Search Results (6,520)

In the context of Poland’s commitments under the European Union’s climate policy, including the European Green Deal and the Fit for 55 package, as well as the decision to ban imports of hard coal from Russia and Belarus, ensuring the stability of the domestic market for energy commodities is becoming a key challenge. The response to these needs is the Coal Platform concept developed by the KOMAG Institute of Mining Technology (KOMAG), which aims to integrate data on hard coal resources, production, and demand. The most important problem is not the just-in-time (JIT) strategy itself, but the lack of accurate, up-to-date data and the high technological and organizational inertia on the production side. The JIT strategy assumes an ability to predict future demand well in advance, which requires advanced analytical tools. Therefore, the Coal Platform project analyses the use of artificial intelligence algorithms to forecast demand and adjust production to actual market needs. The developed mathematical model (2024–2030) takes into account 12 variables, and the tested forecasting methods (including ARX and FLNN) exhibit high accuracy, which together make it possible to reduce overproduction, imports, and CO₂ emissions, supporting the country’s responsible energy transition. This article describes approaches to issues related to the development of the Coal Platform and, above all, describes the concept, preliminary architecture, and data model. As an additional element, a mathematical model and preliminary results of research on forecasting methods in the context of historical data on hard coal production and consumption are presented. The core innovation lies in integrating the just-in-time (JIT) philosophy with AI-driven forecasting and scenario-based planning within a cloud-ready Coal Platform architecture, enabling dynamic resource management and compliance with decarbonization targets. Full article

This study presents a comprehensive analysis of the marginal capacity credit of utility-scale solar and wind power in South Korea using an effective load-carrying capability-based methodology. This research makes three key contributions distinguishing it from previous works. First, the study introduces the concept of marginal capacity credit to quantify the contributions of newly added renewable energy capacities in power systems that already host significant solar and wind power capacities. Second, it evaluates the interaction effects between solar and wind power, revealing their complementary potential in enhancing system adequacy across different penetration levels. Third, it investigates how integrating energy storage systems mitigates intermittency and aligns renewable generation with peak demand. Results indicate that solar power provides relatively high marginal capacity credit at low penetration levels due to its alignment with peak demand, but its contribution declines as deployment expands and peak hours shift. Conversely, wind power maintains more stable marginal capacity credit and eventually surpasses solar power at higher penetration levels due to its broader generation profile. Storage integration notably enhances marginal capacity credit for both resources, with solar power gaining greater benefit from optimized charging and discharging strategies. These findings provide practical guidance for improving power system reliability and capacity planning under growing renewable penetration. Full article

The increasing role of automation systems in energy-efficient buildings creates a need for simulation approaches that support standardized assessment already at the design stage. This paper presents a digital twin-based simulation framework that integrates building information modeling (BIM)-derived building data with MATLAB/Simulink models to enable regulation-oriented evaluation of building automation and control strategies. The proposed approach targets scenario-based analysis of automation maturity levels, covering conventional, advanced, and predictive configurations aligned with EN ISO 52120 and the Smart Readiness Indicator (SRI). A representative academic building model is used to demonstrate how the framework supports reproducible modeling of heating, ventilation, and air conditioning (HVAC), lighting, and shading control functions and enables consistent comparison of their energy-related behavior under unified boundary conditions. The results show that the framework effectively captures performance trends associated with increasing automation sophistication and reveals interaction effects between control subsystems that are not accessible in conventional energy simulation tools. The proposed methodology provides a practical and extensible foundation for early-stage, regulation-aligned evaluation of smart building solutions and for the further development of predictive and artificial intelligence (AI)-assisted control concepts. Full article

The integration of complex system concepts and sustainability in chemical engineering education is often limited to elective or separate courses rather than their integration into the core curriculum. This pedagogical gap can lead to graduates who lack a holistic understanding of the intricate interplay between industrial processes and the Earth’s ecological limits, and the feedback loops required to address complex global challenges. This paper presents a transformative approach to close this gap by embedding the Planetary Boundaries framework and system thinking across core chemical engineering courses, such as Material and Energy Balances, Reaction Engineering, and Process Design, and extending this integration to capstone projects. The framework treats the curriculum itself as an interconnected learning system in which key systems concepts are revisited and deepened through contextualized examples and digital modeling tools, including process simulators and life-cycle assessment. We map each boundary to illustrative process examples and learning activities and discuss practical implementation issues such as curriculum crowding, educator readiness, and data availability. This approach aligns with outcome-based education goals by making system thinking and absolute sustainability explicit learning outcomes, preparing future chemical engineers to design processes that respect planetary limits while balancing technical performance, economic feasibility, and societal needs. Full article

