Search Results (91)

Compressed air systems (CASs) represent a significant portion of energy consumption in large-scale built environments and manufacturing facilities, suffering from both micro-level physical pipeline leaks and macro-level operational inefficiencies. This paper proposes a unified, dual-action artificial intelligence framework aimed at advancing building decarbonization by systematically integrating acoustic-vision leak quantification with schedule-aware machine learning. Specifically, the framework targets pneumatic pipe connection leaks, fitting leaks, and joint degradation faults within compressed air distribution networks, which are the primary sources of micro-level volumetric energy losses in industrial building systems. First, a probabilistic multimodal fusion algorithm (MPSF) using an ultrasonic camera is developed to detect and geometrically quantify physical leaks, successfully translating pixel areas into physical facility energy loss metrics (estimating 11.0 kW of wasted power from detected severe leaks). Second, to optimize the compressor’s supply matching the actual facility demand without risking data leakage from internal flow sensors, an eXtreme Gradient Boosting (XGBoost) model is proposed. By utilizing only external building environmental conditions and the real-time operational schedules of 13 distinct zones, the model achieves highly accurate dynamic power prediction (R² = 0.9698). Finally, comprehensive simulations based on real-world digital monitoring data from a facility-scale built environment demonstrate that only the concurrent application of both modules ensures stable end-point pressure. The integrated framework achieves a substantial system-wide building energy reduction of over 20% to 40% compared to baseline constant-pressure operations, yielding an estimated annual reduction of 116 tons of CO₂ emissions, thereby providing a direct pathway toward carbon-neutral building operations. Full article

This research paper demonstrates how to manage Caputo fractional neutral integro-differential equations which include both integral and nonlinear elements through a unified framework that models dynamic systems with memory-based dynamics. The research establishes sufficient conditions for controllability through fixed point theory in a Banach space framework which requires particular assumptions while the study focuses on the ${\mathfrak{K}}_1<1$ condition which leads to the existence of a controllable solution. The proposed criteria are demonstrated through a numerical example which tests the theoretical results. The real-world case study uses artificial neural network (ANN) technology to predict Litecoin prices through the application of the fractional controllability model which analyzes historical financial data. The hybrid framework enables precise forecasting of nonlinear time series because it combines fractional calculus mathematical principles with ANN learning abilities. The proposed method demonstrates its predictive efficiency. The method shows robust performance through experimental results using cross-validation and performance metrics. The proposed model demonstrates competitive performance while providing additional advantages such as incorporation of memory effects and theoretical controllability. The research establishes a novel connection between fractional dynamical systems and machine learning which serves as an essential tool for studying complicated systems in theoretical research and practical applications. Full article

While it seems sensible that human-centred artificial intelligence (AI) means centring “human behaviour and experience,” it cannot be any other way. AI, I argue, is usefully seen as a relationship between technology and humans where it appears that artefacts can perform, to a greater or lesser extent, human cognitive labour. This is evinced using examples that juxtapose technology with cognition, inter alia: abacus versus mental arithmetic; alarm clock versus knocker-upper; camera versus vision; and sweatshop versus tailor. Using novel definitions and analyses, sociotechnical relationships can be seen as varying types of: displacement (harmful), enhancement (beneficial), and/or replacement (neutral) of human cognitive labour. Ultimately, all AI implicates human cognition; no matter what. Obfuscation of cognition in the AI context—from clocks to artificial neural networks—results in distortion, in slowing critical engagement, perverting cognitive science, and indeed in limiting our ability to truly centre humans and humanity in the engineering of AI systems. To even begin to de-fetishise AI, we must look the human-in-the-loop in the eyes. Full article

This article examines how artificial intelligence and algorithmic systems are reconfiguring truth regimes in digital societies, introducing the concept of “Artificial Truth” to describe an emerging form of epistemic governance where knowledge production and validation become infrastructural functions of sociotechnical systems. The study develops an integrated theoretical framework combining Foucault’s notion of truth regimes, Bourdieu’s theory of symbolic capital and fields, and Actor-Network Theory’s constructivist approach. Through conceptual analysis, the article investigates how algorithmic recommendation systems, generative AI, and automated fact-checking operate as epistemic devices that actively shape what is recognized as credible, authoritative, and true in public discourse. The analysis reveals three fundamental transformations: (1) the restructuring of trust economies, with epistemic authority shifting from institutional expertise to platform-native capital based on engagement metrics and affective proximity; (2) the emergence of generative AI as an epistemic actor producing “synthetic truth” through linguistic fluency rather than propositional understanding; (3) the institutionalization of computational veridiction in algorithmic fact-checking systems that translate situated epistemic judgments into probabilistic classifications presented as neutral. These dynamics configure a regime where truth is evaluated less by correspondence with reality and more by computational plausibility and platform integration. The article’s primary contribution lies in providing a unified theoretical framework for understanding contemporary transformations of epistemic authority, moving beyond disinformation studies to analyze AI as an epistemic actor. By integrating classical sociological perspectives with Science and Technology Studies, it conceptualizes algorithmic systems as epistemic infrastructures that embody specific power relations, restructure symbolic capital economies, and distribute epistemic authority asymmetrically, with profound implications for democratic knowledge, citizen epistemic agency, and public sphere pluralism. Full article

