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The supply chain ecosystem (SCE) operating within an urban environment is characterised by continuous interactions among manufacturing, logistics, service, information, and energy flows across multiple smart city manufacturing-service clusters (SCMSCs). Within the SCE, intelligent production buildings (IPBs) emerge as multifunctional multistorey production-service infrastructures developed under conditions of limited urban land availability and increasing demand for localised manufacturing and service integration. These buildings operate under heterogeneous and dynamically changing energy-demand conditions, substantially complicating energy consumption management. This study develops a knowledge-driven coordination approach for the energy consumption management of IPBs operating within SCMSCs from the perspective of the urban SCE. IPBs are conceptualised as distributed environments with finite building-level power supply system capacity, where manufacturing, logistics, service, and digital processes dynamically compete for shared energy resources. A hierarchical representation of the SCMSC energy environment is proposed, capturing distributed interactions and heterogeneous electricity-demand profiles across interconnected clusters. An information-analytical system integrating monitoring, data acquisition, analysis, ML-based demand prediction, planning, and decision-support functions is developed to support predictive electricity-demand coordination. The proposed framework combines IoT-enabled monitoring with digital-twin-supported synchronisation of energy states for distributed coordination among IPBs. The proposed approach is evaluated through scenario-based analysis of an IPB operating within an urban manufacturing-service environment. The results indicate the potential of knowledge-driven coordination to improve energy-capacity utilisation, mitigate peak-load formation, and enhance operational stability within SCMSCs. Full article

The rapid growth of artificial-intelligence (AI) and high-performance computing workloads has reshaped the power-delivery requirements of high-bandwidth memory (HBM), neural processing units (NPUs), and advanced systems-on-chip (SoCs). These platforms draw large currents that vary rapidly at aggressively scaled supply voltages, so their on-chip regulators must combine high current density, nanosecond-scale settling with minimal droop, wideband power-supply rejection (PSR), and stable capacitor-less operation, while also mitigating issues such as power supply-induced jitter (PSIJ) in high-speed clock and data paths. On-chip low-dropout (LDO) regulators have become the key building block at the point of load, and a wide range of architectures have emerged to meet these demands. This paper reviews on-chip LDOs for HBM and SoC power integrity. We translate application-level power-integrity requirements, including PSIJ, into circuit specifications; organize the design space into fast-transient, wideband high-PSR, high-current and distributed, and capacitor-less and digital/hybrid architectures; benchmark representative state-of-the-art designs using both conventional and application-relevant metrics such as data rate, jitter, and eye margin; and distill the resulting technology trends. Full article

Time-fractional generalizations of the Lindblad master equation describe open quantum systems whose coupling to the environment exhibits power-law memory. We develop the optimal-control theory of such systems and analyse the consistency of the adjoint calculus on which every gradient-based pulse-design method relies. Casting the density operator in a fractional Bochner–Sobolev space of Hilbert–Schmidt operator valued functions, we establish well-posedness through Mittag–Leffler resolvent families, prove that the completely positive trace-preserving (CPTP) structure is preserved for the controlled, time-dependent generator without recourse to subordination, and obtain existence and uniqueness of optimal controls by the direct method. The adjoint is governed by the right Riemann–Liouville derivative with a fractional-integral terminal condition, a structure established for Caputo dynamics with a Mayer cost by Bergounioux and Bourdin, who also showed that a pointwise terminal costate cannot exist. Our central result concerns the discrete counterpart of that fact, where existence is never lost: imposed on the right-Caputo adjoint of a convergent scheme, the pointwise condition yields a bounded costate and a well-defined reduced gradient carrying an error that is mesh-independent. We further establish a sharp rate in the classical limit: the defect vanishes exactly linearly, $\parallel \Delta \left(\beta \right)\parallel =C(1-\beta )+O\left({(1-\beta )}^2\right)$, with C given in closed form through a digamma series. Two consequences follow: monotonicity of the defect in the memory order is proved near $\beta =1$, and the memory order is locally identifiable from gradient data alone. A corrected adjoint restores consistency with proven convergence rates. Numerical experiments on two-level, three-level and two-qubit open systems (Liouville dimension up to 16) confirm the mesh-independence, reproduce C to three significant digits, and recover the full rate on graded meshes. Full article

