Search Results (902)

The Dazhuzhuang, Biting, and Likou Sites are located along the Hui River basin in Yongcheng, eastern Henan. These three sites are situated close to each other and all yielded Longshan Culture period (2300–1800 BCE) remains, including large quantities of pottery with similar stylistic characteristics. However, archaeological surveys did not discover kiln sites at any of the three locations. To investigate the sources of Longshan period pottery in this region, its firing technology, and whether pottery circulated between the sites, this study employed a combination of X-ray fluorescence spectroscopy (XRF), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) to conduct a comprehensive scientific analysis of pottery unearthed from Longshan Culture contexts at the Dazhuzhuang, Likou, and Biting Sites in the Huai River basin, Yongcheng, Henan Province. The results reveal significant differences among the sites in terms of raw material selection, chemical composition, and technological characteristics. Pottery from the Dazhuzhuang Site exhibits with diverse clay sources. The Likou Site is characterized by highly homogeneous compositions derived from relatively high-alumina, low-iron clays, indicating standardized production practices. In contrast, the Biting Site shows greater variability in raw materials and functional differentiation. Thermal and microstructural analyses indicate that the dense glassy phase of black pottery was achieved through reducing firing conditions. In contrast, gray pottery was manufactured with calcareous additives to produce a porous structure. Full article

High-value patents are a key indicator of new product development, the emergence of innovative technology, and a source of innovation incentives. Multiple studies have shown that patent value exhibits a significantly skewed distribution, with only about 10% of patents having high value. Identifying high-value patents from a large volume of patent data in advance has become a crucial problem that needs to be addressed urgently. However, current machine learning methods often rely on manual hyperparameter tuning, which is time-consuming and prone to suboptimal results. Existing optimization algorithms also suffer from slow convergence and local optima issues, limiting their effectiveness on complex patent datasets. In this paper, machine learning and intelligent optimization algorithms are combined to process and analyze the patent data. The Fire Hawk Optimization Algorithm (FHO) is a novel intelligence algorithm suggested in recent years, inspired by the process in nature where Fire Hawks capture prey by setting fires. This paper firstly proposes the Enhanced Fire Hawk Optimizer (EFHO), which combines four strategies, namely adaptive tent chaotic mapping, hunting prey, adding the inertial weight, and enhanced flee strategy to address the weakness of FHO development. Benchmark tests demonstrate EFHO’s superior convergence speed, accuracy, and robustness across standard optimization benchmarks. As a representative real-world application, EFHO is employed to optimize Random Forest hyperparameters for high-value patent recognition. While other intelligent optimizers could be applied, EFHO effectively overcomes common issues like slow convergence and local optima trapping. Compared to other classification methods, the EFHO-optimized Random Forest achieves superior accuracy and classification stability. This study fills a research gap in effective hyperparameter tuning for patent recognition and demonstrates EFHO’s practical value on real-world patent datasets. Full article

Dairy wastewater is highly polluting and requires treatment before being discharged into receiving surface waters or destined for reuse. This study aimed to evaluate the performance of a wastewater treatment plant (WWTP) at a dairy facility, which includes the following treatment stages: screening, grease trap, and an upflow anaerobic filter (UAF). Monitoring data from a WWTP at a dairy situated in the southern region of Minas Gerais, Brazil, were assessed based on pollutant removal efficiency in accordance with Brazilian environmental regulations. The results showed that the WWTP achieved average removal efficiencies of 96.2% for COD and 97.1% for BOD₅. The BOD₅/COD ratio of raw and treated wastewater averaged 0.46 and 0.30, respectively, indicating preferential removal of the biodegradable organic fraction. The treated wastewater complied with legal standards for pH, settleable solids, and total suspended solids. However, at least one sample did not meet regulatory limits for discharge into water bodies regarding surfactants and oils & greases. Strong linear correlations (R² ~ 0.8) between COD and BOD₅ data were observed for both raw and treated wastewater. While the treated wastewater was not suitable for use in the facility’s wood-fired boiler, it may be reused for agricultural irrigation. Full article

