Search Results (365)

Small unmanned aerial vehicles are difficult short-range radar targets because their millimeter-wave radar cross-sections often fall between −10 and −25 dBsm. This paper presents a modular analytical and simulation-based benchmark of a cascaded 77 GHz TDM-MIMO FMCW radar with 12 transmitters and 16 receivers, yielding a 192-element virtual ULA over a 40 m instrumented range. The framework is organized around the main counter-UAV sensing functions: range–Doppler processing first evaluates target observability and provides range–Doppler gates; Doppler-dependent TDM phase compensation is then required before virtual-array snapshots are formed for DoA estimation; and a separate long-dwell single-transmitter branch evaluates micro-Doppler separability using handcrafted features and a nearest-centroid Mahalanobis classifier. Four benchmarks are considered: detection under Swerling fluctuation models, residual TDM phase error caused by Doppler quantization, DoA estimation under an idealized far-field snapshot model, and micro-Doppler separability among UAV and bird classes. Under Swerling I, targets with a mean RCS of $-10$ dBsm or larger maintain detection probability above 0.9 throughout the 40 m window, whereas the $-20$ and $-25$ dBsm classes fall below that level at about 28 m and 21 m. In the far-field DoA benchmark, TLS-ESPRIT gives the lowest conditional RMSE and remains about 13–14 dB above the subarray CRLB at moderate SNR; however, these angular results are reference ceilings because the short-range operating region violates the full-aperture far-field condition and because residual TDM phase error can be severe without accurate compensation. In the micro-Doppler benchmark, birds exceed 95% per-class accuracy at 20 dB total SNR, but overall four-class accuracy saturates near 72–75% and UAV-only three-class accuracy near 63%, with most confusion between the micro-quadrotor and fixed-wing classes. This study therefore identifies architecture-specific performance margins and limitations before measured-data field validation, rather than claiming complete deployment-level performance. Full article

Experimentation in science education faces significant access limitations, both in face-to-face and distance learning settings; in light of this situation, remote laboratories are emerging as a strategic solution. The aim of this study is to present the design and development of an ultra-concurrent remote laboratory focused on the study of projectile motion. Using the Design-Based Research methodology, the resource has been structured around an iterative five-phase approach: design, data capture, development, test and improvement, and integration. The data acquisition system was developed using a hardware setup comprising a projectile launcher, photo gates, a digital interface and a time sensor, implemented and managed via the LabsLand platform. The laboratory integrates semi-parabolic and full-parabolic configurations via an interactive interface that guides the user from connecting components to the multimedia observation of real experimental data. The results of the experimental validation confirm the system’s viability, as the data obtained compare with ideal kinematic equations and reflect, as expected, the behaviour and physical limitations of the real-world environment. This laboratory offers a potential pedagogical advantage, reporting percentage errors around $3$–$11\%$, as it exposes students to experimental uncertainty whilst simultaneously ensuring simultaneous and free access for multiple users in science education. Full article

In logic device development, gate-all-around nanosheet field-effect transistors (GAA NSFETs) are widely regarded as the future mainstream architecture. Due to an innovative stacked-channel design, a novel process module of channel release has been introduced, posing significant challenges to device manufacturing. The channel release quality plays a decisive role in the device’s turn-on voltage and operating speed. Meanwhile, the complex interferences are undoubtedly brought by diverse structures and manufacturing thermal budgets of GAA NSFETs. Here, the non-plasma gas etching, which is not yet widely used in the current industry, is adopted for channel release. The influences of nanosheet width, spacing, and annealing conditions on the etching process are systematically studied. A SiGe/Si etching selectivity as high as 87 is achieved. With increasing channel width, a downward trend in the single-sided damage in the central region of Si nanosheets is shown. At >100% over-etching, the Si single-sided damage in structures with different channel spacing is controlled below 1 nm. The intensified diffusion of Ge elements in the SiGe layer and a gradual slowdown of the SiGe etching rate are caused by increasing the annealing temperature. The root mean square (RMS) value of the channel surface roughness is reduced from 0.087 to 0.069 nm by adding the *H radical pretreatment into the process. These findings provide valuable guidance for developing a channel release etching process with high selectivity, low damage, a stable process window, and low fabrication difficulty. Full article

