Search Results (298)

This study proposes a reproducible exploratory framework to link long-term territorial development with electricity demand in data-scarce contexts, and applies it to Ecuador’s Costa region. The pipeline combines three commonly available input streams: periodic census microdata, an official demand series, and macroeconomic aggregates. Socioeconomic heterogeneity across five non-uniform census rounds (1974, 1982, 1990, 2001, 2010) is summarized through Principal Component Analysis (PCA), and territorial indicators are projected to the demand horizon using a univariate linear trend. Eleven regression specifications are compared on a log-transformed demand variable, and a rolling-origin backtesting scheme plus a 2020–2024 holdout are used for validation. The selected Trend OLS log model attains $R^2=0.551$ and MAPE = 6.08%, and projects a regional demand of approximately 7055 MW by 2050, equivalent to a compound annual growth rate of 3.46%. Beyond the Ecuadorian case, the results show that transparent, low-data pipelines based on harmonized census information, macroeconomic drivers and simple regression models can provide defensible medium- and long-term demand signals for planners in other emerging economies with limited high-frequency data. Full article

Electrical power systems are exposed to interacting electrical, thermal, environmental, and resource-related faults such as leakage current, voltage and frequency deviations, overcurrent, harmonic distortion, phase-sequence error, humidity, fire, wind-speed variability, water insufficiency, and solar-resource loss. This study introduces a software-based database-generation and smart-learning framework that converts 22 candidate risk factors into six normalized severity levels and then maps the simultaneous system state to low-, medium-, and high-level protection decisions. The main novelty is that the software not only evaluates existing measurements; it also produces a literature- and standards-informed synthetic database when long-term real field measurements are not yet available. The database is generated by defining variable limits, sampling realistic operating states, computing severity labels, and storing input–output pairs that can later train or validate predictive maintenance models. The proposed framework therefore, links protection logic, database construction, and reusable training data in a single workflow. The results show how simulated annual operating scenarios can be transformed into structured risk records, warning classes, and shutdown decisions, supporting early fault detection, maintenance planning, and resilience improvement in renewable-integrated electrical networks. Full article

The increasing penetration of inverter-based renewable energy sources and the growing share of underground cable lines significantly modify the frequency-dependent characteristics of medium-voltage distribution networks, increasing the risk of harmonic resonance. Existing resonance studies are often based on detailed electromagnetic models that are difficult to apply during routine distribution network planning and operation. This paper proposes an engineering-oriented methodology for the preliminary assessment of harmonic resonance risk using an equivalent lumped-parameter model that incorporates overhead and cable lines, transformer inductance, photovoltaic generation, and the short-circuit strength of the supplying system. The methodology is applied to a representative 20 kV distribution network to investigate the influence of cable penetration, photovoltaic capacity, transformer loading, and grid strength on resonance conditions. The results show that increasing network capacitance and reducing short-circuit power shift the resonance frequency toward lower-order harmonics, increasing the probability of harmonic amplification. The highest resonance risk is observed under the combined conditions of high photovoltaic generation, low transformer loading, and weak-grid conditions. Unlike detailed electromagnetic simulation models, the proposed methodology enables rapid engineering assessment using parameters readily available to distribution system operators, thereby supporting network planning and operational decision-making in medium-voltage distribution systems with high photovoltaic penetration. Full article

To address the challenges of analyzing vibration signals, overlapping fault characteristic frequency bands, and difficulties in accurately distinguishing similar faults, this study proposes a diagnostic classification scheme for GIS disconnect switch mechanical fault detection. The scheme integrates multi-scale RGB image representation with a Squeeze-and-Excitation Convolutional Neural Network Attention (SE-CNN-Attention) mechanism. First, continuous wavelet transform is employed to decouple the one-dimensional vibration signal into low-, medium-, and high-frequency physical bands, which are then mapped to the red, green, and blue channels to generate a 64 × 64 × 3 RGB time–frequency feature image, enabling three-dimensional encoding of time, frequency, and energy intensity. Based on this approach, a SE-CNN-Attention fusion model is developed. The two-dimensional CNN automatically extracts region-specific features from the time–frequency images. Concurrently, the squeeze-and-excitation (SE) attention mechanism adaptively assigns weights to enhance fault-sensitive frequency bands and suppress noise interference. The model adopts global average pooling instead of traditional fully connected layers, combined with Dropout regularization and a Softmax classifier, to achieve efficient and robust fault classification. Experiments are conducted under four operating conditions with a total of 19,800 sample groups collected. The findings indicate that the proposed approach attains an overall classification accuracy exceeding 94%, outperforming traditional support vector machine (SVM), baseline convolutional neural network (CNN), Vision Transformer (ViT), and ResNet-18 models. The SE-Attention module significantly enhances the model’s focus on key frequency band features while accelerating convergence speed and improving classification stability. This method provides reliable technical support for intelligent maintenance and fault warning of GIS disconnect switch equipment and holds significant engineering application value. Full article

