Search Results (180)

With the expansion of metro networks and the increasing integration of underground retail and transit facilities, metro-connected underground commercial spaces have become a common yet safety-sensitive urban form. In fire scenarios, evacuation in such environments is constrained not only by enclosure and limited egress capacity, but also by the interaction between smoke spread and strongly coupled pedestrian flows across connected zones. Existing studies have examined smoke propagation or evacuation performance in underground spaces, but fewer have explicitly addressed how smoke constraints reshape node-level safety and the relative effectiveness of different intervention strategies in metro-connected commercial environments. This study investigates smoke-constrained evacuation dynamics in a representative metro-connected underground commercial space in Nanjing, China. A coupled simulation framework integrating PyroSim and Pathfinder is employed to examine multiple fire-source scenarios. Available safe egress time (ASET) at critical evacuation nodes is assessed using tenability criteria including visibility, temperature, and CO concentration, and is then compared with evacuation performance to diagnose hazardous routes and node-level failures. On this basis, three intervention strategies—corridor widening, stair widening, and pedestrian diversion—are comparatively evaluated. The results show that, within the modeled case, visibility most frequently becomes the controlling tenability criterion, and stairway nodes tend to lose safety margins earlier than final exits. This indicates that smoke constraints in connected underground commercial environments can trigger an early node-failure process before overall exit capacity is exhausted. The comparison further shows that behavior-oriented pedestrian diversion is more effective than geometric enlargement alone in reducing critical-node pressure and improving system-level evacuation performance under the modeled conditions. Rather than proposing universally transferable design rules, this study provides case-grounded evidence on how smoke propagation and pedestrian convergence jointly shape evacuation vulnerability in metro-connected underground commercial spaces, and offers a structured basis for critical-node diagnosis and intervention comparison in similarly configured environments. Full article

Radiation therapy is central to the management of gynecological cancers, including endometrial, cervical, ovarian, and vaginal malignancies. Despite advances in photon-based techniques, treatment-related toxicity remains significant owing to the anatomical proximity of pelvic targets to critical organs at risk (OARs), including the bowel, bladder, and bone marrow. Proton beam therapy exploits the Bragg peak to deliver a precise dose at depth with minimal exit dose, potentially reducing OAR exposure. This review develops the physical principles, dosimetric evidence, and early clinical data for proton therapy in gynecological malignancies, including cervical, endometrial, ovarian, vaginal, and vulvar cancers. Major focus is given to clinical conditions where conventional brachytherapy is not practical, and proton therapy may offer the greatest advantages, such as reirradiation for recurrent disease, post-operative pelvic irradiation, and extended field nodal treatment. This review also emphasizes current constraints that have slowed down wide clinical implementation, such as the lack of mature prospective data, cost, and accessibility. Finally, we emphasize future directions, including well-designed comparative trials, integration with systemic and immunotherapies, and adaptive treatment strategies. As the body of accumulated evidence evolves, the proton beam therapy potential for the treatment of gynecological malignancies has tremendously increased due to its role in safety and personalization of radiation treatment. Full article

Background: Hematopoietic stem cell (HSC) ex vivo culture causes severe loss of repopulation and regenerative capacity without compromising multilineage differentiation, which greatly limits the efficacy of HSC transplantation. The molecular mechanisms underlying culture-triggered HSC dysfunction remain poorly understood. Methods: Human CD34⁺ HSCs were cultured ex vivo for 96 h to establish a culture-induced HSC dysfunction model. Single-cell RNA sequencing was applied to screen key regulatory genes. TSC22D3 function was verified via overexpression assays, and immunodeficient mice were used to assess HSC engraftment. Transcriptomic profiling were performed to explore downstream molecular mechanisms. Results: Ex vivo culture induced G0 quiescence exit, elevated early apoptosis and impaired in vivo repopulation in human CD34⁺ HSCs. TSC22D3 was highly enriched in freshly isolated quiescent HSCs and gradually downregulated during culture. TSC22D3 overexpression restored HSC G0 arrest and improved hematopoietic engraftment in mice. Mechanically, TSC22D3 upregulated HSC self-renewal genes, suppressed cell cycle-related genes (CDK2/4), and activated the P53-P21-P27 pathway. Conclusions: This study demonstrates that TSC22D3 preserves HSC function during ex vivo culture by maintaining stem cell quiescence and restricting excessive proliferation. These findings reveal a novel transcriptional mechanism regulating HSC homeostasis and provide a promising target for improving functional HSC ex vivo expansion for clinical transplantation. Full article

