Search Results (558)

Transparent and stable complementary inverters that can simultaneously support analog amplification and digital oscillation under optical stimulation are highly demanded for future see-through optoelectronic systems such as smart windows, augmented-reality interfaces, and on-skin photonic sensors. In this work, we report fully transparent, low hysteresis complementary inverters composed of a p-channel single-walled carbon nanotube (SWNT) thin-film transistor (TFT) with a top-gate iCVD pC1D1 polymer dielectric and an n-channel indium–gallium–zinc oxide (IGZO) TFT with a bottom-gate Al₂O₃ dielectric, both fabricated on ITO-coated glass substrates. The two TFTs exhibit well-matched output characteristics in opposite carrier polarities, enabling a CMOS-like inverter operation that delivers rail-to-rail switching, high voltage gain and a small hysteresis of less than 200 mV between forward and reverse sweeps. Furthermore, the UV photoresponse of the IGZO channel allows for systematic modulation of the inverter switching voltage (V_M) and small-signal gain with the incident UV intensity. Cascading the proposed inverter into three-, five-, and seven-stage ring oscillators yields stable rail-to-rail oscillation in which the oscillation frequency (f_osc) and the power consumption (P = C_L · V_DD² · f_osc · N) are tunable in real time by the UV intensity, realizing transparent light-to-frequency conversion at the circuit level. These results establish a route to low-power, see-through optoelectronic logic platforms that combine carbon-nanotube and oxide-semiconductor technologies on a common transparent substrate. Full article

Benchmarking deep learning forecasters against classical and physically based numerical baselines remains uncommon in the time-series forecasting literature. Meteorological station networks offer an under-exploited evaluation environment, uniquely providing a physically based climate-model comparator alongside standard baselines. We evaluated eight forecasting approaches—climatology, SARIMA, Random Forest, and five deep learning architectures (TFT, N-HiTS, PatchTST, TiDE, xLSTM)—against bias-corrected output from a five-member CMIP6 ensemble, on 100 meteorological stations across four Western Balkan countries (monthly temperature and precipitation, 1961–2020), using non-parametric significance testing, a rolling-origin backtest (five windows, 2011–2020), and a five-seed robustness check. For temperature, all five deep learning architectures achieved lower MAE than the classical baselines (p < 10⁻⁹⁹), though PatchTST’s advantage over climatology was not significant; the best-performing architecture varied across seeds and evaluation windows, so we characterise a leading cluster (N-HiTS, TFT, TiDE, PatchTST) rather than a single winner. The primary temperature advantage was geographically broad-based, while the comparison against the physical-model baseline was robust to the choice of comparator GCM. For precipitation, by contrast, a simple climatological-mean baseline outperformed all five deep learning architectures with no exception across all five rolling-origin windows. The deep learning advantage over classical and physical baselines is thus variable-specific rather than universal. Meteorological station networks, combined with a physically based climate-model comparator, constitute a well-suited evaluation environment for the broader time series forecasting community. Full article

Metal oxide thin-film transistors (MO TFTs) have drawn wide interest in photodetectors and optoelectronic synaptic devices owing to their wide bandgap, low off-state current, high optical transparency, low-temperature processing, and large-area uniformity. Gate modulation in the TFT structure can tune the channel’s initial state and interfacial electric field, enhancing the tunability of photogenerated carrier transport, defect trapping/release, and interfacial charge regulation. This article reviews the progress of MO TFT photodetectors and optoelectronic synaptic devices, and examines the roles of light absorption, carrier transport, defect-related carrier dynamics, interfacial charge control, and persistent photoconductivity in different device functions. For photodetectors, key goals include broadening the response spectrum, reducing dark current, improving spectral selectivity, and enhancing response stability. For optoelectronic synaptic devices, post-illumination conductance retention and slow relaxation enable memory retention and synaptic weight modulation. Thus, rather than being separate, photodetection and optoelectronic synapses are functional extensions of the MO TFT optoelectronic response under different application targets. This article further discusses the synergy between these two functions in array sensing, visual preprocessing, and intelligent vision systems. Future development requires advances in targeted defect engineering, interface and structure optimization, array uniformity, standardized evaluation, and device–circuit–algorithm co-design for low-power, integrable intelligent vision hardware. Full article