Parking space availability forecasting is challenging due to noise contamination, non-stationary dynamics, and complex multi-scale temporal dependencies. This study proposes a wavelet-enhanced Autoformer with dynamic multi-kernel decomposition for parking space availability forecasting. Discrete wavelet transform is used to enhance the input sequence by suppressing high-frequency disturbances while preserving the dominant temporal structure. Within the Autoformer framework, a dynamic multi-kernel decomposition module is developed by introducing multiple moving-average kernels with different scales and an input-dependent dynamic weighting mechanism, enabling the adaptive decomposition and fusion of trend and fluctuation components and thereby improving the representation of complex multi-scale temporal patterns. The proposed framework is evaluated using real parking operation data collected from the Liangshan Jingyuan parking lot in Chongqing, China, including 1248 observations with a 2 h sampling interval. Multi-step forecasting experiments are conducted under short-, medium-, and long-term prediction settings with different label-length configurations. Across different forecasting horizons and label-length settings, the proposed model consistently outperforms the original Autoformer, PatchTST, and a range of conventional and recent forecasting baselines. In particular, under the 24-step forecasting setting, the proposed method reduces MAE, RMSE, and WMAPE by approximately 10% relative to the original Autoformer. Moreover, the results suggest that the dynamic multi-kernel mechanism improves the model’s ability to adaptively capture scale-dependent temporal patterns across different forecasting tasks. Full article

Modern power systems increasingly operate under distorted waveform conditions caused by distributed generation, renewable energy sources, and power-electronic loads. In medium- and high-voltage networks, power measurement accuracy depends on voltage and current transformers, whose ratio and phase displacement errors vary with frequency. This paper investigates how these errors propagate into indirect measurements of active, reactive, and apparent power. The analysis considers the conventional 0.2 accuracy class at 50 Hz and the extended 0.2 WB2 optional accuracy class for individual higher harmonic components up to the 400th order. Analytical expressions are derived for power measurement errors, and the results are presented as deterministic error envelopes. In addition, 16 global sign scenarios are evaluated to determine how different combinations of transformer error signs affect total power measurement. The results show that apparent power errors are mainly determined by ratio errors, whereas active and reactive power errors are additionally influenced by differential phase displacement errors. For higher harmonics, the WB2 frequency band limits produce step changes in the error envelopes, and relative errors may become large when the reference active or reactive power of a harmonic component is small. The proposed method extends conventional power error propagation analysis by linking harmonic power components with the frequency band limits of the IEC 61869 WB2 optional accuracy class and by introducing deterministic error envelopes and global sign scenarios for worst-case-oriented assessment under nonsinusoidal operating conditions. Full article

Foxtail millet (Setaria italica L.) is an important food crop worldwide and serves as a model for studying C4 photosynthesis and drought resistance. However, its application in functional genomics and molecular breeding has been severely constrained by the absence of a reliable genetic transformation system. In this study, 66 varieties were systematically evaluated for embryogenic callus induction capacity, with rates ranging from 0 to 70.22%. Among these, five varieties exhibited high embryogenic potential (>50%). Jingu51, an elite cultivated variety in northern China, was selected as the transformation recipient due to its rapid embryogenic response and high induction frequency. Optimization of culture conditions revealed that Murashige & Skoog (MS) medium supplemented with 30 g·L⁻¹ sucrose, 3 g·L⁻¹ phytagel, 1 mg·L⁻¹ CuSO₄, and 1 mg·L⁻¹ 2,4-dichlorophenoxyacetic acid combined with 0.5 mg·L⁻¹ 6-benzylamino purine was optimal for embryogenic callus induction. Key parameters of Agrobacterium-mediated transformation, including 100 μM acetosyringone concentration, callus pretreatment (heat shock, followed by ice bath), bacterial density (OD₆₀₀ = 0.3), and cocultivation temperature (19 °C), were optimized. Under these conditions, a robust genetic transformation system was successfully established, yielding an average efficiency of 9.25% by PCR across multiple expression vectors; half transgenic event exhibited single copy. Notably, the genetic transformation system of Jingu51, an elite variety of foxtail millet, provided a robust platform for functional genomics research and accelerated molecular breeding efforts in this important crop. Full article

