Search Results (1,973)

Porphyrin complexes are of great importance due to their possible applications as sensors, solar cells and photocatalysts, as well as their ability to bind additional ligands. A valuable source of knowledge on their nature is their electric properties, which can be evaluated employing density functional theory (DFT) methods, supporting the experimental research. The present work aims at the application of small property-oriented basis sets in calculation of electric properties in transition metals, their oxides and test coordination complexes. Firstly, the existing polarized ZPol basis set for the first-row transition metals is modified in order to improve atomic polarizability results. For this purpose, optimization of the f-type polarization function exponent is carried out with respect to the value of average atomic polarizability of investigated metals. Next, both the original and the modified basis sets are employed in finite field CCSD(T) calculation of transition metal oxides’ dipole moments, as well as DFT calculation of polarizabilities in porphyrin–zinc and porphyrin–zinc–thiazole complexes. The obtained results show that the ZPol and ZPol-A basis sets can be successfully employed in the calculation of linear electric properties in large systems. The optimization procedure used in the present work can be employed for other source basis sets and elements, leading to new efficient polarized basis sets. Full article

This paper proposes a lateral-stability-oriented path-tracking controller for four-wheel independent drive (4WID) autonomous vehicles. The proposed controller aims to maintain vehicle stability under extreme conditions while minimizing lateral deviation. Firstly, a tiered control framework comprising upper-level and lower-level controllers is introduced. The upper-level controller is a lateral stability path-tracking controller that incorporates tire dynamic characteristics, developed using model predictive control (MPC) theory. This controller dynamically updates the tire lateral force constraints in real time to account for variations in tire dynamics under extreme conditions. Additionally, it enhances lateral stability and reduces path-tracking errors by applying additional yaw torque based on minimum tire utilization. The lower-level controllers execute the required steering angles and yaw moments through the appropriate component equipment and torque distribution. The joint simulation results from CarSim and MATLAB/Simulink show that, compared to the traditional MPC controller with unstable sideslip, this controller can maintain vehicle lateral stability under extreme conditions. Compared to the MPC controller, which only considers lateral force constraints, this controller can significantly reduce lateral tracking errors, with an average yaw rate reduction of 31.62% and an average sideslip angle reduction of 40.21%. Full article

Background: Serious secondary disasters caused by extreme natural weather conditions occur frequently, making it essential to establish a scientific and efficient modern emergency management system to maximize life-saving efforts. Methods: This study focuses on the uncertain environment of urban road networks and employs fuzzy theory to construct a 0–1 integer programming model for emergency evacuation paths that minimizes the average expected travel time. Results: We enhanced the neighborhood search strategy of the traditional ACO_time by incorporating the 2-opt and 3-opt perturbation mechanisms from the SA algorithm. Additionally, we utilized improved ant-volume and ant-perimeter models, along with their combinations, in the pheromone-updating mechanism of the basic ACO. The heuristic principles of the A* algorithm were integrated, introducing the joint influence of path and time into the heuristic function of the ACO algorithm. Conclusions: The IACO3 algorithm was tested on the Sioux Falls network and the Berlin Heisenheimer Center network. The computation time of the improved IACO3 algorithm was reduced by up to 20% compared to the original IACO3 algorithm in relation to the SA algorithm, with only a 4–5% increase in computation time compared to the ACO_time algorithm, which translates to an increase of merely 4–5 s. This demonstrates the superior solution efficiency of the IACO3 algorithm. Full article

