Search Results (155)

Background: Artificial intelligence (AI) is increasingly being used in healthcare. Despite its promise, physicians and trainees remain cautiously optimistic. This systematic review and meta-analysis aimed to assess knowledge and attitudes toward AI and to provide recommendations for AI buy-in by physicians. Methods: Searches of PubMed-OVID-IEEE-Scopus, and Web-of-Science for studies in 2013–2024 identified 11,437 records. One-hundred-and-fifteen met inclusion criteria. Fifty-three studies reported quantitative data on physicians’/trainees’ knowledge and were included in the meta-analysis. Results: Our meta-analysis estimated that only 19.6% of physicians and trainees have high overall AI knowledge, while 36.3% have low knowledge. Fifty-five studies evaluated the depth of AI knowledge. These studies consistently concluded that most physicians or trainees possess only moderate conceptual knowledge of AI, and their technical knowledge is usually limited. Qualitative evaluations also highlighted that a high level of conceptual AI knowledge is associated with greater receptiveness to AI implementation in medicine. We identified five major barriers to translating AI to the bedside with physician buy-in. Conclusion: Although physicians and trainees are generally receptive to AI, many barriers hinder adoption. To address them, we recommend establishing standardized AI education and workforce training, involving clinicians early in AI design, clarifying legal and regulatory issues, leveraging insights from clinical decision support system implementation to reduce workflow challenges, and integrating patient-centered communication principles to enhance trust and transparency. Full article

Background/Objectives: Non-receptive endometrium is associated with recurrent implantation failure, which leads to a decrease in the frequency of pregnancy during IVF; therefore, new treatment methods such as the use of Platelet-Rich Plasma (PRP) are gaining popularity in the treatment of infertility in women with repeated unsuccessful IVF attempts. Methods: A total of 38 women were included in this study, with the main complaint being the inability to conceive or maintain pregnancy. Medical examination, laboratory tests, ultrasound of the pelvic organs and hysteroscopy were performed. After that, whole blood was taken to prepare an autologous PRP; then, the PRP was inserted into the uterine using an intrauterine catheter in the first phase of the menstrual cycle (1–7 procedures). The primary outcome of this study was an increase in endometrial thickness and improvement of the receptive endometrial layer. The secondary outcome was pregnancy rate. This was a single-center prospective interventional clinical study. Results: Statistical analysis of changes in endometrial thickness after PRP therapy showed that endometrial thickness indicators after treatment significantly exceeded the values before the intervention. This may be evidence of the effectiveness of PRP therapy for thin endometrium. When analyzing pregnancy status, it was noted that after receiving PRP, more than half (56% of cases) became pregnant and the majority of them successfully gave birth. Conclusions: Based on the results of our study, we can conclude that intrauterine injection of PRP may be a new therapeutic approach in the treatment of thin endometrium and associated infertility. The use of PRP demonstrated effectiveness in increasing the thickness of the endometrium, regardless of pregnancy, while the secondary indicator was the frequency of successful pregnancies among the participants. Full article

We propose an end-to-end pipeline for Orthogonal Time–Frequency Space (OTFS) sensing that integrates deterministic signal processing with a Machine-Learning (ML) inference stage. The pipeline first generates a complex delay–Doppler grid via standard Symplectic Fast Fourier Transform (SFFT)-based OTFS reception. We then employ an ’oracle’ Ground-Truth (GT) association process to deterministically label signal peaks, extracting their complex gain ($\alpha$) and absolute indices $(m,n)$ to deduce physical targets (range, radial velocity). These oracle-aligned labels are used to train a Random-Forest (RF) classifier. The RF model learns to map normalized $33\times 33$ complex patches, centered on signal peaks, to their corresponding target parameters. On an 80/20 split of 10,000 samples, the classifier achieved a 0.966 accuracy, 0.965 macro-F1 score, and 0.998 macro Receiver Operating Characteristic–Area Under the Curve (ROC–AUC). Notably, when tested on held-out scenes, the model’s derived range and velocity predictions achieved $100\%$ coincidence with the GT, while amplitude and phase corresponded in $89\%$ of instances. This hybrid oracle-and-ML approach demonstrates a highly effective and robust method for precise target extraction in OTFS-based sensing systems. Full article

