Search Results (244)

Smart and sustainable construction increasingly depends on automation, yet robotic steel assembly still lacks task-specific perception data for bespoke connection systems. The Intermeshed Steel Connection (ISC) is a novel steel connection system that can reduce bolting effort and support faster, more reusable assembly, but dependable perception for ISC-aware robotic assembly remains underdeveloped. No public image corpus exists for ISC components, and collecting real site imagery is constrained by access, safety, privacy, and the limited deployment of ISC in practice. This paper introduces ISC-Perception, a hybrid vision dataset for near-field robotic assembly with novel Intermeshed Steel Connections. The dataset combines photorealistic CAD renders from SolidWorks Visualize, automatically annotated synthetic scenes generated in Unity, and a limited curated set of real ISC and human images. For a normalised 10,000-image Unity-based pipeline example, the proposed pipeline reduces estimated human effort to 30.5 h compared with 166.7 h for manual labelling, while the full training and validation set contains 15,928 images. Detectors trained on the hybrid dataset outperform synthetic-only and photorealistic-only alternatives, achieving mAP@0.50 of 0.756 on the complete test set. A near-size-matched comparison indicates that the improved performance is associated with the hybrid composition rather than dataset size alone under the evaluated training conditions. In a 1200-frame multi-view benchtop robotic assembly experiment, the detector achieves mAP@0.50/mAP@[0.50:0.95] of 0.943/0.823. These results show that ISC-Perception provides a practical route to data generation for emerging construction robotics applications where real imagery is scarce and supports the development of perception modules for robotic steel assembly in smart construction. Full article

Grounded in structural biomimetics, this study extracts the multi-segmented tapered geometry from the 10-segmented lamellate antenna of Allomyrina dichotoma and maps it to microwave antenna design. Through biological characterization and parametric modeling, key geometric features—multi-segmented configuration, irregular contour, and bilateral symmetry—were extracted. Along the 2D pathway, a 10 × 10 × 1 mm³ PCB microstrip antenna was designed and fabricated, achieving 111% fractional bandwidth from 4.86 to 17.05 GHz with a peak gain of 2.15 dBi and a radiation efficiency of 67–72% across the operating band, plus two additional bands at 24.76–28.58 GHz and 32.66–37.73 GHz. The multiple resonance valleys on S₁₁ curves and frequency-dependent surface current evolution indicate that multi-mode resonant coupling, perimeter increment, and symmetric aperture efficiency together underpin the ultra-wideband performance. Along the 3D pathway, a dipole antenna replicated via metallic 3D printing attains an electrical length of 0.17λ, with 66% bandwidth and 1.45 dBi gain, confirming the same geometric principle in a shape-preserving form. The 2D route favors planar integration and bandwidth, while the 3D route offers extreme miniaturization. This work provides experimental validation of cross-domain geometric mapping from biology to electromagnetics within structural biomimetics, and offers engineering evidence for the intrinsic versatility of this morphology across physical domains. Full article

Temporal action localization (TAL) requires a detector to recognize action categories and estimate temporal boundaries in untrimmed videos. Mamba supports linear-complexity long-sequence modeling, yet a uniform allocation of state-space operators does not explicitly differentiate the context requirements associated with temporal scales and prediction branches. We present Multi-Scale Decoupled Routing Mamba (MSDR-Mamba), a multi-scale branch-decoupled routing state-space detector. The method combines a phase-dilated multi-rate Mamba temporal pyramid, multi-band state-time initialization, level-wise local–global gating guided by duration priors, and a branch role-decoupled head. The final head uses CNNs for classification and center-offset estimation, with Mamba used for class-specific start/end boundary neighbor modeling. With frozen InternVideo2-6B features on THUMOS14, MSDR-Mamba achieves a five-threshold mAP of 73.09%, exceeding TriDet by 0.43 percentage points. Supplementary experiments on ActivityNet-1.3 and P²ANet further evaluate the complete configuration under longer-duration and dense short action distributions. The results support scale- and branch-aware state-space modeling as a practical design strategy for TAL. Full article