Volatility is a fundamental indicator for assessing the risk of financial assets. By integrating unstructured data, such as earnings call transcripts, the limitations of traditional time series data can be transcended, enabling collaborative forecasting from multiple data sources, enhancing the robustness of volatility prediction, and improving the efficiency of risk management. Although current research has effectively utilized earnings call data to predict asset volatility, price trends, and stock correlations, it often overlooks the inherent challenges of integrating textual and time series data, as well as the self-exciting and clustering characteristics of financial events. While conventional Long Short-Term Memory (LSTM) networks excel in processing fused data, they lack the structural capacity to explicitly model event-driven temporal decay, often failing to differentiate the varying influence of historical shocks over time. To surmount this limitation, we have significantly enhanced the predictive model by focusing on extracting salient information and integrating temporal dependency modeling with dynamic state adjustment mechanisms. The core innovation is introducing the Hawkes process to explicitly capture the self-exciting effect of financial events, which is the key to modeling volatility clustering around earnings releases. The proposed Hawkes LSTM model introduces a decay gating module and a textual information knowledge enhancement module. The decay gating module is specifically designed to more effectively capture the temporal dependencies between events within an event sequence. This allows the model to focus more on recent significant events, with the influence of an event on subsequent events typically diminishing as the temporal interval between them increases. By integrating temporal dependency modeling, the model is enabled to utilize historical data in a more flexible manner. The dynamic state adjustment mechanism further enhances its capacity to capture dynamically changing characteristics. Together, these features provide a more robust and precise solution for volatility prediction. Experimental results on two real-world earnings call datasets show that this approach significantly outperforms existing benchmark models on most prediction horizons, achieving competitive and superior performance and verifying its effectiveness and robustness. Full article

Rotor imbalance and abnormal vibration are classical operating conditions in rotating machinery and can often be identified by conventional vibration analysis. However, the development of low-power, self-powered, and distributed sensing nodes remains important for long-term condition monitoring, particularly in scenarios where external power supply, wiring, and maintenance are constrained. Existing vibration sensors, including piezoelectric and capacitive types, are constrained by power consumption and degraded performance under low-frequency and weak excitation. To address this issue, a magnetically repulsive cushion triboelectric nanogenerator (MRCT) is proposed to enable self-powered vibration sensing. The magnetic-repulsion cushion allows the upper friction layer to undergo stable contact–separation motion under a non-contact restoring force, while the microstructured strip electrode array (MSEA) enhances the triboelectric output and signal stability. A hybrid convolutional neural network–gated recurrent unit (CNN-GRU) deep-learning model is employed to extract time-domain and frequency-domain features from the collected signals, enabling real-time identification of rotor vibration amplitude, frequency, and imbalance weight. Experimental results show that the MRCT provides stable output, a high signal-to-noise ratio, and an identification accuracy above 98% for predefined rotor imbalance-weight states under laboratory conditions. In addition, a shaft-misalignment-related abnormal vibration condition was examined on the motor platform. The corresponding time-domain and frequency-domain analyses show that the MRCT voltage signal exhibits distinguishable signal variations under normal and misalignment-related conditions, including spectral changes around the 2× rotational frequency. A laboratory-scale AIoT-oriented demonstration further verifies the feasibility of integrating MRCT signal acquisition, CNN-GRU inference, wireless transmission, and GUI-based visualization. It should be noted that the present work mainly focuses on imbalance-state recognition, while the misalignment-related experiment provides an additional sensor-response verification. Broader validation involving mechanical looseness, bearing defects, variable-speed operation, cross-machine testing, and long-term industrial conditions remains necessary. Full article

