Search Results (629)

Indonesia has identified clean hydrogen as one of the strategic initiatives for its energy transition, recognizing its potential as an energy carrier that can support the achievement of net zero emissions. To deepen the understanding of this emerging technology, this study assesses the readiness of green hydrogen development in Indonesia through a multi-stakeholder perspective combined with a technology readiness evaluation and insights from global developments. Based on stakeholder interviews across government, industry, academia, and energy institutions, this analysis identifies key enabling conditions and barriers for hydrogen deployment in the Indonesian context. This analysis indicates that the readiness level of green hydrogen technology in Indonesia has reached approximately technology readiness level (TRL) 5–TRL 6, suggesting that most initiatives remain at the pilot and demonstration stages. In addition, seven key factors influencing green hydrogen adoption were identified: infrastructure and technology, policy and regulation, finance, application sectors, public acceptance, standardization, and private sector participation. These results provide policy-relevant insights for accelerating hydrogen development and highlight priority areas for advancing Indonesia’s transition toward a low-carbon energy system. Full article

Electroencephalogram (EEG) signals serve as the core information carrier for brain–computer interfaces (BCIs); however, their highly non-stationary nature, extremely low signal-to-noise ratio, and significant inter-individual variability pose considerable challenges for signal decoding. Existing deep learning methods struggle to strike a balance between multi-scale, fine-grained feature extraction and efficient long-range temporal modeling. To overcome this limitation, this study proposes a novel deep cascaded architecture, MSResBiMamba, which deeply integrates multi-scale spatiotemporal feature learning with cutting-edge long-sequence modeling techniques. The model first utilizes an enhanced multi-scale spatiotemporal convolutional network (MS-CNN) combined with a SE-channel attention mechanism to adaptively extract local multi-band features and dynamically suppress redundant artefacts. Subsequently, it innovatively introduces an enhanced bidirectional Mamba (Bi-Mamba) module to efficiently capture non-causal long-range temporal dependencies with linear computational complexity, whilst cascading multi-head self-attention mechanisms to establish global higher-order feature interactions. Extensive experiments on the BCI Competition IV-2a dataset demonstrate that MSResBiMamba achieves outstanding classification performance in multi-class motor imagery tasks, significantly outperforming traditional methods and existing state-of-the-art neural networks. Ablation studies and t-SNE visualisations further confirm the model’s robustness in feature decoupling and cross-subject applications, providing a high-precision, high-efficiency decoding solution for BCI systems. Full article

To address the long-standing bottleneck of inherent trade-off between thermoelectric performance and mechanical stability in pure In₂O₃ thermoelectric materials, this study puts forward a novel optimization route by innovatively adopting Yb₂O₃ as the dopant, pioneering the dual regulation of defect engineering and electronic structure reconstruction to achieve synchronous thermoelectric–mechanical property synergy, which breaks the limitation of traditional single-property doping modification for oxide thermoelectrics. For electrical transport, Yb³⁺ induces oxygen vacancy donor defects to boost carrier concentration, and targeted orbital hybridization narrows the band gap and elevates density of states near the Fermi level, synergistically lifting conductivity and offsetting the weakened Seebeck coefficient to optimize power factor with he maximum power factor improved from 1.83 μWm⁻¹K⁻² to 5.67 μWm⁻¹K⁻². For thermal transport, doping-induced lattice distortion and multi-scale defect system build intensive phonon scattering centers, sharply suppressing lattice thermal conductivity and lowering total thermal conductivity. This synergistic optimization pushes the maximum ZT value to 0.358, a remarkable breakthrough for In₂O₃-based materials. Meanwhile, Yb₂O₃ doping reinforces Vickers hardness via lattice distortion strengthening and defect bonding enhancement, eliminating the inherent performance trade-off. This work verifies Yb₂O₃ doping as a highly efficient strategy, offering solid theoretical basis and practical guidance for developing high-performance, high-stability oxide thermoelectric materials for practical applications. Full article