Geothermal energy is a clean, renewable, and baseload-stable resource of strategic importance for carbon neutrality. Hot dry rock (HDR) reservoirs are characterized by high temperatures, great depths, and abundant reserves. However, their extremely low natural permeability requires artificial fracturing to establish effective heat exchange networks. Conventional hydraulic fracturing in enhanced geothermal systems (EGS) faces major challenges under HDR conditions, including excessive water consumption, strong water–rock interactions, and elevated induced seismicity risks, limiting its engineering applicability. Waterless or low-water fracturing technologies offer alternative stimulation pathways due to their distinctive physicochemical properties. Existing reviews have mainly addressed individual aspects, such as specific fracturing media or proppant transport, without systematically integrating recent advances in supercritical CO₂ fracturing, foam fracturing, liquid nitrogen fracturing, and hybrid-fluid fracturing technologies, or comprehensively evaluating their engineering implications. This review systematically analyzed the fracturing mechanisms, heat exchange performance, environmental risks, and HDR-specific engineering challenges of these technologies. Results indicate that waterless/low-water fracturing technologies enhance heat extraction efficiency by generating complex fracture networks while mitigating seismic and reservoir damage risks. However, large-scale application requires further advances in the high-temperature stability of fracturing media, material durability, multiphase flow control, and field validation. Full article

Biological soil crusts (BSCs) are critical ecological components in arid lands. Their formation and stability hinge on the assembly and interactive networks of cyanobacteria-led bacterial communities. Yet, how different functional cyanobacteria shape the underlying microbial structure and assembly rules is poorly understood. Here, we cultivated artificial algal crusts using two representative cyanobacteria: the nitrogen-fixing Leptolyngbya sp. and the non-nitrogen-fixing Microcoleus vaginatus (M. vaginatus CM01). A total of six treatments were established based on the presence or absence of spraying with in situ BSCs leachate: a control group without inoculation of algae or bacteria (soil, S); a treatment group sprayed only with bacterial suspension (soil + bacteria, SB); a treatment group sprayed only with M. vaginatus CM01 (soil + M. vaginatus CM01, SM); a treatment group co-inoculated with both BSCs leachate and M. vaginatus CM01 (soil + M. vaginatus CM01 + bacteria, SMB); a treatment group inoculated only with Leptolyngbya sp. CT01 (soil + Leptolyngbya sp. CT01, SL); and a treatment group co-inoculated with Leptolyngbya sp. CT01 and biocrust leachate (soil + Leptolyngbya sp. CT01 + bacteria, SLB). By integrating 16S rRNA gene sequencing, neutral community modeling (NCM), and structural equation modeling (SEM), we dissected differences in Cyano-BSCs development, bacterial community composition, co-occurrence networks, and assembly mechanisms. Inoculation with M. vaginatus CM01 (SM, SMB) superiorly promoted Cyano-BSCs development: the SM group achieved the highest coverage (23.33%), while the SMB group showed marked increases in organic matter (OM, 4.10 g·kg⁻¹) and chlorophyll a (Chla, 13.40 μg·g⁻¹), alongside a >5-fold rise in bacterial, cyanobacterial, and nitrogen-fixation gene abundances versus controls. The mechanism centers on extracellular polymeric substances (EPS) secreted by M. vaginatus, which homogenized the microenvironment, suppressed stochastic bacterial dispersal (NCM, SM: R² = 0.698), and enhanced deterministic selection. This process forged a highly cooperative network (89.74% positive links, average degree 34.71) that directionally enriched Cyanobacteria (relative abundance 40.40%). The Shannon index of Cyano-BSCs from the group (SMB) reached 7.72 ± 0.09, reflecting high microbial community diversity. SEM confirmed M. vaginatus directly regulated bacterial assembly (path coefficient = 0.59, p < 0.05) and indirectly improved the soil environment (path coefficient = 0.64, p < 0.05), establishing a “cyanobacteria-community-environment” feedback loop. Conversely, the Leptolyngbya sp. groups (SL, SLB), despite enriching nitrogen-fixing bacteria and fungi, exhibited low carbon fixation efficiency (notably 1.26 g·kg⁻¹ OM in SL) and lack of EPS; communities remained stochastic (NCM, SL: R² = 0.751) with no effective regulatory pathway—a pattern mirrored in S and SB groups. Our findings demonstrate that M. vaginatus acts as a core engineer of biological soil Cyano-BSCs formation via an “EPS-mediated habitat filtering—functional group enrichment—cooperative network assembly” cascade, enforcing deterministic community construction. Leptolyngbya sp., with limited niche-constructing ability, fails to exert comparable control. This work provides a targeted framework for the artificial restoration of Cyano-BSCs in arid zones. Full article