Blockchain-based electronic voting has yet to reach the electoral environments that could benefit from it most. Existing systems are designed for well-connected, grid-powered urban settings and fail precisely where digital voting could widen participation the most; this gap motivates a framework engineered from the ground up for low-infrastructure conditions rather than one adapted to them. Across much of Africa, four deployment barriers stand in the way: intermittent connectivity that excludes an estimated 43% of the population, identity documents that remain valid for up to ten years and degrade conventional face recognition from 99% to below 80%, power demands of 200–250 W that rule out battery-powered operation, and cryptographic designs locked to a single algorithm regardless of operating context. This paper presents AdaptVote, a five-layer framework built on the premise that these barriers must be removed jointly rather than in isolation. At its core, the AI-FOLM algorithm anchors INT8-quantised ArcFace embeddings to age-stable craniofacial ratios on a Xilinx Zynq-7020 FPGA, reaching a 96.7% True Accept Rate at 0.1% False Accept Rate over 6–10 year age gaps—2.2 percentage points beyond the state of the art. A nullifier-based protocol allows ballots to be cast entirely offline with Merkle-tree integrity, an adaptive ECDSA/EdDSA/BLS layer cuts Ethereum settlement costs by 60–86%, and the complete polling station runs at 4.2 W peak power and 28 Wh per 12 h session, a 99.3% energy reduction over GPU baselines. A twelve-hour field deployment with 300 voters at Mohammed First University, Nador, Morocco, conducted under 35% network availability, recorded a 95.7% first-attempt authentication rate (95% CI: 92.7–97.5%), 99.2% system uptime, and a 53.3% reduction in average voting time relative to paper ballots ($p<0.001$). Together, these results suggest that trustworthy electronic voting can be engineered for, rather than merely adapted to, low-infrastructure electoral settings. Full article

The growing deployment of distributed renewable generation, storage, electric vehicles, heat pumps, smart buildings, and controllable demand is expanding the flexibility available to local energy systems. Energy communities provide the governance context for collective participation and value creation, while community virtual power plants provide the operational mechanism for aggregating distributed resources and connecting them to local optimisation, flexibility markets, grid services, and resilience functions. This PRISMA-ScR-informed framework development study synthesises the literature from Scopus, Web of Science, and IEEE Xplore to examine how artificial intelligence supports community VPP operation, where agentic AI adds capabilities beyond established optimisation, reinforcement learning, and multi-agent systems, and which design requirements follow governed orchestration. The synthesis shows that current evidence is strongest for component-level forecasting, scheduling, bidding, adaptive control, distributed coordination, and digital-twin validation, whereas integrated agentic orchestration remains an emerging direction. Classical multi-agent systems already provide decentralised representation, communication, negotiation, and coordinated control; the additional role proposed for agentic AI is therefore narrower and concerns context-aware multi-step workflow orchestration, governed tool use, exception handling, grounded explanation, and bounded delegation across existing analytical and control services. The study introduces operational flexibility intelligence as the capability to transform potential distributed flexibility into deployable, authorised, market-, grid-, resilience-, and community-compatible action. It further develops a conceptual layered reference architecture in which agentic orchestration operates through governed tools and interfaces rather than bypassing validated resource controllers. Fairness, comfort, privacy, cybersecurity, resilience, auditability, and human-in-command authority are treated as cross-cutting operational constraints. The architecture defines a design and validation agenda rather than an empirically validated implementation. Full article

With the rapid development of clean energy, hydro–wind–photovoltaic multi-energy systems require centralized control centers with stronger operational support, coordinated dispatch, and emergency response capabilities. However, safety constraints in real control systems limit their use for operator training, fault handling, and emergency drills. In addition, existing simulation-training systems are still largely oriented toward individual plants or single-energy systems, with limited support for coordinated training in hydro–wind–photovoltaic multi-energy centralized-control scenarios. To address these limitations, this paper reviews the development and research status of hydro–wind–photovoltaic centralized control monitoring systems and simulation training platforms. First, the typical architectures and functions of hydropower, wind power, photovoltaic, and multi-energy centralized control systems are summarized, with emphasis on centralized monitoring, unified management, operational analysis, and coordinated decision support. Second, based on established digital-twin architectures and existing simulation-training technologies, a generalized digital-twin-based simulation training framework for hydro–wind–photovoltaic centralized control is proposed, integrating multi-energy dynamic simulation, centralized-control functions, coordinated dispatch, operator interaction, and training assessment. Finally, the key platform functions are discussed, including high-fidelity virtual environment construction, dynamic signal simulation, coordinated dispatch optimization, operator training and assessment, and emergency drills. This review provides a systematic reference for the development and engineering application of digital-twin-based simulation training platforms for hydro–wind–photovoltaic multi-energy centralized control. Full article

