Search Results (17,951)

Photocatalytic water splitting is considered a promising pathway for hydrogen production. However, the practical application of this process is hindered by various challenges, including (i) rapid charge carrier recombination, (ii) photocorrosion, and (iii) particle aggregation, which collectively limit the efficiency and stability of photocatalysts. Herein, cadmium sulfide (CdS) has gained significant attention as a visible-light-active photocatalyst due to its suitable band gap and favorable band edge positions. Nevertheless, pristine CdS suffers intrinsically from ultrafast charge recombination and severe photocorrosion under illumination, restricting its practical application. This review provides critical insights into the design and performance of pristine, binary, and ternary CdS-based photocatalysts for efficient water splitting. This review comprehensively surveys density functional theory (DFT) studies elucidating the effects of defect engineering, cocatalysts, heterojunctions, and CdS-related systems, including CdS/polymer composites, CdS/MOFs, and other hybrid structures, on photocatalytic performance. Furthermore, we examine the emerging role of machine learning (ML) in accelerating the discovery and optimization of CdS-based systems through predictive modeling and high-throughput screening. We conclude by identifying critical research gaps and offering future recommendations, emphasizing the integration of DFT and ML within closed-loop experimental frameworks to rationally design stable, high-performance CdS photocatalysts for practical solar hydrogen production. Full article

In high-density cities, limited roof and ground areas constrain conventional photovoltaic (PV) deployment. Vertical bifacial photovoltaic (bPV) modules offer an alternative by making good use of building and infrastructure surfaces while harvesting irradiance on both sides. This study develops an integrated module-level framework for evaluating tilted and vertical bPV modules. It couples two-sided anisotropic irradiance calculations with five-parameter electrical and steady-state thermal models. Unlike irradiance-only or configuration-specific assessments, the framework consistently compares bPV and monofacial PV (mPV) modules across tilt and azimuth configurations while jointly quantifying power output, module temperature, bifacial gain, and angular losses. Predicted power output agreed well with outdoor measurements across four representative mounting configurations, and annual predictions were comparable to PVsyst and SAM results. Applied to Hong Kong, the framework identified optimum tilt angles of approximately 20° for bPV and 18° for mPV modules. A vertical west-facing bPV module achieved 96.3% of the annual energy yield of optimally tilted mPV, with a bifacial gain of 67.2% and an angular-loss-related power loss of 4.8%. These results show that vertical bPV can approach optimally tilted mPV performance while utilizing otherwise unused building surfaces, supporting preliminary design decisions in land-constrained cities. Full article

This work presents a laboratory prototype of an Electron-Bombarded Active Pixel Sensor (EBAPS) to investigate the effects of passivation film thickness on electron energy loss and bombardment gain. Through combined experimental characterization and numerical simulations, we systematically examine the correlations among accelerating voltage, passivation layer thickness, electron gain, and dead-layer energy dissipation. Experimental results show that the fabricated EBAPS achieves a spatial resolution of 18 lp/mm at an accelerating voltage of 8000 V. Reducing the passivation layer thickness from 70 nm to 30 nm decreases dead-layer energy loss from 2000 eV to 1000 eV. An optimized Monte Carlo model is developed to simulate electron penetration behaviors under different thicknesses and voltages, and its predictions are in good agreement with experimental data. This study confirms that thinning the passivation layer effectively lowers the required bombardment voltage and improves the long-term operational reliability of EBAPS devices. These findings offer both experimental evidence and theoretical guidance for substrate thinning and surface modification strategies in EBAPS development. Full article

The transition toward renewable energy has accelerated globally in response to climate commitments and the need for sustainable energy systems, raising important questions about its fiscal implications. Focusing on India, this study investigates the relationship between renewable energy consumption and public debt while controlling for key macroeconomic factors, including economic growth, inflation, and non-renewable energy consumption. Using annual data from 1992 to 2022, the analysis employs the Autoregressive Distributed Lag (ARDL) model and Dynamic ARDL simulations to examine both short-run and long-run dynamics. In addition, Kernel-based Regularized Least Squares (KRLS) is applied to explore heterogeneous marginal effects and potential nonlinearities in the relationship between renewable energy expansion and government debt. The results reveal a time-dependent relationship between renewable energy consumption and public debt. In the short run, renewable energy expansion contributes to a reduction in public debt through efficiency gains and reduced dependence on fossil fuels. However, in the long run, renewable energy consumption exerts a positive and statistically significant impact on government debt, reflecting the substantial investment requirements associated with renewable energy infrastructure development. Economic growth consistently reduces public debt, while inflation provides only temporary relief in the short term. Robustness checks using FMOLS and DOLS confirm the stability of the long-run estimates. These findings highlight the importance of integrating renewable energy policies with prudent fiscal planning and expanding private investment mechanisms to support sustainable energy transitions. Full article