The growing demand for electricity in residential sectors and the global need to decarbonize power systems are accelerating the transformation toward smart and sustainable energy networks. Smart homes and smart grids, integrating renewable generation, energy storage, and intelligent control systems, represent a crucial step toward achieving energy efficiency and carbon neutrality. However, ensuring real-time optimization, interoperability, and sustainability across these distributed energy resources (DERs) remains a key challenge. This paper presents a comprehensive review of artificial intelligence (AI) applications for sustainable energy management and low-carbon technology integration in smart grids and smart homes. The review explores how AI-driven techniques include machine learning, deep learning, and bio-inspired optimization algorithms such as particle swarm optimization (PSO), whale optimization algorithm (WOA), and cuckoo optimization algorithm (COA) enhance forecasting, adaptive scheduling, and real-time energy optimization. These techniques have shown significant potential in improving demand-side management, dynamic load balancing, and renewable energy utilization efficiency. Moreover, AI-based home energy management systems (HEMSs) enable predictive control and seamless coordination between grid operations and distributed generation. This review also discusses current barriers, including data heterogeneity, computational overhead, and the lack of standardized integration frameworks. Future directions highlight the need for lightweight, scalable, and explainable AI models that support decentralized decision-making in cyber-physical energy systems. Overall, this paper emphasizes the transformative role of AI in enabling sustainable, flexible, and intelligent power management across smart residential and grid-level systems, supporting global energy transition goals and contributing to the realization of carbon-neutral communities. Full article

Decisions made by pilots and drivers suffering from depression can endanger the lives of hundreds of people, as demonstrated by the tragedies of Germanwings flight 9525 and Air India flight 171. Since the detection of depression is currently based largely on subjective self-reporting, there is an urgent need for fast, objective, and reliable detection methods. In our study, we present an artificial intelligence-based system that combines iris-based identification with the analysis of pupillometric and eye movement biomarkers, enabling the real-time detection of physiological signs of depression before driving or flying. The two-module model was evaluated based on data from 242 participants: the iris identification module operated with an Equal Error Rate of less than 0.5%, while the depression-detecting CNN-LSTM network achieved 89% accuracy and an AUC value of 0.94. Compared to the neutral state, depressed individuals responded to negative news with significantly greater pupil dilation (+27.9% vs. +18.4%), while showing a reduced or minimal response to positive stimuli (−1.3% vs. +6.2%). This was complemented by slower saccadic movement and longer fixation time, which is consistent with the cognitive distortions characteristic of depression. Our results indicate that pupillometric deviations relative to individual baselines can be reliably detected and used with high accuracy for depression screening. The presented system offers a preventive safety solution that could reduce the number of accidents caused by human error related to depression in road and air traffic in the future. Full article

Heart failure with preserved ejection fraction (HFpEF) accounts for about half of heart failure cases and is linked to aging, obesity, diabetes, and multimorbidity, yet disease-modifying therapies remain limited. A major barrier is heterogeneity: HFpEF comprises overlapping inflammatory, fibrotic, cardiometabolic, and hemodynamic/vascular endophenotypes embedded within systemic cardiorenal and cardiohepatic cross-talk, which conventional metrics such as left ventricular ejection fraction (LVEF), natriuretic peptides (NPs), and standard imaging capture incompletely. In this narrative review, we synthesize clinical, mechanistic, and trial data to describe HFpEF endophenotypes and their multi-organ interactions; critically appraise why traditional diagnostic and enrollment strategies contributed to neutral outcomes in landmark trials; and survey emerging cardiovascular multi-omics studies. We then outline an integrative systems-biology framework that applies (i) within-layer analyses and cross-layer integration, (ii) network-based driver nomination and biomarker discovery, and (iii) target nomination to link molecular programs with circulating markers and candidate therapies. Finally, we discuss practical challenges in implementing multi-omics HFpEF research and highlight future directions such as artificial intelligence (AI)-enabled multi-omics integration, cross-organ profiling, and biomarker-guided, endotype-enriched platform trials. Collectively, these advances position HFpEF as a proving ground for precision cardiology, in which therapies are matched to molecularly defined disease programs rather than ejection-fraction cutoffs alone. Full article

