Search Results (3,564)

Tensegrity structures, by definition composed of compressed members suspended in a network of tensile cables, are characterised by a high strength-to-weight ratio and the ability to undergo reversible deformations. Their application as cores of sandwich panels represents an innovative approach to lightweight design, enabling the regulation of mechanical properties while reducing material consumption. This study presents a finite element modelling procedure that combines analytical determination of prestress using singular value decomposition with implementation in the ABAQUS™ 2019 software. Geometry generation and prestress definitions were automated with Python 3 scripts, while algebraic analysis of individual modules was performed in Wolfram Mathematica. Two models were investigated: M1, composed of four identical modules, and M2, composed of four modules arranged in two mirrored pairs. Model M1 exhibited a linear elastic response with a constant global stiffness of 13.9 kN/mm, stable regardless of the prestress level. Model M2 showed nonlinear hardening behaviour with variable stiffness ranging from 0.135 to 1.1 kN/mm and required prestress to ensure static stability. Eigenvalue analysis confirmed the full stability of M1 and the increase in stability of M2 upon the introduction of prestress. The proposed method enables precise control of prestress distribution, which is crucial for the stability and stiffness of tensegrity structures. The M2 configuration, due to its sensitivity to prestress and variable stiffness, is particularly promising as an adaptive sandwich panel core in morphing structures, adaptive building systems, and deployable constructions. Full article

This study presents a novel approach to intensifying the anaerobic digestion of sewage sludge through enzymatic pretreatment using hydrolytic enzymes—cellulase and lysozyme. It aims to determine how enzymatic activation affects the efficiency of methane fermentation, defined as the degree of organic matter decomposition and yield and composition of biogas. An experiment was carried out under mesophilic conditions over 20 days, analyzing the physicochemical properties of sludge, biogas production, methane content, and sanitary parameters. The addition of cellulase and lysozyme significantly enhanced process efficiency, increasing both the rate of organic matter degradation and biogas yield. The highest biogas production values (0.73 L·g⁻¹ d.m. for cellulase and 0.72 L·g⁻¹ d.m. for lysozyme) were obtained at a 4% (w/w) enzyme concentration, with a corresponding increase in the degree of organic matter decomposition to 78.7% and 80.0%, respectively. The produced biogas contained 58–61% methane, exceeding the values observed in the control sample, which indicates a positive effect of enzymatic activation on methane selectivity. Enhanced biogas production was attributed to improved hydrolysis of complex organic compounds, resulting in greater substrate bioavailability for methanogenic microorganisms. Moreover, methane fermentation led to the complete elimination of E. coli from all supernatants, confirming the hygienization potential of the process. The results of this study indicate that enzymatic pretreatment may serve as a viable strategy to improve both the energy efficiency and hygienic safety of anaerobic digestion processes, with relevance for future optimization and full-scale wastewater treatment applications. Full article

Let $\mathcal{G}=\mathcal{G}(\mathcal{A},\mathcal{B},\mathcal{M},\mathcal{N})$ be a generalized matrix algebra. A linear map $\Delta :\mathcal{G}\to \mathcal{G}$ is called a Lie derivation at $E\in \mathcal{G}$ if $\Delta \left(\right[U,V\left]\right)=[\Delta (U),V]+[U,\Delta (V\left)\right]$ for all pairs $U,V\in \mathcal{G}$ such that $UV=E$. In this paper, we use techniques of matrix decomposition and algebraic identity analysis to fully characterize the general form of Lie derivations at $E=\left(\begin{array}{cc}{e}_0& 0\\ 0& 0\end{array}\right)$, where $e_0$ is an arbitrary fixed element in $\mathcal{A}$. Our main result establishes a necessary and sufficient condition for a Lie derivation at $E=\left(\begin{array}{cc}{e}_0& 0\\ 0& 0\end{array}\right)$ to be decomposable into the sum of a derivation of $\mathcal{G}$ and a center-valued linear map. This characterization significantly extends the classical results concerning global Lie derivations and provides a deeper insight into the local Lie-type behavior in operator algebras. Full article