Stock markets are complex adaptive systems whose feature dependencies evolve over time, and identifying which signals genuinely drive returns is fundamentally a problem of information. Traditional feature selection methods and linear models rely on statistical correlations and overlook causality, which weakens their predictive performance in non-linear, evolving markets. This paper develops an information-theoretic approach to causal feature selection based on Markov Blanket discovery. We apply the Iterative Parent–Child-based search of Markov Blanket (IPCMB), whose conditional-independence tests are conditional mutual information measures, to recover the Markov Blanket of next-month returns—the minimal feature set that carries all Shannon information about the target. We then pair it with a Classification and Regression Tree (CART), whose impurity-based splitting admits an information-gain interpretation, to predict the direction of Chinese A-share returns non-linearly. Using data on 2760 stocks, IPCMB selects 13 causal features from 72 candidates, and CART forecasts whether the next month’s return is positive. The empirical results show an accuracy of 58.0%, 7.7 percentage points above the all-features CART benchmark, and a 13-month cumulative return 35.88% higher than that of the CSI 300 index. The findings indicate that selecting features according to their causal information content and combining them with interpretable tree-based prediction can support more reliable investment decisions in emerging markets. Full article

With advances in emerging material technologies, intelligent reflecting surface (IRS)-assisted vehicular networks have been gaining growing interest. By adaptively shaping the wireless propagation environment, IRSs can improve vehicular network quality of service (QoS). However, most IRS-assisted vehicular network studies are limited to individual RF or VLC frameworks, while only a few investigate IRS-assisted aggregated VLC-RF vehicular networks that combine wide RF coverage with high VLC data rates. In this paper, aggregated VLC-RF vehicular networks are supported by both optical IRSs (OIRSs) and RF IRSs, and a resource allocation scheme is developed to improve the total achievable rate. First, we establish a system model for IRS-assisted aggregated VLC-RF vehicular networks, and then formulate a problem to maximize the total achievable rate. Furthermore, we decompose the maximization of the total achievable rate into five subproblems and solve them iteratively via an efficient alternating optimization scheme based on block coordinate descent (BCD). Moreover, simulation results validate the convergence and efficiency of our algorithm, while highlighting the effects of crucial parameters on system performance, providing valuable insights for resource allocation in IRS-assisted aggregated VLC–RF vehicular networks. Full article

Sustainable agriculture requires simultaneously increasing food production and mitigating climate change, yet the extent to which crop improvement strategies deliver co-benefits at regional scales remains poorly understood. Improving radiation-use efficiency (RUE) has been widely proposed as a pathway to increase crop productivity, but its potential benefits such as soil organic carbon (SOC) sequestration are not well understood. Here, we developed a hybrid modeling framework that integrates a process-based agricultural system model (APSIM) with machine learning to capture genetic × environment × management (G × E × M) interactions and their effects on crop yield and SOC dynamics across the North China Plain. The results show that improving RUE increases both crop yields and SOC, but the magnitude of these benefits is strongly modulated by nitrogen inputs and varies widely across the region. In the future period (2021–2060) under a moderate-emissions scenario SSP2-4.5, increasing RUE of current cultivars by 10% and 20% led to additional wheat yield gains of 1.1 (+16%) and 1.8 t ha⁻¹ (+26%) and maize gains of 0.8 (+11%) and 1.1 t ha⁻¹ (+14%), respectively. These productivity gains also translated into an increase in SOC sequestration (+10% and +26%, respectively), as a consequence of enhanced carbon inputs. Notably, the coupling between yield gains and SOC sequestration varied substantially across the region, indicating spatially differentiated benefits. Our results highlight that improving RUE can contribute to both productivity and soil carbon gains, but these co-benefits are not universal and depend on local environmental and management contexts. This study provides a scalable and feasible approach for evaluating crop improvement strategies and their environmental consequences represented by SOC dynamics, as well as demonstrate that RUE improvement offers great opportunities for sustainable agriculture. Full article

