Search Results (60)

When LiDAR detects underwater targets, the water severely attenuates the laser beams, making it impossible to extract valid echo information during 3D reconstruction of urban water bodies. This study proposes a Multi-Scale Spectral Adaptive Loss Generative Adversarial Network Based on Morphology-Spatiotemporal Decoupled Attention (MSAGAN) that effectively enhances far-field underwater echo signals for LiDAR. Its core components consist of three parts: Morphology-Aware Dynamic Receptive Field Attention (MADRA), Spatial-Temporal Decoupled Frequency-Enhanced Global Feature Fusion Block (STDFBlock), and Adaptive Dynamic Adjustment Loss Function Based on Frequency-Domain Decomposition and Gradient Response (FGADLoss). The model precisely identifies the narrow and curved local structures of the echo signals during the feature extraction process, improving the precise detection of subtle structural changes in the echo signals and enabling the extraction of valid echo signal features from a background of numerous invalid echo signals. The model reduces image fragmentation and center-of-mass drift during echo signal augmentation, improving the accuracy of water body environments’ 3D reconstruction. Through this model, the average point cloud density per square meter for lakes and ponds increased by 2.12 and 3.54, respectively, enabling effective reconstruction of urban water bodies information and offering a high-quality data basis for underwater object recognition and bathymetric surveying. Furthermore, this method effectively addresses the challenge of simultaneously obtaining degraded and ideal streak images that match the echo signals of underwater detection targets, and it also offers advantages in terms of training data requirements, making it particularly well-suited for real-world underwater detection scenarios where paired ideal-degraded data is scarce. Full article

Background/Objectives: This study assesses the outcomes of integrating deep learning-augmented zero echo time (ZTE DL) MRI sequences into standard MRI protocols for assessment of the hands and feet. Methods: In this single-center, retrospective study, a standard MRI protocol of hands and feet at 1.5 T was compared with the same protocol with an added ZTE DL sequence. Pathological changes in the bone, including subcortical sclerosis, osteophytes, joint space narrowing and fractures, were rated as present or absent. Indeterminate intraosseous lesions (erosions/ganglia) and indeterminate extraosseous lesions (ossicles/soft tissue calcifications) were additionally assessed on a semi-quantitative 4-point Likert scale. Diagnostic confidence was rated as low, moderate, or high. Standard MRI vs. ZTE-DL-augmented MRI comparisons were evaluated using a mixed-effects model with reader as a random effect, and X-ray vs. ZTE-DL-augmented MRI comparisons using a paired Wilcoxon signed-rank test. Interreader agreement (two readers) was assessed using Kappa statistics. Results: The cohort encompassed 59 datasets (feet = 22, hands = 37) of 40 patients with an average age of 51.79 (SD ± 11.84) years (58% women (n = 34), 42% men (n = 25)). Additional ZTE DL sequences resulted in similar findings, but with higher diagnostic confidence for assessment of bone changes when compared to conventional MR sequences (p-values < 0.05) and, overall, when compared to radiographs in a subgroup (p < 0.05 for five of six pathologic bone changes). Interreader agreement of diagnostic confidence was moderate to substantial (kappa 0.59–0.71). Conclusions: Addition of ZTE DL sequences to standard MRI protocols of hands and feet at 1.5 T demonstrated similar findings in the assessment of pathological bone changes, but with higher diagnostic confidence compared to conventional MR sequences and radiographs. However, further validation against a reference standard is required to determine diagnostic accuracy. Full article

Opinion polarization, echo chambers, and the rapid formation of opinion clusters have become defining features of debates on contemporary online social platforms. To explain these phenomena from a control-theoretic perspective, this paper investigates opinion dynamics in social networks with edge-heterogeneous confidence bounds, focusing on clustering and polarization behaviors driven by pair-dependent trust and asymmetric influence. Two discrete-time models are proposed, including an unsigned bounded-confidence model and a more general signed model that incorporates both supportive and oppositional interactions. The interaction structures are described by time-varying unsigned and signed digraphs, respectively, in which heterogeneous interpersonal influence is characterized by edge-dependent confidence bounds that naturally encode platform-mediated trust. For the proposed models, rigorous sufficient conditions are established for invariant cluster consensus and structurally balanced polarization. Numerical simulations, including a case study on the Slashdot Zoo signed social network with 50 controversial users, illustrate the theoretical results and demonstrate their relevance for understanding opinion evolution on internet-scale platforms. Full article

