Search Results (360)

In recent years, the energy revolution has driven the rapid development of lithium-ion batteries (LIBs). A fire suppression system capable of rapidly and effectively extinguishing LIB fires constitutes the last line of defense for ensuring the safe operation of the LIB industry. In this study, an experimental platform simulating the storage environment of LIBs in energy-storage stations was constructed, and liquid nitrogen (LN) was employed to conduct fire suppression tests on LIBs. The effective utilization of 17.4 kg of LN during the suppression process inside the battery module was quantified. In addition, fire compartments were established within the battery module, and a strategy for enhancing the LN suppression effectiveness was proposed. The results indicate that, without intervention, the thermal runaway propagation (TRP) rate within the LIB module gradually accelerates. After LN injection, the effective utilization of LN for extinguishing individual LIBs decreases progressively along the sequence of TRP. Creating fire compartments inside the PACK using 6 mm aerogel blankets effectively reduces the transfer of energy from the region undergoing thermal runaway (TR) to other regions, while simultaneously enhancing the extinguishing performance of LN. Under the same LN dosage, the introduction of fire compartments increases the effective utilization from 0.037 to 0.051. However, as the compartment volume decreases, the degree of improvement in LN utilization is reduced. This work is expected to provide guidance for the engineering application of LN-based fire suppression systems to inhibit LIB TR and its propagation. Full article

Maintenance and repair play a crucial role in industry. Smart systems for technical diagnostics can help to save money and to prevent the breakdown of machines and plants. These systems and its classifiers benefit from plausible features because they tend toward robust classification. Although concepts for knowledge discovery are well-known in various scientific fields, they are not established in the field of rotating machines. Knowledge discovery from experimental data is a framework that combines valid methods for knowledge discovery with expert knowledge and automated experiments. For the central data mining step, feature selection algorithms based on heuristic or meta-heuristic search are established. The objective is to identify plausible pattern with a limited number of features and the best combination of these features. The results in this work show which strategies align the best with the requirements of knowledge discovery using experimental data to find plausible features. For this study, well-configured search strategies, namely, sequential forward selection and ant colony optimization, were applied on real data. The data represent several fault severity levels for parallel misalignment and cavitation. The plausible feature vectors and features exhibited good behavior when applied to new targets. It is expected that the obtained knowledge will be transferable to new classification tasks with only minimal optimization of the reference data or the classifier. Full article

Noise regulations often apply penalties (e.g., +5 dB) to A-weighted equivalent sound pressure levels ($L_{\text{Aeq}}$ [dB]) to account for increased annoyance from tonal or impulsive features. Psychoacoustic evidence indicates that spectral characteristics also affect annoyance, with some spectra being substantially more disturbing than others. Yet, no established method exists for determining spectrum-based penalties from measured sound spectra. This study aimed to develop a simple, objective model for assigning penalties to steady-state broadband sounds based on spectral properties. Using experimental data comprising annoyance ratings and penalties for 23 spectrally distinct broadband sounds at three $L_{\text{Aeq}}$ levels (32, 40, and 48 dB), we evaluated several single-number noise descriptors from the literature. Room The Noise Criterion showed the strongest association with direct annoyance ratings, while the spectral centroid (SC) and sharpness were most closely related to spectrum-based penalties. Due to its simplicity, the spectral centroid was selected for the final model: $k=6.9·{\text{log}}_{10}\left(\mathit{SC}\right)-16.3$. The proposed model is expected to be applicable for broadband sounds within 32–48 dB $L_{\text{Aeq}}$ and offers a practical approach for incorporating spectral effects into noise assessment. Full article

Diamond coring bits exhibit stable rock-breaking and coring processes as well as a long service life. However, when drilling in complex and challenging formations are characterized by high hardness, strong plasticity, and high abrasiveness, issues such as low rock-breaking efficiency, rapid failure, and shortened service life frequently occur. To prevent premature bit failure and enhance rock-breaking efficiency, this study investigated the effects of drilling pressure and rotational speed on rock-breaking performance through bench-scale experiments using typical rock samples. A total of 15 experimental groups were included in this study, with one independent trial performed for each group. ROP is calculated as the ratio of effective drilling depth to time consumed, and MSE is derived based on axial force, torque, and rock-breaking volume. The experimental results indicated that (1) sandstone is more sensitive to rotational speed, whereas limestone and dolomite are more sensitive to drilling pressure; (2) the minimum mechanical specific energy (MSE) of sandstone was achieved at a drilling pressure of 15 kN and rotational speed of 50 r/min; (3) limestone exhibited the lowest MSE at 10 kN drilling pressure and 50 r/min rotational speed; and (4) dolomite showed the minimum energy consumption at 10 kN drilling pressure and 25 r/min rotational speed. On this basis, this paper establishes a cutting mechanics model for single-crystal diamond and a working load calculation model for the entire bit, respectively. The cutting mechanics model for single-crystal diamond is re-established based on Hertzian contact theory and elastic-plastic deformation theory. The findings of this study are expected to provide a working load calculation method for diamond coring bits in typical complex and challenging drilling formations and offer technical support for the design of coring bit cutting structures and the development of customized new products. It should be noted that the conclusions of this study are limited to the experimental parameter range (drilling pressure: 5–15 kN; rotational speed: 25–80 r/min), and their applicability under higher load conditions requires further verification. Full article