The rapid development of the Internet of Things (IoT) has imposed increasingly stringent power consumption requirements on receiver design. Unlike phase-locked loops (PLLs), free-running oscillators eliminate power-hungry loop circuitry. However, the inherent frequency offset of free-running oscillators introduces uncertainty in the intermediate frequency (IF), preventing the receiver from aligning with the desired channel. To address this, we present a reconfigurable channel receiver employing a free-running oscillator and frequency estimation for low-power IoT applications. The proposed receiver first captures a specific preamble sequence corresponding to the desired channel through multiple parallel sub-channels implemented in the digital baseband (DBB), which collectively cover the expected IF frequency range. The desired IF frequency is estimated using the proposed preamble-based frequency estimation (PBFE) algorithm. After frequency estimation, the receiver switches to a single-channel mode and tunes its passband center frequency to the estimated IF frequency, enabling high-sensitivity data reception. Measurement results demonstrate that the PBFE algorithm achieves reliable frequency estimation with a minimum IF signal-to-noise ratio (SNR) of 2 dB and an estimation error below 22 kHz. In single-channel mode, with a residual frequency offset of 30 kHz, an 8-point energy accumulation decoding scheme achieves a bit error rate (BER) of 10⁻³ at an IF SNR of 5.2 dB. Compared with the case of the original 50 kHz IF frequency offset, the required SNR is improved by 4.1 dB. Full article

Neurorehabilitation has become increasingly data-enabled, yet the conditions that most strongly modulate recovery, sleep consolidation, circadian alignment, medication ecology, and social–environmental context are rarely captured or acted upon. This opinion paper argues that an exposome perspective, defined as the cumulative pattern of external and internal exposures and their biological imprints across the life course, is not ancillary to rehabilitation but foundational to making therapy learnable, timely, and equitable. We propose a pragmatic model that centers on a minimal exposure dataset collected in minutes and interpreted at the point of care. Two clinical exemplars illustrate feasibility and utility. First, sleep and circadian rhythms: brief actigraphy and standardized reporting can make daily alertness windows visible, allowing teams to align high-intensity sessions to receptive states and to justify environmental adjustments as clinical interventions. Second, anticholinergic burden: a simple, trackable index can be integrated with functional goals to guide deprescribing and optimize cognitive availability for training. Implementation hinges less on new infrastructure than on workflow design: a short intake that surfaces high-yield exposures; embedding targets, e.g., sleep efficiency thresholds or anticholinergic load reductions, into plans of care; enabling secure import of device data; and training staff to interpret rhythm metrics and burden scores. We outline a parallel research agenda comprising pragmatic trials of bundled, exposure-informed care; longitudinal cohorts with time-stamped exposure streams; and causal methods suited to time-varying confounding, all under explicit equity and ethics safeguards. By measuring a few modifiable exposures and linking them to routine decisions, neurorehabilitation can convert context from a source of unexplained variance into actionable levers that improve outcomes and narrow unjust gaps in recovery. Full article

Tourism, a key driver of regional economies and perceived “green industry,” faces challenges from irrational resource allocation and spatial overlaps, undermining sustainability. This study examines 825 tourism resources in China’s Hulunbuir Grassland, analyzing spatiotemporal patterns, influencing factors, and ecological impacts using GPP and NDVI data. Three development phases emerged: essential development, rapid growth, and upgrading. They present a spatial pattern with Hailar and Chen Barag as the center, and multiple other points, mainly affected by ethnic minority population proportions, tourist reception, tourist attraction density, and river networks. Ecological analysis reveals that tourism-induced disturbances cause less environmental stress than other human activities, with grassland NDVI in tourism areas improving during upgrading. However, the NDVI of grasslands under non-tourism disturbance is still superior to that of grasslands under tourism disturbance. The findings emphasize the need for optimized resource allocation and proactive monitoring of tourism’s ecological footprint to advance sustainable grassland tourism. Full article

Orbital angular momentum (OAM) beams exhibit divergence during transmission, which constrains the capacity of communication system channels. To address these challenges, intelligent reflecting surfaces (IRSs), which can independently manipulate incident electromagnetic waves by adjustment of their amplitude and phase, are employed to construct IRS-assisted OAM communication systems. By introducing additional information pathways, IRSs enhance diversity gain. We studied the simulations of two placement methods for an IRS: arbitrary placement and standard placement. In the case of arbitrary placement, the beam reflected by the IRS can be decomposed into different OAM modes, producing various reception powers corresponding to each OAM mode component. This improves the signal-to-noise ratio (SNR) at the receiver, thereby enhancing channel capacity. In particular, when the IRS is symmetrically and uniformly positioned at the center of the main transmission axis, its elements can be approximated as a uniform circular array (UCA). This configuration not only achieves optimal reception along the direction of the maximum gain of the orbital angular momentum beam but also reduces the antenna radius required at the receiver to half or even less. Full article