Climate change and population growth are intensifying global food security challenges by reducing agricultural productivity and increasing reliance on fertilizers. In this context, developing sustainable and economically efficient agricultural solutions becomes essential. The study presents the synthesis of an interpenetrating polymer network (IPN) of hydrogels by combining bacterial cellulose (BC) with poly(acrylic acid) crosslinked with N, N-methylene-bis-acrylamide (PAA–co–MBA) via free radical copolymerization. To explore their potential as bioactive compound carriers, an ethanolic hydroalcoholic phytoextract (EHP) obtained from Hypericum perforatum L. and Melissa officinalis L. was directly encapsulated within the IPN hydrogels. The EHP is valued for its rich bioactive profile and antifungal, antimycobacterial, and antioxidant properties. The results of rheology measurements and thermal gravimetric analysis (TGA) revealed that incorporating BC into the IPN hydrogels significantly enhanced the mechanical stiffness, thermal resistance, and overall stability of the resulting IPN structures. Fourier Transform Infrared (FTIR) spectroscopy and Scanning Electron Microscopy (SEM) confirmed the structural organization and the porosity of the developed composite, as well as the successful fabrication of IPN hydrogels in the EHP medium. Under optimal conditions, the IPN hydrogels exhibited a reduced swelling capacity, thereby slowing the diffusion of the bioactive agents, reducing the application frequency, and enhancing the utilization efficiency. Taken together with the controlled-release performance, these findings demonstrate the potential of BC (PAA-co-MBA) IPN hydrogels as biodegradable and sustainable carrier systems for controlled delivery applications and suggest that they may be promising candidates for hydrogel-based agricultural delivery systems. Full article

Artificial intelligence (AI) technology has become the core force driving industrial transformation in today’s world. In-depth exploration of the spillover effects between artificial intelligence and new energy, as well as high-carbon-emission industries is of great significance for optimizing the industrial structure, preventing systemic risks in the industrial system, and achieving high-quality development. Based on the DY and BK spillover index model under the TVP-VAR framework, this paper analyzes the spillover effects between artificial intelligence and new energy, as well as high-carbon-emission industries from a time–frequency perspective, and constructs a spillover network to analyze the risk spillover transmission path. Finally, it explores the optimal investment portfolio weights and investment hedging strategies in the financial market. The results show that there is a significant static spillover effect between artificial intelligence and new energy, as well as high-carbon-emission industries. The intensity of this effect follows the pattern of “short-term > medium-term > long-term”. Moreover, new energy and some high-carbon-emission industries (such as the non-ferrous metals industry, the petrochemical industry, and the chemical industry) are the net spillover sources, while artificial intelligence and some high-carbon-emission industries (such as the power industry, the building materials industry, and the aerospace industry) are the net receiving parties. The dynamic spillover effect exhibits significant time-varying characteristics, being significantly impacted by major events such as environmental protection policies, the COVID-19 pandemic, and technological innovations. The chemical industry is the largest spillover outputter in all frequency domains, while the building materials industry is the largest receiver. From the perspective of the spillover network, the artificial intelligence industry, as a key node of the spillover network, plays a crucial role in the transmission of risk spillover. From the perspective of investment practice, the minimum connectedness portfolio (MCoP) performs well in terms of risk hedging effectiveness and return performance and may be the best choice for investors to balance risk and return. Full article

This paper presents a simulation-based 16-quadrature amplitude modulation (16-QAM) direct current-biased optical orthogonal frequency-division multiplexing (DCO-OFDM) visible-light optical wireless system using a 520 nm InGaN directly modulated laser (DML) and direct detection over 500 m. A 1024-point transform with 511 data subcarriers provides approximately 15 Gb/s gross and 14.82 Gb/s payload rates without external optical modulators or amplifiers. Under the adopted static line-of-sight model, the simulated bit-error rate (BER) falls below ${10}^{-3}$ at a receiver-side equivalent optical signal-to-noise ratio (OSNR) of about 17 dB and remains below this threshold for beam divergence up to 9 mrad. Gamma–Gamma simulations show that a 5 cm aperture maintains $\mathrm{BER}<{10}^{-3}$ at 20 dB OSNR up to $C_n^2\approx 5\times {10}^{-14}{\mathrm{m}}^{-2/3}$. Pointing-error analysis gives per-axis angular-jitter standard deviations of 0.425, 0.515, and 0.564 mrad at 1% outage for 5, 10, and 15 cm apertures. The clear-air margin is exhausted at $V_{2\%}\approx 0.66\mathrm{km}$, corresponding to $V_{5\%}\approx 0.50\mathrm{km}$, or near 107 mm/h rain. For a 1.5 GHz bandwidth-limited DML, adaptive bit loading reaches 16.5 Gb/s at 28 dB OSNR. The results support a low-complexity medium-range architecture but remain numerical estimates requiring experimental validation under practical device, alignment, and weather conditions. Full article