Guanidinoacetate methyltransferase (GAMT) is an essential enzyme in the biosynthesis of creatine, an important molecule in energy recycling. GAMT loss of function leads to GAMT deficiency (GAMT-D), an autosomal recessive disorder resulting in low creatine levels and the accumulation of a toxic intermediate, guanidinoacetate (GAA). GAMT-D patients present with intellectual disability and epilepsy, emphasizing the detrimental consequences of disturbed creatine metabolisms in the central nervous system (CNS). Current treatments are not curative and may not restore creatine metabolism in the brain. Here, we present a proof-of concept study testing the first CNS-directed, Adeno-associated virus serotype 9 (AAV9)-based gene therapy for the treatment of GAMT-D. the delivery of GAMT construct to cellular models of GAMT-D effectively restored protein and mRNA expression of GAMT while increasing intracellular creatine content and decreasing GAA accumulation. In murine models of GAMT-D, treatment with scAAV9.hGAMT, delivered intrathecally, resulted in increased creatine content as well as significant decreases in GAA accumulation in the CNS and peripheral organs. Overall, we found that scAAV9.hGAMT represents a promising gene therapy for treating GAMT-D, warranting further investigation in animal models to determine an appropriate therapeutic window for both efficacy and safety that allows for translation into human patients in the future. Full article

Population aging is driving the need for unobtrusive, continuous monitoring solutions in residential care environments. Radio-frequency (RF)-based technologies such as Ultra-Wideband (UWB) and millimeter-wave (mmWave) radar are particularly attractive for providing detailed information on presence and movement while preserving privacy. Building on a UWB–mmWave localization system deployed in a senior living residence, this paper focuses on the data-processing methodology for extracting quantitative mobility indicators from long-term indoor monitoring data. The system combines a device-free mmWave radar setup in bedrooms and bathrooms with a tag-based UWB positioning system in common areas. For mmWave data, an adaptive short-term average/long-term average (STA/LTA) detector operating on an aggregated, normalized radar energy signal is used to classify micro- and macromovements into bedroom occupancy and non-sedentary activity episodes. For UWB data, a partially constrained Kalman filter with a nearly constant velocity dynamics model and floor-plan information yields smoothed trajectories, from which daily gait- and mobility-related metrics are derived. The approach is illustrated using one-day samples from three users as a proof of concept. The proposed methodology provides individualized indicators of bedroom occupancy, sedentary behavior, and mobility in shared spaces, supporting the feasibility of combined UWB and mmWave radar sensing for longitudinal routine analysis in real-world elderly care environments. Full article

The connectivity demands of high-performance computing (HPC), artificial intelligence (AI) and data centers are driving the development of a new generation of multimode optical components. This paper discusses the vertical cavity surface emitting laser (VCSEL) bandwidth and noise performance needed to support 106 Gbd line rates with PAM4 modulation for 200 Gbps per lane multimode optical links. A −3 dB bandwidth greater than 35 GHz and a RIN of less than −152 dB/Hz are demonstrated. No uncorrectable errors were observed over 50 m of OM4 fiber, demonstrating good link stability. VCSEL device performance and the associated wear-out life are presented. Leveraging good device reliability and low power consumption of VCSEL-based links, a novel VCSEL near-packaged optics (NPO) concept is proposed for optical interconnects in AI scale-up network applications. Optical interconnects allow for longer reaches, compared to copper interconnects, which facilitate larger AI clusters with network disaggregation. The proposed VCSEL NPO can achieve an energy efficiency of ~1 pJ/bit, which is the highest among optical interconnects. Full article

It is undeniable that rapid population increase coupled with growing resource constraints are making the demand for smart and sustainable solutions more urgent than ever to secure future resources for the transition to sustainable energy production. To address these issues, it is necessary to define innovative approaches that can exploit more efficiently and extensively the resources we have at our disposal. Consequently, this paper provides an overview of the potential benefits of processing waste-printed circuit boards (WPCBs) that are generated in large quantities and, due to their high metal content, can emerge as an adequate and profitable supply of critical metals, such as copper, aluminum, and nickel, which are essential for green energy transition. The review promotes the idea of industrial symbiosis as a concept that goes beyond circular economy and can integrate WPCB treatment and manufacturing processes related to sustainable energy transition, although they are different industrial sectors that can be even regionally separated. Major metal recovery processes from WPCBs are examined and discussed, with the primary focus on the performances of copper, aluminum, and nickel production, while additional metals relevant to the energy transition are also highlighted. Finally, the review paper argues and exemplifies that the recovered metals from WPCBs have the required properties to be supplied into the manufacturing processes of wind turbines, solar panels, and lithium-ion batteries. Full article