Contact electrification (CE), also referred to as triboelectrification, describes electron transfer occurring at the interface of dissimilar materials. Its microscopic mechanism remains unclarified due to the complex coupling of multiple physical fields, yet the rapid development of triboelectric nanogenerators (TENGs) has rendered CE a prominent research hotspot in tribology on account of its promising application prospects. Metal/polymer combinations have been widely employed for CE research due to their significant differences in electron gain and loss. Nevertheless, most existing studies focus solely on saturated polymers, and systematic comparative analyses between saturated and unsaturated molecular structures are rarely reported. Accordingly, the intrinsic microscopic origin of enhanced interfacial electrification performance induced by unsaturated groups has not been fully understood. In this work, first-principles calculations based on density functional theory (DFT) are implemented to establish interfacial models consisting of an Al substrate and three types of PTFE single chains: fully saturated-PTFE, PTFE with unsaturated bonds at the chain terminus, and PTFE with unsaturated bonds in the middle of the chain. The inherent mechanism governing the modulation of CE behaviors by unsaturated structures are comprehensively revealed from multiple perspectives, including charge transfer, electrostatic potential, and frontier orbital distribution. Computational results demonstrate that unsaturated groups drastically elevate local electrostatic potential and strengthen the electron-trapping capability of molecular chains, thereby substantially boosting CE performance. Moreover, this modulation effect exhibits remarkable position dependence, where unsaturated structures located in the middle of molecular chains deliver better performance improvement than terminal unsaturated moieties. The electron-donating and electron-accepting properties of materials are dominated by the energy level characteristics of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), respectively. This study elucidates the microscopic mechanism of CE at unsaturated polymer/metal interfaces at the molecular scale, and provides theoretical support for optimizing the output performance of TENGs through surface modification strategies. Full article

Energy detection is widely used in cooperative spectrum sensing because it requires little prior information about the primary signal, but its performance is sensitive to node-dependent noise power and the signal-to-noise ratio (SNR). This paper proposes a dynamic dual-threshold method under bounded noise power uncertainty. For each sensing node, the local energy statistic is modeled under the idle and occupied hypotheses, and a Bayes-optimal one-sided threshold is evaluated for every admissible noise power value in a multiplicative interval. The lower and upper thresholds are defined as the minimum and maximum of these candidate thresholds. Observations outside the interval are transmitted as one-bit hard decisions, whereas uncertain region observations are normalized, uniformly quantized, and combined at the fusion center by equal gain fusion. Controlled simulations at a matched global false alarm probability show that a well-calibrated fixed dual-threshold benchmark can be competitive near its design point, while node-specific dynamic adaptation becomes advantageous as the uncertainty level increases. The uncertain region reporting probability rises with the uncertainty bound, making the robustness–reporting tradeoff explicit. The results support dynamic threshold adaptation under moderate or relatively large noise power uncertainty and clarify its associated communication cost. Full article

Biomedical vision–language models increasingly support image-grounded clinical dialogue, yet most deployable systems still depend on autoregressive language generation. Such systems tend to truncate answers, react poorly to length instructions, and offer no principled way to signal uncertainty when image evidence is weak. We present MedDiffVL, a biomedical vision-language model that pairs a masked language diffusion backbone with a SigLIP-2 visual encoder and a multimodal alignment pipeline that injects modality and question-type cues. Three inference-time mechanisms target the failure modes of diffusion-based generators in the clinical setting. An adaptive confidence-guided remasking rule uses a time-aware threshold and a short-window stability check to remove repetitive low-quality candidates. A clinically aware length controller selects a target length from question type, modality, and an internal uncertainty estimate. A reliability gate combines visual-evidence and answer-confidence scores to emit, hedge, or escalate a response. On VQA-RAD, SLAKE, and PathVQA, the model reaches 85.42, 92.78, and 94.91% closed-form accuracy and an overall conversation score of 53.42 against a fixed reference. Token repetition falls from 0.18 to 0.06. An ECE falls from 0.137 to 0.034, but this reflects an ECE-surrogate training loss and is not independently validated. These gains are not uniform. The closed-form gains over the prior diffusion model lie within run-to-run variance, and latency stays higher than autoregressive baselines. The main contribution is controllability and reliability-aware decoding, not higher closed-form accuracy. The results indicate that confidence-guided masked diffusion with reliability-aware decoding is a useful direction for controllable and reliability-aware clinical assistants. Full article

