Search Results (1,164)

The fastest growth in electricity demand in the industrialized world will likely come from the broad adoption of artificial intelligence (AI)—accelerated by the rise of generative AI models such as OpenAI’s ChatGPT. The global “data center arms race” is driving up power demand and grid stress, which creates local and regional challenges because people in the area understand that the additional data center-related electricity demand is coming from faraway places, and they will have to support the additional infrastructure while not directly benefiting from it. So, there is an incentive for the data center operators to manage the fast and unpredictable power surges internally so that their loads appear like a constant baseload to the electricity grid. Such high-intensity and short-duration loads can be served by hybrid energy storage systems (HESSs) that combine multiple storage technologies operating across different timescales. This review presents an overview of energy storage technologies, their classifications, and recent performance data, with a focus on their applicability to AI-driven computing. Technical requirements of storage systems, such as fast response, long cycle life, low degradation under frequent micro-cycling, and high ramping capability—which are critical for sustainable and reliable data center operations—are discussed. Based on these requirements, this review identifies lithium titanate oxide (LTO) and lithium iron phosphate (LFP) batteries paired with supercapacitors, flywheels, or superconducting magnetic energy storage (SMES) as the most suitable HESS configurations for AI data centers. This review also proposes AI-specific evaluation criteria, defines key performance metrics, and provides semi-quantitative guidance on power–energy partitioning for HESSs in AI data centers. This review concludes by identifying key challenges, AI-specific research gaps, and future directions for integrating HESSs with on-site generation to optimally manage the high variability in the data center load and build sustainable, low-carbon, and intelligent AI data centers. Full article

This paper proposes an event-triggered extension of duty-ratio-based model predictive direct speed control (DR-MPDSC) for permanent magnet synchronous motor (PMSM) drives in electric vehicle (EV) applications. The main contribution is the development of an event-triggered execution framework specifically tailored to DR-MPDSC, in which control updates are performed only when the speed tracking error violates a prescribed condition, rather than at every sampling instant. Unlike conventional MPDSC and time-triggered DR-MPDSC schemes, the proposed strategy achieves a significant reduction in control execution frequency while preserving fast dynamic response and closed-loop stability. An optimized duty-ratio formulation is employed to regulate the effective application duration of the selected voltage vector within each sampling interval, resulting in reduced electromagnetic torque ripple and improved stator current quality. An extended Kalman filter (EKF) is integrated to estimate rotor speed and load torque, enabling disturbance-aware predictive speed control without mechanical torque sensing. Furthermore, a unified field-weakening strategy is incorporated to ensure wide-speed-range operation under constant power constraints, which is essential for EV traction systems. Simulation and experimental results demonstrate that the proposed event-triggered DR-MPDSC achieves steady-state speed errors below 0.5%, limits electromagnetic torque ripple to approximately 2.5%, and reduces stator current total harmonic distortion (THD) to 3.84%, compared with 5.8% obtained using conventional MPDSC. Moreover, the event-triggered mechanism reduces control update executions by up to 87.73% without degrading transient performance or field-weakening capability. These results confirm the effectiveness and practical viability of the proposed control strategy for high-performance PMSM drives in EV applications. Full article

Modern 5G+grade low power wide area network (LPWAN) technologies such as Narrowband Internet-of-Things (NB-IoT) operate utilizing a multi-channel slotted ALOHA algorithm at the random access phase. As a result, the random access phase in such systems is characterized by relatively low throughput and is highly sensitive to traffic fluctuations that could lead the system outside of its stable operational regime. Although theoretical results specifying the optimal transmission probability that maximizes the successful preamble transmission probability are well known, the lack of knowledge about the current offered traffic load at the BS makes the problem of maintaining the optimal throughput a challenging task. In this paper, we propose and analyze a new reactive access-barring scheme for NB+IoT systems based on machine learning (ML) techniques. Specifically, we first demonstrate that knowing the number of user equipments (UE) experiencing a collision at the BS is sufficient to make conclusions about the current offered traffic load. Then, we show that through utilizing ML-based techniques, one can safely differentiate between events in the Physical Random Access Channel (PRACH) at the base station (BS) side based on only the signal-to-noise ratio (SNR). Finally, we mathematically characterize the delay experienced under the proposed reactive access-barring technique. In our numerical results, we show that by utilizing modern neural network approaches, such as the XGBoost classifier, one can precisely differentiate between events on the PRACH channel with accuracy reaching 0.98 and then associate it with the number of user equipment (UE) competing at the random access phase. Our simulation results show that the proposed approach can keep the successful preamble transmission probability constant at approximately 0.3 in overloaded conditions, when for conventional NB-IoT access, this value is less than 0.05. The proposed scheme achieves near-optimal throughput in multi-channel ALOHA by employing dynamic traffic awareness to adjust the non-unit transmission probability. This proactive congestion control ensures a controlled and bounded delay, preventing latency from exceeding the system’s maximum load capacity. Full article