Traditional time-frequency diagnostics for high-speed bearings face an entrenched trade-off between resolution and real-time feasibility. We present a fast Dual-Phase Short-Time Root-MUSIC pipeline that exploits Hankel structure via FFT-accelerated Lanczos bidiagonalization and Sliding-window Singular Value Decomposition to deliver sub-Hz super-resolution under millisecond budgets. Validated on the Politecnico di Torino aerospace dataset (seven fault classes, three severities), fDSTrM detects 150 μm inner-race and rolling-element defects with 98% and 95% probability, respectively, at signal-to-noise ratio down to −3 dB (78% detection), while Short-Time Fourier Transform and Wavelet Packet Decomposition fail under identical settings. Against classical Root-MUSIC, the approach sustains approximately 200 times speedup with less than ${10}^{-11}$ relative frequency error in offline scaling, and achieves 1.85 milliseconds per 4096-sample frame on embedded-class hardware in streaming tests. Subspace order pre-estimation with adaptive correction preserves closely spaced components; Kalman tracking formalizes uncertainty and yields 95% confidence bands. The resulting early warning margin extends maintenance lead-time by 24–72 h under industrial interferences (Gaussian, impulsive, and Variable Frequency Drive harmonics), enabling field-deployable super-resolution previously constrained to offline analysis. Full article

The Lomb–Scargle method (LSM) constitutes a robust method for frequency and amplitude estimation in cases where data exhibit irregular or sparse sampling. Conventional spectral analysis techniques, such as the discrete Fourier transform (FT) and wavelet transform, rely on orthogonal mode decomposition and are inherently constrained when applied to non-equidistant or fragmented datasets, leading to significant estimation biases. The classical LSM, originally formulated for univariate time series, provides a statistical estimator that does not assume a Fourier series representation. In this work, we extend the LSM to multivariate datasets by redefining the shifting parameter $\tau$ to preserve the orthogonality of trigonometric basis functions in ${\mathbb{R}}^n$. This generalization enables simultaneous estimation of the frequency, phase, and amplitude vectors while maintaining the statistical advantages of the LSM, including consistency and noise robustness. We demonstrate its application to solar activity data, where sunspots serve as intrinsic markers of the solar dynamo process. These observations constitute a randomly sampled two-dimensional binary dataset, whose characteristic frequencies are identified and compared with the results of solar research. Additionally, the proposed method is applied to an ultrasound velocity profile measurement setup, yielding a three-dimensional velocity dataset with correlated missing values and significant temporal jitter. We derive confidence intervals for parameter estimation and conduct a comparative analysis with FT-based approaches. Full article

This study provides a comprehensive analysis of lemon peel (LP) combustion behaviour using combined physicochemical characterization and non-isothermal thermogravimetric kinetics. To achieve this, LP was characterized for its proximate and ultimate composition, with its structure analysed via FTIR, XRD, and SEM. Thermogravimetric analysis (TGA) was then performed at high heating rates (20–80 K min⁻¹) to investigate combustion stages, and kinetic and thermodynamic parameters were determined using six model-free and one model-fitting method. The results revealed a high heating value (23.02 MJ kg⁻¹) and high volatile matter (73.2 wt%), establishing LP’s significant energy potential. TGA displayed four distinct decomposition stages corresponding to dehydration, pectin/hemicellulose, cellulose, and lignin/char combustion. Kinetic analysis yielded activation energies that varied with conversion, peaking at approximately 304 kJ mol⁻¹, and a three-dimensional diffusion (D3) mechanism was identified as the rate-limiting step. In conclusion, while its high energy content and low nitrogen (1.26 wt%) and sulphur (0.20 wt%) content make LP an attractive low-emission biofuel, its viability is challenged by a high potassium concentration in the ash (34.8 wt% K₂O), posing a severe risk of slagging. This study provides the comprehensive combustion kinetic data for LP at high heating rates, which is essential for designing appropriate energy conversion technologies and ash management strategies. Full article