Reliable underwater acoustic (UWA) communication is fundamental to marine sensing applications, including environmental monitoring, underwater sensor networks, and autonomous platforms, yet remains severely challenged by multipath propagation, Doppler effects, and limited bandwidth. This paper investigates a memory-based multicarrier modulation framework in which controlled phase continuity is introduced at the symbol-mapping stage to enhance robustness against channel-induced distortions. Unlike conventional memoryless multicarrier schemes, the proposed approach embeds intentional phase memory at the transmitter and exploits it at the receiver, improving reliability in highly dispersive underwater environments. A comprehensive bit-error-rate (BER) evaluation is conducted using extensive simulations over realistic shallow-water acoustic channel models. The analysis examines rational modulation indices, pulse-shaping filters, roll-off factors, transmitter–receiver separation distances, and receiver structures. Both matched-filter and zero-forcing receivers are considered to assess trade-offs between interference mitigation and noise amplification. Results demonstrate consistent and significant BER improvements compared with conventional memoryless multicarrier systems. A modulation index of 7/16 achieves the minimum BER with matched-filter detection, while 3/10 yields optimal performance with zero-forcing detection. The Dirichlet pulse provides the most robust performance across operating conditions. These findings establish phase-memory-aware multicarrier design as a practical strategy for reliable underwater sensing and communication systems. Full article

Urban logistics systems face growing delivery demand and complex traffic and operational constraints, which make unmanned delivery carriers, including unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), a promising solution. Existing studies typically focus on a single delivery carrier type and rely on idealized assumptions, overlooking heterogeneous cooperation under multiple stations, multiple time windows, and real-world transport conditions. To address these gaps, we propose the Multi-Station UAV–UGV Cooperative Delivery Scheduling Problem with Temporally Discontinuous Service Availability (MSUUCDSP) to minimize the total travel and waiting time of UAVs and UGVs. To solve the problem, we propose a mixed-integer linear programming (MILP) model with a novel mathematical approach and a Hybrid Large Neighborhood Search (HLNS) algorithm. Additionally, we adopt a Hidden Markov Model (HMM)-based map-matching method and big data techniques to capture realistic operational characteristics. Computational experiments are conducted on various realistic instances under four diverse scenarios. Results show that UAV–UGV cooperation significantly improves efficiency, reducing total time cost by 17.12% compared with single-mode delivery, and they reveal substantial discrepancies between idealized assumptions and realistic scenarios. We further develop an ArcGIS-based simulation to support practical implementation. The findings provide valuable insights for decision-making and engineering applications for logistics operators. Full article

Energy management of multi-carrier energy hubs (MCEHs) is a challenging task, particularly when fuel cell electric vehicle (FCEV) stations are included, due to the stochastic nature of FCEV demand, system loads, and integrated renewable energy resources (RERs) such as wind turbines (WTs) and photovoltaic (PV) systems. This paper aims to optimize the energy management of an MCEH-based microgrid to simultaneously minimize total operating costs and emissions. To this end, a novel enhanced quadratic interpolation optimization (EQIO) algorithm is proposed. The proposed EQIO algorithm incorporates two key improvements: a best-to-mean quasi-oppositional-based learning (BMQOBL) strategy and an evaluation mutation (EM) strategy. The performance of EQIO is evaluated using the CEC 2022 benchmark functions, and the obtained results are compared with those of other optimization techniques. Three case studies are investigated: (i) energy management of the MCEH microgrid without RERs, (ii) sustainable operation (with RERs), and (iii) sustainable operation with RERs combined with the application of demand-side response (DSR). Moreover, the proposed framework explicitly supports long-term sustainability goals by enhancing renewable energy utilization, reducing the carbon footprint, and promoting cleaner transportation through efficient integration of FCEV infrastructure. The results demonstrate that integrating RERs reduces operating costs and emissions by 51.47% and 59.69%, respectively, compared to the case without RERs. Furthermore, the combined application of RERs and DSR achieves cost and emission reductions of 55.26% and 53.93%, respectively, compared to the case without RERs. Full article

The global energy transition demands solutions that balance intermittent renewable energy generation while decarbonizing heat and power sectors. Hydrogen has appeared as a versatile energy carrier, enabling sector coupling across electricity, heat, and industry. This work explores the integration of hydrogen into combined heat and power (CHP) systems, with a regional focus on Saarland, Germany. It depicts H₂-ready technologies including combustion engines, gas turbines, and fuel cells, and introduces a custom Python-based (Version 3.13) techno-economic optimization model to simulate multi-energy system operations. The analysis reveals that high hydrogen costs, electricity price volatility, and market design significantly constrain economic viability. However, Saarland’s industrial structure and cross-border infrastructure projects offer strategic opportunities for scalable hydrogen deployment. The article concludes with targeted recommendations for technology development, policy reform, and regional replication, positioning hydrogen CHP as a flexible and decarbonizing solution in energy-intensive regions. Full article