Across all kingdoms of life, ribosomes are indispensable molecular machines that translate genetic information into the proteome of living cells. The fundamental catalytic centers of the ribosome, constructed primarily from ribosomal RNA (rRNA), exhibit remarkable conservation between the major domains of life. The ribosome’s A-site deciphers the mRNA’s triplet code, while the P-site synthesizes the growing protein chain and the E-site provides exit for deacylated tRNA; a distinct tunnel facilitates nascent polypeptide export. While the conservation of ribosomal proteins is less pronounced between bacteria and eukaryotes, striking homology exists from simple eukaryotes to humans. Ribosomal proteins were traditionally viewed mainly as scaffolding agents, steering rRNA folding during ribosome biogenesis and maintaining structural stability during translation. However, since the early 2000s, advances in structural and functional ribosome analysis have ushered in a more nuanced paradigm: ribosomes are no longer considered uniform machines. Instead, an array of rRNA and ribosomal protein modifications generates a spectrum of ribosome populations capable of specialized translation. RiboScreen^TM technology leverages this regulatory potential of individual ribosomal proteins, enabling deliberate modulation of target protein output and representing a promising tool for correcting dysregulated protein expression involved in rare and common diseases. This review will first introduce relevant aspects of ribosome biology and then showcase the tools of this new technology. Finally, we report examples for the delivery of small molecules to target ribosomal proteins for tailored restoration of protein production levels in rare and prevalent diseases. Full article

Purpose: To provide an evidence-calibrated, time-bound clinical framework for using 38% silver diamine fluoride (SDF) as interim stabilization for severe early childhood caries (SECC) in young children, addressing gaps in existing guidelines regarding treatment duration, exit criteria, equity, and system accountability. Methods: This framework was developed from the American Academy of Pediatric Dentistry (AAPD) guidance (2017–2025), the 2024 Cochrane review, real-world utilization studies, and a narrative review proposing a preservation-to-precision heuristic. Recommendations are expressed using GRADE terminology. Results: The framework includes ten recommendations, a systems drift principle, explicit time thresholds (<6 months, 6–12 months, >12 months), a 12-month reassessment mandate, equity guardrails, a bridge vs. destination consent model, and a future research agenda. A clinical vignette contrasts appropriate short-term bridging with prolonged temporization due to access barriers. Conclusions: SDF is conditionally recommended for caries arrest in primary teeth. In children with SECC, SDF should be used within a documented, time-bound preservation-to-precision pathway. SDF should not become an open-ended substitute for definitive restorative care. Explicit equity implementation prevents the framework from penalizing underserved children. Full article

Automatic modulation classification (AMC) is a core capability for spectrum monitoring, adaptive receivers, and electronic support. Most radio-frequency machine learning (RFML) studies train multi-class classifiers on benchmark datasets that contain a single modulation per recording at baseband. In operational settings, however, the objective is often to detect only a small set of signals of interest, making large multi-class models unnecessarily expensive to train and deploy. In addition, multi-class formulations can increase false-alarm risk due to confusion among non-essential classes and may allocate model capacity inefficiently to distinctions that are irrelevant for the operational objective. This paper investigates an alternative workflow based on targeted binary transformer detectors and evaluates their robustness under practical RF complications. Using the RadioML 2018.01A dataset, we construct binary detection tasks with BPSK as the signal of interest and introduce three increasingly realistic conditions: (i) center-frequency shifts away from baseband, (ii) sampling-rate mismatches via decimation and interpolation, and (iii) multi-signal mixtures where modulations co-occur either in frequency (simultaneous transmissions) or in time (temporal concatenation). The results show that baseband-trained detectors do not generalize to center-frequency-shifted signals, and multi-signal interference can cause complete detection failure unless explicitly modeled during training. We investigate early-exit transformer inference to reduce computation on high-confidence examples, showing it maintains (and occasionally improves) detection performance. We also evaluate inter-modulation transfer learning and intra-modulation adaptation from baseband to mixed- and multi-signal scenarios. Full article