As an eco-friendly flame-retardant additive, magnesium hydroxide (MH) is widely employed in low-smoking, halogen-free polymer materials due to its environmentally benign nature. In order to enhance flame retardancy performance, the modified MH was modified with tetrakis(hydroxymethyl)phosphonium sulfate (THPS) by a one-pot hydrothermal method. The resulting morphology was characterized using scanning electron microscopy (SEM), and it shows the dispersion of nanometer particles and almost no aggregation. The X-ray photoelectron spectroscopy (XPS) along with Raman spectroscopy show that the THPS is connected with the Mg(OH)₂ by chemical bond. The sample was incorporated into ethylene–vinyl acetate (EVA) to evaluate the flame retardancy was assessed via limiting oxygen index (LOI) and vertical burning tests (UL-94). The results show that THPS modified MH effectively enhanced the flame retardancy, achieving a V-0 rating and an LOI value of 31.3%. In addition, the composites retain good mechanical integrity. The thermal analysis with TGA and DTG shows the formation of the MgO decomposition product, along with water vapor and phosphorus-containing radicals released by modified MH in the combustion process, forming a strong flame-retardant protective layer. In addition, the maximum smoke density of EVA/MHP-3 composite was 155.4, lower than 411.3 for EVA/MH, with a 62.2% reduction in total smoke production. The result shows that THPS is effective for improving the flame-retardant efficiency of inorganic metal hydroxide in polymer composites. Full article

The Global Cement and Concrete Association (GCCA) estimated that by 2050, 36% industry-wide sustainable value will be created, which includes sequestering CO₂ into the cement and concrete industry to produce commercially feasible high-value products. Direct utilization of CO₂ in the cement and concrete industry, which utilizes natural and sustainable materials, is gaining momentum. Naturally occurring mixtures of hydro magnesite and huntite are important industrial minerals which, upon endothermic decomposition over a specific temperature range, will release water and CO₂. This unique chemistry has led to such mixtures being successfully utilized as fire retardants, replacing aluminum hydroxide or Alumina Tri-Hydrate (ATH). Despite the developed marketplace for magnesium-based fire-retardant products, there is little mention of CO₂ mineral carbonation methods, which attempt to recover and convert magnesium from natural seawater or industrial waste into oxides or carbonates as part of the carbon sequestration initiative. The hypothesis to be proven in this work states that if the process of seawater mineral carbonation is prematurely quenched, Mg²⁺ ionic species in seawater adsorbed on the calcite lattice formation will be trapped and therefore recovered in various oxidized forms, such as magnesium oxides, magnesium hydro magnesite, and magnesium carbonate precipitates. A novel method to recover magnesium Mg²⁺ ions from seawater was successfully explored and documented; as such, from an initial concentration of 1250 ppm Mg²⁺ in raw seawater, the average concentration of spent Mg²⁺ ions after the reaction was as low as 20 ppm. A very efficient near-total recovery of Mg²⁺ from the seawater into the solid precipitates was recorded. Subsequently, the process for continuous seawater mineral carbonation for the production of magnesium/brucite/huntite products was successfully proven and optimized to operate with a 30 s reaction time, a dynamic feedstock concentration, [CaO] at 1 gpl in seawater and a room temperature reaction temperature (30 °C), where the average yield of the fire-retardant magnesium-based compounds was 26% of the synthesized precipitates. Approximately 5000 g of the hydro magnesite materials was molded into a fire-retardant brick or concrete wall, which was subjected to an accredited fire performance and durability testing procedure BS476-22:1987. There were encouraging results from the fire resistance testing, where the fire-retardant material passed BS476-22:1987, with performance criteria such as physical integrity failure, the maximum allowable face temperature, and a minimum duration before failure, which was up to 104 min, evaluated. Full article

Physics-based simulations of lithium-ion battery thermal runaway (TR) and thermal propagation (TP) enable the assessment of diverse temperature behaviors among individual cells. These behaviors are primarily driven by variations in thermal properties and the amount of heat released during thermal decomposition. However, given the inherent variability in thermal property measurements, the specific values adopted can lead to substantial differences in predicted temperature behavior. In this study, we developed a 1-dimensional TP model for an array of three prismatic lithium-ion battery cells, in consideration of the uncertainty of key thermal parameters including specific heat, thermal conductivity, activation energy, and the latent heat of the thermal decomposition reaction. The validity of the model and the identification of calibration parameters are ensured through comparison with experimentally measured temperatures. We evaluated the influence of these parameter variations on the temperature and thermal runaway behavior of each cell. Our findings indicate that the variation in thermal runaway timing increases with distance from the trigger cell, and the probability of thermal runaway in the end cell was significantly higher than in the center cell. A sensitivity analysis using a surrogate model revealed that cell temperature is more sensitive to variations in thermal conductivity and latent heat than to variations in specific heat and activation energy. Full article