According to conclusions agreed to in the 1995 Report of the World Summit for Social Development and the 2015 Sustainable Development Summit, seventeen sustainable development goals (SDGs) have been ratified and published as the 2030 Agenda for Sustainable Development. In 2022, the 8th Environment Action Programme was legally agreed upon, following the six European Green Deal priorities. These SDGs serve as a constant reminder of the importance of globally coordinated actions in compliance with the theory of sustainable development. However, more than a constant reminder, this international agreement should become the foundation for necessary change. On 22 July 2024, the daily global average temperature reached a new record high. The EU treaties signed between 1951 and 2007 laid the foundation for the creation of EU environmental policy. However, those EU treaties, along with environmental policy, form merely a non-binding and minimum set of priorities without any sanctions imposed for illegal practices. In 2021, EU member countries adopted the European Climate Law as the first legally binding document seeking to achieve goals set by the Paris Agreement and the European Green Deal. Any further EU sustainable development policies are dependent on global cooperation as a key element of survival. With the EU’s dependent on the rest of the world for its energy, the forcing of any obligatory change will be hard to achieve. This proves the importance of the 17th SDG, agreed in 2015. Only global partnership for sustainable development can prevent further damage to our ecosystem and achieve priorities set by the EU and UN agendas. The review aims to present the connection between sustainable development (SD) goals defined by the European Commission, for which the most important aspects are the need to meet the environmental requirements to protect future needs in the long run, and to confront the shortcomings of European law-making practices, in which most crucial reforms are presented as non-binding legal acts. Finally, in 2024 members of the European Parliament established an extended list of environmental crimes to be regarded as punishable offences and replaced the Environmental Crime Directive, making criminal activities and offences potentially legally punishable; however, it is yet to be seen how this initiative will be incorporated within the national legislations of each EU member country and to what extent. Full article

The graph theory is one of the fundamental structures in computer science used to model various scientific and engineering problems. Many problems within the graph theory are categorized as NP-hard and NP-complete. One such problem is the minimum dominating set (MDS) problem, which seeks to identify the minimum possible subsets in a graph such that every other node in the subset is directly connected to a node in this subset. Due to its inherent complexity, developing an efficient polynomial-time method to address the MDS problem remains a significant challenge in graph theory. This paper introduces a novel algorithm that utilizes a centrality measure known as the Malatya Centrality to effectively address the MDS problem. The proposed algorithm, called the Malatya Dominating Set Algorithm (MDSA), leverages centrality values to identify dominating sets within a graph. It extends the Malatya centrality by incorporating a second-level centrality measure, which enhances the identification of dominating nodes. Through a systematic and algorithmic approach, these centrality values are employed to pinpoint the elements of the dominating set. The MDSA uniquely integrates greedy and dynamic programming strategies. At each step, the algorithm selects the most optimal (or near-optimal) node based on the centrality values (greedy approach) while updating the neighboring nodes’ criteria to influence subsequent decisions (dynamic programming). The proposed algorithm demonstrates efficient performance, particularly in large-scale graphs, with time and space requirements scaling proportionally with the size of the graph and its average degree. Experimental results indicate that our algorithm outperforms existing methods, especially in terms of time complexity when applied to large datasets, showcasing its effectiveness in addressing the MDS problem. Full article

In mines where the natural caving method is used, the frequent occurrence of underground debris flows and the complex mine environments make it difficult to prevent and control underground debris flows. The source is one of the critical conditions for the formation of debris flows, and studying the impact of source material gradation on underground debris-flow disasters can effectively help prevent and control these occurrences. This paper describes a multiscale study of underground debris flows using physical model experiments and the discrete-element method (PFC^3D) to understand the impact of the source material gradation on the disaster mechanism of underground debris flows from macroscopic and microscopic perspectives. Macroscopically, an increase in content of medium and large particles in the gradation will enhance the instantaneous destructive force. Large particles can more easily cause disasters than medium and fine particles with the same content, but the disaster-causing ability is minimized when the contents of medium and large particles exceed 50% and 60%, respectively. With increasing fine particle content, the long-distance disaster-causing ability and duration is increased. On the microscopic level, the source-level pairs affect the initial flow mode, concentration area of the force chain, average velocity, average runout distance, and change in energy of the underground debris flow. Among them, the proportion of large particles in the gradation significantly affects the change in kinetic energy, change in dissipative energy, time to reach the peak kinetic energy, and time of coincidence of dissipative energy and gravitational potential energy. The process of underground debris flow can be divided into a “sudden stage”, a “continuous impact stage”, and a “convergence and accumulation stage”. This work reveals the close relationship between source material gradation and the disaster mechanism of underground debris flows and highlights the necessity of considering the source material gradation in the prevention and control of underground debris flows. It can provide an important basic theory for the study of environmental and urban sustainable development. Full article