High-stakes logistics, defined as supply chains where delays, quality loss, or noncompliance have serious human, safety, financial, or geopolitical consequences, are a prominent case of a broader reality: global supply chains are safety-, cost-, and time-critical socio-technical systems where forecasting quality, vendor coordination, and operational decisions shape service levels and stakeholder welfare. At the same time, decarbonization pressures and the growing use of AI for planning and control introduce new risks and trade-offs across energy, computation, and physical logistics. We develop a multi-agent framework that models supply chain system-of-systems dynamics drawing on (1) supply chain decision functions (shipment planning, sourcing and vendor management), (2) national energy-transition conditions that determine grid carbon intensity, and (3) carbon-aware computation accounting for AI-enabled decision support. Methodologically, we combine predictive analytics, unsupervised segmentation, and a carbon-cost-of-intelligence layer in a scenario-based assessment of how national energy-transition profiles–from Norway to India–affect the intensity of AI compute carbon, meaning the carbon emissions generated by the hardware and data centers required to train and run AI models. We introduce the carbon-adjusted supply chain performance (CASP) metric that integrates physical transport carbon, cold-chain overhead where applicable, and AI compute carbon into a per-package-type performance measure. Our analysis yields three actionable outputs for systems engineering and environmental management: carbon, service, and cost trade-off frontiers; governance levers (sourcing portfolio rules, buffers, and compute policies); and system-level early-warning indicators for disruption amplification. This study implements a tool-augmented multi-agent system (orchestrator, risk, and sourcing agents) using AWS bedrock and strands agents, where LLM-based agents orchestrate deterministic analytical engines through structured tool interfaces with adaptive query generation. Theoretically, we extend previous systems-of-systems and sustainable supply chain findings by formalizing package-type-specific carbon–service frontiers and by embedding AI compute carbon into a socio-technical resilience framework. Practically, the CASP benchmark, governance lever analysis, and multi-agent implementation provide decision-makers with concrete tools to compare carriers, routes, and compute strategies across countries while making transparent the trade-offs between service reliability and total carbon. Full article

Selectively modulating specific brain-rhythm bands with physical stimuli helps both to reveal neural mechanisms and to provide non-pharmacological treatment avenues for brain disorders. This study proposes and implements a multi-channel transcranial magneto-acoustic stimulation driving system based on amplitude-modulated (AM) sine waves (AM-TMAS) intended to supply a reliable hardware platform for noninvasive, focal low-frequency rhythmic electrical stimulation of deep-brain structures. The driving system implements a 64-channel AM module based on an FPGA plus high-speed DACs. Multi-channel precision is achieved via a unified high-speed clock and a global UPDATE trigger. To overcome the large separation between envelope and carrier frequencies, we developed a high-fidelity AM waveform generation method based on DDS + LUT + envelope multiplication. The algorithm first centers the carrier samples to preserve waveform symmetry, then applies LUT-based envelope coefficients and fixed-point envelope multiplication, enabling high-precision AM outputs with carrier frequencies from 100 kHz to 2 MHz and envelope frequencies from 0.1 Hz to 100 kHz. We tested the system’s rhythmic multi-channel AM output performance across frequencies and also measured magneto-acoustic-coupled rhythmic electrical signals produced by the AM-TMAS driving setup. Any single channel reliably produced high-fidelity AM waveforms with a 500 kHz carrier and 8 Hz/40 Hz envelopes; the measured carrier was 499.998 kHz with excellent frequency stability. Both envelope and carrier frequencies are flexibly tunable. At the nominal 500 kHz carrier, envelope fidelity was further quantified: the extracted envelopes achieved NRMSEs of 1.0795% (8 Hz) and 1.9212% (40 Hz), confirming high-fidelity AM synthesis. Under a 0.3 T static magnetic field, the AM-TMAS driving system generated rhythmic electrical responses in physiological saline that carried the expected 40 Hz envelope. The proposed AM-TMAS driver achieves high accuracy in AM waveform generation and robust multi-channel performance, and—when combined with an external static magnetic field—can produce rhythmically modulated magneto-acoustic electrical stimulation. This platform provides a practical technical tool for brain-function research and the development of rhythm-targeted neuromodulation therapies. Full article