The rapid growth of e-commerce has intensified the need for efficient last-mile delivery, making unmanned aerial vehicles (UAVs) a promising solution. However, despite their potential, practical deployment remains limited by how to effectively plan depot locations and UAV fleet sizes under stochastic customer demands with probabilistic same-day modifications. Existing approaches often address the strategic and operational decisions separately, leading to inefficiencies and infeasible solutions in practice. This study develops a unified two-stage decision framework integrating strategic depot location and UAV fleet allocation with operational assignment and scheduling. Three strategic models are considered: a deterministic model, a stochastic model solved via Sample Average Approximation (SAA), and a robust optimization model. Operational decisions assign UAV trips to realized requests while respecting time-slot and UAV availability constraints. Deterministic and SAA models are solved directly as integer programs, whereas the robust model is tackled via a logic-based Benders decomposition framework, with all approaches evaluated through simulation. The results show that the robust model provides overly conservative solutions, resulting in higher costs; the deterministic model minimizes cost but risks service failures; and the SAA approach balances cost and service across demand scenarios. The findings demonstrate the value of jointly considering strategic and operational decisions in UAV delivery design and provide practical guidance for UAV logistics operators. The proposed framework helps firms select appropriate planning models that align with their risk tolerance and service reliability goals, thereby improving the feasibility and competitiveness of UAV-based delivery systems. Full article

Packing approaches enhance training efficiency by filling the padding space in each batch with shorter sequences, thereby reducing the total number of batches per epoch. This approach has proven effective in both pre-training and supervised fine-tuning of large language models (LLMs). However, most packing methods necessitate a packing-aware masking (PAM) mechanism to prevent cross-contamination between different text segments in the multi-head attention (MHA) layers. This masking ensures that the scaled dot-product attention operates only within segment boundaries. Despite its functional utility, PAM introduces significant implementation complexity and computational overhead during training. In this paper, we propose a novel method that eliminates the need for PAM during supervised fine-tuning with packing. Instead of masking, we introduce a learnable tensor derived from Low-Rank Adaptation (LoRA) with the query and value parameters of the attention mechanism. This tensor is trained to attenuate the subspace corresponding to cross-contamination, effectively replacing the function of PAM. Through component-wise decomposition of attention head outputs, we isolate the contamination component and demonstrate that it can be attenuated using the LoRA-derived tensor. Empirical evaluations on 7B-scale LLMs show that our method reduces training time and runtime overhead by completely removing the implementation associated with PAM. This enables more scalable and efficient supervised fine-tuning with packing, without compromising model integrity. Full article

Aiming at the problems of high complexity in source domain data, inaccessibility of target domain data, and unknown fault patterns in real-world industrial scenarios for mechanical fault diagnosis, this paper proposes a Feature Decomposition-based Source-Free Universal Domain Adaptation (FD-SFUniDA) framework for mechanical equipment fault diagnosis. First, the CBAM attention module is incorporated to enhance the ResNet-50 convolutional network for extracting feature information from source domain data. During the target domain adaptation phase, singular value decomposition is applied to the weights of the pre-trained model’s classification layer, orthogonally decoupling the feature space into a source-known subspace and a target-private subspace. Then, based on the magnitude of feature projections, a dynamic decision boundary is constructed and combined with an entropy threshold mechanism to accurately distinguish between known and unknown class samples. Furthermore, intra-class feature consistency is strengthened through neighborhood-expanded contrastive learning, and semantic weight calibration is employed to reconstruct the feature space, thereby suppressing the negative transfer effect. Finally, extensive experiments under multiple operating conditions on rolling bearing and reciprocating mechanism datasets demonstrate that the proposed method excels in addressing source-free fault diagnosis problems for mechanical equipment and shows promising potential for practical engineering applications in fault classification tasks. Full article