Accurate pavement distress identification is essential for infrastructure maintenance. However, prevailing models frequently underperform in complicated environments due to extreme scale variations, atypical defect geometries, and severe background noise. To mitigate these limitations, this study presents BDC-YOLO, an upgraded detection network built upon the YOLOv8 baseline. The proposed architecture structurally incorporates three specialized mechanisms: Bi-level Routing Attention (BRA) to isolate relevant target features from background artifacts; Dynamic Snake Convolution (DySnakeConv) to capture the topological characteristics of elongated and irregularly shaped cracks; and Content-Aware ReAssembly of FEatures (CARAFE) to minimize information degradation during upsampling and refine multi-scale feature fusion. Evaluated on the RDDChina dataset, BDC-YOLO demonstrates superior accuracy over the baseline and comparative state-of-the-art methods. Specifically, the framework yields a mAP_0.5 of 88.9%, representing an absolute gain of 4.7% against the standard YOLOv8 model while achieving an inference speed of 175.4 FPS. Full article

Overcoming complex background noise and poor small-target detection in industrial settings, this paper introduces YOLOv8-BBP2, an enhanced YOLOv8 model. To better extract dynamic features, the backbone integrates a BiFormer dual-level routing attention mechanism. Moreover, a learnable Bi-directional Feature Pyramid Network (BiFPN) replaces the standard module, optimizing multi-scale feature integration. A P2 detection head is also added to accurately identify tiny objects, such as early flames and thin smoke. Tested on a custom factory fire dataset, YOLOv8-BBP2 yields 95.231% precision, 94.612% recall, and 89.677% mean average precision (mAP@0.5). These metrics represent respective gains of 3.31%, 4.934%, and 7.451% over the baseline YOLOv8s. Ultimately, with an inference speed of 20 ms per frame, the proposed network ensures highly robust, real-time performance. Full article

Search-and-rescue (SAR) missions following earthquakes and other disasters require aerial video perception systems that do more than detect objects in isolated frames. Operators must maintain the identities of access points, vehicles, responders, hazards, and other mission-relevant targets despite UAV ego-motion, dust, debris, occlusion, scale variation, and abrupt scene transitions. This paper presents E’CHIT (Edge-Oriented Colour Histogram Instance-Guided Tracking), a deployment-oriented, operator-centric UAV tracking framework for real-world disaster-response applications. Its primary scientific contribution is an identity-stabilised, detector-assisted tracking methodology. YOLOv8-seg proposals trained on D’RespNeT initialise and refresh tracks; a Custom-RE3 recurrent module propagates target states through short detector dropouts; and a lightweight EOMC verifier, based on edge orientation, mean colour, and shape consistency, determines whether tracks should be accepted, refreshed, or reacquired. A scene-cut watchdog that combines luminance mean absolute difference (MAD) with HSV histogram divergence prevents stale identities from carrying over after hard edits or sudden feed changes. Custom-RE3 is the continuation module implemented and evaluated in this study. The surrounding E’CHIT wrapper follows an initialise–reseed–verify–reset cycle and is tracker-adaptable at the software-interface level: another compatible SOT or MOT continuation module can be integrated through adapter modifications, state and bounding-box conversion, and method-specific retuning, followed by independent validation. All reported quantitative results therefore apply to the Custom-RE3 implementation. D’RespNeT, the optional reinforcement learning (RL) warm start, the HUD, and the deployment stack support this central tracking contribution. D’RespNeT provides 28 polygon-annotated SAR classes. An author-developed PPO/SAC script is used only during offline detector training. In the reported runs, it produces different early optimisation trajectories for selected difficult or under-represented classes, while the default supervised schedule remains the strongest final global mAP reference. No RL policy runs during deployment; the detector architecture, parameter count, and inference graph remain unchanged. Evaluation on D’RespNeT and authentic disaster-response UAV footage shows that E’CHIT increases Success@IoU ≥ 0.5 from 0.62 to 0.79, reduces identity switches by approximately 71%, and maintains real-time 1080p performance, achieving 164–330 FPS for single-target tracking and 24–100+ FPS for end-to-end multi-target operation on an RTX-class GPU using FP16. The VOT2014, NT-VOT211, and VOTS2024 figures reproduce historical result spaces reported in the literature and include a clearly labelled, non-official E’CHIT operating-point marker solely for context. This marker was not produced using the corresponding official datasets, toolkits, reset rules, or submission routes; it is excluded from the primary quantitative claims and must not be interpreted as a leaderboard rank or a protocol-identical comparison. Overall, the system demonstrates how identity-stable UAV tracks can provide actionable operator cues for target monitoring, entry-point assessment, and UAV–UGV/ground-team coordination in cluttered disaster scenes. Full article