The deployment of object detection models on resource-constrained edge devices remains a substantial challenge, primarily because conventional static networks expend the same worst-case computational cost on every input, regardless of intrinsic difficulty. This paper proposes an input-adaptive dynamic neural network architecture for object detection in embedded environments. The present study investigates two orthogonal axes of input-adaptive inference for embedded object detection: The system demonstrates depth adaptivity through the implementation of Early Exit, and width adaptivity via group-wise Adaptive Routing. The proposed framework is constructed on a frozen Ultralytics YOLO26s backbone and incorporates two YOLO-style early-exit heads positioned at approximately 33% and 66% of the backbone depth. Furthermore, the framework incorporates two Straight-Through Gumbel-Softmax routers, which are appended after Layers 4 and 8 with group-wise hard gating. Both axes additionally accept an explicit external control signal that allows the host system to override the input-conditional policy at inference time. The dual-mode design facilitates the functionality of the trained checkpoint as either an input-adaptive policy, in which the depth and width are determined per sample from the input distribution, or an externally controlled policy. The experimental findings demonstrate two strongly asymmetric input-adaptive policies on a frozen YOLO26s backbone. The early-exit profile reduces the compute per sample from 12.739 to 10.532 GFLOPs—a 17.32% reduction according to our in-house Conv2d/Linear MAC-based GFLOPs estimator—while preserving baseline accuracy (mAP50 = 0.1545 vs. baseline = 0.1528; ΔmAP50 = +0.0017, within evaluation noise; mAP50–95 = −0.0033). Evaluating the router-only profile in the same validator pipeline with a sparsity penalty of γ = 0.05 results in a 12.3% decrease in logical GFLOPs (from 12.739 to 11.172), while maintaining an accuracy level that is at or above the PEFT baseline (mAP50 = 0.2324 and mAP50–95 = 0.1040). In our small-domain PEFT setup, training the dynamic-policy modules yields per-checkpoint mAP shifts in this magnitude. Therefore, we interpret the width-axis accuracy result as preservation of the baseline rather than an improvement. Our contribution on the width axis is reducing computing power while maintaining baseline accuracy. Importantly, the router profile’s logical GFLOP savings are not currently reflected in wall-clock latency under our dense-kernel PyTorch implementation. Achieving practical speedup requires sparse-kernel deployment, such as structured-sparse kernels, TensorRT, TVM, or Triton paths. We will address this in future deployment-level work. Our results indicate that the depth axis can yield genuine end-to-end speedup today, while the width axis offers deployment-pending compute reduction. Full article