To address the energy and environmental challenges, this study targets the need for ultra-low energy buildings in China’s hot summer-cold winter region (HSCW) by developing high-performance alkali-activated foam concrete (AAFC) insulation material. Initially, a target performance indicator system was established. Subsequently, a mix proportion design method based on the volume method was proposed, and preliminary mix proportions were designed and tested to achieve the target performance. Accordingly, eight factors, including alkali equivalent and SiO₂ aerogel content, were selected for further optimization. A systematic optimization of performance was then conducted using an L₃₂(4⁸) orthogonal experimental design. Range analysis and analysis of variance indicated that foam content significantly affected all target properties. The water-to-binder ratio notably influenced mechanical performance and dry density. Alkali equivalent and activator modulus directly regulated the reaction process. Notably, the incorporation of 2.5 wt% SiO₂ aerogel reduced the thermal conductivity to 0.1107 W/(m·K), highlighting its significant role in improving thermal insulation and effectively resolving the common trade-off between insulation and mechanical properties in FC. Furthermore, the waterproofing agent played a critical role in reducing water absorption and enhancing frost resistance. Finally, the optimal mix proportion was determined through matrix analysis, with all material properties meeting the expected targets. Test results confirmed that the optimized FC achieved a dry density of 576.34 kg/m³, compressive and flexural strengths of 5.83 MPa and 1.41 MPa, respectively, a drying shrinkage rate of only 0.614 mm/m, a mass water absorption of 3.87%, and strength and mass loss rates below 10.5% and 1.8% after freeze–thaw cycles. Therefore, this material presents a novel solution for the envelope structures of low-energy buildings. Full article

The construction sector faces pressure to decarbonise while addressing rising resource demands and agricultural waste. Ordinary Portland cement (OPC) is a major CO₂ emitter, yet biomass residues are often open-burned or landfilled. This study explores corncob ash (CCA) as a sustainable supplementary cementitious material (SCM), examining how calcination conditions influence pozzolanic potential and support circular economy and climate goals, which have not been adequately explored in literature. Ten CCA samples were produced via open-air burning (2–3.5 h) and electric-furnace calcination (400–1000 °C, 2 h), alongside a reference OPC. Mass yield, colour, XRD, XRF, LOI, and LOD were analysed within a process–structure–property–performance–sustainability framework. CCA produced in a 400–700 °C furnace window consistently achieved high amorphous contents (typically ≥80%) and combined pozzolanic oxides (SiO₂ + Al₂O₃ + Fe₂O₃) above the 70% ASTM C618 threshold, with 700 °C for 2 h emerging as an optimal condition. At 1000 °C, extensive crystallisation reduced the expected reactivity despite high total silica. Extended open-air burning (3–3.5 h) yielded chemically acceptable but more variable ashes, with lower amorphous content and higher alkalis than furnace-processed CCA. Simple industrial ecology calculations indicate that valorising a fraction of global CC residues and deploying optimally processed CCA at only 20% OPC replacement could displace 180 million tonnes CC waste and clinker avoidance on the order of 5–6 Mt CO₂ per year, while reducing uncontrolled residue burning and primary raw material extraction. The study provides an experimentally validated calcination window and quality indicators for producing reactive CCA, alongside a clear link from laboratory processing to clinker substitution, circular resource use, and alignment with SDGs 9, 12, and 13. The findings establish a materials science foundation for standardised CCA production protocols and future life cycle and performance evaluations of low-carbon CCA binders. Full article