This paper presents a digital signal processing (DSP) architecture that decouples the many entangled impairments encountered at the 1000Base-T link startup such as Inter-Symbol Interference (ISI), echo, near-end crosstalk (NEXT), pair swap, line delay skew, etc., and thus considerably simplifies physical layer (PHY) implementation. Code-subset remapping and delay alignment are employed in the DSP such that neither pair swap nor line delay skew would cause tap retraining of the adaptive equalizers during link startup. As a result, the echo and NEXT cancellers and the decision feedback equalizer (DFE) can be implemented in an efficient hybrid FIR architecture that incorporates a compact time-interleaved 4D-LMS circuit with the input delay line shared for both LMS adaptation and FIR filtering, leading to a 25.32% and 37.59% reduction in area and combinational cells, respectively, as synthesized in a 28 nm CMOS standard cell library. Co-simulations of the DSP with a realistic analog front-end (AFE) model show that the system meets BER < 10⁻¹⁰ for 150 m CAT-5 unshielded twisted-pair (UTP) cables while tracking up to ±200 ppm frequency deviation with a critical path of 6 ns. Full article

Background/Objectives: The composition of atherosclerotic plaques is increasingly recognized as a key factor determining cardiovascular risk. Features such as intraplaque hemorrhage, a necrotic lipid core, and the integrity of the fibrous cap are strongly associated with plaque instability and the occurrence of adverse clinical events. Magnetic resonance imaging (MRI) allows for non-invasive characterization of plaque microstructure through quantitative mapping of T1 and T2 relaxation times; however, image noise may limit the accuracy of these measurements. Methods: In this experimental study, a total of 15 ex vivo atherosclerotic plaque samples were imaged using a 1.5T scanner with a fast spin-echo sequence featuring variable repetition times (TR: 200–12,000 ms) and echo times (TE: 21–240 ms) to obtain T1 and T2 maps. An Attention–Residual–Dense U-Net neural network was trained on pairs of noisy and reference images to reduce Rician noise while preserving structural details. Results: The 15 samples examined exhibited T1 values ranging from 1768 to 3294 ms and T2 values ranging from 138 to 202 ms, which were shorter than those for water (T1: 3323 ms; T2: 114 ms), which is consistent with the presence of collagen, lipids, and mineral deposits. Variability among samples reflected differences in composition, with the shortest relaxation times suggesting advanced calcifications. The application of deep learning methods allowed for a threefold improvement in the signal-to-noise ratio (SNR) while preserving the microarchitecture of the lamina. Conclusions: Quantitative T1/T2 mapping combined with deep learning-based image enhancement methods constitutes a robust tool for high-resolution characterization of atherosclerotic plaque composition under ex vivo conditions. The results obtained indicate the potential for translating this method to in vivo studies to better detect tissue heterogeneity and features associated with plaque instability. Full article

The rapid progress of large language models (LLMs) has fostered the development of domain-specific variants in law, medicine, and finance. However, existing legal LLMs still struggle to generate contextually grounded and regulation-compliant responses in complex scenarios of fair competition review. To address this, we present FairAgent, a collaborative multi-agent framework that unifies data refinement and reinforcement learning for legal reasoning. FairAgent integrates two core modules: (1) EchoCourt, a closed-loop data generation and refinement pipeline that constructs high-quality question–answer pairs through generation, critique, and optimization guided by a hierarchical Fairness Knowledge Forest; and (2) a two-stage outcome-based reinforcement learning mechanism that progressively teaches the model to invoke and integrate external retrieval in reasoning. We further enhance learning stability through a RAG-based rollout and retrieval-mask loss. Extensive evaluations demonstrate that FairAgent significantly improves reasoning accuracy, interpretability, and compliance in fair competition review compared with state-of-the-art baselines, establishing a scalable framework for retrieval-augmented legal intelligence. Full article