Background/Objectives: Burn injuries are a major public health concern. This study estimated global, regional, and national burn burdens by total body surface area from 1990 to 2021 and projected trends to 2050. Methods: Utilizing data from the Global Burden of Disease Study 2021, we examined the prevalence, mortality, and years lived with disability (YLDs) according to age, sex, and region. Future trends were predicted using Bayesian meta-regression models and Das Gupta decomposition analysis. Results: In 2021, global prevalence was 12.99 million for severe burns and 235.34 million for mild burns, with age-standardized rates of 158.75 and 2815.26 per 100,000. Severe burns were highest in Southern Latin America (7836.51 per 100,000) and mild burns in the Caribbean (626.94 per 100,000). The largest declines from 1990 to 2021 were in high-income North America for severe burns (−38.22%) and East Asia for mild burns (−73.03%). Females had higher severe burn prevalence at younger and older ages, while males had higher mild burn prevalence from early adulthood. Leading risk factors were fire, heat, and hot substances (38.22% of severe burn YLDs; 53.87% for mild burns). By 2050, severe burns are projected to rise by 233.4% and mild burns by 142.5%, with Eastern Europe showing the largest growth. Conclusions: Although age-standardized burn rates are declining, absolute cases are projected to rise due to population growth and aging, particularly in low- and middle-income countries, underscoring the need for stronger prevention and improved burn care infrastructure. Full article

This work presents an integrated experimental and machine learning study on the fire performance of sisal fiber-reinforced polyester composites treated with magnesium hydroxide as a flame retardant. A total of 43 small-scale fire resistance tests were conducted in a custom-built gas-fired furnace following ISO 834 and NCh935/2 standards. Key parameters—including fiber content, flame retardant proportion, catalyst, and accelerator—were correlated with burn time and mass loss. Linear regression revealed negligible to weak correlations, while nonlinear models (Random Forest, Support Vector Regression, and Deep Neural Network) showed improved predictive capacity. The Deep Neural Network achieved the best performance (MSE = 0.061, R² = 0.334). Experimental results confirm that magnesium hydroxide substantially increases burn time, whereas sisal fiber content alone has a minimal effect on fire resistance. This study highlights an affordable strategy for enhancing the fire safety of bio-based composites and demonstrates the potential of machine learning to optimize material formulations. Future research should expand the dataset and validate the models through standardized large-scale fire tests. However, the findings are limited to small-scale fire resistance tests under controlled laboratory conditions and should not be generalized to full-scale structural applications without further validation. Full article

This study proposes an equivalent furniture fire model based on standard combustible assembly and verifies its feasibility as a substitute for real furniture through full-scale experiments and numerical simulations. Experiments show that the peak heat release rate and total heat release of the standard combustible assembly are highly consistent with those of the single-seat sofa. The numerical model has been verified by experimental data. The dynamic characteristics of the heat release rate (HRR) curve are consistent with the temperature evolution process, confirming its reliability for the numerical model. The research on optimizing fire extinguishing parameters is carried out based on this numerical simulation. The results show that the response time of the horizontal sprinkler is 22 s shorter than that of the vertical sprinkler, and the fire extinguishing efficiency is improved. Reducing the sprinkler height to 3 m can accelerate activation and reduce CO₂ release. A flow rate of 91.4 L/min can effectively control the fire, but when it exceeds 150 L/min, the fire extinguishing efficiency is significantly reduced. The low response time index sprinkler starts up 88 s faster than the standard type, significantly enhancing the initial fire suppression capability. This scheme provides a safe, economical, and repeatable standardized combustible assembly for fire training and offers theoretical support for the parameter design of intelligent fire extinguishing systems. Full article