This study presents a photovoltaic (PV)-based electric vehicle (EV) charging system designed to optimize energy use and support isolated microgrid operations. The system integrates PV panels, DC/AC, AC/DC, and DC/DC converters, voltage and frequency droop control, and two energy management algorithms: Power Sharing and SEWP (Spread Energy with Priority). The DC/AC converter demonstrated high efficiency, with stable AC output and Total Harmonic Distortion (THD) limited to 1%. The MPPT algorithm ensured optimal energy extraction under both gradual and abrupt irradiance variations. The DC/DC converter operated in constant current mode followed by constant voltage regulation, enabling stable power delivery and preserving battery integrity. The Power Sharing algorithm, which distributes PV energy equally, favored vehicles with a higher initial state of charge (SOC), while leaving low-SOC vehicles at modest levels, reducing satisfaction under limited irradiance. In contrast, SEWP prioritized low-SOC EVs, enabling them to achieve higher SOC values compared to the Power Sharing algorithm, reducing SOC dispersion and enhancing fairness. The integration of voltage and frequency droop controls allowed the station to support microgrid stability by limiting reactive power injection to 30% of apparent power and adjusting charging current in response to frequency deviation. Full article

Insects achieve millisecond sensor–motor loops with tiny sensors, compact neural circuits, and powerful actuators, embodying the principles of Edge AI. We present a comprehensive architectural blueprint translating insect neurobiology into a hardware–software stack: a latency-first control hierarchy that partitions tasks between a fast, dedicated Reflex Tier and a slower, robust Policy Tier, with explicit WCET envelopes and freedom-from-interference boundaries. This architecture is realized through a neuromorphic Reflex Island utilizing spintronic primitives, specifically MRAM synapses (for non-volatile, innate memory) and spin-torque nano-oscillator (STNO) reservoirs (for temporal processing), to enable instant-on, memory-centric reflexes. Furthermore, we formalize the biological governance mechanisms, demonstrating that, unlike conventional ICEs and miniturbines that exhibit narrow best-efficiency islands, insects utilize active thermoregulation and DGC (Discontinuous Gas Exchange) to maintain nearly constant energy efficiency across a broad operational load by actively managing their thermal set-point, which we map into thermal-debt and burst-budget controllers. We instantiate this integrated bio-inspired model in an insect-like IFEVS thruster, a solar cargo e-bike with a neuromorphic safety shell, and other safety-critical edge systems, providing concrete efficiency comparisons, latency, energy budgets, and safety-case hooks that support certification and adoption across autonomous domains. Full article

High-precision prediction of the crystal diameter during the growth of electronic-grade silicon single crystals is a critical step for the fabrication of high-quality single crystals. However, the process features high-temperature operation, strong nonlinearities, significant time-delay dynamics, and external disturbances, which limit the accuracy of conventional mechanism-based models. In this study, mechanism-based models denote physics-informed heat-transfer and geometric models that relate heater power and pulling rate to diameter evolution. To address this challenge, this paper proposes a hybrid deep learning model combining a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and self-attention to improve diameter prediction during the shoulder-formation and constant-diameter stages. The proposed model leverages the CNN to extract localized spatial features from multi-source sensor data, employs the BiLSTM to capture temporal dependencies inherent to the crystal growth process, and utilizes the self-attention mechanism to dynamically highlight critical feature information, thereby substantially enhancing the model’s capacity to represent complex industrial operating conditions. Experiments on operational production data collected from an industrial Czochralski (Cz) furnace, model TDR-180, demonstrate improved prediction accuracy and robustness over mechanism-based and single data-driven baselines, supporting practical process control and production optimization. Full article