This study introduces a novel predictive model for atomization droplet size, developed using comprehensive data collected under elevated temperature and pressure conditions using a twin swirl airblast nozzle. The model, grounded in flow instability theory, has been meticulously parameterized using the Particle Swarm Optimization (PSO) algorithm. Through rigorous analysis, including analysis of variance (ANOVA), the model has demonstrated robust reliability and precision, with a maximum relative error of 19.3% and an average relative error of 6.8%. Compared to the classical atomization model by Rizkalla and Lefebvre, this model leverages theoretical insights and incorporates a range of interacting variables, enhancing its applicability and accuracy. Spearman correlation analysis reveals that air pressure and the air pressure drop ratio significantly negatively impact droplet size, whereas the fuel–air ratio (FAR) shows a positive correlation. Experimental validation at ambient conditions shows that the model is applicable with a reliability threshold of We₁/Re₁ ≥ 0.13 and highlights the predominance of the pressure swirl mechanism over aerodynamic atomization at higher fuel flow rates (q > 1.25 kg/h). This research effectively bridges theoretical and practical perspectives, offering critical insights for the optimization of airblast nozzle design. Full article

Volume-phase holographic gratings are suitable for use in greenhouse gas detection imaging spectrometers, enabling the detection instruments to achieve high spectral resolution, high signal-to-noise ratios, and high operational efficiency. However, when utilized in the infrared wavelength band with high dispersion requirements, gratings struggle to meet the demands for low polarization sensitivity due to changes in diffraction performance caused by phase delays in the incidence of light waves with distinct polarization states, and current methods for designing bulk-phase holographic gratings require a large number of calculations that complicate the balance of diffraction properties. To overcome this problem, a design method for transmissive bulk-phase holographic gratings is proposed in this study. The proposed method combines two diffraction theories (namely, Kogelnik coupled-wave theory and rigorous coupled-wave theory) and establishes a parameter optimization sequence based on the influence of design parameters on diffraction characteristics. Kogelnik coupled-wave theory is employed to establish the initial Bragg angle range, ensuring that the diffraction efficiency and phase delay of the grating thickness curve meet the requirements for incident light waves in various polarization states. Utilizing rigorous coupled-wave theory, we optimize grating settings based on criteria such as a center wavelength diffraction efficiency greater than 95%, polarization sensitivity less than 10%, maximum bandwidth, and spectral diffraction efficiency exceeding 80%. The ideal grating parameters are ultimately determined, and the manufacturing tolerances for various grating parameters are analyzed. The design results show that the grating stripe frequency is 1067 lines per millimeter, and the diffraction efficiencies of TE and TM waves are 96% and 99.89%, respectively. The diffraction efficiency of unpolarized light is more than 88% over the whole spectral range with an average efficiency of 94.49%, an effective bandwidth of 32 nm, and a polarization sensitivity of less than 7%. These characteristics meet the performance requirements for dispersive elements based on greenhouse gas detection, the spectral resolution of the detection instrument is up to 0.1 nm, and the signal-to-noise ratio and working efficiency are improved by increasing the transmittance of the instrument. Full article

The state of the art in methane pyrolysis does not yet provide a definitive answer as to whether the concept of an elementary reaction is universally applicable to the apparently simple process of methane dissociation. Similarly, the literature currently lacks a comprehensive and unambiguous description of the methane pyrolysis process and, in particular, a single model that would well represent its course at both the micro and macro scales. Given the wide range of conditions under which this reaction can occur—whether thermal or thermo-catalytic, in solid or fluidized bed reactors—it is crucial to evaluate the usefulness of different kinetic models and their compatibility with basic thermodynamic principles and design assumptions. To address these research gaps, the authors analysed the thermodynamic and kinetic dependencies involved in the thermal decomposition of methane, using the synthesis of methane from its elemental components and its reversibility as a basis for exploring suitable kinetic models. Using experimental data available in the literature, a wide range of kinetic models have been analysed to determine how they all relate to the reaction rate constant. It was found that regardless of whether the process is catalytic or purely thermal, for temperatures above 900 °C the reversibility of the reaction has a negligible effect on the hydrogen yield. This work shows how the determined kinetic parameters are consistent with the Kinetic Compensation Effect (KCE) and, by incorporating elements of Transition State Theory (TST), the possibility of the existence of Entropy–Enthalpy Compensation (EEC). The indicated correspondence between KCE and EEC is strengthened by the calculated average activation entropy at isokinetic temperature $({∆S}_B=-275.0 \mathrm{J}·(\mathrm{m}\mathrm{o}\mathrm{l}·\mathrm{K}{)}^{-1}).$ Based on these results, the authors also show that changes in the activation energy $(E=20–421 \mathrm{k}\mathrm{J}·{\mathrm{m}\mathrm{o}\mathrm{l}}^{-1})$ can only serve as an estimate of the optimal process conditions, since the isoconversion temperature ($T_{iso}=1200-1450 \mathrm{K}>T_{eq}$) is shown to depend not only on thermodynamic principles but also on the way the reaction is carried out, with temperature (T) and pressure (P) locally compensating each other. Full article