With the continuous integration of a large amount of renewable energy sources such as wind and solar power into the power system, the economic and secure scheduling of the power grid, as a crucial carrier for electricity transmission, becomes of paramount importance. However, issues such as voltage fluctuations at grid nodes, low renewable energy consumption rates, and increased active power losses, caused by the widespread integration of high proportions of renewable energy, urgently need to be addressed. To effectively solve these problems, this paper proposes a multi-objective coordinated optimization scheduling method for the economy and security of source–grid–load–storage based on an effective scenario-screening approach. Firstly, an iterative self-organizing data analysis algorithm based on density noise application spatial clustering is designed to efficiently generate typical output scenarios for renewable energy sources such as wind and solar power. Meanwhile, to achieve low-carbon scheduling objectives, green certificate and carbon trading mechanisms are introduced. A multi-objective coordinated scheduling and trading model for the economy and security of large power grids, sources, loads, and storage is constructed with the goal of enhancing renewable energy consumption, and it is solved using the weight assignment method and an improved particle swarm optimization algorithm. Finally, the effectiveness and feasibility of the proposed method are validated and illustrated based on an improved IEEE standard node test system. Full article

Background/Objectives: Although triple antibiotic paste is effective in managing infected primary teeth, its incomplete removability from tooth structure remains a major limitation, prompting the search for alternative drug-delivery systems. The aim of this study was to obtain a multi-antibiotic porous composite system for tailored drug delivery, to develop a formulation strategy, and to characterize the physicochemical properties and drug release. Methods: The developed composites consisted of a porous composite matrix (PCM; chitosan/bioactive filler) and two or three antibiotics (ciprofloxacin [CIP], metronidazole [MET], clindamycin [CLI]). Three methods of incorporating antibiotics were used: applying an antibiotic solution to the stabilized PCM; introducing an antibiotic solution into the polymer matrix; and introducing an antibiotic into the polymer matrix as nanoparticles. The physicochemical properties of the composites, including microstructure, compressive strength, and swelling, were assessed. The antibiotic release profile was assessed for up to 168 h. Results: The most advantageous method for introducing MET and CLI, in terms of release profile, was applying them to the PCM surface, whereas ciprofloxacin exhibited stable release when incorporated directly into the polymer matrix and entrapped during the stabilization process. The composites with nanoparticles, including MET or CIP, did not release any active substances during the experimental period. Conclusions: The results demonstrate that the developed formulation strategy enables the production of composites that rapidly release substantial amounts of the active substances within a short time frame and maintain their concentration for an extended period, which may be beneficial for the treatment of bacterial infections. Full article

This paper presents a novel dual-mode memristor-based ring oscillator designed for energy-efficient, wireless biomedical signal conditioning systems. The proposed architecture leverages a compact DTMOS memristor emulator, consisting of only two transistors and one capacitor, to replace the conventional NMOS pull-down devices in a three-stage PMOS ring oscillator. This integration enables two distinct operating modes within a single compact core: a fixed-frequency mode for stable clock generation and carrier synthesis, and a programmable chirp mode for frequency-modulated signal generation. The fixed-frequency mode achieves continuous tuning from 3.142 GHz to 4.017 GHz via varactor control, with an ultra-low power consumption of only 111 µW at 4.017 GHz. The chirp mode generates linear frequency sweeps starting from 0.8 GHz, with the sweep range independently controllable through the state capacitor value and the pulse width of the control signal (SW_Chirp). Designed in a standard 0.18 µm CMOS process, the oscillator exhibits a low phase noise of −87.82 dBc/Hz at a 1 MHz offset for the three-stage configuration, improving to −94.3 dBc/Hz for the five-stage design. The overall frequency coverage spans 0.8–4.017 GHz, representing a 133.6% fractional range. The calculated figure of merit (FoM) is −169.45 dBc/Hz. Experimental validation using a discrete CD4007 prototype confirms the oscillation principle, while comprehensive simulations demonstrate robust performance across process corners and temperature variations. With its zero-static-power memristor core, wide tunability, and dual-mode reconfigurability, the proposed oscillator is ideally suited for multi-standard wireless biomedical applications, including implantable telemetry, neural stimulation, ultra-wideband (UWB) transmitters, and non-contact vital sign monitoring. Full article

