-storytelling has emerged as a critical strategy to link consumers’ emotional resonance with value recognition, thereby enhancing brand trust and loyalty across various industries. While existing research emphasizes the symbolic and emotional value of brand storytelling, it often overlooks measurable economic outcomes supported by specific quantitative data. To address this gap, this study selects Hermès and Pop Mart as case studies to examine how storytelling marketing strategies influence consumer behaviour and brand economic performance. Drawing on secondary data from corporate reports, industry databases, and other authoritative sources, the study measures key indicators including sales volume, revenue, price elasticity of demand (PED), and repurchase rate, while also analyzing consumers’ narrative processing of brand stories. The results show that storytelling significantly strengthens the emotional bond between consumers and products: specifically, causes consumers’ sensitivity to price change decreases, increases revenue and repurchase rates, and fosters long-term brand loyalty. This study provides empirical evidence for the economic value of storytelling marketing and provides direction for companies to enhance consumer emotional participation and improve performance.

Bluetooth positioning technology is vulnerable to multipath interference, signal occlusion and other factors in a complex environment, which cannot meet the requirements of high precision positioning. In order to improve the positioning accuracy of Bluetooth, this paper summarizes several technical principles of Bluetooth positioning, focusing on three key influencing factors: environment, hardware and algorithm. Then, through integrating the research results of recent years, this paper puts forward precision improvement strategies from three perspectives: hardware optimization, algorithm improvement and system design. The effectiveness of the technology is validated through real-world applications in commercial, industrial, and medical sectors. Finally, addressing technical bottlenecks in complex environments, this study explores future development directions such as multi-technology integration and intelligent algorithm iteration, providing theoretical and practical references for high-precision Bluetooth positioning applications in smart scenarios.

In the era of the digital economy, digital transformation is an inevitable choice for enterprises to enhance their competitiveness; however, vague strategic positioning often leads to poor transformation results. Data-driven decision-making based on business analytics can solve the empirical dilemma of strategy positioning. This study focuses on how business analytics can help strategic positioning amid corporate digital transformation while clarifying its core mechanism and corresponding practice models. This study suggests that business analytics plays a crucial role throughout the whole process of data-driven decision-making: in the initial stage, it lays a strategic foundation by digital maturity assessment along with internal and external analysis; In the middle stage, the strategic positioning will be transformed into a set of quantifiable indicator systems; In the later stage, data-driven method is used to ensure the synchronization between strategy and environment. Regarding practice models of different enterprises, large enterprises can cooperate through unified analysis platforms, while small and medium-sized enterprises (SMEs) rely on lightweight tools to build advantages. To cope with key challenges during transition, including ambiguous strategic goals and insufficient digital foundation, enterprises are advised to acquire systematic and self-generated digital capabilities, such as strengthened data governance, improved organizational digital literacy, and an integrated decision loop. This research promotes business analytics from a mere tool to a strategic decision enabler, providing enterprises with a feasible path to convert their strategic positioning from experience-driven to data-driven, which is of guiding significance for reducing risks and optimizing allocation.

Against the backdrop of rapid Artificial Intelligence (AI) and Internet of Thing (IoT) advancement, traditional HCI modalities (keyboards, mice) show limitations, spurring attention to intuitive gesture interaction—a core part of NUIs. This technology mainly has two routes. Vision-based systems use cameras, with deep learning (e.g., YOLOv5 for hand detection, CNN for feature extraction) enabling tasks like keypoint and spatio-temporal feature analysis, and real-time recognition via stereo image fusion. Sensor-based systems include wearable (EMG sensors, smart gloves) and non-wearable (Leap Motion, Kinect, WDHS) devices, with multi-sensor fusion enhancing robustness; bioimpedance measurement is an emerging non-wearable method. These technologies apply to intelligent HCI, disability assistance, VR/AR, and healthcare but face challenges like poor environmental robustness and high computing demands, playing a key role in advancing HCI. Looking forward, gesture recognition is expected to evolve toward more natural, adaptive, and multimodal interaction modes by integrating speech, eye tracking, and emotion recognition for context-aware communication.

Human pose estimation leverages methods for computer vision to automatically find and recognize the principal joints of the human body. In the last few years, numerous deep learning techniques for estimating human posture have made great strides. Among these, since it forms the basis for 3D human pose estimation, 2D human pose estimation is crucial. Top-down and bottom-up approaches are the two broad categories into which 2D multi-person pose estimation methodologies can be separated. The former detects each object in the input data and then performs key point localization on each object individually. Its advantages include high accuracy and suitability for scenarios involving a single person or a small, dispersed group of people; however, its disadvantages include reliance on detection technology, high computational requirements, and poor real-time performance. The bottom-up method initially focuses on detecting all the key points contained in the input data, and then combines these key points based on their spatial relationships to form complete skeletons of different objects. The advantage is that it doesn't rely on object detection, making it suitable for scenarios with many people and occlusions; however, the disadvantage is that key point grouping is complex and prone to mis-matching. Furthermore, contrasting the two approaches' outcomes on the COCO dataset, this paper analyzes their performance in specific scenarios. It also discusses the remaining issues and future research directions in this field.