Scene text detection in multilingual environments poses significant challenges. Traditional detection methods often struggle with language-specific features and require extensive annotated training data for each language, making them less practical for multilingual contexts. The diversity of character shapes, sizes, and orientations in natural scenes, along with text deformation and partial occlusions, further complicates the task of detection. This paper introduces LICS (Locating Inter-Character Spaces), a method that detects inter-character gaps as language-agnostic structural cues, enabling more feasible multilingual text detection. A two-stage approach is employed: first, we train on synthetic data with precise character gap annotations, and then apply weakly supervised learning to real-world datasets with word-level labels. The weakly supervised learning framework eliminates the need for character-level annotations in target languages, substantially reducing the annotation burden while maintaining robust performance. Experimental results on the ICDAR and Total-Text benchmarks demonstrate the strong performance of LICS, particularly on Asian scripts. We also introduce CSVT (Character-Labeled Street View Text), a new scene-text dataset comprising approximately 20,000 carefully annotated streetscape images. A set of standardized labeling principles is established to ensure consistent annotation of text locations, content, and language types. CSVT is expected to facilitate more advanced research and development in multilingual scene-text analysis. Full article

Single-nucleotide polymorphism (SNP) genotyping arrays are important tools for crop genetic research. Addressing the current issues of insufficient accuracy in upland cotton cultivar identification and difficulties in distinguishing closely related germplasm and hybrids, developing an SNP array enabling rapid and accurate cotton cultivar identification and applicable to molecular breeding is a key demand in cotton cultivar identification and genetic breeding. This study aims to develop a low-cost and high-precision SNP array for upland cotton (Gossypium hirsutum L.) based on Genotyping by Target Sequencing (GBTS) technology. The array will integrate high accuracy in cultivar identification with applicability to molecular breeding, and this study further aims to clarify its application in cultivar identification. The Cotton 13K SNP array contains 13,571 high-quality SNP loci, including 8658 polymorphic sites derived from resequencing data and 4913 functional loci linked to key agronomic traits. All these loci are relatively evenly distributed across the genome. Genotyping 219 upland cotton cultivars/lines accurately clustered them into four genetic subgroups (K = 4), which closely matched their breeding institutions and geographical origins. Analysis of 44 experimental cotton materials (including sister lines and backcross materials) established a genetic similarity threshold of ≥90% for effectively distinguishing closely related germplasm. Comparative analysis of 38 F₁ hybrids and conventional cotton cultivars demonstrated that the average heterozygosity (Het) of hybrids (16.01%) was significantly higher than that of conventional cultivars (5.52%, p < 0.001). A preliminary threshold of Het ≥ 10% was identified for accurate discrimination of cotton hybrids. In conclusion, the Cotton 13K SNP array is a robust tool for population genetic analysis, discrimination of closely related cultivars, and hybrid identification. It also facilitates key molecular breeding steps, including parental evaluation, backcross monitoring, and marker-assisted selection (MAS). Its integration into breeding pipelines is expected to accelerate the development of new cotton varieties. Full article

With the advancement of aerospace equipment toward high-speed and heavy-duty applications, conventional forced lubrication systems are facing significant challenges in terms of reliability and adaptability to complex operating conditions. Porous medium materials, owing to their unique self-lubricating and oil-retention capabilities, are regarded as an ideal lubrication solution. However, their seepage behavior is governed by the strong coupling effects of microscopic pore structures and fluid physicochemical properties, the mechanisms of which remain inadequately understood, thereby severely constraining the design and application of high-performance lubricating materials. To address this, this paper systematically reviews recent research progress on seepage behavior in porous media, with the aim of establishing a correlation between microstructural characteristics and macroscopic performance. Starting from the characterization of porous media, this work comprehensively analyzes the structure–seepage relationships in porous polymers, metal foams, and porous ceramics, and constructs a multi-scale theoretical framework encompassing macroscopic continuum theories, mesoscopic lattice Boltzmann methods (LBM), pore network models, and microscopic molecular dynamics. The advantages and limitations of experimental measurements and numerical simulation approaches are also compared. In particular, this study critically highlights the current neglect of key interfacial parameters such as surface wettability and pore roughness, and proposes an in-depth investigation into the seepage mechanisms of polyimide porous cage materials based on LBM. Furthermore, the potential application of emerging research paradigms such as data-driven approaches and intelligent computing in seepage studies is discussed. Finally, it is emphasized that future efforts should focus on developing deeply integrated cross-scale simulation methodologies, strengthening multi-physics coupling and artificial intelligence-assisted research, and advancing the development of intelligent porous lubricating materials with gradient structures or stimulus-responsive characteristics. This is expected to provide a solid theoretical foundation and technical pathway for the rational design and optimization of high-performance lubrication systems. Full article