This study examined the anthropometric, lower-limb flexibility, power, and kinematic correlates of 5 m and 7.5 m post-entry passive underwater glide performance during forward-weighted (FW) and rear-weighted (RW) swim starts in adolescent female swimmers. Twenty-three trained female swimmers aged 14–16 years completed FW and RW starts in a randomized within-subject repeated-measures design. Anthropometry, ankle dorsiflexion range of motion, lower-limb muscle extensibility, vertical jump performance, and start kinematics were assessed. FW starts produced shorter block exit time (mean difference = −0.06 s; p < 0.001; Cohen’s dz = −1.63), shorter water-entry time (mean difference = −0.07 s; p < 0.001; Cohen’s dz = −1.65), and higher average water-entry velocity (mean difference = 0.17 m·s⁻¹; p < 0.001; Cohen’s dz = 1.36) compared with RW starts. FW also yielded a faster 5 m post-entry passive underwater glide completion time (mean difference = −0.04 s, approximately 40 ms; p = 0.005; Cohen’s dz = −0.66), whereas 7.5 m post-entry passive underwater glide completion time did not differ between techniques (p = 0.725). Exploratory regression models accounted for 29.0–64.4% of the adjusted variance across outcomes, but these models were not externally validated and should be interpreted as exploratory, hypothesis-generating associations. Technique-related differences were specific to the block exit and early post-entry passive glide phases; selected physical characteristics may complement kinematic assessment in this population but should not be used as stand-alone criteria for start-technique selection. Full article

Early-exit cascades over a frozen convolutional backbone enable adaptive inference but suffer from three sources of train–inference mismatch: branches train on samples they will never see at inference; their per-class precision thresholds are calibrated on the wrong distribution; the standard cross-entropy target on backbone argmax labels discards the backbone’s uncertainty signal. We close all three gaps with CalexNet (cascade-aligned early exits), a training-recipe-only modification: branches train under continuously weighted importance sampling that matches the cascade-survivor distribution; per-class precision thresholds are calibrated on the actual cascade-survivor subset of the validation set; the classification head is trained against the backbone’s full softmax via a temperature-scaled KL objective. Combined with an augmented prototype-pooling branch head, CalexNet is evaluated on ResNet18 and ResNet50 backbones across CIFAR-100 (20-supe-class coarse, the harder primary setting) and CINIC-10 (10-class, the easier cross-validation counterpart). On the accuracy–FLOPs Pareto frontier, CalexNet matches or exceeds three published baselines (PTEEnet, ZTW, BoostNet) and a within-paper “no-alignment, no-KD” reference. The largest gains appear in the practically relevant 30–70% FLOPs-reduction regime and show consistent trends across $n=3$ training seeds. CalexNet requires no inference-time architectural change and is a drop-in for any frozen-backbone early-exit cascade. Full article

Severe early childhood caries (SECC) in preschool children is a progressive, multifactorial disease with far-reaching consequences for child health, family functioning, and health systems. Minimally invasive dentistry (MID), particularly 38% silver diamine fluoride (SDF), is increasingly used to arrest lesions and “buy time” when definitive restorative care is delayed. This narrative review synthesizes current evidence-based guidelines and real-world utilization data to clarify the appropriate role and limits of SDF in SECC management. Professional guidance supports SDF for lesion arrest within an ongoing caries management plan, but does not endorse it as a universal long-term substitute for durable restorative care. Observational studies show that many SDF-treated primary teeth receive additional intervention within approximately 2 years, and any delay in sedation/general anesthesia is typically measured in weeks to months. A large recent private practice study found that 35% of children with caries progressed to higher-intensity treatment (restoration or extraction) over a median of 547 days, reinforcing the time-limited nature of interim stabilization. We propose a “preservation-to-precision” framework that prioritizes child-centered outcomes—freedom from pain and infection, durable function, and acceptable psychosocial impact—through risk-based, tooth- and child-specific planning, realistic follow-up assessment, and clear exit criteria for transition to definitive care. In high-income settings, the ethical value of “buying time” depends on whether systems use that time to advance children toward timely, definitive care rather than normalizing prolonged temporization as routine practice. Full article