In the Mediterranean basin, wildfires burn an average of 600,000 ha per year, causing severe ecological, economic, and social impacts. Fire behavior modeling is essential for wildfire prevention and control. Three-dimensional physics-based fire behavior models, such as Fire Dynamics Simulator (FDS), can represent heterogeneous fuels and simulate fire behavior processes with greater detail than conventional models. However, they require accurate information about species composition and 3D distribution of fuel mass and bulk density at the voxel level. Working in a Mediterranean ecosystem study area we developed a methodology based on the use of geometric and spectral features from UAS-based digital aerial photogrammetric point clouds for (i) species segmentation and classification using machine learning algorithms, (ii) generation of biomass prediction models at individual plant level, and (iii) creation of 3D fuel scenarios and modeling wildfire behavior. Field measurements were conducted on 22 circular plots with a radius of 5 m. Data from the field measurements, combined with species-specific allometric equations, were used for the evaluation of classification and prediction models. Fire behavior variables such as rate of spread, heat release rate, and mass loss rate were monitored and assessed as outputs from 20 different scenarios using FDS. The overall species classification accuracy was 80.3%, and the biomass regression R² values obtained by cross-validation were 0.77 for Pinus halepensis and 0.83 for Anthyllis cytisoides. These results are encouraging further improvement based on the integration of sensors onboard UAS, and the characterization of fuels for fire behavior modeling. These high-resolution fuel representations can be coupled with standard risk assessment tools, enabling fire managers to prioritize treatment areas and plan for resource deployment. Full article

The following article aims to implement a hybrid modulation methodology based on the Selective Harmonic Elimination Pulse Width Modulation (SHE-PWM) technique to work with the fundamental frequency of the system and find the optimal firing angles using the PSO optimization algorithm, capable of reducing the voltage THDv present in the output signals of three-phase multilevel inverters. To develop this approach, three case studies are proposed, developed in MATLAB/Simulink software, which feature three-phase inverters with five, seven, and nine levels, respectively, of the CHB topology. The impact of adequate modulation is assessed, resulting in a voltage output signal with reduced distortion. The national regulation ARCERNNR 002/20 will be used as a reference point to evaluate the results before and after implementing the methodology. It was verified that the developed methodology can effectively eliminate the selected harmonics, especially those of lower order (3rd, 5th, 7th, 9th, 11th, 13th, and 15th), achieving an improvement of up to 17.93% in the voltage THDv concerning the standard S-PWM modulation present in the CHB-MLI. Full article

The precise forecasting of renewable energy production and usage is essential for the stability, efficiency, and sustainability of contemporary power systems. This requirement is especially urgent in South Africa, a nation currently grappling with considerable energy issues, such as recurrent load shedding, outdated coal-fired power plants, and an increasing electricity demand. As the country moves towards a more renewable-focused energy portfolio, the capacity to anticipate future energy requirements is crucial for effective planning, operational stability, and grid resilience. This study introduces a hybrid approach that combines deep learning and machine learning techniques, specifically integrating long short-term memory (LSTM) neural networks with extreme gradient boosting (XGBoost) to provide more accurate and detailed forecasts of energy demand. LSTM networks are particularly effective in capturing long-term temporal dependencies in sequential data, such as patterns of energy usage. At the same time, XGBoost delivers high-performance gradient-boosted decision trees that can manage non-linear relationships and noise present in extensive datasets. The proposed hybrid LSTM-XGBoost model was trained and assessed using high-resolution data on energy consumption and weather conditions gathered from a coastal municipality in KwaZulu-Natal, South Africa, a country that exemplifies the convergence of renewable energy potential and challenges related to energy reliability. The preprocessing steps, including normalization, feature selection, and sequence modeling, were implemented to enhance the input data for both models. The performance of the model was thoroughly evaluated using standard statistical metrics, specifically the mean absolute error (MAE), the root mean squared error (RMSE), and the coefficient of determination (R²). The hybrid model achieved an MAE of merely 192.59 kWh and an R² of approximately 0.71, significantly surpassing the performance of the individual LSTM and XGBoost models. These findings highlight the enhanced predictive capabilities of the hybrid model in capturing both temporal trends and feature interactions in energy consumption behavior. Full article