Hierarchical retail forecasting requires predictions that are both accurate and coherent across multiple planning levels, from total demand to individual product–store series. Because these forecasts guide inventory, replenishment, storage, and distribution decisions, improving their coherence and reliability can support more efficient resource use, reduce avoidable overstock and product waste, and limit the need for emergency logistics. This study investigates how global deep-learning architectures interact with post hoc reconciliation in point and probabilistic forecasting. Using an M5-derived hierarchical and grouped structure comprising 42,840 series, we compare three MLP-oriented models, MLP, N-BEATS, and N-HiTS, with five transformer-based models, Transformer, Temporal Fusion Transformer, Informer, PatchTST, and Autoformer. All models are evaluated under a common 28-day forecasting horizon, temporal partition, Optuna-based tuning protocol, and three complete seeded runs. Coherence is imposed using Bottom-Up reconciliation and four MinTrace variants, while probabilistic forecasts are generated through residual-block bootstrap reconciliation. Point and probabilistic performance are assessed level-wise and globally using MASE and scaled CRPS, respectively. The results show that the strongest transformer-based combination outperforms the strongest MLP-based combination at every hierarchy level. PatchTST combined with MinTrace-WLS-struct is particularly effective at aggregate and intermediate levels, achieving a Total-level MASE of 0.537 and sCRPS of 0.037. At the Product–Store level, Bottom-Up reconciliation becomes preferable, with the Transformer attaining the lowest MASE of 1.367 and sCRPS of 0.912. Because granular series dominate the hierarchy-wide average, the lowest overall MASE is obtained by TFT with Bottom-Up reconciliation (1.428), whereas the lowest overall sCRPS is shared by the Transformer and Informer with Bottom-Up reconciliation (0.813). These findings demonstrate that neither the forecasting architecture nor the reconciliation method should be selected independently of hierarchy depth and forecasting objective. Strategic and tactical levels benefit primarily from PatchTST with MinTrace reconciliation, whereas highly granular operational forecasting favors Bottom-Up reconciliation with transformer-based models. From a sustainability perspective, this level-aware framework provides a basis for aligning forecasting decisions with resource efficiency, waste reduction, service reliability, and greater resilience across the retail supply chain. Full article

Zn–Sn–O (ZTO) thin-film transistors (TFTs) are promising indium-free oxide semiconductor devices, but their electrical stability is limited by oxygen-related defect states. In this study, Ga and Si incorporated ZTO TFTs were systematically compared using an identical bottom-gate top-contact device architecture to investigate dopant-dependent defect modulation and bias-temperature stability. Both Ga and Si incorporation induced a positive threshold-voltage shift and reduced the relative contribution of oxygen-deficient bonding components, suggesting modification of oxygen-related defect environments in the ZTO channel. Optical analysis further showed reduced Urbach energies after dopant incorporation, suggesting a decrease in localized band tail states and reduced structural disorder. Under negative bias temperature stress (NBTS), SZTO exhibited the smallest threshold-voltage shift, demonstrating the most effective stability enhancement. These results indicate that Ga incorporation preserves high field-effect mobility while improving stability, whereas Si incorporation more effectively reduces oxygen-related defect features and provides enhanced NBTS stability. This study provides insight into the dopant-dependent defect engineering for the improved reliability of indium free oxide TFTS. Full article

Photovoltaic (PV) power interval forecasting models are commonly trained on data dominated by non-ramp samples, which may weaken uncertainty characterization during rapid power changes. This study proposes a ramp-aware quantile regression Temporal Fusion Transformer (RQR-TFT) that jointly estimates PV power quantiles and the probability of a future ramp event. Ramp labels are constructed from the normalized power change between adjacent sampling instants. A shared Temporal Fusion Transformer (TFT) encoder extracts temporal representations from historical PV power and meteorological variables, and two output branches perform quantile forecasting and ramp-event identification. Ramp-sample-weighted quantile loss and positive-class-weighted classification loss are jointly optimized to increase the influence of minority ramp samples. The proposed method is evaluated for 4 h ahead forecasting using measurements collected from a 50 MW PV power station during 2019–2020. For the nominal 90% prediction interval, RQR-TFT achieves a ramp-sample prediction interval coverage probability (${\mathrm{PICP}}_R$) of 0.864, an overall prediction interval normalized average width (PINAW) of 0.209, and an overall normalized interval score (NIS) of 0.365. The area under the precision–recall curve for ramp-event identification is 0.906. The results demonstrate improved ramp-sample coverage and overall interval quality, although ramp-sample coverage remains below the nominal level. Full article