High penetrations of renewable generation are increasing the need for long-duration energy storage capable of intertemporal balancing and reserve provision. However, the market value of heterogeneous storage portfolios under shared grid-connection constraints remains insufficiently quantified. This study develops a risk-averse day-ahead co-bidding model for a hybrid pumped-hydro and compressed-air energy storage (CAES) portfolio participating jointly in energy and spinning-reserve markets. Monte Carlo sampling and scenario reduction are used to represent price uncertainty, while conditional value-at-risk (CVaR) captures downside-profit risk. Shared point-of-common-coupling (PCC) constraints explicitly couple electricity sales, purchases, and reserve offers. Compared with homogeneous pumped-hydro expansion, replacing the equivalent incremental pumped-hydro capacity with CAES increases the cumulative reserve bid by 65.71%, while expected profit decreases by 1.17% and raw-scenario back-test CVaR remains nearly unchanged, decreasing by only 0.05%. Relative to the unconstrained hybrid-storage case, the shared PCC constraints reduce expected profit, raw-scenario back-test CVaR, and reserve bids by 1.01%, 1.34%, and 18.62%, respectively. Scenario-reduction sensitivity and synthetic price–spread analyses indicate that the main operating mechanisms remain stable within the assumed scenario-generation framework, while sensitivity analyses reveal diminishing returns from CAES expansion and saturation of PCC-related profit gains near 5000 MW. Because all price scenarios are synthetic and neither historical nor independent out-of-sample market data are used, these analyses constitute model-based robustness tests rather than seasonal or real-market validation. The findings support the coordinated configuration of heterogeneous storage, grid-interface capacity, and risk preferences, but should be interpreted as market-bidding-level comparative evidence under the adopted equivalent CAES representation rather than as market-specific profitability forecasts. Full article

In this study, an Artificial Neural Network (ANN)-based adaptive DC-link voltage (V_dc) controller is developed for a Parallel Hybrid Active Power Filter (PHAPF). The proposed controller aims to simultaneously determine the DC-link reference voltage (V_{dc_ref}) and the PI controller gains (K_i, K_p) as a function of the operating conditions. Training data for the ANN are obtained from simulations performed in the MATLAB/Simulink environment. Simulations performed using this training set show that adapting the DC-link reference voltage reduces total harmonic distortion (THD) compared to a PHAPF with a fixed V_{dc_ref} and reduces the DC-link voltage at low-power loads, which has the potential to lower switching losses, while adaptive PI gains improve transient behavior after large load changes. Therefore, the two adaptive quantities affect complementary aspects of performance. The trained ANN model is coded in the C programming language and implemented on a microcontroller-based control card. A 5 kVA PHAPF system is designed and fabricated for experimental verification. Experimental results demonstrate that the proposed ANN-based adaptive DC-link voltage control algorithm achieves lower total harmonic distortion (THD) than PHAPFs employing a constant $V_{dc\_ref}$. In addition, reducing the DC-link voltage under low-power operating conditions has the potential to decrease voltage stress across the power switches and reduce switching losses. Furthermore, the proposed ANN-based adaptive DC-link voltage control algorithm exhibits better harmonic suppression performance despite the processing load and filtering delays. Full article

Modern Global Navigation Satellite System (GNSS) architectures incorporate authorized signals like GPS M-code; however, conventional extraction methods suffer from performance degradation under low signal-to-noise-ratio (SNR) conditions and exhibit strong dependence on high-gain antennas and precise synchronization. This paper proposes a machine learning-based end-to-end extraction framework leveraging time-frequency domain feature fusion. This method breaks through the constraint of relying solely on either time-domain or frequency-domain features. It jointly feeds the time-domain waveforms and spectral features of baseband signals into models such as Multi-Layer Perceptron (MLP) and Transformer, enabling automatic learning of the nonlinear time-frequency characteristics of M-code. This approach effectively suppresses interference from P(Y) code sidelobes and fully exploits the information contained in both the main and side lobes of the M code spectrum. Experimental results demonstrate that under the extremely low SNR condition of −10 dB, the extraction accuracy of the proposed method is improved by 13.7% compared with conventional methods. Systematic accuracy–efficiency trade-off analysis shows that the lightweight MLP model achieves comparable accuracy to the complex Transformer model, with only 6.8% of the parameter scale and 6.2 times faster inference speed, making it the most competitive solution for real-time engineering deployment. In the real-world measurement scenario using a 7.5-m antenna, an extraction accuracy of 94.7% is achieved with only 10 ms of small-sample training data. This method significantly enhances the extraction performance of authorized signals under low-SNR non-cooperative reception conditions. Full article

