Search Results (905)

Vector hydrophones play an extremely important role in marine exploration. How to reduce the size of vector hydrophones while improving their directional detection capability is a critical issue that needs to be addressed. The auditory organ of the fly Ormia ochracea represents a prime example of achieving high-resolution directional detection within a compact size range. This paper proposes a vector hydrophone that integrates an Ormia ochracea fly-inspired mechanically coupled structure with an optical fiber vibration sensing structure, offering advantages of small size and strong electromagnetic interference immunity. The hydrophone demonstrates a good response to acoustic pulse trains and can accurately demodulate acoustic waves from 1 kHz to 10 kHz. Directional response experiments show that this hydrophone can significantly amplify the time delay differences of incoming acoustic waves. At an acoustic frequency of 9.25 kHz, the time delay amplification factor reaches approximately 50 times within the range of −90° to +90°, exhibiting good cosine directionality. Full article

Contemporary surveys in the search for extraterrestrial intelligence (SETI) typically make one-off “spot scans” across the sky to search planetary systems for narrow-band radio signals that would indicate the presence of intelligent life. Spot scans may span a duration of seconds to minutes in order to observe a large number of targets with limited resources, but such a strategy does not necessarily consider the timing of exactly when to listen for extraterrestrial signals. Several ideas for possible time markers were suggested in the first few decades of SETI, such as the use of recurrent supernovae, gamma ray bursts, or pulsars as a way of establishing directionality and attracting attention toward an extraterrestrial beacon. Civilizations in binary systems might even choose the points of periastron and apastron in its host system to send transmissions to other single-star civilizations. However, all of these timing considerations were developed prior to the age of exoplanets, which enables a more detailed assessment of targets suitable for SETI. This paper suggests SETI strategies for circumbinary and circumprimary planets based upon the timing of orbital events in such systems. Events such as orbital extremes could represent a logical time marker for extraterrestrial civilizations to transmit, if they desire to be detected. Likewise, a transiting binary pair with inhabited planets around each star could yield maximum detectability of leakage radiation when both stars eclipse within our field of view. As planets in binary systems continue to be discovered, limited-duration SETI surveys should selectively target such systems based upon the occurrence of reasonable time markers. Full article

Breaking the maize yield plateau necessitates the development of density-tolerant varieties, for which recurrent selection is a key breeding strategy. However, a systematic understanding of how the interaction between selection density (parental screening environment) and evaluation density (variety testing environment) modulates genetic gain remains a critical knowledge gap. This study aimed to systematically elucidate this interaction and its impact on genetic gain and combining ability. We established two F2 base populations from distinct heterotic groups: Zheng 58 × LH196 (Stiff Stalk, SS) and Chang 7-2 × MBUB (Non-Stiff Stalk, NSS). Through bulk selection, we advanced populations for three cycles (C0, C2, C4) under three selection densities: low (60,000 plants/ha), medium (90,000 plants/ha), and high (120,000 plants/ha). Hybrids were generated using a double tester design and evaluated in multi-environment trials at Shijiazhuang in Hebei province and Xinxiang in Henan province in 2023 across matching density gradients. We employed analysis of variance (ANOVA) and general combining-ability (GCA) estimates to assess the genetic gains for yield and combining ability across 14 parental materials and 28 hybrids. Our results demonstrate that density compatibility between selection and evaluation environments is paramount. Genetic gain decreased by 0.89–26.52% with a density discrepancy of >30,000 plants/ha and plummeted by 19.71–77.44% when the discrepancy exceeded 60,000 plants/ha, underscoring the necessity of aligning selection density with the target environment. Under matched densities, population yield increased significantly with escalating density, with the high-density selection regime showing a maximum yield improvement of 53.78% from C0 to C4. Materials selected under high density exhibited superior performance and significantly higher combining ability (averaging a 238.35% increase) and genetic gain (averaging a 263.39% improvement) in medium-to-high-density environments, confirming strong positive selection pressure. Conversely, materials from low-density selection processes were better adapted to environments of ≤60,000 plants/ha. This study provides a crucial theoretical and practical foundation for establishing density-optimized recurrent breeding systems to directionally enhance genetic gain in maize. Full article