Stock index price series are composed of superimposed multi-frequency components, including long-term trends, cyclical fluctuations, and stochastic noise. Effectively decoupling these heterogeneous components and modeling them separately is key to improving forecasting accuracy. Existing methods under the “decomposition–prediction” paradigm mostly employ fixed-scale decomposition, and the forecasting models are not specifically adapted to the non-stationary and high-noise characteristics of financial data, resulting in limitations in adaptivity and local dynamic capture. This paper proposes a frequency-aware adaptive multi-scale decomposition Transformer hybrid model (FAMS-Transformer). At the decomposition level, the fast Fourier transform is used to dynamically identify dominant cycles, thereby adaptively decoupling trends and fluctuations, overcoming the limitations of fixed-scale decomposition. At the forecasting level, a lightweight depthwise separable convolution is embedded between the self-attention and feedforward network of the Transformer encoder, enhancing the model’s ability to capture local temporal dynamics and achieving collaborative modeling of global dependencies and local information. Comparative experiments with 15 baseline models including LSTM, Transformer, TimesNet, and FreTS on three representative Chinese market indices—Shanghai Composite Index, Shenzhen Component Index, and Small and Medium Enterprises 100 Index—across four prediction horizons from one step to 15 steps demonstrate that FAMS-Transformer achieves the best forecasting accuracy in all scenarios. The coefficient of determination for 15-step prediction remains stably between 0.730 and 0.928. Moreover, the model still performs well on the S & P 500 dataset. Ablation studies and significance tests further validate the effectiveness of each core module and the statistical significance of the performance improvements. Full article

Tool wear in CNC milling increases friction and torque demand at the tool-workpiece interface, which is reflected in spindle motor current. This study develops a non-intrusive tool wear condition classification method using spindle motor current monitoring during practical CNC milling of commercial medium-carbon steel workpieces (JIS S50C/AISI SAE 1050-equivalent; as-received and non-heat-treated; nominal laboratory hardness approximately 4.3 HRC). Experiments were performed on a Tongtai MDV-508 vertical machining center at fixed cutting conditions (3000 rpm spindle speed, 2 mm axial depth of cut, 5 mm cutting width, and 300 mm/min feed rate) using eight TiAlN-coated fine-grain WC–Co solid carbide end mills (10 mm diameter, four flutes; nominal Co binder approximately 10 wt%). An oil-based HS Highstart/HS-SSHS-BH10 cutting fluid was applied through the machine external coolant nozzle in flood mode at an estimated nominal flow rate of approximately 3 L/min and near-room coolant temperature (25 ± 2 °C), and was used as supplied without dilution. A clamp-type AC current sensor was installed on one phase line supplying the spindle motor, and current was acquired using an NI-9221 module at 20 kHz. Cutting intervals were isolated by envelope-based segmentation, concatenated, and divided into 1 s windows (0.5 s overlap) for feature extraction. Three feature sets were evaluated: time-domain statistics, frequency-domain statistics, and an FFT→PCA hybrid representation. Tool states (New, Mid-life, Old) were labeled using post-process surface roughness Ra thresholds supported by microscope observation. The PCA transformation was fitted only on training data and then applied to the held-out test data. A logistic regression classifier achieved 97.44% test accuracy (152/156 windows; 95% Wilson CI: 93.59–99.00%) with the PCA-hybrid features, outperforming time-domain (89.74%) and frequency-domain (94.87%) models. The results support spindle current monitoring as a low-cost approach for quality-aligned tool condition monitoring, while the external validity remains limited to the tested machine, material, tool, coolant, and cutting-parameter combination. Full article