This work is devoted to the investigation of an actual scientific and applied problem in the identification of exit points for early-exit DNNs based on the analysis of neural chains, which is one of the complex tasks related to the scientific and applied problems of DNN optimization, including, in particular, those based on the existing early-exit concept. The obtained computational complexity of the developed method is not limited by the latter itself, but instead, it mainly depends on the chosen algorithm for analyzing the occurrences of particular substrings (i.e., trimmed neural chains) into a defined list of strings (i.e., full neural chains). For example, in the framework of the conducted research, the Python operator “in” has been used (for this purpose), which uses an in-built optimized algorithm based on the combination of the Boyer–Moore and Horspool algorithms with a linear scalability, and computational complexity that approaches the arithmetic product of the total number of strings (i.e., full neural chains) in the array by the average length of the string in the same array. The performed practical approbation of the developed method gave positive results in decreasing the overall time for obtaining the final result of the considered DNN, as well as significantly decreasing the following timing parameters of the considered DNN: the minimal time to obtain the final result (reduced by more than 5 times); the average time to obtain the final result (reduced by ~1.4 times); and the total time spent processing all 22,500 modeling cases in total (reduced by ~1.39 times). In terms of the main positive aspects and advantages of the developed method, we could highlight its maximal versatility (in terms of the studied DNNs, their architectural and/or structural features, application areas, and input data representation, as well as further software implementation of the proposed method), together with its maximal simplicity of representation and understanding, which ensures the possibility of working with this method even for novice and inexperienced researchers and users who have only basic knowledge of DNNs. In addition, the main results and conclusions of the conducted research are given, and the prospects for further research are considered. Full article

High-density urban plazas hosting multi-session public events often experience pulsed inflows, prolonged crowd retention, and localized bottleneck congestion, creating crowd-safety risks that cannot be fully captured by static capacity or total evacuation time alone. This study develops an agent-based simulation framework to evaluate ultra-high-density exposure and evacuation dynamics in Guangzhou Huacheng Plaza during the International Light Festival. The model was constructed in AnyLogic using site-layout data, event organization records, official attendance information, historical event timelines, and publicly available video observations. Two scenarios were examined: normal dynamic entry–exit operation under different inter-performance intervals, and overload-triggered evacuation under alternative spatial management strategies. Model calibration and event-process validation were conducted by comparing simulated congestion hotspots, key event timing, delayed dispersal patterns, and evacuation-duration ranges with historical observations and documented event records. The results show that extending the inter-performance interval from 90 min to 120 min reduced the overload duration from 75 min to 5 min and decreased cumulative ultra-high-density exposure from 25.62 to 13.93. Under overload evacuation, zonal guidance mainly improved early-stage crowd redistribution, whereas increased exit capacity produced a stronger reduction in total evacuation time and sustained congestion. Total evacuation time decreased from 185 min in the baseline condition to 160 min under the combined strategy, while effective discharge capacity increased from 231.12 to 338.28 pedestrians/min. These findings indicate that crowd safety in open urban plazas depends not only on total attendance, but also on event pacing, bottleneck recovery time, and effective discharge capacity. The proposed exposure-oriented framework provides a quantitative basis for evaluating crowd accumulation and evacuation strategies in high-density open public spaces. Full article

Deploying deep neural networks-based intrusion detection systems on resource-constrained edge devices demands inference strategies that balance latency, energy, and accuracy under shifting threat landscapes. This paper presents Edge-Prioritize IDS, a class-prioritized early-exit framework that accelerates inference for high-risk attack classes without post-deployment retraining. A lightweight K-dimensional control vector encodes per-class runtime priorities and steers samples toward earlier exits via adaptive normalization and cost-sensitive training. Evaluation across five benchmarks NSL-KDD, CIC-IDS2017, UNSW-NB15, WISDM, and CIFAR-10 on an NVIDIA Jetson TX2 shows that Edge-Prioritize IDS preserves baseline accuracy (up to 99.6%) while reducing latency by up to 55% and energy by up to 50% for prioritized classes. Ablation studies isolate each component’s contribution, and a controlled distribution-shift experiment demonstrates the sliding-window heuristic’s ability to recover near-baseline latency within 500 samples under synthetic class-frequency drift. Once trained under the proposed framework, the model requires no additional retraining, firmware updates, or additional memory beyond the priority vector itself when runtime priorities change. Full article