Background: Elite soccer performance emerges from the interplay of cognitive, emotional, psychophysiological, and tactical processes that operate in real time during matches. Advances in wearable sensors and artificial intelligence (AI) now allow continuous monitoring of physiological and psychological states. They also allow modeling of how these states relate to tactical and physical performance. Existing reviews have examined machine learning in soccer, heart rate variability (HRV) monitoring, and psychological determinants of performance separately. No scoping review has mapped the intersection of AI analytics, wearable psychophysiological monitoring, and psychological performance constructs as one integrated decision-support framework in elite soccer. Aim: The aim of this study was to map the available evidence on the integration of AI and machine learning with psychophysiological monitoring for performance modeling in elite soccer, to identify the psychological constructs already used as model inputs, to describe the wearable technologies and AI methods applied, and to set out the translational challenges and evidence gaps that need priority attention. Methods: The review followed the PRISMA extension for Scoping Reviews (PRISMA-ScR) and the updated Joanna Briggs Institute (JBI) methodology. The protocol was registered on the Open Science Framework (OSF). Six databases (PubMed/MEDLINE, Scopus, Web of Science, SPORTDiscus, IEEE Xplore, and PsycINFO) were searched from January 2000 to March 2026 using the Population–Concept–Context (PCC) framework. Two reviewers independently screened titles, abstracts, and full texts (Cohen’s kappa = 0.82). Results: Thirty-six sources met the eligibility criteria after screening of 3104 records. AI and machine learning have been applied widely to predict physical and tactical performance in soccer, yet they rarely include psychological constructs. Reported models (decision trees, gradient boosting, and artificial neural networks) reach high accuracy for physical outcomes in internal validation, for example, above 66% for injury risk. Multi-modal models that add physiological and psychological inputs report stronger prediction. These figures come mostly from internal validation, and external validation and overfitting controls are seldom reported, so they should be read as optimistic upper bounds. Psychological and psychophysiological inputs remain under-represented. Explainable AI (XAI) methods, in particular Shapley Addictive exPlanations (SHAP) values, are appearing, but validation with domain experts is scarce. HRV has been reviewed as a psychophysiological marker in soccer, yet its use within AI decision-support tools for real-time psychological readiness has not been mapped. Three translational challenges stand out: the ecological validity gap between laboratory cognitive tests and match-embedded psychophysiology; the interpretability problem of opaque AI in high-stakes decisions; and the data fragmentation problem created by disconnected physical, tactical, and psychological data streams. Conclusions: Integrating AI with wearable psychophysiological monitoring offers a credible route toward integrated performance modeling in elite soccer. Closing this gap calls for multi-modal frameworks that combine psychological constructs, physiological markers, and tactical data within explainable AI. Research priorities include ecologically valid psychophysiological assessment protocols, position-specific psychological profiling, and practitioner-validated tools that turn AI outputs into usable coaching recommendations. Full article

Accurate flood mapping is essential for disaster response, yet optical–SAR fusion remains difficult when clouds, speckle noise, and terrain interference cause the two sensors to provide uneven or conflicting evidence. Multimodal models may suffer from “modality collapse”, while fixed scan patterns make water boundaries difficult to delineate. To address these issues, we propose CISRMamba, a multimodal state space model (SSM) based on a lightweight MobileMamba backbone. A Deformable Scan Router (DSR) adapts the scan trajectory to irregular flood contours while preserving SSM efficiency. Instead of forcing all regions into a rigid “align-then-fuse” process, the Feature Complementary Module (FCM) suppresses discrepant responses, supplies complementary information where one modality is incomplete, or enhances consistent evidence according to the local optical–SAR relationship. The Refined Feature Integration (RFI) module reduces distributional shift and modality collapse through a difference-guided residual, while the Spectral Refinement Block (SRB) recovers high-frequency spatial details weakened by downsampling. Experiments on CAU-Flood and Wuhan show that CISRMamba achieves mIoU scores of 91.53% and 58.67%, respectively, outperforming baselines based on CNNs, Transformers, and Mamba. These results indicate that CISRMamba combines context modeling with boundary-level refinement for efficient flood monitoring under heterogeneous weather and scene conditions. Full article

