Search Results (221)

The electrooxidation (EO) of three important environmental contaminants, anticorrosive 1H-benzotriazole (BTA), plasticizer dibutyl phthalate (DBP), and non-ionic surfactant Triton X-100 (tert-octylphenoxy[poly(ethoxy)] ethanol, t-OPPE), was studied as a possible means to improve their elimination from wastewaters, which are an important emission source. EO was performed in a batch reactor with a boron-doped diamond (BDD) anode and a stainless steel cathode. Different supporting electrolytes were tested: NaCl, H₂SO₄, and Na₂SO₄. Results were analysed from the point of their efficacy in terms of degradation rate, kinetics, energy consumption, and transformation products. The highest degradation rate, shortest half-life, and lowest energy consumption was observed in the electrolyte H₂SO₄, followed by Na₂SO₄ with only slightly less favourable characteristics. In both cases, degradation was probably due to the formation of persulphate or sulphate radicals. Transformation products (TPs) were studied mainly in the sulphate media and several oxidation products were identified with all three contaminants, while some evidence of progressive degradation, e.g., ring-opening products, was observed only with t-OPPE. The possible reasons for the lack of further degradation in BTA and DBP are too short of an EO treatment time and perhaps a lack of detection due to unsuitable analytical methods for more polar TPs. Results demonstrate that BDD-based EO is a robust method for the efficient removal of structurally diverse organic contaminants, making it a promising candidate for advanced water treatment technologies. Full article

This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying inclusion criteria, 143 peer-reviewed studies published between January 2019 and April 2025 were analyzed. This review shows that AI-driven controllers—especially deep-reinforcement-learning agents—deliver median energy savings of 18–35% for HVAC and other major loads, consistently outperforming rule-based and model-predictive baselines. The evidence further reveals a rapid diversification of methods: graph-neural-network models now capture spatial interdependencies in dense sensor grids, federated-learning pilots address data-privacy constraints, and early integrations of large language models hint at natural-language analytics and control interfaces for heterogeneous IoT devices. Yet large-scale deployment remains hindered by fragmented and proprietary datasets, unresolved privacy and cybersecurity risks associated with continuous IoT telemetry, the growing carbon and compute footprints of ever-larger models, and poor interoperability among legacy equipment and modern edge nodes. The authors of researches therefore converges on several priorities: open, high-fidelity benchmarks that marry multivariate IoT sensor data with standardized metadata and occupant feedback; energy-aware, edge-optimized architectures that lower latency and power draw; privacy-centric learning frameworks that satisfy tightening regulations; hybrid physics-informed and explainable models that shorten commissioning time; and digital-twin platforms enriched by language-model reasoning to translate raw telemetry into actionable insights for facility managers and end users. Addressing these gaps will be pivotal to transforming isolated pilots into ubiquitous, trustworthy, and human-centered IoT ecosystems capable of delivering measurable gains in efficiency, resilience, and occupant wellbeing at scale. Full article

Integrated photonics is a transformative technology for enhancing communication and computation in Cloud and Fog computing networks. Photonic integrated circuits (PICs) enable significant improvements in data-processing speed, energy-efficiency, scalability, and latency. In Cloud infrastructures, PICs support high-speed optical interconnects, energy-efficient switching, and compact wavelength division multiplexing (WDM), addressing growing data demands. Fog computing, with its edge-focused processing and analytics, benefits from the compactness and low latency of integrated photonics for real-time signal processing, sensing, and secure data transmission near IoT devices. PICs also facilitate the low-loss, high-speed modulation, transmission, and detection of RF signals in scalable Radio-over-Fiber (RoF) links, enabling seamless IoT integration with Cloud and Fog networks. This results in centralized processing, reduced latency, and efficient bandwidth use across distributed infrastructures. Overall, integrating photonic technologies into RoF, Fog and Cloud computing networks paves the way for ultra-efficient, flexible, and scalable next-generation network architectures capable of supporting diverse real-time and high-bandwidth applications. This paper provides a comprehensive review of the current state and emerging trends in integrated photonics for IoT sensors, RoF, Fog and Cloud computing systems. It also outlines open research opportunities in photonic devices and system-level integration, aimed at advancing performance, energy-efficiency, and scalability in next-generation distributed computing networks. Full article

Marsh vegetation dampens wave energy, providing protection to coastal communities from storms. A new modeling framework was applied to study wave height evolution over the saltmarsh bordering Newbury, MA. A regional Delft3D hydrodynamic model generated wind driver waves in the open water portions of the study area, which were then one-way coupled with an analytical model, the Marsh Transect Wave Attenuation (MTWA) model, which tracked wave evolution along select transects throughout the marsh. Field observations of vegetation and wave height evolution were used to calibrate MTWA. Seven scenarios were run covering a range of possible future management and environmental conditions, in addition to projected sea level rise. Results underscore the importance of vegetation and elevation to wave attenuation. Full article