Background and Objectives: Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by social communication deficits, restricted interests, and repetitive behaviors. At present, there is no pharmacological intervention that reliably targets the core symptoms of ASD; instead, medications are primarily used to manage associated or concurrent symptoms such as irritability, aggression, anxiety, attention difficulties, and sleep disturbances. This review summarizes the current evidence for pharmacological treatments in ASD, emphasizing how these interventions are used in a symptom-focused, adjunctive manner, and highlighting efficacy, mechanisms, limitations, and emerging therapeutic targets. Methods: A comprehensive literature review was conducted across PubMed, Cochrane Library, and Embase to identify clinical trials, systematic reviews, meta-analyses, and preclinical studies on pharmacological interventions for ASD. Seventy-seven references were integrated to reflect the current state of evidence. Results: Established pharmacological strategies include atypical antipsychotics for severe irritability and aggression, as well as antidepressants, stimulants and non-stimulant agents, mood stabilizers, and anxiolytics for selected comorbid symptoms, although efficacy is often modest and variable, and side effects can be significant. Adjunctive and investigational approaches targeting glutamatergic and GABAergic neurotransmission, monoaminergic systems, and neuroinflammatory and oxidative stress pathways show preliminary promise but remain experimental. Across all categories, pharmacological treatments are most effective when embedded in individualized, multimodal care plans that integrate behavioral, rehabilitative, and psychological interventions. Conclusions: This review maps pharmacologic strategies in ASD onto their underlying neurobiological mechanisms and clarifies how evidence strength differs across drug classes and symptom domains. Ongoing advances in genetics, synaptic and circuit-level neuroscience, and neuroimmune signaling are expected to yield more specific, mechanism-based pharmacological approaches for autistic behaviors, with the potential to improve long-term functioning and quality of life when combined with comprehensive psychosocial care. Full article

Portfolio optimization is fundamental to modern finance, enabling investors to construct allocations that balance risk and return while satisfying practical constraints. When transaction costs and cardinality limits are incorporated, the problem becomes a computationally demanding mixed-integer quadratic program. This work demonstrates how principles from biomimetics—specifically, the computational strategies employed by biological neural systems—can inspire efficient algorithms for complex optimization problems. We demonstrate that this problem can be reformulated as a constrained quadratic program and solved using dynamics inspired by spiking neural networks. Building on recent theoretical work showing that leaky integrate-and-fire dynamics naturally implement projected gradient descent for convex optimization, we develop a solver that alternates between continuous gradient flow and discrete constraint projections. By mimicking the event-driven, energy-efficient computation observed in biological neurons, our approach offers a biomimetic pathway to solving computationally intensive financial optimization problems. We implement the approach in Python and evaluate it on portfolios of 5 to 50 assets using five years of market data, comparing solution quality against mixed-integer solvers (ECOS_BB), convex relaxations (OSQP), and particle swarm optimization. Experimental results demonstrate that the SNN solver achieves the highest expected return (0.261% daily) among all evaluated methods on the 50-asset portfolio, outperforming exact MIQP (0.225%) and PSO (0.092%), with runtimes ranging from 0.5 s for small portfolios to 8.4 s for high-quality schedules on large portfolios. While current Python runtimes are comparable to existing approaches, the key contribution is establishing a path to neuromorphic hardware deployment: specialized SNN processors could execute these dynamics orders of magnitude faster than conventional architectures, enabling real-time portfolio rebalancing at institutional scale. Full article

In this study, the operation features of a transformerless voltage divider, with transistor–diode commutation of switchable capacitors, designed to operate as a part of low-power autonomous electronic systems with reduced output voltage are studied both theoretically and experimentally. The analysis is carried out for a divider operation with a constantly or periodically connected voltage source V₀ with unlimited power. It is found that the divider’s efficiency during operation with a constantly connected primary voltage source V₀ with unlimited power is very low. However, the efficiency can reach 60% during the divider’s operation using a periodically connected voltage source V₀ with unlimited power. It has been shown that the efficiency can only reach 40% in the case of using a voltage source with limited power connected to the divider periodically. It has been established that for circuits with transistor–diode commutation of the capacitors, the stabilization effect is much stronger than for circuits with diode commutation of the capacitors. Therefore, an excess of the maximum load voltage relative to the expected value V₀/N is significantly lower for transistor–diode commutation in comparison with diode commutation (N is the number of divider stages). Based on the ideas developed regarding the divider operation, analytical expressions are obtained, enabling us to calculate the parameters of the studied divider circuits in a wide range. The good agreement between the analytical estimations and experimental data suggests that these calculations adequately describe the operation of the dividers, and that the derived analytical expressions can be successfully used during the preliminary design stage. In general, the analysis carried out herein and the developed approach make it possible to significantly narrow the range of search for the necessary system parameters when designing voltage dividers. Full article