This study investigates the metaphysics of fasting according to the hugely influential mystic Muhyi al-Din ibn ‘Arabi (d. 638/1240). Ibn ‘Arabi argues that fasting holds an unparalleled position in ritual worship. While his predecessor Abu Hamid al-Ghazali (d. 505/1111) in his magnum opus—The revival of the religious sciences (Ihya’ ‘ulum al-din)—addresses the ethical dimension of fasting, Ibn ‘Arabi’s concern is the metaphysical reality of it. There are six principal reasons Ibn ‘Arabi gives for fasting being superior to other forms of worship, all of which revolve around fasting’s uniqueness that adverts to the uniqueness of God: (1) Fasting is elevated because God has connected it to Himself in prophetic traditions. All other acts of worship are connected to humans. Fasting is thus elevated from the servant to God. (2) Fasting is not an action like other forms of worship; it is an inaction since it entails refraining from eating, drinking, and sexual intercourse. This makes its essence incomprehensible as it is not an entity but the lack of one. The incomprehensibility of the essential inaction of fasting connects it to the incomprehensibility of God. (3) Fasting, insofar as it displays independence from food, drink, and sexual intercourse, mirrors the divine attribute of true independence from all things (samadaniyya). (4) Fasting is described as a shield (junna) in prophetic traditions because although it is not an action in itself, the state of fasting becomes a protection against evil actions. Awareness of God (taqwa), likewise, protects against evil actions. Thus, fasting is related to God as it begets awareness of God. (5) The breath of the person who fasts, though malodourous to humans, is said to be fragrant for God by Prophet Muhammad. This ‘fragrance’ is produced by the breath of the person who fasts, which adverts to the Breath of the Compassionate (nafas al-Rahman) that brings all things into existence according to Ibn ‘Arabi. (6) The people who fast shall enter heaven through the Gate of Rayyan (quenched thirst) as reported in prophetic traditions. Ibn ‘Arabi argues that the quenching of thirst represents an endpoint or ‘perfection’ (kamal) after which one does not require more drink. This ‘perfection’ (of satiation) mirrors God’s perfection. Fasting is the only form of worship that has a gate that alludes to its perfection, which demonstrates that it is unique. In all these ways, then, there is nothing like fasting, which connects it to God because there is nothing like God. Full article

Thyroid diseases are very common endocrine diseases that afflict millions of people around the world and need proper and timely diagnosis to ensure proper treatment. Although machine learning and hybrid metaheuristic methods have advanced, current models have high computation costs, low interpretability, and low probability calibration, which limit their use in clinical settings. In this research, a new DFSel-FT (Differentiable Feature Selection and an FT-Transformer) system is suggested, which combines DFSel-FT to allow one to diagnose thyroid disease effectively and interpretably. It employs Concrete (Gumbel-Softmax) gates to select the features end-to-end to make sure that only the most relevant clinical attributes are carried through the training. A Transformer-based architecture is then used to process the chosen features to learn intricate interdependencies. The model is trained with class-balanced focal loss and temperature scaling to better enhance calibration. Experimental evaluation on the UCI Thyroid Disease Dataset (22,632 samples) showed that the proposed model achieved 97.85% accuracy, 97.65% Macro-F1, and 98.10% AUC-OVR, showing competitive performance compared with traditional machine learning models, modern tabular deep learning baselines, and hybrid metaheuristic methods. Other indicators of robustness and reliability include MCC (0.955), Cohen Kappa (0.951), and small calibration error (ECE = 0.021). SHAP and LIME explainability analysis reveals clinically relevant features that include TSH, TT4, and T3. The proposed framework provides a balanced integration of predictive performance, interpretability, and probability calibration, making it a promising benchmark-level framework for interpretable and calibrated thyroid disease classification, requiring external clinical validation before real-world deployment. Full article