Counterfactual quantum control is a novel control method, in which no actual material particles or energy are transported and exchanged between the controller and the controlled. By introducing the quantum Zeno effect where the evolution of a quantum system can be suppressed by continuous observation, this paper presents a review of research progress in counterfactual quantum control. The basic concept of counterfactual quantum control is presented and macro counterfactual quantum control is thoroughly discussed. In addition, related experimental verification and applied exploration are also discussed. This review paper covers the progress toward counterfactual quantum communication, non-invasive imaging and specific applications. Full article

The energy sector in all countries around the world is undergoing a transformation, with the main trend being the increasing share of renewable sources. Some countries, such as those in the European Union, have set themselves the goal of completely phasing out fossil fuels by 2050. Currently, the energy systems of European countries are far from this goal, and fossil fuels play a key role in balancing energy systems. This article presents a one-year simulation of a hypothetical Polish energy system based solely on renewable sources and utilizing biomethane, synthetic ammonia, and solid biomass as sources to ensure energy supply in the event of unfavorable weather conditions, which means a lack of wind and solar radiation. Six variants of these systems were analyzed, demonstrating the feasibility of such a system using only biogas as a stabilizing fuel. The required generating capacities of wind turbines, photovoltaic panels, and installations for converting biomethane, ammonia, and solid biomass into electricity were determined. Calculations were based on historical data recorded in 2024 in the Polish energy system. It was found that by increasing currently installed PV and wind turbines by a factor of 4.8 and installing 24 GW of ICE engines fueled with biomethane and an additional 10 GW of ORC modules, current electricity demand would be covered 100% by renewable energy sources. The same goal can be achieved without ORC modules by increasing the installed power of PV and wind turbines by a factor of 6.8. The novelty of this research is the application of the fully renewable concept of electricity generation systems to Polish reality using real-life data. Full article

Obesity is a systemic metabolic disorder characterized by chronic low-grade inflammation and insulin resistance, with growing evidence indicating that the brain represents a primary and particularly vulnerable target organ. Beyond peripheral metabolic consequences, obesity induces region-specific structural, functional, and biochemical alterations within the central nervous system, contributing to cognitive impairment, dysregulated energy homeostasis, and increased susceptibility to neurodegenerative diseases. This narrative review examines key neurometabolic and neuroinflammatory mechanisms underlying obesity-related brain vulnerability, including downstream neuroinflammation, impaired insulin signaling, mitochondrial dysfunction, oxidative stress, blood–brain barrier disruption, and impaired brain clearance mechanisms. These processes preferentially affect frontal and limbic networks involved in executive control, reward processing, salience detection, and appetite regulation. Advanced neuroimaging has substantially refined our understanding of these mechanisms. Magnetic resonance spectroscopy provides unique in vivo insight into early neurometabolic alterations that may precede irreversible structural damage and is complemented by diffusion imaging, volumetric MRI, functional MRI, cerebral perfusion imaging, and positron emission tomography. Together, these complementary modalities reveal microstructural, network-level, structural, hemodynamic, and molecular alterations associated with obesity-related brain vulnerability and support the concept that such brain dysfunction is dynamic and potentially modifiable. Integrating neurometabolic and multimodal neuroimaging biomarkers with metabolic and clinical profiling may improve early risk stratification and guide preventive and therapeutic strategies aimed at preserving long-term brain health in obesity. Full article