Rock hardness significantly impacts comminution efficiency, one of mining’s most energy-intensive processes. Accurate, rapid, and non-invasive hardness characterization can enhance mine-to-mill optimization and energy management. This study investigates sensor-based technologies, hyperspectral imaging, and portable X-ray fluorescence (pXRF) integrated with machine learning (ML) algorithms for characterizing rock hardness in open-pit gold mining contexts. A total of 159 rock samples from two Canadian open-pit gold mines were analyzed through Leeb rebound hardness (LRH), short-wave infrared (SWIR) hyperspectral imaging, and a pXRF analyzer for chemical characterization. The most critical spectral features of SWIR images were extracted using a novel and automated feature extraction approach and further refined by applying a recursive feature elimination (RFE) algorithm to reduce the dimensionality of the spectral feature space. Three ML algorithms, including Random Forest Regressor (RFR), Adaptive Boosting (AdaBoost), and Multivariate Linear Regression (MLR), were applied to develop predictive hardness models considering three scenarios: using chemical features, using refined spectral features, and their combination. The findings underscore the potential of advanced sensor integration and analytics in remotely characterizing rock hardness, which could contribute to enhancing efficiency and sustainability in modern mining operations. Full article

Single-column hybrid-reinforced self-centering segmental assembled bridges (SHR-SCSAB) exhibit vertical stiffness discontinuities, significantly impacting the refinement of their seismic design methodology. In this study, we investigate SHR-SCSAB by employing the finite strip method to calculate the maximum transverse bearing capability of segmental assembled piers, and the corresponding horizontal displacement at the pier top. By leveraging the mechanical properties of hybrid reinforcement materials, we further derive an analytical expression for the equivalent elastic stiffness of SHR-SCSAB as an integrated system. OpenSees software was used to establish a finite element model of the SHR-SCSAB, and the agreement between numerical simulations and analytical solutions validates the accuracy of the derived equivalent elastic stiffness expression. Additionally, this study evaluates the seismic performance of single-column SHR-SCSAB and examines the influence of key parameters on its behavior. The results demonstrate that hybrid reinforcement effectively addresses the low energy dissipation capacity inherent in self-centering bridges while preserving their advantage of minimal residual displacement. These findings significantly advance the refinement of seismic design methods for SHR-SCSAB. Full article

Rail-based crop phenotypic platforms operating in open-field environments face challenges such as environmental variability and unstable data quality, highlighting the urgent need for intelligent, online data acquisition strategies. This study proposes a digital twin-based data acquisition strategy tailored to such platforms. A closed-loop architecture “comprising connection, computation, prediction, decision-making, and execution“ was developed to build DT-FieldPheno, a digital twin system that enables real-time synchronization between physical equipment and its virtual counterpart, along with dynamic device monitoring. Weather condition standards were defined based on multi-source sensor requirements, and a dual-layer weather risk assessment model was constructed using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation by integrating weather forecasts and real-time meteorological data to guide adaptive data acquisition scheduling. Field deployment over 27 consecutive days in a maize field demonstrated that DT-FieldPheno reduced the manual inspection workload by 50%. The system successfully identified and canceled two high-risk tasks under wind-speed threshold exceedance and optimized two others affected by gusts and rainfall, thereby avoiding ineffective operations. It also achieved sub-second responses to trajectory deviation and communication anomalies. The synchronized digital twin interface supported remote, real-time visual supervision. DT-FieldPheno provides a technological paradigm for advancing crop phenotypic platforms toward intelligent regulation, remote management, and multi-system integration. Future work will focus on expanding multi-domain sensing capabilities, enhancing model adaptability, and evaluating system energy consumption and computational overhead to support scalable field deployment. Full article

This review provides a comprehensive and multidisciplinary overview of recent advancements in solar tracking systems (STSs) aimed at improving the efficiency and adaptability of photovoltaic (PV) technologies. The study systematically classifies solar trackers based on tracking axes (fixed, single-axis, and dual-axis), drive mechanisms (active, passive, semi-passive, manual, and chronological), and control strategies (open-loop, closed-loop, hybrid, and AI-based). Fixed-tilt PV systems serve as a baseline, with single-axis trackers achieving 20–35% higher energy yield, and dual-axis trackers offering energy gains ranging from 30% to 45% depending on geographic and climatic conditions. In particular, dual-axis systems outperform others in high-latitude and equatorial regions due to their ability to follow both azimuth and elevation angles throughout the year. Sensor technologies such as LDRs, UV sensors, and fiber-optic sensors are compared in terms of precision and environmental adaptability, while microcontroller platforms—including Arduino, ATmega, and PLC-based controllers—are evaluated for their scalability and application scope. Intelligent tracking systems, especially those leveraging machine learning and predictive analytics, demonstrate additional energy gains up to 7.83% under cloudy conditions compared to conventional algorithms. The review also emphasizes adaptive tracking strategies for backtracking, high-latitude conditions, and cloudy weather, alongside emerging applications in agrivoltaics, where solar tracking not only enhances energy capture but also improves shading control, crop productivity, and rainwater distribution. The findings underscore the importance of selecting appropriate tracking strategies based on site-specific factors, economic constraints, and climatic conditions, while highlighting the central role of solar tracking technologies in achieving greater solar penetration and supporting global sustainability goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). Full article