Digital fluxgate magnetometers have been widely used in deep space exploration due to their low noise, high sensitivity, and high reliability. This paper presents a digital fluxgate magnetometer using a fifth-order single-loop 1-bit Sigma–Delta modulator. With a consistent system structure, measurement range, test setup, and calculation method, the characteristics of magnetic field measurement noise and non-linear error are obtained under four OSR configurations through simulation analysis and experimental testing. The test results show that within the range of ±65,000 nT, the system achieves its optimal performance with a non-linearity of 0.024%, an RMS noise of 0.106 nT, and a noise power spectral density of 5.7 pT·Hz^−1/2 at 1 Hz. These results indicate that increasing the OSR can effectively improve the performance of this digital fluxgate magnetometer, enabling high linearity and low noise measurement in Earth’s magnetic field. Full article

Chinese ethnic minority cultures are integral to the Chinese cultural system and play a significant role in shaping the nation’s image through international dissemination. Amid the profound integration of internationalization and digitalization, ethnic minority cultures draw on Guochao (national tide)—a distinctive contemporary Chinese dissemination paradigm—to merge traditional ethnic heritage with modern artistic forms, thereby enabling a structural shift from passive heritage preservation to dynamic cultural reproduction. This process helps consolidate China’s national memory and enhance the international dissemination of its cultural soft power. Taking Kam Big Song (in Chinese, Dong Zu Da Ge) as a typical case, this paper examines how ethnic minority cultures can adopt Guochao as a contemporary paradigm for cultural dissemination to transcend mere protection of intangible cultural heritage (ICH). The paper proposes a fourfold strategic framework—encompassing talent cultivation, symbolic translation, dissemination improvement, and technological empowerment—to facilitate the international outreach of ethnic minority cultures while preserving their cultural subjectivity and authenticity. By integrating locally rooted core values, modern cultural translation, and international recognition, ethnic minority cultures can fully showcase the cultural diversity and interconnectedness of Chinese civilization. Through this systematic approach to strengthening the Chinese national community, these efforts help enhance China’s international cultural influence. Full article

Against the backdrop of global carbon-neutrality targets, the circular utilization of end-of-life power batteries has become a critical pathway for securing resources and reducing environmental risks. Digital technologies are increasingly reshaping circular supply-chain management. High-performing circular suppliers are essential to the efficient operation of digital circular supply chains. However, existing research on circular supplier selection (CSS) still faces three gaps: a theoretical gap in which digitalization is neglected in circular supply chains, an information-representation gap causing evaluation distortion, and an aggregation-mechanism gap arising from the inadequate treatment of interdependencies among decision variables. To address these issues, this study constructs a decision model based on probabilistic uncertain linguistic T-spherical fuzzy sets (PULTSFSs) and Choquet-integral-based aggregated sum product assessment (CASPAS). First, this study develops a multilevel evaluation system that closely integrates digital management and control capabilities. By combining PULTSFSs with the Choquet integral (CI), a CASPAS group decision-making model that captures interaction effects among decision elements is proposed. The strategic procurement of nickel-cobalt-manganese 811 (NCM811) cathode-material precursors by CATL is used as a case study. The results show that this indicator system is better suited to digital circular supply-chain management scenarios than traditional frameworks. In the CATL case, the recycled-material supplier h₃ achieves a comprehensive score of 0.660, 5.60% higher than that of the second-ranked alternative. By capturing interdependencies among evaluation elements, the proposed model exhibits stronger ranking discriminability and robustness than traditional independent-attribute methods, thereby providing a practical foundation and an efficient tool for digital supply-chain management in the power-battery recycling industry. Full article

As the photovoltaic (PV) generation sector expands, ensuring reliable maximum power point tracking (MPPT) in embedded controllers becomes increasingly important. However, many MPPT studies rely on idealized simulation assumptions that neglect practical sensing limitations, including ADC quantization, finite measurement resolution, sensor noise, offset, ripple, and scaling constraints. This paper presents a sensing-aware simulation-based digital twin framework for photovoltaic arrays that integrates dynamic environmental excitation with a virtual instrumentation layer and firmware-level execution on an ESP32 microcontroller (Espressif Systems, Shanghai, China). Unlike conventional model-in-the-loop simulations, the proposed framework ensures that the embedded control algorithm operates on reconstructed and quantized measurements rather than on ideal internal model states. The platform is evaluated using an Incremental Conductance MPPT implementation as a representative embedded workload. The results demonstrate that the proposed simulation-based digital twin framework enables repeatable firmware-level experimentation while exposing instrumentation-induced effects on tracking stability, control dynamics, and measurement-driven behavior under non-ideal sensing conditions. Furthermore, it provides structured datasets to support early-stage development, tuning, and validation of embedded MPPT algorithms before physical laboratory deployment. Full article