Background/Objectives: The CT features of shock thyroid are enlarged thyroid glands with inhomogeneous enhancement and peri-thyroid fluid. However, if peri-thyroid fluid is absent or equivocal, shock thyroid may be confused with multinodular goiter or Hashimoto’s thyroiditis on enhanced chest CT. Thus, we investigated CT characteristics that differentiate shock thyroid from multinodular goiter or Hashimoto’s thyroiditis. Methods: We retrospectively identified 10 patients with shock thyroid who underwent enhanced chest CT and thyroid function tests (TFT) from March 1, 2025, to May 31, 2026. A control group consisted of 21 randomly selected patients with multinodular goiter (n = 10) or Hashimoto’s thyroiditis (n = 11). CT features, including capsular sparing with low attenuation, a prominent vessel sign, and a nodule with a sharp margin, were compared between groups. Results: There was a significant difference in the prevalence of capsular sparing with low attenuation between the shock thyroid and control groups [90.0% (9/10) versus 0.0% (0/21), p < 0.001]. There was also a significant difference in the frequency of a prominent vessel sign between the shock thyroid and Hashimoto’s thyroiditis groups [0.0% (0/10) versus 63.6% (7/11), p < 0.001], and a nodule with a sharp margin between the shock thyroid and multinodular goiter groups [0.0% (0/10) versus 80.0% (8/10), p < 0.001]. Conclusions: CT findings such as capsular sparing with low attenuation accompanied by peri-thyroid fluid collection may represent potential imaging markers of shock thyroid and require validation in a larger multicenter cohort. Full article

Data centers can provide demand-side flexibility by coordinating computing workloads, cooling systems, and on-site energy resources. However, facility demand varies with information technology (IT) load and cooling operation, making a fixed power usage effectiveness (PUE) or a forecast independent of planned controls inconsistent with dispatch. This paper proposes a control-aware, multi-horizon PUE forecasting framework for coordinated data center demand-side management (DSM) and microgrid dispatch. The key idea of this control-aware approach is to forecast PUE using planned workload and cooling schedules as inputs. A power-consistent Temporal Fusion Transformer (PC-TFT) predicts quantiles of non-IT overhead power and rack inlet temperature from telemetry, weather forecasts, admitted requests, and candidate workload and cooling schedules. Facility power and PUE are derived from the algebraic power balance, ensuring consistency among IT, overhead, and facility power and PUE values no lower than 1. Empirical split conformal calibration and temporally dependent scenarios characterize forecast uncertainty. A trajectory-conditioned piecewise-affine control response map with a recursive thermal state links the forecasts to a risk-informed model predictive controller that coordinates workloads, cooling, photovoltaic generation, battery storage, and grid exchange. The proposed framework is validated through EnergyPlus simulations of a Shenzhen data center, coupled with workload and microgrid simulations. Forecasting performance is compared with persistence and matched-input neural baselines, while dispatch is benchmarked against deterministic and oracle controllers. The results demonstrate improved multi-horizon PUE forecasting accuracy and empirical interval calibration, lower operating cost and peak grid demand, higher renewable energy utilization, and fewer service quality violations. These findings indicate that control-aware, power-balance-constrained probabilistic PUE forecasts can provide a reliable basis for coordinated data center DSM and microgrid dispatch. Full article

This study examines whether modern deep learning architectures can improve multi-horizon value-at-risk (VaR) forecasting by learning nonlinear tail-risk dynamics that are difficult to capture with conventional econometric models. Using S&P 500 return data and realized volatility measures, we compare quantile regression (QR), Light Gradient Boosting Machine (LGBM), and five neural architectures—MLP, LSTM, TCN, TiDE, and TFT—within HAR, CAViaR, and realized-volatility-augmented CAViaR specifications across 1% and 5% VaR at 1-day, 5-day, 10-day, and 22-day horizons. Forecast performance is evaluated using pinball loss, formal VaR backtests, and the model confidence set procedure. The results suggest that the performance of deep neural architectures depends on the forecast horizon and the structure of the underlying tail-risk dynamics. In particular, gated memory, attention-based learning, and multi-horizon sequence design appear to improve conditional quantile forecasting by better capturing persistence, nonlinear dependence, and regime-sensitive behavior. At the same time, stronger statistical forecasting accuracy does not automatically imply regulatory validity, since a VaR model must also satisfy formal coverage and independence tests. Overall, the findings highlight the distinction between predictive skill and regulatory adequacy in financial risk measurement. Full article