Mixed-viewpoint pavement crack detection remains challenging because aerial (Drone) and ground-level (Ground) images exhibit substantially different feature distributions, while repeated downsampling inevitably weakens the representation of fine crack structures in UAV (unmanned aerial vehicles) imagery. To address these issues, this study proposes VCDH-Net, a lightweight mixed-viewpoint crack detection framework built upon YOLOv11n. The framework introduces a Viewpoint-Conditioned Dual-Head Detection (VCDH) architecture that dynamically routes features to viewpoint-specific detection heads through a lightweight viewpoint classifier, enabling specialized optimization while maintaining a shared feature extraction backbone. On this basis, a Lightweight Structure Enhancement (LSE) module is incorporated into mid-level feature layers to reinforce directional crack structures by exploiting local contrast and geometric priors. Furthermore, a Pyramid Detail Refinement (PDR) module is developed for the Drone branch to recover fine-grained spatial information of ultra-small cracks through a lightweight upsample–refine–downsample residual pathway. To provide a more comprehensive evaluation of mixed-viewpoint detection performance, a cross-view assessment framework is further established by introducing three complementary metrics, namely Cross-View Gap (CV-Gap), Worst-View Score (VWS), and Cross-View Balance (CVB). Experiments conducted on a dual-viewpoint pavement crack dataset collected from roads in and around Nanjing, China demonstrate that the proposed method achieves an mAP50-95 of 57.88%, improving the baseline YOLOv11n by 1.55 percentage points. Meanwhile, the Drone-view mAP50-95 increases from 46.98% to 48.83%, and the proposed framework attains the highest CVB score of 0.4965, indicating improved cross-viewpoint detection consistency under the tested conditions. These results, validated on a single-region dataset, demonstrate that VCDH-Net effectively alleviates viewpoint-induced feature discrepancies on the tested data while enhancing the representation of fine crack structures. Generalization to other geographic regions requires further validation on multi-viewpoint datasets not yet publicly available. Full article

Background: Accurate identification and classification of white blood cell (WBC) subtypes in peripheral blood smears (PBS) is essential for the diagnosis and monitoring of hematological malignancies, including leukemia. Conventional manual microscopy, although clinically established, is labor-intensive and subject to inter- and intra-observer variability. Deep learning-based object detection offers a route to automation, yet most prior studies are limited by small datasets, restricted cell taxonomies, or single-microscope acquisition. This study evaluates a YOLOv11-large (YOLOv11L) detector for simultaneous localization and classification of 13 leukemia-relevant WBC subtypes plus an artifact class (14 classes total), trained on the large-scale, multi-domain, open-source LeukemiaAttri dataset. Methods: From the LeukemiaAttri dataset, 18,664 annotated images (67,347 objects) acquired at 40× and 100× magnification were partitioned by stratified sampling into training (70%), validation (15%), and test (15%) sets. The training set was expanded to 65,785 images through extensive geometric, photometric, and AugMix augmentation. A YOLOv11L model pretrained on MS COCO was fine-tuned for 250 epochs (640 × 640 input) on a single NVIDIA H200 SXM GPU, using an auto-selected optimizer (momentum 0.9; weight decay 5 × 10⁻⁴), automatic mixed precision (AMP), and mosaic augmentation for the first 240 epochs. Results: On an internal held-out test set, the model achieved an mAP50 of 93.9%, mAP50-95 of 77.9%, precision of 94.1%, recall of 88.8%, and an F1 score of 0.913, with similar performance in the validation and test sets. Class-wise average precision (AP) ranged from 89.3% (monocyte) to 98.2% (monoblast), confirming consistent detection across morphologically diverse subtypes. Conclusions: The YOLOv11L detector achieved high performance across all 14 categories on the internal test set, with metrics exceeding those previously reported for subset-specific baselines. These findings support further evaluation of the model as a decision-support tool for peripheral blood smear analysis. External validation is required to determine its clinical utility and generalizability. Full article

Radioactive source cleanup in nuclear environments is an important task for facility decommissioning and autonomous radiation management. In multi-target cleanup scenarios, each source removal action alters the subsequent radiation risk distribution. This action-dependent evolution invalidates the static planning assumptions commonly adopted in conventional routing and path planning methods. To address this challenge, this work formulates multi-target radioactive hotspot cleanup as a preference-conditioned sequential combinatorial optimization problem. We propose a neural sequential optimization framework that integrates hotspot map encoding and radiation field image encoding through cross-modal interaction. The proposed framework generates preference-conditioned risk-aware cleanup policies, enabling effective coordination between target selection and safe navigation under different risk–efficiency trade-offs. The nuclear hot-cell simulation environment is constructed using the Robot Operating System (ROS) and the Gazebo physics simulation engine. Compared with classical heuristic methods, evolutionary optimization methods, and reinforcement learning-based baselines, the proposed method achieves lower cumulative radiation exposure across different problem scales while maintaining competitive task efficiency. The results demonstrate that the proposed framework provides an effective task-level sequencing strategy for radiation-aware robotic cleanup in action-dependent risk fields. Full article