Pregabalin (PGB) exerts its therapeutic effects by binding to the α₂δ auxiliary subunits of voltage-gated calcium channels and modulates synaptic transmission in the brain. However, its influence on cerebellar parallel fiber–Purkinje cell (PF–PC) synaptic transmission remains unclear. In the present study, we investigated the effects of PGB on PF–PC synaptic transmission using whole-cell patch-clamp recording, glutamate fluorescence imaging, immunohistochemistry, co-immunoprecipitation, Western blotting, and pharmacological approaches. Micro-application of PGB to the cerebellar molecular layer induced a concentration-dependent inhibition of PF–PC excitatory postsynaptic currents (EPSCs), accompanied by an increased paired-pulse ratio. The inhibitory effect of PGB on PF–PC EPSCs was abolished by extracellular blockade of N-methyl-D-aspartate receptors (NMDAR) or their GluN2A subtype, as well as by disruption of α₂δ-1–NMDAR complexes, but not by intracellular NMDAR inhibition. Glutamate sensor imaging further showed that PGB markedly reduced the fluorescence intensity of glutamate release evoked by PF stimulation. In the presence of tetrodotoxin (TTX) and a gamma-aminobutyric acid type A (GABAA) receptor antagonist, PGB reduced the frequency of miniature excitatory postsynaptic currents (mEPSCs) without affecting their amplitude. The PGB-induced reduction in mEPSC frequency was fully abolished by extracellular blockade of GluN2A-containing NMDARs or disruption of α₂δ-1–NMDAR complexes. Similarly, the inhibitory effects of PGB on PF–PC EPSCs and mEPSCs were eliminated by extracellular PKA inhibition, but not by intracellular protein kinase A (PKA) inhibition. Western blot analysis showed that PGB significantly increased PKA phosphorylation in the molecular layer of the cerebellar cortex. Immunoreactivity for GluN2A and α₂δ-1 subunits was colocalized within the molecular layer and abundantly distributed around the dendrites and somata of PCs. Co-immunoprecipitation further verified that α₂δ-1 was co-precipitated with GluN1 in cerebellar molecular layer tissue samples. The results indicate that PGB depresses glutamate release from parallel-fiber terminals in the mouse cerebellar cortex through the presynaptic α₂δ-1-coupled GluN2A-containing NMDAR/PKA signaling pathway, thereby attenuating PF–PC synaptic transmission. Full article

Social media sentiment analysis demands systems that are simultaneously accurate, scalable, and interpretable. Lexicon-based methods offer transparency but ignore context, while pre-trained language models (PLMs) capture contextual semantics yet encode sentiment only implicitly. Existing integration strategies inject lexicon signals at the input, attention, or feature layer—all outside the recurrent gating mechanism that controls how affective evidence accumulates over a sequence. We propose the SAGU-Net, a framework built around the Sentiment-Aware Gated Unit (SAGU), a gated recurrent unit (GRU) variant with a dedicated sentiment gate conditioned on external lexicon signals. A complementary Context-Adaptive Sentiment Scoring (CASS) module transforms static polarity scalars into context-dependent vectors via learned projections over PLM representations, bridging the gap between discrete lexicon scores and continuous embeddings. The sentiment gate activations provide token-level explainability without post hoc attribution. On a 12,700-sample Chinese social media corpus of intellectual property co-branding reviews (Fleiss’ $\kappa =0.82$) and two public benchmarks, the SAGU-Net achieves 93.62% accuracy and 93.21% Macro-F1, outperforming nine baselines and matching or exceeding LoRA-fine-tuned large language models (GPT-5, Claude Sonnet 4.6, DeepSeek V3.2, Qwen3.5) while requiring three to four orders of magnitude fewer parameters. Ablation confirms the sentiment gate as the single most impactful component. Full article

Forecasting scrap steel prices is crucial for the economic sustainability of recycling operations, yet it remains challenging due to inherent volatility and non-stationary behavior. In this study, we develop and evaluate a delta-targeted Hybrid forecasting pipeline for short horizons of 1, 3, and 7 days. We benchmark classical baselines (Naive, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Exponential Smoothing (ETS)) against recurrent deep learning models (Simple Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM)) and recent neural forecasting baselines, including Decomposition-Linear (DLinear), Convolutional Kolmogorov–Arnold Network (C-KAN), and Neural Basis Expansion Analysis for Time Series (N-BEATS), using real-world daily scrap steel price data. The results indicate that delta-targeting generally yields more stable predictive performance than direct raw-price forecasting as the prediction horizon increases. For example, at the 7-day horizon, the predictive fit improves from approximately $R^2\approx 0.87$ for raw-price LSTM to around $R^2\approx 0.90$ for delta-trained recurrent models. At the same horizon, a delta-based RNN achieves the lowest Mean Absolute Percentage Error (MAPE) among the evaluated models (approximately $1.39\%$), while the proposed Hybrid model remains competitive across all tested horizons and maintains a goodness-of-fit of approximately $R^2\approx 0.90$ without uniformly minimizing point error relative to the best-performing recurrent baseline. Attention profiling and permutation-based feature importance analyses indicate that the model places relatively higher weight on calendar-related inputs, consistent with the presence of weekly patterns in the data; these results should be interpreted as sensitivity diagnostics rather than causal evidence. Overall, the findings suggest that delta-transformed targets provide a more suitable prediction space than raw-price targets for short-horizon scrap steel forecasting, while the Hybrid design offers a balanced combination of predictive performance and diagnostic interpretability for operational decision support. Full article