In this work, a plasma-free atomic layer deposition (ALD)-based H₂O post-treatment method is proposed to precisely modulate hydrogen-related (H-related) traps in indium gallium zinc oxide (IGZO) thin-film transistors (TFTs) by the number of H₂O treatment cycles. Under the optimized condition, the scaled device with a channel length of 70 nm exhibits a near-ideal subthreshold swing of 62.9 mV/dec, a low threshold voltage (V_TH) of 0.18 V, an acceptable static leakage current, and a high drive current of 3.29 μA/μm at an overdrive voltage and drain voltage of 1 V. In addition, the treated device shows only a 13 mV of V_TH shift after 1000 s positive bias stress (PBS), corresponding to a 94% improvement compared with the pristine device. These improvements are attributed to the introduction of two different polarities of hydrogen-related traps after H₂O treatment. Furthermore, the influence of H-related traps on bias stability and the mechanisms responsible for V_TH shift are systematically clarified. These results establish that an optimized hydrogen incorporation window that maximizes the beneficial effects while balancing severe hydrogen-induced degradation caused by excessive hydrogen incorporation. Consequently, scaled IGZO TFTs with fast switching, low-power operation, high performance, and high reliability can be achieved, providing strong potential for back-end-of-line (BEOL)-compatible electronics and monolithic three-dimensional integrated applications. Full article

Hot dry rock (HDR) represents a highly promising sustainable clean energy resource for the future, with the fracture network induced by hydraulic fracturing serving as the cornerstone for thermal energy extraction efficiency. However, conventional numerical simulation tools are computationally expensive and fail to meet the requirements for real-time, dynamic on-site predictions. While machine learning models, such as traditional artificial neural network (ANN) and Long Short-Term Memory (LSTM), suffer from severe “black-box” limitations, they also lack the capability to capture the dynamic evolutionary characteristics of complex time series. To resolve these limitations, this study proposes a deep learning framework based on the Temporal Fusion Transformer (TFT) to dynamically predict the temporal evolution of fracture morphology. The research dataset was automatically generated in batches using GOHFER software (version 9.5.6), and feature selection was subsequently completed through parametric sensitivity analysis. A comparative analysis between the TFT model and traditional baseline models (ANN and LSTM) demonstrates that the TFT model achieves superior accuracy in capturing non-linear features that evolve dynamically over time, effectively overcoming the severe underfitting issues exhibited by conventional models under complex temporal constraints. Furthermore, leveraging the inherent attention mechanism of the TFT, this study elucidates the influence weights of injection rate, proppant concentration, and carrier fluid volume on fracture propagation across different fracturing stages, thereby enhancing the interpretability of the deep learning model. This research provides a novel decision-making tool that balances high efficiency with interpretability for the efficient and sustainable design of HDR. Full article

The development of flexible thin-film transistors (TFTs) is crucial for the advancement of wearable electronics, bendable displays, and the Internet of Things (IoT). A key challenge in this field is the fabrication of high-performance gate dielectric layers that combine excellent electrical properties with mechanical robustness and low-temperature processability. In this work, we report flexible TFTs based on CdS and hybrid PVPh/PMMA-ZrO₂ as semiconductor and gate dielectric layers, respectively. The hybrid gate dielectric films were deposited on flexible PEN substrates via a facile spin-coating process at a low temperature of 150 °C. On the other hand, CdS layers were deposited through photo-assisted chemical bath deposition at room temperature. Both correspond to deposition methods in solutions, fulfilling the low-temperature condition. The electrical properties of the hybrid gate dielectric layers were characterized by using metal–insulator–metal (MIM) capacitors, which presented excellent insulating properties, low leakage current density and suitable gate capacitance for transistor operation. From the analysis of the electrical response of flexible TFTs, reliable device characteristics and key electrical metrics were extracted. Furthermore, the MIM and TFTs were tested under mechanical bending, demonstrating stable performance. The MIM capacitors showed outstanding mechanical stability, retaining low leakage and stable capacitance after 1000 bending cycles, with changes attributed to reversible interfacial charge redistribution rather than bulk degradation. Meanwhile the TFTs kept full electrical functionality under repeated bending and tight bending radii (down to 0.6 cm), demonstrating reasonable mechanical durability. These results validate the solution-processed PVPh/PMMA-ZrO₂/CdS system as a promising, mechanically robust platform for flexible electronics. Full article