We integrated a Karakuri mechanism into a low-latency (14.2 ms) event-driven software decision support system by combining the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS). Expert matrices ($C.R.=0.0729$) prioritized labor saving with machine idle-time reduction (global weight = 0.1119) as the most critical requirement. TOPSIS results showed that the cam mechanism was the optimal configuration, followed by pulley and roller mechanisms. These top-ranked mechanical solutions serve as a low-power, zero-electricity baseline that enhances shop-floor management efficiency, providing a stable foundation for advanced sensor integration. By filtering mechanical noise and stabilizing structural motion, the developed system enables seamless incorporation of micro-sensors and telemetry systems, effectively bridging mechanical automation with high-precision digital sensing. This systematic approach reduces reliance on trial-and-error design, supports lean and Jidoka principles, and offers a scalable pathway for sustainable manufacturing transformation. Full article

This contribution presents a methodology for optimal placement (OP) of meters in power systems, using the state-space reference frame. The goal is to minimize the state estimation error, specifically, the mean squared error (MSE), through OP of a limited number of measurement devices and keep the total observability of the system. The measurement set is applied to the time-domain state estimation based on the Kalman filter (KF) to obtain voltage and current waveforms in real time, and the harmonic content is evaluated through the application of the Fast Fourier Transform (FFT). The effectiveness of harmonic state estimation (HSE) is demonstrated in case studies considering different operating points in a test electrical network, particularly in estimating the dynamic behavior of nonlinear electrical loads. The HSE method is implemented in physical tests using Lab-Volt^® equipment to monitor the system in real time using MATLAB/Simulink^® software through the RL-LAB^® platform; the real-time experimental tests (RTE) allow validation of the real-time digital simulation (RTS). Full article

This paper presents an FPGA feasibility study of two digital processing blocks for a future real-time Visible Light Positioning (VLP) receiver: a Sliding Discrete Fourier Transform (SDFT) stage for carrier-magnitude extraction and a hardware-optimized Multi-Layer Perceptron (MLP) inference engine for coordinate estimation. The localization model is trained and evaluated offline with experimental data from the four-photodiode VLP system reported in the reference work, whereas the SDFT and MLP FPGA blocks are validated separately on a physical Visible Light Communication (VLC) hardware platform. Therefore, the reported 1–2 mm spatial accuracy belongs to the offline reference dataset and model, whereas the FPGA results quantify module latency, numerical fidelity, implementation resources, and tool-estimated power, not physical end-to-end coordinate accuracy. The design was implemented on a AMD Xilinx Zynq-7000 (xa7z020clg484-1Q) SoC FPGA device and operated at 100 MHz. The SDFT block requires 61,655 clock cycles, corresponding to 616.55 μs, while the MLP variants require between 607 and 6612 clock cycles, corresponding to 6.07 μs and 66.12 μs, respectively. All MLP implementations reproduce the offline software reference with HW/SW MSE values on the order of ${10}^{-6}$. The resulting blocks and the reported first HLS integration estimates establish an implementation path while identifying the acquisition, synchronization, and multi-tone validation work still required for a complete receiver. Full article

Artificial intelligence (AI) is transforming photovoltaic (PV) systems from passive generators into self-forecasting, self-diagnosing, and self-optimizing energy assets. This review synthesizes 170 Scopus-indexed documents published between 2020 and 2026 across six technical domains: solar resource and PV power forecasting; intelligent monitoring, fault diagnosis, and cybersecurity; AI-driven control and design optimization; smart grid integration and real-time energy management; AI-assisted PV materials, devices, and manufacturing; and cross-sectoral smart PV applications. Quantitative synthesis shows that hybrid deep learning forecasters reduce root mean square error by 31.9–43.9% relative to persistence and single-model baselines. Attention-based architectures achieve mean absolute percentage errors of up to 5%. Machine learning classifiers achieve fault detection accuracies of 92.3–99.4% with protection response times below 100 ms, enabling predictive maintenance at the fleet scale. Reinforcement learning energy management lowers electricity costs by 20–55%, reduces peak demand by 13–31.5%, and raises PV self-sufficiency from 71.5% to 89.7%. AI-optimized thermal and material interventions deliver efficiency gains of up to 22.2%, and indoor perovskite devices exceed 40% conversion efficiency. Reported performance metrics are derived from heterogeneous datasets, horizons, and baselines; they are therefore synthesized as indicative ranges rather than directly comparable benchmarks. Persistent barriers include data scarcity, model opacity, cybersecurity vulnerabilities, edge deployment constraints, and energy justice concerns. These barriers are mapped to research directions in explainable AI, federated and transfer learning, digital twins, and blockchain-enabled energy markets. A roadmap to 2040 consolidates the findings and charts the transition toward high-efficiency, resilient, and sustainable solar energy conversion. Full article