Alzheimer’s disease (AD) is the most common form of dementia, characterized by the progressive accumulation of amyloid β (Aβ) plaques and neurofibrillary tangles of tau protein in and around neurons. However, these markers appear relatively late in the disease, and their direct causality is incompatible with clinical observations. Extensive data suggest that dysregulation of Ca²⁺ signaling is an early event in the pathogenesis of AD. In familial AD (FAD), mutations in presenilin are shown to alter Ca²⁺ homeostasis by affecting the gating properties and/or the expression levels of inositol 1,4,5-trisphosphate (IP₃) receptors (IP₃Rs) and ryanodine receptor (RyRs)—the main channels responsible for Ca²⁺ release from the endoplasmic reticulum (ER). Thus, understanding the mechanism through which these channels disrupt Ca²⁺ homeostasis at different spatiotemporal scales is crucial to determining their role in AD. Here, we use computational modeling to investigate how the gating kinetics of single IP₃R in FAD-affected cells differ from those in wildtype (WT) cells and how these differences translate to impaired Ca²⁺ signaling at subcellular and whole-cell levels. Our detailed analysis reveals a significantly lower threshold for Ca²⁺ oscillations at the whole-cell level in terms of agonist concentration, with higher frequency and amplitudes in FAD-affected cells. These results shed new light on the observed Ca²⁺ hyperactivity in the pre-clinical stage of AD, reporting high-frequency Ca²⁺ oscillations in neurons. Full article

AI infrastructure is entering a constraint-dominated regime in which power access, cooling, water conditions, reliability, and financing jointly shape cost, sustainability, and operational risk. Yet the metrics used to evaluate these systems remain fragmented across facility engineering, compute/workload performance, and economic or risk analysis, with definitions that often sit at different layers and under different boundaries. This fragmentation weakens cross-layer reasoning and makes decision-traceable trade-off analysis difficult. This paper proposes a structured, decision-oriented measurement architecture for AI infrastructure metrics. The framework combines a 6 × 3 taxonomy, which organizes metrics across six layers and three semantic domains, with a procedural workflow built around a problem card, variable registry, minimality gate record, activated-cell map, boundary log, metric ledger, and a results sheet with case-pack manifest. Within this protocol, the Metric Propagation Graph is used as a case-specific dependency representation for tracing decision-facing metrics back to minimal boundary-consistent inputs. It is introduced as a traceability layer within the framework rather than as a stand-alone graph-theoretic method. The paper is illustrated through one fully worked case and one scoped portability illustration. The first is a fully worked large-load planning case for the Northern Virginia data-center corridor within PJM’s Dominion zone, showing that a boundary-consistent integrated metric can reverse the ranking obtained under a simpler screening view. The second is a scoped portability illustration for hourly matching under dual Scope 2 boundaries. Its purpose is not to provide a second full empirical validation, but to show how the same dossier logic, boundary discipline, and traceable metric construction transfer to a distinct decision setting. Full article