Valley-dependent topological physics offers a promising avenue for designing nanoscale devices based on gapless single-layer graphene. To demonstrate this potential, we investigate an electrical bias-controlled topological discontinuity in valley polarization within a two-segment armchair nanoribbon of gapless single-layer graphene. This discontinuity is created at the interface by applying opposite in-plane, transverse electrical biases to the two segments. An efficient tight-binding theoretical formulation is developed to calculate electron states in the structure. In a reference configuration, we obtain energy eigenvalues and probability distributions that feature interface-confined electron eigenstates induced by the topological discontinuity. Moreover, to elucidate the implications of interface confinement for electron transport, a modified configuration is introduced to transform the eigenstates into transport-active, quasi-localized ones. We show that such states result in Fano “anti-resonances” in transmission spectra. The resilience of these quasi-localized states and their associated Fano fingerprints is examined with respect to fluctuations. Finally, a proof-of-concept band-stop electron energy filter is presented, highlighting the potential of this confinement mechanism and, more broadly, valley-dependent topological physics in designing nanoscale devices in gapless single-layer graphene. Full article

It is crucial to understand the market structure and the formation of the electricity mix in the context of the increasingly widespread and global introduction of renewable energy sources as primary energy sources. Due to the cyclical nature of energy production from RES, a long-term plan for seasonal storage is mandatory for smooth and effective energy transition. The stability of the energy system remains a key requirement, especially due to the dynamic changes in the generation, consumption, and pricing of energy resources. This article aims to present the concept that, in the absence of a properly structured and balanced market, thermal power plants prove to be flexible and reliable power sources that can be quickly integrated into the energy system at critical moments when maintaining the grid balance is difficult (such as during peak hours of solar generation). Full article

Background: Chest X-ray (CXR) is widely used for the assessment of thoracic diseases, yet automated multi-label interpretation remains challenging due to subtle visual patterns, overlapping anatomical structures, and frequent co-occurrence of abnormalities. While recent deep learning models have shown strong performance, limitations in interpretability, anatomical awareness, and robustness continue to hinder their clinical adoption. Methods: The proposed framework employs a hybrid ConvNeXtV2–Vision Transformer (ViT) architecture that combines convolutional feature extraction for capturing fine-grained local patterns with transformer-based global reasoning to model long-range contextual dependencies. The model is trained exclusively using image-level annotations. In addition to classification, three complementary post hoc components are integrated to enhance model trust and interpretability. A segmentation-aware Gradient-weighted class activation mapping (Grad-CAM) module leverages CheXmask lung and heart segmentations to highlight anatomically relevant regions and quantify predictive evidence inside and outside the lungs. An ontology-driven neuro-symbolic reasoning layer translates Grad-CAM activations into structured, rule-based explanations aligned with clinical concepts such as “basal effusion” and “enlarged cardiac silhouette”. Furthermore, a lightweight out-of-distribution (OOD) detection module based on confidence scores, energy scores, and Mahalanobis distance scores is employed to identify inputs that deviate from the training distribution. Results: On the VinBigData test set, the model achieved a macro-AUROC of 0.9525 and a Micro AUROC of 0.9777 when trained solely with image-level annotations. External evaluation further demonstrated strong generalisation, yielding macro-AUROC scores of 0.9106 on NIH ChestXray14 and 0.8487 on CheXpert (frontal views). Both Grad-CAM visualisations and ontology-based reasoning remained coherent on unseen data, while the OOD module successfully flagged non-thoracic images. Conclusions: Overall, the proposed approach demonstrates that hybrid convolutional neural network (CNN)–vision transformer (ViT) architectures, combined with anatomy-aware explainability and symbolic reasoning, can support automated chest X-ray diagnosis in a manner that is accurate, transparent, and safety-aware. Full article

The increasing challenges of urbanization and environmental degradation have led to a greater need for built environments that minimize ecological consequences while actively contributing to ecosystem services (ES). Bio-Inspired Design (BID) is a promising approach that translates natural-system ideas into architectural and urban solutions. This study investigates how BID can be used to deliver and improve ecosystem services, like climate regulation, air purification, and energy, in the built environment, focusing on applications in hot climates and at the meso scale. The study conducts a qualitative and integrative analysis of bio-inspired concepts derived from existing research and innovative practices. It examines specific ecosystem services—selected based on previous studies—and illustrates how these strategies can improve environmental performance in urban contexts. A conceptual framework for linking biological analogies to urban functions is proposed. The framework emphasizes the interdisciplinary relationships between architecture, urban design, material science, and environmental engineering. This provides a helpful guide for researchers and practitioners on how to use BID to enhance sustainability results. The study suggests that incorporating BID principles into urban design procedures can potentially transform built environments into active contributors to ecosystem functioning, enabling them to provide ES rather than merely consuming resources. While this conclusion is conceptual, the framework highlights pathways for more resilient and sustainable urban futures. Full article