Traditional engineering education struggles to bridge the theory–practice gap in the Industry 4.0 era, as static 2D schematics inadequately convey complex spatial relationships. While advanced visualization tools exist, their adoption is frequently hindered by requirements for specialized hardware and software, limiting accessibility. This study details the development and evaluation of a novel, web-based Digital Twin framework designed for accessible, intuitive e-learning that requires no client-side installation. The framework, centered on a high-fidelity 3D model of a historic radial engine, was assessed through a qualitative pilot case study with seven engineering professionals. Data was collected via a “think-aloud” protocol and a mixed-methods survey with a Likert scale and open-ended questions. Findings revealed an overwhelmingly positive reception; quantitative data showed high mean scores for usability, educational impact, and professional training potential (M > 4.2). Qualitative analysis confirmed the framework’s success in enhancing spatial understanding via features like dynamic cross-sections, improving the efficiency of accessing integrated documentation, and demonstrating high value as an onboarding tool. This work provides strong preliminary evidence that an accessible, web-based Digital Twin is a powerful and scalable solution for technical education that significantly enhances spatial comprehension and knowledge transfer. Full article

Background: Aphasia, a common consequence of left-hemisphere stroke, significantly impairs communication and daily functioning. Various studies have explored language recovery but only few have focused on the predictors of recovery of functional communication in patients with stroke. Objective: To identify clinical and linguistic factors associated with functional communication outcomes in patients with post-stroke aphasia. Methods: We enrolled 61 patients with aphasia due to left-hemispheric stroke, admitted to post-acute neurorehabilitation centers. Patients underwent neuropsychological, functional, and language assessments at admission (T0) and discharge (T1). Language abilities were evaluated with the Brief Exam of Language—II (BEL-II), and functional communication was measured through caregiver-rated I-CETI scores. Depression, basic (ADL) and instrumental (IADL) activities of daily living were also assessed. Correlations and regression models were used to examine predictors of functional communication recovery (ΔCETI). Results: Significant improvements were observed in all language domains, functional independence, and mood symptoms from T0 to T1 (p < 0.003). Regression analysis showed that demographic and general clinical variables (e.g., age, etiology, dysphagia) were not significant predictors of ΔCETI. However, ADL score, comprehension skills (Token test and comprehension sub-score of BEL-II) were significantly associated with functional communication recovery (β = 0.51, β = 0.68 and β = 0.75, respectively; p < 0.05). Conclusions: Functional communication recovery in post-stroke aphasia is strongly associated with initial comprehension abilities and functional autonomy in basic life activities, rather than demographic or general clinical variables. These findings highlight the need for targeted interventions aimed at improving receptive language and the importance of including ecologically valid communication assessments in post-stroke rehabilitation protocols. Full article

The purpose of the present paper is to revisit the interface between biblical studies, reception exegesis, and reception theory. In the first part of the paper, we discuss what we believe to be the most important lessons learned from recent scholarship on the relationship between these fields and highlight what we think is still an underestimated conclusion: if we assume that “meaning” is contextual rather than essential, the full(er) capacity of a biblical text is not discoverable until we have examined how it has appeared in various contexts. Related to this is the question of why and how texts survive and even thrive in new contexts and in what way later authors utilize the “capacity” of the biblical texts, because even if “meaning” is ultimately brought to texts by their readers, texts are in some senses agents as well. To exemplify these discussions and the connection between reception exegesis and biblical criticism, two short examples from the reception of Psalms 110 and 137 in medieval Christian Arabic and Judeo-Arabic sources are presented. In the first example, we recapitulate findings on how inner-biblical reception generates a complex web of potential interpretations but also how the ambivalence created in the process may be the greatest asset of that text. It is also an example of where interpretation may teach us about the life and thought of ancient and medieval communities and how they interacted with one another over the meaning of the biblical text. In contrast, the second example is more centered on the “capacity” of the text and in what sense communities exploit that potential for their larger purposes. Full article

Introduction: The implementation of pedagogical tactics with ethnic-cultural approaches represents essential mechanisms for promoting breastfeeding, ensuring greater effectiveness in knowledge dissemination. These are supported by cultural ideologies that integrate didactic processes tailored to the beliefs and practices of each ethnic group, thus facilitating greater acceptance and adherence to health recommendations. Objective: The study aims to analyze the effectiveness of culturally sensitive educational strategies from the prenatal stage at the Quiroga Health Center, Ecuador, exploring how indigenous cultural dynamics influence the receptivity of education. Methodology: The research is quantitative, quasi-experimental, and employs a descriptive, documentary, bibliographic, and analytical cross-sectional cohort design to thoroughly analyze collected data and identify cause-and-effect factors influencing breastfeeding knowledge and practice. Results: The effectiveness of the educational intervention in promoting cognitive awareness of breastfeeding was confirmed using the McNemar statistic applied to related samples. Conclusions: Breastfeeding knowledge levels are closely tied to reproductive history, a constantly evolving process affected by various social factors. Health education is implemented and strengthened through teaching programs as these strategies can significantly contribute to maternal and child health promotion, adapting to the specific needs and contexts of each community. Full article