Photoelectrochemical (PEC) sensors have emerged as potential analysis techniques in recent years due to PEC’s benefits, which include straightforward operation, quick response times, and basic equipment. In this work, a new PEC sandwich immunoassay was fabricated, which was based on low-toxicity BiOI/BiOCl composites accompanied by enhanced signal detection via AgI-conjugated antibodies (Ab₂-AgI). Specifically, the low-toxicity inorganic semiconductor BiOI/BiOCl composites were first utilized in PEC bioanalysis. Owing to the unique configuration of energy levels between BiOI and BiOCl, the photoelectric response was more excellent than those of BiOI or BiOCl alone. Moreover, the Ab₂-AgI conjugates were utilized as signal amplification components through the specific antibody–antigen immunoreaction. In the presence of target Ag, the immobilized Ab₂-AgI conjugates clearly improve the steric hindrance of the sensing electrode and effectively hinder the transfer of photo-induced holes; meanwhile, AgI NPs can competitively absorb excitation light. A new PEC immunosensing platform for detecting tumor markers at 0 V under visible light excitation was developed, and using carcinoembryonic antigen (CEA) as a model analyte demonstrated an ultra-low detection limit of 4.9 fg·mL⁻¹. Meanwhile, it demonstrated excellent specificity and stability, potentially opening up a novel and promising platform for detecting other critical biomarkers. Full article

Using a Dyson–Schwinger approach, we perform an analysis of the non-trivial ground state of thermal $SU\left(N\right)$ Yang–Mills theory in the non-perturbative regime where chiral symmetry is dynamically broken by a mass gap. Basic thermodynamic observables such as energy density and pressure are derived analytically, using Jacobi elliptic functions. The results are compared with the lattice results. Good agreement is found at low temperatures, providing a viable scenario for a gas of massive glue states populating higher levels of the spectrum of the theory. At high temperatures, a scenario without glue states consistent with a massive scalar field is observed, showing an interesting agreement with lattice data. The possibility is discussed that the results derived in this analysis open up a novel pathway beyond lattice to precision studies of phase transitions with false vacuum and cosmological relics that depend on the equations of state in strong coupled gauge theories of the type of Quantum Chromodynamics (QCD). Full article

Because of the degradation of building materials and the increased design load, concrete parts continually require repair. Special cementitious matrix components, Strain Hardening Cementitious Composites (SHCC), have exceptional ductility, strength growth during cracking, and recurrent controlled-opening crack formation. The purpose of this study was to improve the qualities of SHCC by reinforcing it with steel metal mesh. This study examined the optimization and effects of shear strengthening on the shear capacity of both damaged and undamaged reinforced concrete beams by employing SHCC internally reinforced with steel mesh fabric (SMF). Under bending loading, eight reinforced concrete beams were evaluated. Four of them were loaded to shear crack before any strengthening could be performed. The beams were 1500 mm in length, 200 mm in height, and 120 mm in width, and one, two, or three SMFs were applied. The beams’ whole shear span had external strengthening applied on both sides. Additionally, layers of strengthening in the U-shape were applied. The walls of the strengthening were thirty millimeters thick. The failure, load-deflection response, ultimate load, ultimate displacement, and energy absorbance of the tested beams were determined and discussed. Compared to an unstrengthened beam, the ultimate load of undamaged beams increased by 47%, 57%, and 90% when reinforced with 1, 2, or 3 layers of SMF, respectively, within the SHCC. Additionally, incorporating one, two, or three SMF layers within the SHCC improved the deflection of strengthened undamaged beams by 52%, 87%, and 116%, respectively. For damaged beams, the maximum load was approximately 11% lower than that of their undamaged counterparts, regardless of the number of SMF layers used in the SHCC strengthening. Applying one, two, or three layers of SMFs within the strengthening layer led to increases of the ratios of 163, 334, and 426%, respectively, in the energy absorbed by the strengthened beams in comparison to the control beam. The shear strength of the strengthened beams was determined through analytical modeling by implementing a correction factor (α = 0.5) to take into consideration the debonding action between the SHCC layer and the beam sides. This factor significantly improved the predictive accuracy of the analytical models by matching the mean ratio of the analytical findings to the experimental predictions. Full article