This study investigates the ecological footprint (EF) convergence dynamics of the “Big Ten Emerging Economies” (BTEs) over the period 1967–2024. Employing the Maximum Overlap Discrete Wavelet Transform (MODWT) in conjunction with the Fourier KPSS (FKPSS) stationarity test, the analysis decomposes the EF series into short-, medium-, and long-term frequency components, allowing the stochastic convergence hypothesis to be examined separately across multiple time horizons. The empirical results reveal that convergence is largely absent in the original series, with stochastic convergence detected only for India, Indonesia, and Türkiye at the aggregate level. Once the series are decomposed, convergence becomes considerably more visible. In the short run, convergence is supported for Argentina, Indonesia, Mexico, Poland, and Türkiye. The medium run emerges as the most robust convergence horizon, with all ten economies exhibiting stochastic convergence—a result that becomes visible only after accounting for nonlinear structural breaks through the Fourier framework. In the long run, convergence is supported for Argentina, Brazil, China, Korea, Poland, and South Africa, while India, Indonesia, Mexico, and Türkiye exhibit persistent divergence. No single country maintains convergence consistently across all time horizons, underscoring the heterogeneous and frequency-dependent nature of EF dynamics in major emerging economies. The robustness analysis based on the Fourier ADF and standard ADF tests supports the primary findings. These results contribute to the EF convergence literature by demonstrating that environmental convergence is a multi-layered and frequency-dependent phenomenon, and offer empirical insights relevant to the design of long-run sustainability policies for emerging economies. Full article

Terahertz hot electron bolometer (HEB) mixers, which offer the highest sensitivity in the frequency range above 1.5 THz, are equipped on space observatories to detect the terahertz radiation emitted from the interstellar medium within galaxies. To increase the mapping speed, it is essential to develop large HEB mixer arrays. However, conventional quasi-optical coupling methods, including single large silicon lens approaches and silicon lens array approaches, suffer from the conflict of achieving high filling factor and uniform illumination on the HEB mixer array. This paper reviews the research progress on quasi-optical coupled HEB mixer arrays and proposes an innovative array coupling scheme to overcome the existing limitation. We designed a metasurface beam shaper based on the Gerchberg–Saxton algorithm and COMSOL simulation to transform an incoming Gaussian beam into a flattop beam in the focal plane, thereby forming uniform illumination for an antenna-coupled HEB mixer array. The metasurface is intended primarily for uniform local oscillator (LO) distribution across the array. The simulation of the metasurface beam shaper at 0.6 THz demonstrates a flattop beam with a flat region approximately 3 mm wide, and the intensity across this region varies by only 4.2%. The same simulation is also performed at 1.6 THz, and the flat region is 1.5 mm wide with a 5.5% intensity variation. This work demonstrates the feasibility of using a metasurface to convert a Gaussian beam into a flattop beam at terahertz frequencies as well as a pathway for array-level coupling schemes for HEB mixer arrays with high filling factor and uniform illumination. Full article

Physical-layer key generation (PLKG) is a technique that produces symmetric encryption keys by exploiting the inherent characteristics of wireless channels. It offers advantages including high physical-layer security, elimination of pre-shared keys, dynamic upgradability, and resistance to quantum attacks, making PLKG a promising security solution for next-generation (6G) networks. However, satellite communication channels exhibit high dynamics and long propagation delays. Characteristics such as large Doppler shifts, short coherence times, and orbital predictability pose severe challenges to PLKG, including reciprocity degradation, low key generation rate (KGR), and susceptibility to channel-prediction attacks. This work proposes a delay-Doppler domain time-hopping key generation scheme (KE-DD-TH) based on Orthogonal Time Frequency Space (OTFS) modulation for high-speed links between Low-Earth-Orbit (LEO)/Medium-Earth-Orbit (MEO) satellites and ground terminals in Ka/Ku bands. The scheme performs non-uniform sampling on the DD domain grid of OTFS symbols using an ephemeris-driven pseudo-random time-hopping sequence generated by cascaded linear feedback shift registers (LFSRs) and a nonlinear matrix transformation. Both legitimate parties estimate the channel only at time-hopping instants and multiply two adjacent estimates to construct an “equivalent channel” matrix, yielding a random source with high entropy, high reciprocity, and low predictability. The eavesdropper’s key disagreement rate (KDR) remains close to 0.5 under all signal-to-noise ratio (SNR) conditions, corresponding to the ideal random-guessing baseline. This indicates that Eve obtains negligible mutual information, i.e., $I(K_A;K_E)\approx 0$. By contrast, the conventional KE-DD scheme allows Eve’s KDR to degrade to 0.014 at 30 dB SNR, indicating near-complete key recovery. The generated keys pass all 12 randomness tests of the NIST SP 800-22 statistical test suite. Full article