This paper addresses Region-3 control of a 2.5 MW three-bladed HAWT using a data-driven workflow that links empirical modeling to implementable pitch control. To focus on fundamental regulation dynamics, the turbine is modeled as a rigid single-mass drivetrain driven by identified quasi-steady aerodynamics. First, we identify a compact shaft-power surface $P(\omega ,V,\beta )$ and recover the associated MPP condition, which clarifies why the optimal rotor speed rises with wind and motivates a comparison between capped-MPP operation and constant-speed regulation. We then synthesize a practical Region-3 loop—PI in rate with a first-order pitch servo and saturation handling—and evaluate proportional (P), PI, and PI + servo controllers under sinusoidal and Kaimal-turbulent inflow. Finally, we propose an adaptive PI variant that keeps a fixed acceleration feed-through but retunes the integral path online via ARX(1,1) + RLS to maintain a target closed-loop bandwidth. Performance metrics computed over the full simulation window (t ∈ [0, 50] s) show that P-only control exhibits large steady bias and cap violations; PI recenters speed and power around their targets; adding a pitch servo further trims peaks and ripple. In steady-state turbulent tests, PI + servo achieves tight regulation, Δω_peak ≈ 0.033% (0.079 rad/s), P_RMS ≈ 0.62%, while the adaptive PI maintains similar tightness with the lowest variability overall Δω_peak ≈ 0.0104% (0.025 rad/s), P_RMS ≈ 0.17. The workflow yields a practically implementable β(V) schedule and a lightweight adaptation mechanism that compensates for slow aerodynamic performance drift without changing the control structure. While structural loads and aeroelastic modes are not explicitly modeled, the proposed controller enforces strict speed and power constraints via a rigid-body dynamic analysis. Extensions to IPC, preview/forecast augmentation, and validation on higher-fidelity aeroelastic/drivetrain models are identified as future work. Full article

The progressive replacement of synchronous machines by inverter-based resources (IBRs) reduces system inertia and short-circuit strength, making power systems more vulnerable to frequency and voltage instabilities. Grid-forming (GFM) inverters can mitigate these issues by establishing voltage and frequency references, emulating inertia and enabling autonomous operation during islanding, while grid-following (GFL) inverters mainly contribute to reactive power support. This paper evaluates the capability of GFM inverters to provide grid support under both grid-connected and islanded conditions at the distribution level. Electromagnetic transient (EMT) simulations in MATLAB/Simulink R2022b were performed on a 20 kV radial microgrid comprising GFM and GFL inverters and aggregated load. Small disturbances, including phase-angle jumps and voltage steps at the point of common coupling, were introduced while varying the GFM share and virtual inertia constants. Also, local variables were assessed during islanded operation and separation process. Results indicate that maintaining a GFM share above approximately 30–40% with inertia constants exceeding 2 s significantly enhances frequency stability, supports successful transitions to islanded operation, and improves overall resilience. The study highlights the complementary roles of GFM and GFL in enabling the stable and resilient operation of converter-dominated distribution systems. Full article

Energy efficiency is a key requirement for Internet of Things (IoT) nodes, particularly in applications powered by energy harvesting that operate without batteries. In this work, we present a parametric power model of a LoRa transceiver (Semtech SX1276) aimed at ultra-low power remote sensing scenarios. The transceiver was characterized in all relevant states (startup, transmission, reception, and sleep), and the results were used to build a state-based model that predicts average power consumption as a function of transmission power, sleep strategy, packetization, and input data rate. Experimental validation confirmed that the cubic fit for transmission peaks achieves a determination coefficient of 0.99, while reception is added as a constant consumption. The model was implemented in a Python simulator that provides mean, best-case, and worst-case estimates of system power consumption, and it was validated in an ASIC-based sensor node demonstration, with predictions within 10% of measured values. The framework highlights the trade-offs between energy efficiency and robustness (e.g., minimal SF and no CRC vs. higher spreading factors and error-control) and supports the design of custom controllers for ultra-low power IoT nodes as well as more energy-permissive applications. Full article