This paper presents a simulation-based artificial neural network (ANN) model to predict bit-rock interaction forces during drilling. Drill string vibration poses a significant challenge in the oil, gas, and geothermal industries, leading to non-productive time and substantial financial losses. This research addresses the challenge of modelling bit-rock interaction excitation forces, which is crucial for predicting vibration and component fatigue life. For a PDC bit with multiple cutters, the cutter tangential velocities at various drilling speeds are calculated, and individual cutter forces are predicted with a two-dimensional discrete element method simulation in which a single cutter moves in a straight line through rock modelled as bonded particles. This data is then used to train an ANN model that characterizes the bit-rock force time series in terms of frequency, amplitude, and distribution of force peaks. Once inserted into a dynamic simulation of the drill string, the algorithm reconstructs the expected bit-rock force time series. A case study using a rigid segment axial and torsional drill string model was used to show that the bit-rock model outputs lead to the expected bit-bounce and stick-slip under certain drilling conditions. Next, the model was implemented in a 3D deviated well drill string simulation with non-linear friction and contact, generating complex stress states with good computational efficiency. Full article

Accurate prediction of forage digestibility is essential for efficient livestock management and feed formulation. This study evaluated the performance of machine learning (ML) models to estimate the in vitro digestibility of leaf and stem components of Brachiaria hybrid cv. Ipyporã, using three datasets composed of pasture management variables, chemical composition variables, and their combination. Artificial neural network (Multilayer Perceptron, MLP), decision trees (REPTree and M5P), Random Forest (RF), and Multiple Linear Regression (LR) were tested. The principal component analysis revealed that 61.3% of the total variance was explained by two components, highlighting a strong association between digestibility and crude protein content and an opposite relationship with lignin and neutral detergent fiber. Among the evaluated models, MLP, LR, and RF achieved the best performance for leaf digestibility (r = 0.76), while for stem digestibility the highest accuracy was obtained with the LR model (r = 0.79; MAE = 2.42; RMAE = 2.87). The REPTree algorithm presented the lowest predictive performance regardless of the input data. The results indicate that chemical composition variables alone are sufficient to develop reliable prediction models. These findings demonstrate the potential of ML techniques as a non-destructive and cost-effective approach to predict the nutritional quality of tropical forage grasses and support precision livestock management. Full article

The accelerated integration of photovoltaic (PV) systems, particularly within Hybrid PV–Battery Storage Systems (PV-BSS), establishes a compelling need for advanced control strategies that are fundamental to achieving effective Energy Saving Management. However, conventional proportional–integral (PI) controllers demonstrate limited adaptability and necessitate tedious, manual parameter tuning, frequently resulting in suboptimal dynamic performance, especially under load transients. To specifically address these constraints within the domain of high-power electronics, this paper introduces a novel Artificial Neural Network (ANN)-based current controller tailored for the 1500 VDC Three-Level Hybrid Active Neutral Point Clamped (3L-HANPC) inverter, which is a widely accepted PV-BSS topology. The optimal Multi-Layer Perceptron (MLP) architecture was identified using a multi-criteria methodology, which strategically balanced Total Harmonic Distortion (THD) performance against training efficiency. Simulation results affirm that the proposed ANN controller achieves superior harmonic mitigation and demonstrates faster dynamic responses compared to the PI counterpart. Moreover, the controller fundamentally ensures stable operation while eliminating the necessity for complex PI parameter tuning. Its dependable performance across both trained and unseen operating points strongly validates its robust adaptability. This self-tuning ANN approach thus provides a viable pathway for enhancing the reliability of future hybrid energy storage systems. Full article