To address global carbon reduction demands, oxy-fuel combustion in circulating fluidized beds (oxy-CFB) has emerged as a highly promising carbon capture technology, offering extensive fuel flexibility and facilitating bioenergy with carbon capture and storage (BECCS). However, its commercialization is hindered by significant energy penalties and complex scale-up challenges. This review comprehensively analyzes the fundamental multiphase mechanisms, heat transfer behaviors, and multi-pollutant emission characteristics of oxy-CFB systems, drawing upon multiscale modeling advancements and operational data from pilot to 30 MW_th industrial demonstrations. Replacing air with an O₂/CO₂/H₂O mixture fundamentally alters gas–solid hydrodynamics and char conversion pathways, necessitating active fluidization state re-specification. Despite shifting optimal desulfurization temperatures and introducing recarbonation risks, the technology demonstrates inherent advantages in synergistic pollutant control, including the complete elimination of thermal NO_x. While atmospheric oxy-CFB is technically viable, transitioning to pressurized operation is critical to minimizing system efficiency penalties. Furthermore, integrating oxygen carrier-aided combustion (OCAC) and developing advanced predictive control strategies are essential to managing multi-module thermal inertia and enabling rapid dynamic responsiveness for modern power grids. Full article

This review aims to explore the potential and mechanisms of selenium-biofortified probiotics as an innovative nutritional strategy for alleviating exercise-induced physiological stress. Exercise, particularly high-intensity or exhaustive exercise, triggers a cascade of physiological perturbations, including oxidative stress, inflammatory responses, gut barrier dysfunction, and muscle damage. Traditional single-nutrient strategies, such as inorganic selenium or probiotic supplementation, are often limited by low bioavailability or a narrow scope of action. Selenium-biofortified probiotics are produced via microbial biotransformation, which converts inorganic selenium into bioavailable organic forms like selenoamino acids or selenium nanoparticles that are loaded onto active probiotic carriers. This creates a synergistic entity combining the bioactivity of selenium with the gut-modulating functions of probiotics. Their core mechanism involves establishing a multi-layered defense system: by providing substrate for key selenoproteins like glutathione peroxidase, they directly enhance endogenous antioxidant defenses; by modulating immune cytokine networks, they downregulate excessive post-exercise inflammation; through probiotic colonization and metabolites, they maintain intestinal epithelial barrier integrity, countering exercise-induced intestinal hyperpermeability; and via the gut–muscle axis, they may regulate muscle metabolism and repair. Animal studies provide evidence for improved exercise endurance and reduced damage markers, but human clinical trials show inconsistent results, highlighting the influence of study design, dosage, and individual baseline status. Future research requires high-quality, long-term human trials to elucidate specific molecular pathways and develop personalized application protocols, advancing this synergistic strategy toward precision sports nutrition. Full article

Efficient recovery of critical metals from complex polymetallic ores relies on clarifying their mineralogical occurrence. A Cd-Ag-rich Pb-Zn ore from southwestern China was investigated via a multi-scale process mineralogy approach integrating reflected-light microscopy, TIMA and LA-ICP-MS. Systematic analysis was conducted on ore texture, mineral liberation characteristics, and the occurrence and distribution of Ag and Cd. The ore is a medium–low grade Pb-Zn deposit (Pb 0.81%, Zn 4.33%) with economically recoverable associated Cd (0.066%) and Ag (5.04 ppm), dominated by sphalerite (7.74%), galena (1.39%), pyrite (3.92%), quartz (47.80%) and calcite (18.66%). TIMA analysis revealed poor liberation of sphalerite and galena, with fully liberated particles accounting for <30%. LA-ICP-MS results showed that Cd is highly enriched in sphalerite (average 5982 ppm, 98%) mainly in isomorphous form, while Ag is dispersed in pyrite (average 178 ppm, 56%), galena (average 227 ppm, 25%) and sphalerite (average 31 ppm, 19%), also primarily as isomorphs; partial Cd in pyrite occurs as micro-inclusions. The multi-scale mineralogical data provide a scientific basis for resource utilization, indicating the necessity of fine grinding and differentiated recovery strategies: “zinc depression followed by lead flotation” for Pb-Zn recovery, the establishment of a comprehensive Ag recovery system with Pb-Zn-Fe as carriers for Ag recovery, and “Zn-carried Cd” flotation for Cd recovery. This study verifies the effectiveness of combined TIMA and LA-ICP-MS in elucidating critical metal occurrence, and provides a mineralogy-based process design for the sustainable processing of such complex ores. Full article