The maintenance of gate roadways after longwall retreat is a critical geomechanical and technological problem in underground coal mining, particularly under conditions of increasing mining depth and complex geological settings. This study investigates the influence of support elements on the stress state of surrounding rocks and the stability of gate roadways intended for repeated use in coal mines of Ukraine and Kazakhstan. The research combines numerical modeling and analysis of field experience from Dniprovska Mine of PJSC “DTEK Pavlogradugol”, Kostenco Mine, and PJSC “Mine Administration Pokrovske”. Elastoplastic deformation of the rock mass was simulated using the finite element method within a stationary formulation, with the Mohr–Coulomb criterion applied to describe rock failure. Different support schemes were analyzed, including steel arch frames, protective structures, rock bolts, and cable bolts. The geomechanical response was evaluated using the parameters Q* and P*, which characterize the heterogeneity of the stress field and the degree of stress relief, respectively, as well as the extent of inelastic deformation zones. The results showed that protective structures significantly improve the condition of surrounding rocks at relatively shallow depths by reducing stress heterogeneity and limiting the development of inelastic deformation. Rock bolting promotes the formation of a reinforced rock–bolt arch in the roof, increasing roadway stability after longwall passage. However, under deep mining conditions, protective structures alone are insufficient, and reinforcement with cable bolts becomes necessary to maintain the integrity of the reinforced roof zone and reduce the load on individual bolts. Field observations from operating mines confirmed the practical efficiency of the proposed support approaches. The study demonstrates the role of each support element in forming a stable reinforced structure around the roadway and provides a basis for selecting rational support systems for gate roadways reused for ventilation or repeated use. Full article

Underwater gated time-correlated single-photon-counting (TCSPC) LiDAR is advantageous when weak target echoes coexist with strong backscatter. However, under the first-photon-triggering and SPAD dead-time mechanism, the estimated time of flight becomes dependent on the return strength, thereby producing a range walk error (RWE). This paper develops a condition-calibrated correction framework for accumulated-histogram underwater ranging in the low-photon regime. A non-homogeneous Poisson first-arrival model that jointly includes gate-limited signal photons and in-gate background triggering yields a computable expression for the total trigger probability and the conditional first-arrival time. A first-order expansion around $N_{pe}\approx 0$ leads to an approximately linear RWE–$N_{pe}$ relation under the present system–water condition. A density-based signal-window localization method and a noise-occlusion-compensated estimator of $N_{pe}$ are combined with reference-plane differential calibration. Experiments in a 10 m clear-freshwater tank at 9.11 m show that the mean absolute error is reduced from 39.205 mm to 2.130 mm, corresponding to a 94.57% improvement. Compared with a quadratic model used under higher-photon conditions, the proposed linear model yields an order-of-magnitude smaller residual error in the low-photon region ($N_{pe}<1.6$). Full article

Hydrogen production is gaining increasing importance as a key component of the transition toward carbon-neutral energy systems. In this study, the prediction of hydrogen generation in membraneless alkaline water electrolyzers (MAWEs) is investigated using deep-learning-based time-series modeling. A single-input modeling framework is adopted, where only the system current is used as the input variable. Experimental current signals obtained from long-duration tests conducted at electrolyte concentrations between 5 and 35 g KOH (7200 s per experiment) are employed as the model inputs, while mass-based hydrogen production (in grams) is used as the output variable. Two recurrent neural network architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), are implemented, and their predictive performance is comparatively evaluated using RMSE, MAE, and R² metrics. In addition to deep learning models, classical approaches including Linear Regression, ARIMA, and Naïve Forecast are also considered for comparison. The results show that both models are capable of accurately reproducing the hydrogen-production dynamics across the entire concentration range. In particular, the prediction accuracy improves notably at medium and high electrolyte concentrations, where the coefficient of determination (R²) approaches 0.98. The residual distributions remain narrow and symmetric around zero, indicating the absence of systematic estimation bias. The results also show that classical models can achieve comparable performance under stable operating conditions, while deep learning models provide advantages in capturing nonlinear and dynamic behavior. While LSTM and GRU exhibit comparable accuracy, each architecture provides complementary advantages under different operating conditions. These findings indicate that deep-learning-based time-series modeling constitutes a lightweight and reliable framework for prediction and control applications in MAWE systems. Overall, this study demonstrates the applicability of data-driven models for the dynamic characterization of membraneless water electrolysis. Full article