Yttrium oxide (Y₂O₃) has emerged as a key material for advanced solar-blind ultraviolet (SBUV) photodetectors, attributable to its large bandgap energy (~5.5 eV), high dielectric constant, excellent silicon compatibility, and robust thermal stability. To precisely tune its optical bandgap for optimal alignment with the intrinsic solar-blind region, this study prepared Y_1.5In_0.5O₃ ternary alloy films via co-sputtering, achieving an optimized bandgap of 4.70 eV. After optimizing the photosensitive layer, we fabricated a self-powered Pt/Y_1.5In_0.5O₃/p-GaN back-to-back heterojunction SBUV photodetector was fabricated based on the optimized photosensitive layer. Under photovoltaic operation (0 V), the resulting device exhibited impressive performance metrics: a narrow spectral response (FWHM ~50 nm), quick rise/decay times of 30 and 75 ms, respectively, and high operational durability (less than 0.8% photocurrent degradation over 100 cycles). The detector also maintained a low noise current level (2.95 × 10⁻¹² A/Hz^1/2 at 1 Hz) and a low noise-equivalent power (NEP) of 4.42 × 10⁻⁹ W/Hz^1/2, indicating high sensitivity to weak optical signals. These results establish Y_xIn_2−xO₃ ternary alloy as a viable material platform for SBUV detection and provide a new design strategy for developing highly sensitive, low-noise and spectrally selective ultraviolet photodetectors. Full article

In the digital age, social media platforms homogenize beauty standards and intricately link clothing choices to social norms and class identities. Grounded in Pierre Bourdieu’s concepts of cultural and social capital, supplemented by Erving Goffman’s theory of stigma, this study examines how social media amplifies pre-existing socio-cultural pressures that influence Turkish women’s decisions to abandon the hijab. The research has practical implications for understanding and addressing hijab abandonment. It employs a qualitative design based on semi-structured interviews with 13 participants, analyzed through a phenomenological approach. The findings reveal that the pursuit of social acceptance and resistance to social exclusion are more decisive factors in hijab abandonment than direct social media influence. While social media serves as a crucial amplifier of aesthetic ideals and a gateway to digital legitimacy, the primary drivers are deeply rooted in the pursuit of social acceptance and resistance to long-standing mechanisms of socio-cultural exclusion, stigmatization, and symbolic violence—processes intensified and mediated through digital platforms. The analysis uncovers the operation of a dual-sided neighborhood pressure, whereby women face scrutiny from both religious communities enforcing idealized piety norms and secular circles perpetuating stigmatizing labels such as backwardness or ignorance. Crucially, participants reported that unveiling was strategically employed as a means of overcoming barriers to professional advancement, gaining access to elite social spheres, and escaping the constant burden of representation. The study concludes that hijab abandonment emerges as a complex strategy of social navigation, where digital platforms act as powerful accelerants of pre-existing class- and identity-based conflicts. Full article

This paper is dedicated to the investigation of new generalizations of the classical Gronwall–Bellman–Bihari integral inequalities, which are fundamental tools in the qualitative and quantitative analysis of differential, integral, and integro-differential equations. We establish two primary, novel theorems. The first theorem presents a significant generalization for inequalities involving composite nonlinear functions and iterated integrals. This result provides an explicit bound for an unknown function $u(t)$ satisfying an inequality of the form $\mathsf{\Phi }(u(t))\le a(t)+{\int }_{t_0}^t f(s)\mathsf{\Psi }(u(s))ds+{\int }_{t_0}^t g(s)\mathsf{\Omega }({\int }_{t_0}^s h(\tau )K(u(\tau ))d\tau )ds$. The proof is achieved by defining a novel auxiliary function and applying a rigorous comparison principle. The second main theorem establishes a new bound for a class of fractional integral inequalities involving the Riemann–Liouville fractional integral operator ${\mathcal{I}}^{\alpha }$ and a non-constant coefficient function $b(t)$ in the form $u(t)\le a(t)+b(t){\mathcal{I}}^{\alpha }[\omega (u(s))]$. This result extends several recent findings in the field of fractional calculus. The mathematical derivations are detailed, and the assumptions on the involved functions are made explicit. To illustrate the utility and potency of our main results, we present two applications. The first application demonstrates how our first theorem can be used to establish uniqueness and boundedness for solutions to a complex class of nonlinear integro-differential equations. The second application utilizes our fractional inequality theorem to analyze the qualitative behavior (specifically, the boundedness of solutions) for a generalized class of fractional integral equations. These new inequalities provide a powerful analytical framework for studying complex dynamical systems that were not adequately covered by existing results. Full article