By imposing finite order constraints on Fibonacci anyon braid relations, we construct the finite quotient $G={\mathbb{Z}}_5⋊2I$, where $2I$ is the binary icosahedral group. The Gröbner basis decomposition of its $SL(2,\mathbb{C})$ character variety yields elliptic curves whose L-function derivatives $L^{\prime }(E,1)$ remarkably match fundamental biological structural ratios. Specifically, we demonstrate that the Birch–Swinnerton-Dyer conjecture’s central quantity: the derivative $L^{\prime }(E,1)$ of the L-function at 1 encodes critical cellular geometries: the crystalline B-DNA pitch-to-diameter ratio ($L^{\prime }(E,1)=1.730$ matching $34\AA /20\AA =1.70$), the B-DNA pitch to major groove width ($L^{\prime }=1.58$) and, additionally, the fundamental cytoskeletal scaling relationship where $L^{\prime }(E,1)=3.570\approx 25/7$, precisely matching the microtubule-to-actin diameter ratio. This pattern extends across the hierarchy ${\mathbb{Z}}_5⋊2P$ with $2P\in \{2O,2T,2I\}$ (binary octahedral, tetrahedral, icosahedral groups), where character tables of $2O$ explain genetic code degeneracies while $2T$ yields microtubule ratios. The convergence of multiple independent mathematical pathways on identical biological values suggests that evolutionary optimization operates under deep arithmetic-geometric constraints encoded in elliptic curve L-functions. Our results position the BSD conjecture not merely as abstract number theory, but as encoding fundamental organizational principles governing cellular architecture. The correspondence reveals arithmetic geometry as the mathematical blueprint underlying major biological structural systems, with Gross–Zagier theory providing the theoretical framework connecting quantum topology to the helical geometries that are essential for life. Full article

We report the synthesis of two new biquinoxen-σ^H-adducts (3,3′-diisopropoxy-4,4′-dimethyl-3,3′,4,4′-tetrahydro-2,2′-biquinoxaline (Mbqn-(OⁱPr)₂) (1) and 3,3′-bis(isopropylthio)-4,4′-dimethyl-3,3′,4,4′-tetrahydro-2,2′-biquinoxaline (Mbqn-(SⁱPr)₂) (2)) with the same molecular structure other than the exchange of two oxygen atoms with sulphur atoms. This enables us to directly compare the optical properties and stability of the compounds as a result of this substitution. For freshly prepared solutions of 1, a fluorescence quantum yield of 97% is observed, whereas for 2, the value is much lower at 7%. We furthermore note a decrease in quantum yields for solutions investigated after certain storage times, indicating a reactive channel. We note that this decomposition is much faster for solutions of 2 compared with compound 1. For 1, the decomposition likely proceeds to the biquinoxen dipseudobase via an equilibrium, whereas for 2 the decomposition product remains unidentified. The decomposition of 1 in particular was followed using ultrafast transient absorption spectroscopy, investigating the dynamics of the biquinoxen system after photoexcitation. Given the redox activity of biquinoxens, additionally the oxidation of the compounds was investigated using (spectro)electrochemistry. Full article

Considering the limited availability of meteorological data in practice, this paper investigates the short-term photovoltaic output prediction problem based on data decomposition and error correction to further improve prediction accuracy. Firstly, according to the analysis of the variation characteristics of photovoltaic output data, the Seasonal and Trend decomposition using Loess (STL) method is used to decompose the original data into three components: seasonal term, trend term, and residual term. Considering that the variation patterns of different components are different, based on the division of the dataset, temporal convolutional network (TCN)-based prediction models for each component are constructed separately, and the prediction results are superimposed to obtain the predicted value of the photovoltaic output. Secondly, an error dataset is constructed based on the prediction errors of the training set and validation set, and a TCN error prediction model is established. The error prediction value is used as compensation to correct the photovoltaic output prediction value, and the final photovoltaic output prediction value is obtained. Finally, based on the measured photovoltaic output data of a certain region in China, the effectiveness and advancement of the proposed method are demonstrated through the ablation and comparative experiments. Full article