Background: Parents of school-age children with neurodevelopmental and conduct difficulties face elevated stress, reduced self-efficacy and relational strain, yet evidence for scalable, school-embedded support remains limited. Drawing on mentalization theory—which emphasises parents’ capacity to understand behaviour in terms of underlying mental states—this mixed-methods study evaluated a weekly parent learning group integrating psychoeducation, mentalization-based practice and peer support, delivered within an alternative provision school. Methods: A group of twelve parents who attended at least six sessions completed retrospective pretest–posttest questionnaires assessing parental reflective functioning (PRFQ) and parenting self-efficacy (PSOC). Semi-structured interviews explored parents’ subjective experiences and perceived changes in parent–child interactions and parent–school relationships. Quantitative outcomes were analysed using paired t-tests and effect sizes; qualitative data underwent reflexive thematic analysis. Results: Quantitative analyses revealed statistically significant improvements in parental reflective functioning and self-efficacy. Pre-mentalizing scores decreased substantially (d = 1.34), indicating reductions in non-mentalizing, while interest and curiosity about children’s mental states increased markedly (d = 1.83). Parenting self-efficacy improved significantly (d = 1.61). Although a reduction in excessive certainty about mental states approached significance (d = 0.63, p = 0.053), trends suggested greater epistemic balance. Qualitative analysis identified six themes elucidating mechanisms of change, including enhanced mentalizing capacity, reduced parental stress, transformed parent–child interactions and facilitation style as a critical active ingredient. Integration of findings suggests that psychoeducational content provided conceptual grounding for understanding behaviour, facilitator modelling scaffolded reflective practice, and relational safety within the group enabled authentic engagement with challenging experiences. Conclusions: These preliminary findings indicate that a school-based parent learning group combining psychoeducation, mentalization-based practice and peer support is feasible and associated with meaningful improvements in parental reflective functioning and self-efficacy. Parent narratives of transformed relational practices and shifts from reactive to reflective engagement echo broader literature demonstrating that group-delivered mentalization-oriented programmes can enhance reflective capacities and caregiving quality in diverse family contexts. The school setting may extend the reach of such interventions to families not engaged with clinical services and support collaborative parent–school partnerships. Future research should employ larger, controlled designs, incorporate observational and child outcome measures, and explore scalability across educational contexts. Full article

This article offers a comparative study of two closely linked constellations of early Ṣūfī thought: the ascetic–mystical program of al-Fuḍayl ibn ʿIyāḍ (d. 187/803) and that of his renowned disciple Bishr al-Ḥāfī (d. 227/841). Moving beyond hagiographic anecdote, the study advances the thesis that the pair articulate two complementary modalities of tawba (repentance) that generate distinct ascetic habitus and pedagogical lineages: al-Fudayl’s “ethic of awe” (fear, juridical redress, and renunciation of patronage) and Bishr’s “aesthetics of reverence” (beauty-induced modesty, evident humility, and fame avoidance). Drawing on primary sources (Ḥilyat al-Awliyāʾ, al-Sulamī’s Ṭabaqāt al-Ṣūfiyya, al-Qushayrī’s Risāla, al-Sarrāj’s Lumaʿ), the article reconstructs each thinker’s core concepts, practices (e.g., returning wrongs, ḥafāʾ/barefoot humility), and teaching styles and maps how the teacher–disciple nexus transmits, adapts, and ritualizes these ethics into durable Ṣūfī dispositions. Methodologically, the article combines close textual analysis with practice theory to show how emotions—such as fear and modesty (ḥayāʾ)—are choreographed into public, socially legible acts, thus reframing repentance as embodied discipline rather than interior feeling alone. A prosopographic appendix traces transmission from al-Fudayl to Bishr to Sarī al-Saqaṭī and al-Junayd, clarifying how each modality survives in later Baghdad sobriety and Malāmatī self-effacement. The contribution is twofold: first, it supplies a granular typology of early Ṣūfī repentance that explains divergent stances toward money, publicity, and power; second, it models how to read early Ṣūfī biography as anthropology of practice, recovering the lived grammar by which “conversion stories” become social programs. In doing so, the article nuances standard narratives of early Ṣūfism, showing that Bishr is not merely al-Fuḍayl’s echo but a creative reframer whose “reverential” path complements—rather than imitates—the awe-driven ethic associated with al-Fuḍayl. Full article

Regarding Judah’s exilic realities and forced migration experience, this article proposes that the sage responsible for this poem functioned as a carrier group in articulating a narrative of collective trauma. The paper begins by summarizing key components of cultural trauma theory as developed by Jeffrey C. Alexander. It also situates the shared socio-historical context of the final textual forms of Jeremiah and Proverbs within the exilic/post-exilic realities of the Judahite community. It next traces the trope of wine inebriation across several Jeremiah texts, focusing especially on Jeremiah 25:15–29 to show how this motif is integrally woven into the book’s overarching themes of indictment, judgment, and exile. A conventional wisdom reading of Proverbs 23:29–35 yields a moralistic warning about the self-destructive cycle of wine intoxication of the fools in the book of Proverbs. But a cultural trauma hermeneutic of the poem—when paired with intertextual echoes of Jeremiah 25:15–29—opens the poem to a deeper reading. Within this framework, the sapiential poem emerges as a creative, dramatic and theologically rich act of trauma storytelling, depicting foolish Judah’s metaphorical intoxication as an embodiment of exilic indictment, woes and suffering, yet gesturing toward the possibility of healing and restoration through wisdom reflection and re-narration of their past. Full article