The paper presents measurements of frequency dependence of conductivity and real components of complex permittivity of a nanocomposite consisting of electrical pressboard, bio-insulating oil and water nanodroplets with moisture content ranging from 0.6 wt.% to 5 wt.%. Bio-oil meets high environmental requirements—it is fully biodegradable, and its combustion products are significantly less harmful than those of mineral oil. In addition, the use of bio-oil reduces the carbon footprint of power transformer production. The quantum mechanical phenomenon of electron tunnelling between potential wells created by water nanodroplets was used to analyze the experimental results obtained. The study determined the effect of moisture content on the relative relaxation time values. On this basis, the number of water molecules in nanodroplets, their diameters and the concentration of nanodroplets depending on moisture content were determined. The distances over which electrons tunnel in moist pressboard impregnated with bio-oil were determined. These values are the expected values of the probability distribution of the distance between neighbouring nanodroplets. The values of the number of water molecules in nanodroplets are also the expected values of the probability distribution of the number of molecules in nanodroplets. It has been established that during many years of transformer life, several parallel processes occur as the moisture content in bio-oil-impregnated pressboard increases. One of them involves the accumulation of water molecules collected in the pressboard in nanodroplets. The second is an increase in the concentration of nanodroplets. The third is an increase in the average number of water molecules in nanodroplets. Full article

Bearing health monitoring is essential for ensuring the reliability and operational safety of induction machines, as bearing faults remain among the most frequent failure modes in rotating electrical equipment. This work contributes to condition monitoring by enhancing the robustness of health indicators and developing a supply-frequency-independent health indicator (HI) for bearing fault diagnosis using Motor Current Signature Analysis (MCSA). The objective is to design an HI capable of reliably representing the bearing degradation state under varying operating conditions, particularly when the supply frequency changes. To achieve this, the study briefly examines the key physical mechanisms governing the detectability of bearing-related spectral signatures—including rotational frequency, unbalanced magnetic pull, eddy currents, skin effect, and hydrodynamic forces. The theoretical analysis establishes the overall trend expected under varying supply frequencies and clarifies how these phenomena collectively influence the spectral characteristics of the fault components and the frequency-dependent evolution of their amplitudes. These insights are experimentally validated using induction machines fitted with bearings of two fault severities. Leveraging this physical understanding, a modified regression-based compensation model is introduced to reduce the frequency-dependent variation in the HI. The resulting compensating factor effectively stabilizes the frequency response, producing a more consistent and monotonic degradation trend across the tested conditions. The proposed method is computationally lightweight, does not require run-to-failure data or detailed physical modeling, and is suitable for real-time implementation. By integrating physical insight with data-driven modeling, this work presents a practical and frequency-independent HI framework that can be readily deployed within digital-twin-based condition monitoring architectures for induction machines. Full article

Endocrine-disrupting compounds (EDCs) are emerging environmental pollutants that are known to the disrsupt hormonal system of many vertebrates. Amphibians, with their aquatic larval stages and high sensitivity to waterborne contaminants, are especially vulnerable to EDC exposure. Despite increasing concerns over EDC pollution, systematic monitoring of these compounds in surface waters remains limited in many regions, including the European Union. This study investigates the effects of water from the Warta River, one of the largest rivers in Central Europe, an urban waterway subjected to significant anthropogenic pressure and known to contain EDCs on body condition, digit ratio, and gonadal development in two brown frog species: the common frog Rana temporaria and the moor frog Rana arvalis. We propose DR as a potential biomarker of endocrine disruption, as it is linked to hormonal impact in the early development of vertebrates. In this study, tadpoles were reared in the semi-open experimental setup with tanks containing river or potable tap water as a control. Contrary to expectations, no significant differences were observed in body condition, digit ratio, or gonadal structure, suggesting that EDC concentrations in the river water may not have been high enough to induce detectable effects. However, a consistent relation between DR and sex was observed in both species, underscoring its potential as a biologically meaningful trait. Notably, the potable tap water used as a control exhibited contamination levels comparable to the river water, raising concerns about the efficacy of current water treatment methods and highlighting the challenges of establishing true reference conditions in environmental studies. Full article