Soybean seed variety identification is essential for seed quality control, germplasm management, and variety authentication. However, few-shot classification remains challenging because different varieties often exhibit highly similar one-dimensional hyperspectral signatures, and labeled samples are limited in practical seed-testing scenarios. This study proposes a Residual Derivative-Guided Spectral Fusion (RDSF) module to improve spectral representation under limited-sample conditions. RDSF uses the raw spectrum and its first- and second-order derivatives to characterize global reflectance patterns, local slope variations, and spectral curvature, respectively. The three representations are processed by separate branches and combined through bounded learnable residual fusion, with the raw spectrum serving as the primary representation and the derivatives providing complementary corrections. As a plug-and-play component, RDSF was integrated into Prototypical Network (ProtoNet), Relation Network (RelationNet), and Model-Agnostic Meta-Learning (MAML). The module was evaluated using spectra from 11,000 individual soybean seeds representing 11 varieties under known-class and strict class-disjoint unseen-class protocols. Under the representative known-class 3-way 10-shot setting with 15 query samples per class, RDSF increased the meta-test accuracy of RelationNet from 0.8898 ± 0.0201 to 0.9184 ± 0.0060. Under the unseen-class protocol, RDSF consistently improved ProtoNet and RelationNet across all evaluated shot settings; the largest gain was observed for ProtoNet in the 5-shot setting, with the meta-test accuracy increasing from 0.8848 ± 0.0253 to 0.9094 ± 0.0080. In contrast, RDSF did not consistently improve MAML under this protocol, indicating that its effectiveness depended partly on the underlying meta-learning mechanism. Ablation experiments and architecture comparisons further showed the complementary contributions of the derivative branches and the advantages of bounded residual fusion over a three-channel architecture and direct feature concatenation. Overall, RDSF provides an effective spectral representation module for metric-based few-shot classification of soybean seed varieties under the evaluated known-class and unseen-class conditions. Full article

Background/Objectives: The hospital discharge report is a critical document for care continuity that generates a substantial administrative burden for clinicians. Generative artificial intelligence (AI) offers the potential to reduce this burden while improving documentary quality. This study aims to compare, under real-world conditions with a GDPR-oriented architecture based on prior local anonymisation, the quality of AI-assisted discharge reports (IAIA) against those drafted by the responsible physician (INF). Methods: A retrospective, paired, expert-evaluation study was conducted at a Spanish university hospital. One hundred and twenty consecutive clinical cases from nine departments were included (240 reports total). Each case was independently evaluated by one of ten primary care physicians using a structured rubric covering 13 clinical dimensions (ordinal scale 1–3) and a global rating scale (1–10). The Wilcoxon signed-rank test was applied to all paired comparisons; effect size was estimated using the paired rank-biserial correlation (r). Results: IAIA achieved a significantly higher overall mean rating than INF (8.14 vs. 7.30 out of 10; p < 0.0001; r ≈ 0.76, large effect). IAIA was nominally superior in 9 of 13 clinical dimensions; after Bonferroni correction for the 13 per-dimension comparisons, six of these differences remained statistically significant, with the largest gains in family history, principal diagnosis hierarchy, and structured listing of secondary diagnoses. INF retained an advantage only in allergies and intolerances (2.69 vs. 2.46; p = 0.002), where IAIA tended to use generic formulas. Three dimensions showed no significant difference (prior treatment, physical examination, procedures). Conclusions: AI-assisted discharge reports received higher expert-rated documentary quality scores in a non-blinded paired evaluation across most evaluated dimensions. The physician-written report retained an advantage only in the safety-critical allergy domain, where allergy information must not be inferred by the model but sourced from verified structured fields or explicitly flagged as pending physician validation, supporting the need for a supervised hybrid model in which AI generates the initial draft while the clinician mandatorily validates sensitive content. Prior local anonymisation constitutes a GDPR-oriented approach to generative AI deployment in European hospital settings, substantially reducing the risk of disclosure of identifiable clinical information. Full article