Improving financial time series forecasting presents challenges because models often struggle to identify diverse fault patterns in unseen data. This issue is critical in fintech, where accurate and reliable forecasting of financial data is essential for effective risk management and informed investment strategies. This work addresses these challenges by initializing the weights and biases of two proposed models, Gated Recurrent Units (GRUs) and the Echo State Network (ESN), with different chaotic sequences to enhance prediction accuracy and capabilities. We compare reservoir computing (RC) and recurrent neural network (RNN) models with and without the integration of chaotic systems, utilizing standard initialization. The models are validated on six different datasets, including the 500 largest publicly traded companies in the US (S&P500), the Irish Stock Exchange Quotient (ISEQ) dataset, the XAU and USD forex pair (XAU/USD), the USD and JPY forex pair with respect to the currency exchange rate (USD/JPY), Chinese daily stock prices, and the top 100 index of UK companies (FTSE 100). The ESN model, combined with the Lorenz system, achieves the lowest error among other models, reinforcing the effectiveness of chaos-trained models for prediction. The proposed ESN model, accelerated by the Kintex-Ultrascale KCU105 FPGA board, achieves a maximum frequency of $83.5$ MHz and a power consumption of $0.677$ W. The results of the hardware simulation align with MATLAB R2025b fixed-point analysis. Full article

With respect to distributed MIMO radar systems, conventional far-field detection methods fail under near-field conditions due to significant wavefront curvature, which inevitably results in target energy loss and erroneous parameter estimation. To solve this problem, we propose a near-field target detection framework based on range–angle-coupled matching in this study. Firstly, we design the linear frequency modulation by frequency division (FD-LFM) signal. In addition to offering favorable orthogonality and Doppler tolerance, the transmitter of distributed MIMO radar employs a wide beamwidth to mitigate the low scanning efficiency associated with beam positioning in distributed phased array (PA) radar systems. Secondly, we develop a three-dimensional grid-based echo model for near-field targets in range–azimuth–elevation domain. Specifically, we conceive a coherent pulse integration method via multi-dimensional matching, which enables precise delay alignment and echo accumulation across all transmit–receive pairs for accurate near-field target detection. Thirdly, we propose a parallelization scheme for distributed MIMO radar near-field processing. Our proposal not only compensates effectively for spherical wave propagation effects but also achieves real-time processing through GPU acceleration. Finally, our proposed method’s feasibility of high resolution and effectiveness of near-field detection have been verified by field experimental simulation and actual measurement processing results. Full article

In order to achieve 0.1 m resolution and fully polarimetric observation capabilities for airborne SAR systems, the adoption of stepped-frequency modulation waveform combined with the polarization time-division transmit/receive (T/R) technique proves to be an effective technical approach. Considering the issue of range resolution degradation and paired echoes caused by multichannel amplitude–phase mismatch in fully polarimetric airborne SAR with 0.1 m resolution, an amplitude–phase error estimation algorithm based on echo data is proposed in this paper. Firstly, the subband amplitude spectrum correction curve is obtained by the statistical average of the subband amplitude spectrum. Secondly, the paired-echo broadening function is obtained by selecting high-quality sample points after single-band imaging and the nonlinear phase error within the subbands is estimated via Sinusoidal Frequency Modulation Fourier Transform (SMFT). Thirdly, based on the minimum entropy criterion of the synthesized compressed pulse image, residual linear phase errors between subbands are quickly acquired. Finally, two-dimensional cross-correlation of the image slice is utilized to estimate the positional deviation between polarization channels. This method only requires high-quality data samples from the echo data, then rapidly estimates both intra-band and inter-band amplitude/phase errors by using SMFT and the minimum entropy criterion, respectively, with the characteristics of low computational complexity and fast convergence speed. The effectiveness of this method is verified by the imaging results of the experimental data. Full article