Automatic Dependent Surveillance-Broadcast (ADS-B) systems provide continuous aircraft position reports for aviation surveillance and short-term trajectory prediction. However, many data-driven predictors directly model the longitude, latitude, and altitude contained in ADS-B messages as generic multivariate time-series variables, which can weaken latitude-dependent displacement, bearing, and local motion relationships during multi-step forecasting. This paper proposes Geometry-Informed Adaptive Time-Series Fusion (GeoATF), a forecasting framework for short-term aircraft trajectory prediction from ADS-B position reports. For multi-route learning, each input window is mapped from global absolute coordinates to a local geodesic chart anchored at the last observation. The predicted local offsets are subsequently decoded into World Geodetic System 1984 (WGS-84) coordinates through great-circle forward navigation. GeoATF combines a patch-based time-series Transformer (PatchTST) temporal branch for modeling historical ADS-B position sequences with a geometric–kinematic branch that constructs great-circle distance, bearing, velocity component, and trajectory change rate features from the same sequence. An adaptive branch weight fusion module then learns horizon-dependent geometric–kinematic branch weights for residual correction. Experiments on 945 ADS-B flights from ten routes demonstrate that GeoATF delivers more accurate multi-horizon trajectory forecasts than the evaluated baselines, with more pronounced advantages at longer forecast horizons. Flight-level statistical analysis supports the robustness of these improvements, while resource evaluation indicates a moderate computational footprint under the common inference protocol. These results suggest that explicitly representing geometric–kinematic information and adaptively fusing it with temporal features can improve short-term aircraft trajectory prediction using only historical ADS-B position data. Full article

Main memory carries data outside the processor’s trust boundary, so commodity systems-on-chip (SoCs) increasingly encrypt it; yet, in-line memory encryption engine itself becomes a differential power analysis (DPA) target whose key, if recovered, unlocks all of dynamic random-access memory (DRAM). We present PACE, a page-adaptive, cache-anchored memory encryption engine for RISC-V that makes nth-order DPA resistance practical and keeps cryptographic latency off the cache eviction critical path. PACE inserts a TileLink adapter between the last-level cache and the memory port and applies, per physical page, one of four policies (plaintext/confidentiality/confidentiality+integrity/+masking-order-d) selected from RISC-V page table bits through a memory-mapped control plane. Confidentiality uses counter mode whose per-line keystream is precomputed during cache residency; integrity is tree-free at the embedded operating point via on-chip counters and tags, with a live split counter block-MAC Bonsai Merkle tree for scale-out. DPA resistance is layered: ISAP-style fresh re-keying caps the data complexity per key at $q\approx 1$, and domain-oriented masking (DOM, d + 1 shares) protects the sole key processing block to order d. We implement PACE in Chisel on a Rocket SoC (Chipyard) and evaluate it with open-source tooling. A deterministic TileLink-level harness proves ciphertext-in-memory and detects tamper/replay/splice, and the live Tier-B engine (DRAM counters and per-line message authentication codes (MACs) plus an on-chip-rooted block-MAC tree) is validated from end to end on full Rocket and BOOM SoCs and on the FPGA; the masked Ascon-p S-box is proven order-d secure (d = 1, 2) under a glitch- and transition-aware model by three independent formal tools (COCO, PROLEAD, and SILVER, the last also deciding the full composability lattice and confirming exact glitch-robust order-2 probing security), with COCO extending the exact verdict to the highest synthesized order d = 3 (secure at probing orders 1–3); a simulated trace correlation power analysis (CPA) recovers the full key from an unprotected core and is defeated by masking, with a mutual information analysis confirming the $N\propto {\sigma }^{2(d+1)}$ trace amplification law. We further realize PACE on field-programmable gate array (FPGA) silicon: the engine plus an on-chip ring oscillator power sensor is placed, routed, timing-closed at 100 MHz, and programmed on a Xilinx XC7Z020, and we drive a fixed-vs-random Test Vector Leakage Assessment (TVLA) campaign read back entirely over a JTAG (Joint Test Action Group). A multi-core configuration and a Linux control-plane driver are likewise validated. Across synthetic access patterns and named application kernels (AES, SHA-256, matrix multiplication, pointer chasing) on both in-order Rocket and out-of-order BOOM, application-level overhead is within measurement noise of plaintext for cache resident workloads (masking, in particular, is cycle-identical to plain confidentiality), and we characterize the cost of each policy, masking order, and re-keying interval, demonstrating side-channel-hardened memory encryption on open RISC-V hardware. Full article