Nuclear lamins organize the structural and regulatory architecture of the nucleus, integrating nuclear mechanics, chromatin organization, and genome regulation. During cardiac development, lamin composition undergoes a coordinated transition that parallels the shift from proliferative embryonic cardiomyocytes to mechanically active postnatal cells. Recent findings reveal that B-type lamins support early nuclear plasticity and proliferative capacity, whereas Lamin A/C stabilizes nuclear architecture and transcriptional programs in mature cardiomyocytes. Beyond their structural roles, lamins participate in multiple layers of nuclear regulation, including lamina-associated chromatin organization, nucleo–cytoskeletal mechanotransduction, nucleocytoplasmic transport, and regulation of mitotic progression and cell-cycle exit. Through these interconnected functions, the nuclear lamina coordinates cardiomyocyte proliferation, maturation, and mechanical stress adaptation during heart development. Mutations in lamin genes cause a diverse group of disorders collectively known as laminopathies, many of which prominently affect the cardiovascular system. In this review, we first examine how B-type and A-type lamins are developmentally deployed to regulate cardiomyocyte proliferation and maturation in the heart. We then discuss the mechanistic pathways through which lamins organize nuclear architecture, chromatin dynamics, and nucleo–cytoskeletal signaling to coordinate cardiac cellular function. Finally, we consider how disruption of these lamin-dependent regulatory networks contributes to cardiomyopathy, cardiovascular aging, and the loss of regenerative capacity in the adult mammalian heart. Full article

Background/Objectives: Extraforaminal lumbar interbody fusion provides indirect decompression without entering the spinal canal, but its uptake has been limited by poor visualization and risk of exiting nerve root injury. We describe a minimally invasive exoscopic extraforaminal lumbar interbody fusion (exELIF) technique and evaluate its clinical and radiological outcomes. This study aims to describe the exELIF technique and report its early clinical and radiological outcomes. Methods: Twenty-six patients with lumbar degenerative diseases underwent exELIF using a 3D exoscope (ORBEYE). The procedure was performed through bilateral 30–40 mm posterior incisions. Clinical outcomes were assessed using the Japanese Orthopedic Association score preoperatively and at 1-year follow-up. Postoperative computed tomography evaluated interbody fusion. Operative time, blood loss, and complications were recorded. Results: Mean operative time was 131 ± 51 min, and mean estimated blood loss was 82 ± 99 mL. The mean JOA score improved from 15.2 ± 2.2 to 24.3 ± 2.6, with a mean recovery rate of 66% at 1 year. Interbody fusion was achieved in 96%. In an exploratory CUSUM analysis of 18 single-level fluoroscopy-guided cases, a transition in operative time was observed at approximately the 10th case; operative time and estimated blood loss decreased from 141.5 ± 39.2 min and 89.0 ± 77.8 mL in cases 1–10 to 80.1 ± 6.7 min and 21.2 ± 18.1 mL in cases 11–18 (p < 0.001 and p = 0.035, respectively), indicating a reduction of operative time with accumulated experience rather than a formally established learning curve. Three patients developed transient exiting nerve root symptoms that resolved spontaneously during follow-up. One patient at the L5/S level required revision surgery due to left L5 nerve root palsy caused by posterior migration of the bone graft; this complication led to a modification of the technique, with posterior bone grafting no longer performed at L5/S. Partial screw loosening was observed in 5 patients (19%), all of which were asymptomatic and required no additional intervention. Conclusions: ExELIF provides excellent visualization in deep surgical fields, allowing the use of conventional surgical instruments through minimally invasive incisions. This is an early feasibility report of a single-institution retrospective case series with a heterogeneous cohort and no control group; the present data therefore do not establish superiority over conventional or endoscopic ELIF. Within these limits, exELIF was associated with acceptable early clinical improvement and a high interbody fusion rate, and progressive reduction in operative time with experience suggests that it may be a technically feasible minimally invasive option for selected patients with lumbar degenerative disease and for revision surgery after lumbar decompression. Full article