Background/Objectives: Previous studies suggest that nutrient deficiencies can alter immune responses in animals. However, the impact of micronutrients on autoimmune diseases like type 1 diabetes (T1D) in humans remains unclear since the described associations are based on observational data and they cannot establish causality. This study aims to examine the causal relationship between various micronutrients and T1D using Mendelian randomization (MR). Methods: We performed a two-sample MR analysis using genetic variants from genome-wide association studies (GWASs) of 17 micronutrients as instrumental variables (IVs). We analyzed T1D GWAS datasets of European (18,942 cases/520,580controls), multi-ancestry (25,717 cases/583,311 controls), Latin American/Hispanic (2295 cases/55,134 controls), African American/Afro-Caribbean (6451 cases/109,410 controls), and East Asian (1219 cases/132,032 controls) ancestries. We applied the inverse variance weighted (IVW) method in our main analysis, and additional MR estimators (MR-Egger, weighted median, weighted mode, MR-PRESSO) to address pleiotropy, and the Steiger test to test directionality in sensitivity analyses. Results: Following Bonferroni correction (p < 0.05/17), we found positive association between potassium levels and T1D risk (OR = 1.098, 95% CI [1.075, 1.122] p = 5.5 × 10⁻¹⁸) in the multi-ancestry analysis. Zinc, vitamin B12, retinol, and alpha tocopherol showed nominal associations. Vitamin C, D, K1, B6, beta- and gamma-tocopherol, magnesium, iron, copper, selenium, carotene, and folate showed no significant effects on T1D risk. For the multi-ancestry analysis, we had sufficient power to detect ORs for T1D larger than 1.065. Conclusions: Higher serum potassium levels were associated with increased T1D risk in our MR study, though supporting observational evidence is currently limited. Other micronutrients are unlikely to have large effects on T1D. Full article

Effective detection of acoustic signals plays a crucial role in numerous fields, including industrial equipment fault prediction and environmental monitoring. Acoustic sensing technology, owing to its substantial information carrying capacity and non-contact measurement advantages, has garnered widespread attention in relevant applications. However, the effective detection of weak target acoustic signals amidst strong noise interference remains a critical challenge in this field. The core bottleneck lies in the difficulty of traditional detection methods to simultaneously achieve both high sensitivity and high directionality. To address this limitation, this work proposes a line-defect phononic crystal (PnC) structure that enables directional enhancement and detection of weak target signals under intense spatial noise interference by coupling defect state localization characteristics with anisotropy mechanisms. Through theoretical derivation and finite element numerical simulation, the directional enhancement properties of this structure were systematically validated. Furthermore, numerical simulations were conducted to validate the detection of weak harmonic signals and weak bearing fault signals under strong spatial noise interference. The results demonstrate that this line-defect phononic crystal (PnC) structure exhibits high feasibility and outstanding performance in detecting weak acoustic signals. This work provides novel insights for developing new acoustic detection methods combining high sensitivity with high directivity, showcasing unique advantages and broad application prospects in acoustic signal sensing, enhancement, and localization. Full article

This study presents a novel approach to financial time series forecasting by introducing asymmetric loss functions. This is specifically designed to enhance directional accuracy across major stock indices (S&P 500, DJI, and NASDAQ Composite) over a 33-year time period. We integrate these loss functions into an attention-based Long Short-Term Memory (LSTM) framework. The proposed loss functions are evaluated against traditional metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and other recent research-based losses. Our approach consistently achieves superior test-time directional accuracy, with gains of 3.4–6.1 percentage points over MSE/MAE and 2.0–4.5 percentage points over prior asymmetric losses, which are either non-differentiable or require extensive hyperparameter tuning. Furthermore, proposed models also achieve an F1 score of up to 0.74, compared to 0.63–0.68 for existing methods, and maintain competitive MAE values within 0.01–0.03 of the baseline. The optimized asymmetric loss functions improve specificity to above 0.62 and ensure a better balance between precision and recall. These results underscore the potential of directionally aware loss design to enhance AI-driven financial forecasting systems. Full article

Most graph learning methods remain limited to undirected, pairwise interactions, restricting their ability to capture the multi-entity and directional relationships common in real-world systems. We propose the Directed Higher-Ordered Neural Network (HONN) framework that introduces directionality into hypergraph learning through flexible spectral Laplacian formulations. Unlike fixed-Laplacian methods such as the Generalized Directed Hypergraph Neural Network (GeDi-HNN), a tunable q-parameter in our framework balances local identity preservation with global diffusion, enabling robust and generalizable feature propagation. Experiments on five benchmark datasets show that HONN consistently matches or outperforms state-of-the-art baselines, achieving 84% on NTU-2012, 87.4% on WebKB Texas, and 86.2% on Cornell, while maintaining computational efficiency. Ablation studies confirm the crucial role of Laplacian selection, activation functions, and q-tuning in shaping model performance. By unifying directionality and higher-order reasoning, HONN provides a scalable foundation for predictive modeling in domains such as knowledge graphs, spatio-temporal networks, and recommendation systems. Full article