Enhancement behavior on saturation current of output characteristics in amorphous indium–gallium–zinc oxide (a-IGZO) thin-film transistors under positive bias stress (PBS) is investigated. The threshold voltage (V_th) of the a-IGZO TFT demonstrates a typical positive shift during PBS. Notably, the output current at an identical overdrive voltage (V_ov) initially rises with increasing bias stress duration. The observed phenomena are elucidated by the detrapping of positively charged defects situated at the interface between the dielectric and active layer due to electrons induced by bias stress during PBS, which diminishes carrier scattering at the channel interface. Following the full release of positive interface charge, additional electron trapping states emerge. The presence of trapped electrons intensifies carrier scattering at the channel interface, leading to a degradation in drive current. Low-frequency noise (LFN) measurements are conducted to verify the suggested mechanism of PBS instability in a-IGZO TFTs. Moreover, the energy distribution of PBS-induced traps is shown by C-V characterization to be exponential, dominated by shallow traps. Passivation greatly enhanced the PBS stability, which is attributed to hydrogen doping-induced defect passivation and the shielding of the back-channel interface from the air atmosphere. Full article

The Western Balkans (Serbia, Croatia, Bosnia and Herzegovina, and Montenegro) occupy a transitional climatic position between the Mediterranean hotspot and the continental Balkan interior within Southeast Europe, yet multi-country, multi-model, station-resolved assessments of regional aridification remain scarce. We combined quality-controlled monthly temperature and precipitation records from 100 stations across Serbia, Croatia, Bosnia and Herzegovina, and Montenegro (1961–2020) with bias-corrected projections from a five-member CMIP6 ensemble (EC-Earth3, MPI-ESM1-2-HR, CNRM-CM6-1, MRI-ESM2-0, IPSL-CM6A-LR) under four SSP scenarios to 2100 and benchmarked these projections against five short-term forecasting baselines on a held-out 2019–2020 period. Aridity was quantified using the Ellenberg Climate Quotient (EQ) and De Martonne Index. Results: Ninety-eight of 100 stations showed significant warming (1961–2020, p < 0.05), and 26 showed significant aridification. Ensemble mean end-of-century EQ change ranged from −6.9% (SSP1-2.6) to +44.6% (SSP5-8.5, Montenegro), with the largest absolute increases in the Pannonian lowlands; inter-model uncertainty exceeded inter-scenario uncertainty by roughly a factor of two. Deep learning forecasters (TFT, N-HiTS) outperformed bias-corrected CMIP6 output for short-term, station-scale temperature forecasting, while a simple climatological baseline remained competitive for precipitation. CMIP6 projections and AI forecasting are complementary: multi-model ensembles remain indispensable for long-term, scenario-conditioned planning, while AI offers superior near-term predictive skill for operational decisions. Full article

In this study, we systematically decoupled reversible charge transitions via recombination and irreversible bulk trapping via ionization in solution-processed indium zinc oxide thin-film transistors (TFTs) under positive- and negative-bias-stress (PBS and NBS) conditions. We defined highly decoupled degradation behavior by completely evaluating time-dependent transfer characteristics and saturation leakage currents across a range of indium molarities (0.0125 M to 0.2 M). Results indicate that PBS-induced instability is likely governed by a reversible electrostatic neutralization process reducing total effective shallow and deep acceptor-like states, which are dynamically counteracted by interfacial recombination at the dielectric/semiconductor boundary. Conversely, severe degradation under NBS originated from irreversible bulk trapping triggered by the ionization of donor-like oxygen vacancies in a ZnO amorphous random network. Total effective trapped charges were calculated from threshold voltage shifts to clarify these defect kinetics quantitatively; these calculations demonstrated direct correlation with the integrated theoretical capacities of the deep and shallow acceptor-like gap-state distributions. Finally, we propose a comprehensive density of state–energy band alignment model incorporating thermal activation energies and flat-band voltages. This analytical framework proves that the composition-dependent Fermi level positioning rigorously limits and dictates complex bias-stress instabilities, offering profound insights for designing highly stable amorphous oxide semiconductor TFTs. Full article