Background/Objectives: Lung and cardiovascular diseases are leading causes of mortality worldwide, yet early detection remains challenging due to the subtle symptoms. Digital clubbing, characterized by the bulbous enlargement of the fingertips, serves as an early indicator of these diseases. This study aims to develop an automated system for detecting digital clubbing using deep-learning models for real-time monitoring and early intervention. Methods: The proposed system utilizes the YOLOv8 model for object detection and U-Net for image segmentation, integrated with the ESP32-CAM development board to capture and analyze finger images. The severity of digital clubbing is determined using a custom algorithm based on the Lovibond angle theory, categorizing the condition into normal, mild, moderate, and severe. The system was evaluated using 1768 images and achieved cloud-based and real-time processing capabilities. Results: The system demonstrated high accuracy (98.34%) in real-time detection with precision (98.22%), sensitivity (99.48%), and specificity (98.22%). Cloud-based processing achieved slightly lower but robust results, with an accuracy of 96.38%. The average processing time was 0.15 s per image, showcasing its real-time potential. Conclusions: This automated system provides a scalable and cost-effective solution for the early detection of digital clubbing, enabling timely intervention for lung and cardiovascular diseases. Its high accuracy and real-time capabilities make it suitable for both clinical and home-based health monitoring. Full article

This study introduces a novel multi-criteria group evaluation approach grounded in the theory of basic uncertain information (BUI) to facilitate the selection of green recycling suppliers for shared electric bikes. Firstly, a comprehensive index system of green recycling suppliers is established, encompassing recycling capacity, environmental sustainability, financial strength, maintenance capabilities, and policy support, to provide a solid foundation for the scientific selection process. Secondly, the basic uncertain information generalized power weighted average (BUIGPWA) operator is proposed to aggregate group evaluation information with BUI pairs, and some related properties are investigated. Furthermore, the basic uncertain information-based best–middle–worst TOPSIS (BUI-BMW-TOPSIS) model incorporating the best, middle, and worst reference points to enhance decision-making accuracy is proposed. Ultimately, by integrating the BUIGPWA operator for group information aggregation with the BUI-BMW-TOPSIS model to handle multi-criteria decision information, a novel multi-criteria group decision-making (MCGDM) method is constructed to evaluate green recycling suppliers for shared electric bikes. Case analyses and comparative analyses demonstrate that compared with the BUIWA operator, the BUIGPWA operator yields more reliable results because of its consideration of the degree of support among decision-makers. Furthermore, in contrast to the traditional TOPSIS method, the BUI-BMW-TOPSIS model incorporates the credibility of information provided by decision-makers, leading to more trustworthy outcomes. Notably, variations in attribute weights significantly impact the decision-making results. In summary, our methods excel in handling uncertain information and complex multi-criteria group decisions, boosting scientific rigor and reliability, and supporting optimization and sustainability of shared electric bike green recycling suppliers. Full article

Hydrogen storage has been a bottleneck factor for the application of hydrogen energy. Hydrogen storage capacity for titanium-decorated boron-doped C₂₀ fullerenes has been investigated using the density functional theory. Different boron-doped C₂₀ fullerene absorbents are examined to avoid titanium atom clustering. According to our research, with three carbon atoms in the pentagonal ring replaced by boron atoms, the binding interaction between the Ti atom and C₂₀ fullerene is stronger than the cohesive energy of titanium. The calculated results revealed that one Ti atom can reversibly adsorb four H₂ molecules with an average adsorption energy of −1.52 eV and an average desorption temperature of 522.5 K. The stability of the best absorbent structure with a gravimetric density of 4.68 wt% has been confirmed by ab initio molecular dynamics simulations. These findings suggest that titanium-decorated boron-doped C₂₀ fullerenes could be considered as a potential candidate for hydrogen storage devices. Full article