This article examines how gender shapes Holocaust memory through close analyses of two canonical women’s memoirs: Charlotte Delbo’s Auschwitz and After and Ruth Klüger’s Still Alive (2001), a considerably rewritten and culturally reinterpreted version of her earlier German book Weiter leben (1992). Delbo, a French political deportee, and Klüger, an Austrian Jewish survivor, provide testimonies that challenge the male-centered paradigms that have long dominated the Holocaust literature. Although pioneering feminist scholars have shown that women experienced and remembered the Holocaust differently, gender-based analysis remains underused—not only in Holocaust studies but also in broader memory studies, where it is often assumed to be already complete or exhausted. This view of theoretical saturation reflects a Eurocentric bias that equates critical maturity with Western academic prominence, thereby masking the ongoing influence of gender on the production, circulation, and reception of testimony worldwide. Drawing on trauma theory, concepts of multidirectional memory and postmemory, systems theory of media, and ethical approaches to testimony, this article argues that gender is not merely descriptive of Holocaust experience but also constitutive of how trauma is narrated, circulated, and archived. Testimony, as a cultural form, is inherently mediated, and that mediation is fundamentally gendered. This analysis illustrates how Delbo and Klüger create gendered testimonial forms through unique aesthetic strategies. Delbo’s writing focuses on seeing by invoking a feminist aesthetics of voir as imagined and ethical visualization, while Klüger’s narrative emphasizes voice, utilizing rhetorical sharpness and ambivalent narration to challenge postwar silencing. Instead of equating gender with femininity, the article understands gender as a relational and intersectional system—one that includes masculinity, non-binary identities, and structural power differences. It also questions Eurocentric assumptions that feminist critique has been fully explored within memory studies, urging renewed engagement with gender in transnational contexts, such as the often-overlooked testimonies from wartime Shanghai. Ultimately, this article argues that feminist approaches to Holocaust testimony expose the gendered structures of grievability that determine which kinds of suffering are preserved—and which remain unspoken. Full article

Objectives: Primary bone tumors such as osteosarcoma and chondrosarcoma are rare but aggressive malignancies that require early and accurate diagnosis. Although X-ray radiography is a widely accessible imaging modality, detecting small or multi lesions remains challenging. Existing deep learning models are often trained on small, single-center datasets and lack generalizability, limiting their clinical effectiveness. Methods: We propose the YOLOv11-MTB, a novel enhancement to YOLOv11 integrating multi-scale Transformer-based attention, boundary-aware feature fusion, and receptive field augmentation to improve detection of small and multi-focal lesions. The model is trained and evaluated on two multi-center datasets, including the BTXRD dataset containing annotated radiographs with lesion types and bounding boxes. Results: YOLOv11-MTB achieves state-of-the-art performance on bone tumor detection tasks. It attains a mean average precision (mAP) of 79.6% on the BTXRD dataset, outperforming existing methods. In clinically relevant categories, the model achieves small-lesion mAP of 55.8% and multi-lesion mAP of 63.2%. Conclusions: The proposed YOLOv11-MTB framework demonstrates promising generalization and accuracy for primary bone tumor detection in radiographic images. Its performance in detecting small and multiple lesions suggests potential for clinical application. Full article

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

This paper presents a novel harmonic-selective clocking scheme that facilitates concurrent downconversion of spectrally distant radio frequency (RF) signals using a single low-frequency local oscillator (LO) in an N-path receiver architecture. The proposed scheme selectively generates LO harmonics aligned with multiple RF bands, enabling simultaneous downconversion without modification of the passive mixer topology. The receiver employs a 4-path passive mixer configuration to enhance harmonic selectivity and provide flexible frequency planning.The architecture is implemented on a printed circuit board (PCB) and validated through comprehensive simulation and experimental measurements under continuous wave and modulated signal conditions. Measured results demonstrate a sensitivity of $-55\mathrm{dBm}$ and a conversion gain varying from $-2.5\mathrm{dB}$ to $-9\mathrm{dB}$ depending on the selected harmonic pair. The receiver’s performance is further corroborated by concurrent (dual band) reception of real-world signals, including a GPS signal centered at 1575 MHz and an LTE signal at 1179 MHz, both downconverted using a single 393 MHz LO. Signal fidelity is assessed via Normalized Mean Square Error (NMSE) and Error Vector Magnitude (EVM), confirming the proposed architecture’s effectiveness in maintaining high-quality signal reception under concurrent multiband operation. The results highlight the potential of harmonic-selective clocking to simplify multiband receiver design for wireless communication and global navigation satellite system (GNSS) applications. Full article