The maritime industry faces significant challenges from energy consumption and air pollution. Fuel cells, especially hydrogen types, offer a promising clean alternative with high energy density and rapid refueling, but their slow dynamic response necessitates integration with lithium batteries (energy storage) and supercapacitors (power storage). This paper investigates a hybrid vessel power system combining a fuel cell with a Hybrid Energy Storage System (HESS) to address these limitations. An improved droop control strategy with adaptive coefficients is developed to ensure balanced State of Charge (SOC) and precise current sharing, enhancing system performance. A comprehensive protection strategy prevents overcharging and over-discharging through SOC limit management and dynamic filter adjustment. Furthermore, the Parrot Optimization Algorithm (POA) optimizes HESS capacity configuration by simultaneously minimizing battery degradation, supercapacitor degradation, DC bus voltage fluctuations, and system cost under realistic operating conditions. Simulations show SOC balancing within 100 s (constant load) and 135 s (variable load), with the lithium battery peak power cut by 18% and the supercapacitor peak power increased by 18%. This strategy extends component life and boosts economic efficiency, demonstrating strong potential for fuel cell-powered vessels. Full article

The permeability of cohesive zone plays an important role in the stable operation and production efficiency of blast furnace. Fluidity of the primary slag in the cohesive zone is an important factor affecting the permeability and is usually characterized by the so-called fluidity index. In order to describe the relationship between the viscosity and the fluidity index of the FeO-CaO-SiO₂ ternary slag system (similar to the primary slag) generated by sinter, the fluidity and viscosity of FeO-CaO-SiO₂ ternary slag system was studied in this paper. It includes testing the fluidity under different temperatures and different compositions, calculating the viscosity of FeO-CaO-SiO₂ ternary slag system through the solid–liquid coexistence-phase viscosity model, and coupling the relationship between fluidity index and viscosity. The results show the following: (1) For the FeO-CaO-SiO₂ ternary slag system, when the temperature is constant, the fluidity index of primary slag in non-three-phase region increases with the increase in w (FeO), while that in three-phase region decreases with the increase in w (FeO). (2) The Kondratiev model and the Batchelor model were jointly employed to calculate the primary slag viscosity in the cohesive zone. (3) In FeO-CaO-SiO₂ ternary slag system, there is an approximate power function correlation between the solid–liquid coexistence-phase viscosity and the fluidity index. The research content and results of this paper have a certain theoretical guiding value for further research on more complex cohesive zone slag system and enhanced blast furnace smelting. Full article

This study examines the influence of physical biomass pretreatment on the pyrolysis behavior of woody pruning residues of Carya illinoinensis (pecan tree) processed in a stainless-steel batch reactor heated by concentrated radiative energy. Experiments were conducted with 25.5 g of biomass using a solar simulator equipped with a mirror concentrator, operating at three constant thermal power levels (234, 482, and 725 W). As a pretreatment strategy, the woody residues were deliberately processed without drying, while mechanical size reduction and sieving were applied to obtain a controlled particle size range of 1–4 mm. This approach enabled the isolated assessment of the effects of physical pretreatment, particularly particle size and bulk density, on heat transfer, thermal response, and pyrolysis behavior. The pyrolysis performance of the pretreated woody biomass was systematically compared with that of walnut shell biomass and inert volcanic stones subjected to the same particle size control. Two consecutive experimental cases were implemented: Case A (CA), comprising heating, pyrolysis of fresh biomass, and cooling; and Case B (CB), involving reheating of the resulting biochar under identical operating conditions. An improved analytical methodology integrating temperature–time profiles, their derivatives, and gas composition analysis was employed. The results demonstrated the apparently inert thermal behavior of biochar during reheating and enabled clear temporal identification of the main biomass conversion stages, including drying, active pyrolysis of hemicellulose and cellulose, and passive lignin degradation. However, relative to walnut shell biomass of equivalent volume, the woody pruning residues exhibited attenuated thermal and reaction signals, primarily attributed to their lower bulk density resulting from the selected pretreatment conditions. This reduced bulk density led to less distinct pyrolysis stages and a 4.66% underestimation of the maximum reaction temperature compared with thermogravimetric analysis, highlighting the critical role of physical pretreatment in governing heat transfer efficiency and temperature measurement accuracy during biomass pyrolysis. Full article