To address the difficulty of synergizing multi-source spatial data with geological text and the limited spatial reasoning of large language models (LLMs), this paper proposes an intention-driven hybrid semantic–spatial retrieval–augmented generation (RAG) method and a corresponding GIS visualization system. The method uses intent routing to direct queries to spatial parsing or text retrieval, extracts target entities, attribute constraints, and spatial relations from natural language via an LLM, and dynamically generates parameterized PostGIS (PostgreSQL Spatial Extension) queries through a rule-based parser, achieving deep coupling of semantic understanding and spatial computation. A spatial proximity verification module computes the minimum distances between target and reference entities, producing a verifiable target-reference list that provides precise spatial support for answers. The system implements a multi-source dynamic data management mechanism supporting unified heterogeneous data import, ArcGIS layer style parsing, adaptive point visualization, and user-defined mappings from data tables to geological entity types. It further integrates prospectivity prediction, geochemical association analysis, and intelligent QA into an end-to-end interactive GIS environment. Experimental results show that semantic filtering raises Precision@5 (Precision at rank 5) from 0.171 to 0.829, rule-based ranking improves NDCG@5 (Normalized Discounted Cumulative Gain at rank 5) by about 16%, and the spatial proximity verification module increases the spatial citation rate (distance coverage ratio) from 24.2% to 47.8% when computed over the 17 spatial-relation queries for which distance citations are applicable. Adding textual knowledge further boosts answer relevance to 0.863 while maintaining a comparable spatial citation rate (47.2% vs. 47.8%). To mitigate potential self-preference bias in the LLM-as-Judge setup, an independent evaluation using DeepSeek-V4-Flash was conducted, yielding high inter-evaluator agreement (Pearson r = 0.951 for faithfulness, 0.988 for relevance). These results suggest the method’s potential for query understanding, ranking optimization, and spatial interpretability, while also highlighting the need for larger-scale benchmarks and blinded expert evaluation. Full article

The human placenta is a temporary organ structured to optimize exchange between the maternal and fetal circulatory systems. Its fetal component consists of highly branched chorionic villi, which are anchored to the maternal uterine wall and project into the intervillous space. The outer surface of these villi is lined by a multinucleated, continuous layer called the syncytiotrophoblast, which is supported by an underlying layer of proliferative cytotrophoblast cells and the invasive extravillous trophoblast (EVT). This cellular bilayer forms a selective barrier that directly bathes in maternal blood, allowing for the efficient transfer of oxygen and nutrients while structurally preventing the direct mixing of maternal and fetal blood cells. Human placental studies have been stymied by ethical and accessibility constraints. Stem cell biology has now revolutionized the capacity to model human placental development, in particular with the derivation of human trophoblast stem cells (hTSCs) and organoids. Authentic, self-renewing human trophoblast stem cells (hTSCs) were first derived not from pluripotent stem cells but from primary tissue—first-trimester villous cytotrophoblasts and blastocysts. Derivation from human pluripotent stem cells (PSCs) followed only subsequently, along two principal routes: conversion of naive PSCs, which retain extraembryonic competence, and induction from primed PSCs, as well as by direct reprogramming of somatic cells to induced hTSCs. An important advance underlying these improvements is the mapping of a global reprogramming roadmap. Multi-omic and lineage-tracing experiments have mapped the stepwise transcriptional and epigenetic conversions of fibroblasts to hTSCs, including sequential chromatin reconfiguration, trophoblast gene network activation, and repression of somatic signatures. These results identify major regulatory bottlenecks and intermediate states, improving reprogramming fidelity. The derivation of stem-cell-based trophoblast organoids now enables complex modeling of placental architecture, function, and disease susceptibility in vitro. These organoids accurately recapitulate placental barrier functions and immunological features, allowing for examinations of maternal–fetal health, pregnancy disorders, and placental infection response to viruses like cytomegalovirus and SARS-CoV-2. Looking ahead, the integration of reprogramming and organoid technologies will propel patient-specific and tailor-made models for personalized diagnostics, drug screening, and mechanism studies. As we unravel the molecular ballet of trophoblast induction, such discoveries have the potential to bridge basic translational gaps in reproductive biology and maternal–fetal medicine. Full article