Background/Objectives: Accurate visualization of the Achilles tendon enthesis is critical for distinguishing mechanical, degenerative, and inflammatory pathologies. Although ultrasonography is the first-line modality for suspected enthesis disease, recent technical advances may expand the role of magnetic resonance imaging (MRI). This study evaluated the utility of ultra-short echo time (UTE) and zero echo time (ZTE) sequences versus proton density-weighted imaging (PD-WI) for depicting the enthesis organ in healthy volunteers. Methods: In this institutional review board (IRB)-approved prospective single-center study, 50 asymptomatic adult volunteers underwent 3-Tesla hindfoot MRI with fat-suppressed PD-WI, UTE, and ZTE between 2018 and 2023. Four radiologists assessed image quality, signal-to-noise ratio, visibility, and abnormal high signal intensities (SIs) of the periost, sesamoid, and enthesis fibrocartilages (PCa, SCa, and ECa, respectively). Statistical tests included Chi-square, McNemar, paired Wilcoxon, and Benjamini–Hochberg adjustments for multiple comparisons. Results: The median age was 36 years (range: 20–51); 58% women were included. PD-WI and ZTE sequences were always available while UTE was unavailable in 24% of patients. PD-WI consistently failed to concomitantly visualize all fibrocartilages. ZTE and UTE visualized all fibrocartilages in 72% and 92.1% of volunteers, respectively, with significant differences favoring ZTE and UTE over PD-WI (p < 0.0001) and UTE over ZTE (p = 0.027). Inter-rater agreement exceeded 80% except for SCa on ZTE (68%, 95%CI: 53.2–80.1). Abnormal SCa findings in asymptomatic patients were more frequent with UTE (23.7%) and ZTE (34%) than with PD-WI (2%) (p = 0.0045). Conclusions: At 3-Tesla, UTE and ZTE sequences reliably depict the enthesis organ of the Achilles tendon, outperforming PD-WI. However, the high sensitivity of these sequences also presents challenges in interpretation. Full article

Synthetic aperture radar (SAR) is an all-weather and all-day imaging technique for Earth observation. Achieving efficient observation, high resolution, and wide swath coverage have remained critical development goals in SAR technology, which inherently require extended synthetic aperture time. However, various non-ideal factors, including atmospheric disturbances, orbital perturbations, and antenna vibrations. degrade imaging performance, causing defocusing and ghost targets. Furthermore, the long synthetic time and large imaging swath further enlarge the temporal and spatial variability of these factors and seriously degrade the imaging effect. These inherent challenges make autofocusing indispensable for SAR imaging with a long synthetic time and large swath. In this paper, a novel autofocus method specifically designed to address these non-ideal factors is proposed for SAR imaging with a long synthetic time and large swath. The innovation of the method mainly consists of two parts. The first is the autofocus for multiple non-ideal factors, which is accomplished by an improved phase gradient autofocus (PGA) equipped with amplitude error estimation and discrete windowing. PGA with amplitude error estimation can solve the problem of defocus, and discrete windowing can focus the energy of paired echoes. The second is an error fusion and interpolation method for a long synthetic time and large swath. This method fuses errors among sub-apertures in the long synthetic time and can fulfill autofocus for blocks where strong scatterers are not sufficient in the large swath. The proposed method can effectively achieve SAR focusing with a long synthetic time and large swath, considering spatial and temporal variant non-ideal factors. Point target simulations and distributed target simulations based on real scenarios are conducted to validate the proposed method. Full article

Photoinduced charge separation at donor–acceptor composites (active layer material of organic solar cells) is an important step of photoelectric energy conversion. It results in the formation of the interfacial charge-transfer state (CTS), which is a Coulombically bound electron-hole pair. We developed the mathematical procedure of direct quantification of the electron-hole distance on the basis of time-domain pulse electron paramagnetic resonance data, obtained in an electron spin echo (ESE) experiment. For an ensemble of CTSs characterized by a distribution of electron-hole distances, this procedure derives the average electron-hole distance without numerical simulation of the experimental data, which is a superposition of the oscillating functions, corresponding to CTSs with a certain electron-hole distance. This procedure was tested on model distance distributions, yielding very accurate results. The data for highly efficient organic photovoltaic composite PM6/Y6 were also analyzed; the average electron-hole distance within the CTS and its dependence on temperature were determined. This procedure can be useful for tracing small changes in CTS structure during optimization of the donor–acceptor composite morphology, which is tightly related to the photovoltaic efficiency of the composite. Full article