Phased array technology can be realized with directional control with fixed beam steering. However, its directionally dependent beam pattern limits the efficiency of suppressing undesirable distance interference. This paper presents a guided wave frequency diverse array-based damage location method for thin-walled structures. Firstly, a guided wave frequency diverse array signal model is derived with a relatively small frequency increment that can achieve distance–direction two-dimensional focusing. Secondly, three types of receiving arrays, including a monostatic array, following array, and symmetric array, are constructed to achieve the maximum damage-induced signal amplitude. Finally, a two-dimensional multiple signal classification (MUSIC)-based damage location method is applied for damage imaging in thin-walled structures. Simulations on an aluminum plate and the experiments on an epoxy laminate plate demonstrate the validity and effectiveness of the proposed method. Full article

This paper introduces a method for estimating the Downlink Angle of Departure (DL-AoD) of 5G User Equipment (UE) from measured signal strengths of directionally transmitted synchronization signals. Based on estimated DL-AoD values, from two or more anchor nodes, the position of the UE was estimated. Unlike most prior work, which is simulation-based or relies on custom testbeds, this study uses real measurements from an operational 5G network in an industrial factory environment. A deterministic estimator was derived, but multipath and unknown beam characteristics limit its accuracy. To address this, machine learning was applied to automatically adapt to the environment. Previous simulation studies reported 90th-percentile DL-AoD estimation errors below 2°, while experimental works achieved best-case accuracies of 5–6°. In this study, the experimental DL-AoD estimation error remained below 4° for 90% of the measurements, indicating improved real-world performance. Reported positioning errors in the literature range from 3.8 m to 140 m, whereas the 13.2 m error obtained here lies near the midpoint of this range, confirming the practicality of the proposed method in industrial environments. Compared to existing approaches, this work demonstrates high angular accuracy using only sub-6 GHz beams in a realistic industrial scenario without detailed knowledge of antenna beam patterns and channel state. The findings demonstrate that standard 5G signals can provide accurate indoor localization without additional infrastructure, offering a practical path toward cost-effective positioning in industrial IoT and automation. Full article

Preschool children develop essential mathematical concepts through play, yet little is known about how traditional board games can support this process. This small-scale microgenetic case study investigates how children unfamiliar with the Ethiopian game Gebeta (a mancala-type game) learn to play the game and what mathematical competencies emerge. Video observations were conducted with 5-year-olds in Norway as they engaged in repeated play sessions. Event logs and transcripts were analysed with a focus on children’s developing strategies and difficulties. The results show that children quickly understood the basic rules but faced challenges with directionality (i.e., maintaining the correct direction of play) and differentiating between different types of game moves. Over time, they demonstrated evident progress in following the rules. They began to treat groups of counters as units, moving from one-by-one counting to bundling and unitising. These developments align with Bishop’s fundamental activities locating and counting. We conclude that Gebeta provides a playful context in which children can practice directionality and bundling, both of which are crucial for avoiding common errors in later school mathematics, such as off-by-one counting errors and misunderstandings of the number system. The study suggests that Gebeta can be introduced in early childhood settings without simplifying the rules, providing an engaging resource for early mathematics education. Full article

We investigate the robustness of transcript-based estimators for properties of interactions against various types of noise, ranging from colored noise to isospectral noise. We observe that all estimators are sensitive to symmetric and asymmetric contamination at signal-to-noise ratios that are orders of magnitude higher than those typically encountered in real-world applications. While different coupling regimes can still be distinguished and characterized sufficiently well, the strong impact of noise on the estimator for the direction of interaction can lead to severe misinterpretations of the underlying coupling structure. Full article

Rapidly pinpointing the origin of accidental chemical gas releases is essential for effective response. Prior vision pipelines—such as 3D CNNs, CNN–LSTMs, and Transformer-based ViViT models—can improve accuracy but often scale poorly as the temporal window grows or winds meander. We cast recursive backtracking of concentration fields as a finite-horizon, multi-step spatiotemporal sequence modelling problem and introduce Recursive Backtracking with Visual Mamba (RBVM), a Visual Mamba-inspired, directionally gated state-space backbone. Each block applies causal, depthwise sweeps along ${\mathrm{H}}^{\pm }$, ${\mathrm{W}}^{\pm }$, and ${\mathrm{T}}^{\pm }$ and then fuses them via a learned upwind gate; a lightweight MLP follows. Pre-norm LayerNorm and small LayerScale on both branches, together with a layer-indexed, depth-weighted DropPath, yield stable stacking at our chosen depth, while a 3D-Conv stem and head keep the model compact. Computation and parameter growth scale linearly with the sequence extent and the number of directions. Across a synthetic diffusion corpus and a held-out NBC_RAMS field set, RBVM consistently improves Exact and hit $\le 1$ over strong 3D CNN, CNN–LSTM, and ViViT baselines, while using fewer parameters. Finally, we show that, without retraining, a physics-motivated two-peak subtraction on the oldest reconstructed frame enables zero-shot dual-source localization. We believe RBVM provides a compact, linear-time, directionally causal backbone for inverse inference on transported fields—useful not only for gas–release source localization in CBRN response but more broadly for spatiotemporal backtracking tasks in environmental monitoring and urban analytics. Full article