The ordinary Portland cement (OPC) manufacturing process is highly resource-intensive and contributes to over 5% of global CO₂ emissions, thereby contributing to global warming. In this context, researchers are increasingly adopting geopolymers concrete due to their environmentally friendly production process. For decades, industrial byproducts such as fly ash, ground-granulated blast-furnace slag, and silica fume have been used as the primary binders for geopolymer concrete (GPC). However, due to uneven distribution and the decline of coal-fired power stations to meet carbon-neutrality targets, these binders may not be able to meet future demand. The UK intends to shut down coal power stations by 2025, while the EU projects an 83% drop in coal-generated electricity by 2030, resulting in a significant decrease in fly ash supply. Like fly ash, slag, and silica fume, natural clays are also abundant sources of silica, alumina, and other essential chemicals for geopolymer binders. Hence, natural clays possess good potential to replace these industrial byproducts. Recent research indicates that locally available clay has strong potential as a pozzolanic material when treated appropriately. This review article represents a comprehensive overview of the various treatment methods for different types of clays, their impacts on the fresh and hardened properties of geopolymer concrete by analysing the experimental datasets, including 1:1 clays, such as Kaolin and Halloysite, and 2:1 clays, such as Illite, Bentonite, Palygorskite, and Sepiolite. Furthermore, this review article summarises the most recent geopolymer-based prediction models for strength properties and their accuracy in overcoming the expense and time required for laboratory-based tests. This review article shows that the inclusion of clay reduces concrete workability because it increases water demand. However, workability can be maintained by incorporating a superplasticiser. Calcination and mechanical grinding of clay significantly enhance its pozzolanic reactivity, thereby improving its mechanical performance. Current research indicates that replacing 20% of calcined Kaolin with fly ash increases compressive strength by up to 18%. Additionally, up to 20% replacement of calcined or mechanically activated clay improved the durability and microstructural performance. The prediction-based models, such as Artificial Neural Network (ANN), Multi Expression Programming (MEP), Extreme Gradient Boosting (XGB), and Bagging Regressor (BR), showed good accuracy in predicting the compressive strength, tensile strength and elastic modulus. The incorporation of clay in geopolymer concrete reduces reliance on industrial byproducts and fosters more sustainable production practices, thereby contributing to the development of a more sustainable built environment. Full article

With the global energy structure transitioning towards clean and low-carbon alternatives, electrochemical energy storage technologies have emerged as pivotal enablers for achieving efficient renewable energy utilization and carbon neutrality objectives. However, conventional electrode materials remain constrained by inherent limitations, including low specific surface area, sluggish ion diffusion kinetics, and insufficient mechanical stability, which fundamentally hinder the synergistic fulfillment of high energy density, superior power density, and prolonged cycling durability. Three-dimensional printing technology offers a revolutionary paradigm for designing and fabricating carbon-based electrochemical energy storage devices. By enabling precise control over both the microstructural architecture and macro-scale morphology of electrode materials, this additive manufacturing approach significantly enhances energy/power densities, as well as cycling stability. Specifically, 3D printing facilitates biomimetic topological designs (e.g., hierarchical porous networks, vertically aligned ion channels) and functional hybridization strategies (e.g., carbon/metal oxide hybrids, carbon/biomass-derived composites), thereby achieving synergistic optimization of charge transfer kinetics and mechanical endurance. This review systematically summarizes recent advancements in 3D-printed carbon-based electrodes across major energy storage systems, including supercapacitors, lithium-ion batteries, and metal–air batteries. Particular emphasis is placed on the design principles of carbon-based inks, multiscale structural engineering strategies, and process optimization methodologies. Furthermore, we prospect future research directions focusing on smart 4D printing-enabled dynamic regulation, multi-material integrated systems, and artificial intelligence-guided design frameworks to bridge the gap between laboratory prototypes and industrial-scale applications. Through multidisciplinary convergence spanning materials science, advanced manufacturing, and device engineering, 3D-printed carbon electrodes are poised to catalyze the development of next-generation high-performance, customizable energy storage systems. Full article

Research on self-imagined emotional imagery supports the development of practical affective brain–computer interface (BCI) systems. This study proposes a hybrid emotion induction approach that combines facial expression image cues with subsequent emotional imagery, involving six positive and six negative emotions across two- or four-class valence and arousal categories. Machine learning (ML) techniques were applied to interpret these self-generated emotions from electroencephalogram (EEG) signals. Experiments were conducted to observe brain activity and validate the proposed feature and classification algorithms. The results showed that absolute beta power features computed from power spectral density (PSD) across EEG channels consistently achieved the highest classification accuracy for all emotion categories with the K-nearest neighbors (KNN) algorithm, while alpha–beta ratio features also contributed. The nonlinear parametric ML models achieved high effectiveness; the K-nearest neighbor (KNN) classifier performed best in detecting neutral states, while the artificial neural network (ANN) achieved balanced accuracy across emotional stages. The proposed system supports the use of the hybrid emotion induction paradigm and PSD-derived EEG features to develop reliable, subject-independent affective BCI systems. In future work, we will expand the datasets, employ advanced feature extraction and deep learning models, integrate multi-modal signals, and validate the proposed approaches across broader populations. Full article