In this review work, we outline a conceptual path that, starting from the numerical investigation of the transition between weak chaos and strong chaos in Hamiltonian systems with many degrees of freedom, comes to highlight how, at the basis of equilibrium phase transitions, there must be major changes in the topology of submanifolds of the phase space of Hamiltonian systems that describe systems that exhibit phase transitions. In fact, the numerical investigation of Hamiltonian flows of a large number of degrees of freedom that undergo a thermodynamic phase transition has revealed peculiar dynamical signatures detected through the energy dependence of the largest Lyapunov exponent, that is, of the degree of chaoticity of the dynamics at the phase transition point. The geometrization of Hamiltonian flows in terms of geodesic flows on suitably defined Riemannian manifolds, used to explain the origin of deterministic chaos, combined with the investigation of the dynamical counterpart of phase transitions unveils peculiar geometrical changes of the mechanical manifolds in correspondence to the peculiar dynamical changes at the phase transition point. Then, it turns out that these peculiar geometrical changes are the effect of deeper topological changes of the configuration space hypersurfaces ${\sum }_v=V_N^{-1}\left(v\right)$ as well as of the manifolds ${\{M_v=V_N^{-1}((-\infty ,v]\left)\right\}}_v\in \mathbb{R}$ bounded by the ∑_v. In other words, denoting by vc the critical value of the average potential energy density at which the phase transition takes place, the members of the family ${\left\{{\sum }_v\right\}}_{v<v_c}$ are not diffeomorphic to those of the family ${\left\{{\sum }_v\right\}}_{v>v_c}$; additionally, the members of the family ${\left\{M_v\right\}}_{v>v_c}$ are not diffeomorphic to those of ${\left\{M_v\right\}}_{v>v_c}$. The topological theory of the deep origin of phase transitions allows a unifying framework to tackle phase transitions that may or may not be due to a symmetry-breaking phenomenon (that is, with or without an order parameter) and to finite/small N systems. Full article

Physical properties of the binary solutions, toluene with 2-propanol and 2-methyl-1-propanol, were measured at T = 293.15, 298.15, 303.15, 308.15, and 313.15 K and P = 100 kPa. The experimental density values were tested with the Emmerling et al. and Gonzalez-Olmos–Iglesias equations. The results indicate that the equation by Emmerling et al. is the best to correlate the density for toluene + 2-methyl-1-propanol system, while for toluene + 2-propanol, both proposed equations are proper to correlate the density with composition and temperature. The viscosity results were verified with different models containing two adjustable parameters. The values of viscosity deviation ($∆\eta$), excess molar volume (V^E), excess Gibbs energy (ΔG^*E), partial molar volumes ($\overline{V_1}$ and $\overline{V_2}$), and apparent molar volume ($V_{\phi ,1}$ and $V_{\phi ,2}$) were calculated. The values of the excess molar volume were positive for both systems, while negative values were obtained for the viscosity deviation and the excess Gibbs energy. The excess properties of the mixtures were adjusted to the Redlich–Kister equation. The values of thermodynamic functions of activation of viscous flow were computed and analyzed. Additionally, the Prigogine–Flory–Patterson (PFP) theory was applied to calculate V^E and then compared with experimental values. The values of the percentage absolute average deviation obtained suggest the validity of this theory. The Fourier transform infrared spectroscopy (FTIR) spectra of the binary solutions studied in this work allowed for the understanding of the interactions between the molecules of these systems. Full article

Background: The averaged cortical thickness of meta-ROI is currently being used for the diagnosis and prognosis of Alzheimer’s disease (AD) using structural MRI brain images. The purpose of this work is to present a hybrid meta-ROI for the detection of AD. Methods: The AD detectability of selected cortical and volumetric regions of the brain was examined using signal detection theory. The top performing cortical and volumetric ROIs were taken as input nodes to the artificial neural network (ANN) for AD classification. Results: An AD diagnostic accuracy of 91.9% was achieved by using a hybrid meta-ROI consisting of thicknesses of entorhinal and middle temporal cortices, and the volumes of the hippocampus and inferior lateral ventricles. Pairing inferior lateral ventricle dilation with hippocampal volume reduction improves AD detectability by 5.1%. Conclusions: Hybrid meta-ROI, including the dilation of inferior lateral ventricles, outperformed both cortical thickness- and volumetric-based meta-ROIs in the detection of Alzheimer’s disease. Full article