To address the limitations of conventional electrochemical impedance spectroscopy (EIS) testing, we propose an efficient rapid EIS testing system. This system utilizes an AC pulse excitation signal combined with an “intelligent fast fourier transform (IFFT) optimization algorithm” to achieve rapid “one-to-many” impedance data measurements. This significantly enhances the speed, flexibility, and practicality of EIS testing. Furthermore, the conventional model-fitting approach for EIS data often struggles to resolve the issue of overlapping impedance arcs within a limited frequency range. To address this, the present study employs the Regularization Distributed Relaxation Time (RDRT) method to process EIS data obtained under AC pulse conditions. This approach avoids the workload and analytical uncertainties associated with assuming equivalent circuit models. Finally, the practical utility of the proposed testing system and the RDRT impedance analysis method is demonstrated through the estimation of battery state of health (SOH). In summary, the method proposed in this study not only addresses the issues associated with conventional EIS data acquisition and analysis but also broadens the methodologies and application scope of EIS impedance testing. This opens up new possibilities for its application in fields such as lithium-ion batteries (LIBs) energy storage. Full article

Climate change in Eastern Europe requires introducing automated irrigation systems and monitoring agricultural and climatic parameters to ensure food security. The automation of irrigation, together with the generation of climate reports based on AI (artificial intelligence) using OpenAI models for Internet of Things (IoT) data processing, contributes to the optimization of resources by reducing excessive water and energy consumption, supporting plant health through proper irrigation and increasing sustainable agricultural productivity by providing suggestions and statistics to streamline the agricultural process. In this paper, the authors present a system that allows continuous data collection of parameters such as temperature, humidity, and soil moisture, providing detailed information and advanced analytics for each device and area monitored using AI to generate predictive recommendations. The data transmission is performed wirelessly via WebSocket to the central database. This system uses data from all devices connected to the application to assess current climate conditions at a national level, identifying trends and generating reports that aid in adapting to extreme events. The integration of artificial intelligence in the context of monitoring and irrigation of agricultural areas is a step forward in the development of sustainable agriculture and for the adaptation of agriculture to increasingly aggressive climate phenomena, providing a replicable framework for vulnerable regions. Full article

Grafting of paramagnetic transition and rare earth metal ions onto the surface of detonation nanodiamonds (DNDs) was successfully implemented in the recent decade and opened new opportunities in the biomedical application of these compounds, particularly as novel contrast agents for magnetic resonance imaging. The grafting was studied mainly using EPR, NMR, and magnetic measurements. Such a highly surface-sensitive, quantitative, chemical analytic technique as X-ray photoelectron spectroscopy (XPS) was very rarely used. In this paper, we report the XPS study of grafting transition and rare-earth metal ions (Cu²⁺, Co²⁺, Mn²⁺, and Gd³⁺) onto the surface of DNDs. Binding energies for metal, carbon, oxygen, and nitrogen atoms were determined and attributed to the corresponding ion states and atomic groups. Comparing XPS and EPR findings, we showed that the developed synthesis route resulted in almost complete grafting of manganese and gadolinium atoms in the form of paramagnetic ions Mn²⁺ and Gd³⁺ to the diamond surface, while only 30% of the copper atoms on the surface are in the paramagnetic state Cu²⁺, and the rest 70% are in the non-magnetic Cu⁺ state. It was not possible to draw a similar conclusion regarding Co²⁺ ions due to the lack of data on the amount of these paramagnetic ions on the DND surface. Full article

The fields of catalysis and energy storage nowadays quote the use of nanomaterials with well-defined size, morphology, chemical composition, and thermal stability in the high-temperature range and under harsh conditions of reactions. We present herein an approach based on in situ environmental scanning transmission electron microscopy (STEM), combined with analytical STEM and electron tomography (ET), for the evaluation of the thermal stability of hollow Pt nanospheres under vacuum and high-pressure hydrogen environments. Spherical Pt hollow nanospheres (HNSs) with an average diameter of 15 and 34 nm were synthesized by a galvanic replacement-based procedure using either steep or continuous addition of Pt salts during synthesis. The as-synthesized HNSs exhibit complex 3D structures with shells of a few nm constituted by small Pt nanoparticles and marked by the presence of open channels. The thermal stability of Pt-based HNSs under TEM vacuum and 1 bar of hydrogen flow is reported by considering microstructural changes, e.g., the build-up of a continuous shell and its evolution until HNSs collapse at elevated temperatures (>500 °C). Experimental findings are discussed considering fundamental phenomenological issues, i.e., NP faceting, NP diffusion, and subsequent NP sintering, with respect to the behavior of the systems investigated. Full article