Background/Objectives: Bladder cancer (BC) carries a substantial global burden. Although lead (Pb) exposure has been linked to cancer, its molecular impact on bladder tumors remains unclear. We asked whether Pb-responsive transcriptional programs are present and clinically relevant in BC by integrating toxicogenomic resources with tumor transcriptomes and whether a composite lead-response score has prognostic value. Methods: Differential expression was performed on TCGA bladder urothelial carcinoma (BLCA) RNA-seq data (tumor vs. normal). Lead-associated genes were curated from the Comparative Toxicogenomics Database (CTD) and tested for over-representation among BLCA differentially expressed genes (DEGs) using a hypergeometric framework, with a stricter |log₂FC| ≥ 1 sensitivity. A tumor-level lead-response score was derived from the Pb–DEG overlap. Associations with overall survival (OS) were assessed using Cox models adjusted for age, sex, and pathological stage; secondary endpoints included PFI/DFI/DSS. Results: Lead-associated genes were significantly enriched among BLCA DEGs (background M = 20,530; K = 2618; n = 11,436; k = 1595; p = 4.21 × 10⁻⁹), and enrichment persisted under |log₂FC| ≥ 1 (n = 4275; k = 698; p = 9.86 × 10⁻¹⁵). Pathway over-representation highlighted synaptic/neuronal-like adhesion and transmission, MAPK-centered signaling, and cell-cycle control. Among top candidates, AQP12B was independently prognostic for OS (HR per 1 SD increase = 0.76; 95% CI 0.63–0.92; p = 0.0038; N = 404). The composite lead-response score showed a directionally protective but non-significant association in multivariable OS models (HR per 1 SD = 0.93; 95% CI 0.81–1.05; p = 0.244), while median-split Kaplan–Meier (KM) curves separated (p = 0.045). Conclusions: Lead-responsive transcriptional programs are detectable in BLCA and intersect adhesion, MAPK signaling, and cell-cycle pathways. AQP12B emerges as a plausible prognostic marker, and a composite lead-response score warrants external validation for risk stratification and clinical translation. Full article

Amidst global imperatives for environmental sustainability, this study investigates the nexus between carbon emissions reduction (CER), ownership structures, and total factor productivity (TFP) in Chinese enterprises—recognized as vital economic drivers facing carbon emissions pressures. Based on the theoretical frameworks of innovation offsets, agency cost theory, and upper echelons theory, with data from CSMAR (2009–2023), we proposed a positive effect of CER on TFP while examining the moderating roles of ownership structure metrics: chairman shareholding ratio, manager shareholding ratio, and ownership–control separation ratio. TFP estimation employed dual approaches: mean consolidation (TFP-Mean) and entropy weighting (TFP-Entropy) methods. The results confirmed CER exerts significantly positive impacts on TFP, with ownership structures demonstrating statistically significant yet directionally heterogeneous moderation effects. Heterogeneity analysis reveals heightened TFP sensitivity to carbon emission initiatives among private enterprises, foreign-owned enterprises, and small enterprises. Notably, the entropy weighting method exhibits substantial comparative advantages in TFP measurement. These findings underscore that advancing TFP necessitates simultaneously optimizing carbon emissions efficiency and ownership governance. Full article

Aluminum alloys under long-term service or repetitive stress are prone to small fatigue cracks (FCs) with arbitrary orientations, necessitating eddy current probes with focused magnetic fields and directional selectivity for reliable detection. This study presents a flexible printed circuit board (FPCB) probe with a double-layer planar excitation coil and a double-layer differential receiving coil. The excitation coil employs a reverse-wound design to enhance magnetic field directionality and focusing, while the differential receiving coil improves sensitivity and suppresses common-mode noise. The probe is optimized by adjusting the excitation coil overlap and the excitation–receiving coil angles to maximize eddy current concentration and detection signals. Finite element simulations and experiments confirm the system’s effectiveness in detecting surface cracks of varying sizes and orientations. To further characterize these defects, two time-domain features are extracted: the peak-to-peak value ($\Delta$P), reflecting amplitude variations associated with defect size and orientation, and the signal width ($\Delta$W), primarily correlated with defect angle. However, substantial overlap in their value ranges for defects with different parameters means that these features alone cannot identify which specific parameter has changed, making prior defect classification using a Transformer-based approach necessary for accurate quantitative analysis. The proposed method demonstrates reliable performance and clear interpretability for defect evaluation in aluminum components. Full article