Flame-retardant poly (ethylene terephthalate) composites (FR PET) have been developed with the potential to be used as substrates in applications where flexibility and transparency are required. Several phosphorous-based flame retardants of a different nature were selected here for compounding by melt blending with a low-molecular-weight PET polymer. The fire reaction, transparency, and mechanical properties were analyzed. TGA and cone calorimetry were used to elucidate the gas-phase and condensed-phase actions of flame retardants and their effectivity. Cone calorimeters showed an improved performance with the addition of flame retardants, particularly a reduction in generated heat, improving the FGI (fire growth index) value. However, a V0 classification (following the UL94 standard) was achieved only with the addition of an organic phosphonate, Aflammit PCO900, to the PET matrix. This behavior was linked to the early reaction of this flame retardant in the gas phase, in addition to a plastification effect that causes the removal of the polymer from the incident flame. The presence of flame retardants reduced the transparency of composites over the neat PET, but, nevertheless, a good optical performance remained. No special effect was observed on the crystallization parameters. Therefore, the increase in opacity can be attributed to the poor miscibility of flame retardants and/or differences in the diffraction index of the polymer and FR additives. Full article

To investigate the residual load-bearing capacity of composite steel truss bridge girders after vehicle fire, a 100 m simple supported composite steel truss bridge girder was selected as the research object, and a typical oil tanker fire was taken as the fire scenario. This study identifies the most critical conditions associated with an oil tanker fire and outlines the degradation pattern of the residual load-bearing capacity of composite steel truss bridge girders after a vehicle fire. It also proposes a damage classification standard and an evaluation method for the load-bearing capacity based on the structural failure path and load-displacement curve. The results indicate that the most critical scenario during a vehicle fire occurs when the fire is located on the bridge deck, particularly in the middle section of the longitudinal bridge and the outermost lane of the transverse bridge. During a vehicle fire, the top chord is the component most affected by the thermal history. Under immersion cooling conditions, the remaining load-bearing capacity of the girder decreases more significantly compared with natural cooling. After the fire, the upper chord first reaches the yield strength, causing load transfer to adjacent horizontal inclined members. The stress of the horizontal inclined rod will develop rapidly, leading to structural instability and eventual failure. Four grades of load-bearing capacity damage for composite steel truss bridge girders after vehicle fire are defined to serve as references for practical engineering applications. Full article

Hydrofluorocarbons (HFC) are today widely used as refrigerants, solvents, or aerosols for fire protection. Due to their non-negligible environmental impact, there exists an increasing interest towards their effective separation and recovery, which still remains a major challenge. This work presents a comprehensive thermodynamic and transport modeling approach able to describe HFC sorption and transport in different amorphous polymers, including glassy, rubbery, and copolymers, as well as in supported Ionic Liquid membranes (SILMs). In particular, the literature solubility data for refrigerants such as R-32, R-125, R-134a, and R-152a is analyzed by means of the Sanchez–Lacombe Equation of State (SL-EoS), and its non-equilibrium extension (NELF), to predict gas uptake in complex polymeric materials. The Standard Transport Model (STM) is then employed to describe permeability behaviors, incorporating concentration-dependent diffusion using a mobility coefficient and thermodynamic factor. Results demonstrate that fluorinated gases exhibit strong affinity to fluorinated and high free-volume polymers, and that solubility is primarily governed by gas condensability, molecular size, and polymer structure. The combined EoS–STM approach accurately predicts both solubility and permeability across different pressures in all polymers, including SILM. The thorough study of HFC transport in polymer membranes provided both systematic insights and predictive capabilities to guide the design of next-generation materials for refrigerant recovery and low-GWP separation processes. Full article

This article focuses on the fire safety risks associated with conventional glass–aluminum façades—with a particular focus on stick and unitized curtain walling systems—providing an overview of possible fire spread mechanisms, considering the role of the curtain wall in maintaining compartmentation at the spandrel zone. First, it analyzes some of the relevant requirements of different European building regulations. Then, it provides a test evidence-based critical analysis of the gaps and loopholes in the relevant fire resistance standard for partial curtain wall configurations (EN 1364-4), where the evaluation of the propagation within the façade system is not necessarily considered in the fire-resistant spandrel zone. Finally, it presents a proposal for addressing these gaps in the form of a theoretical concept for a new test configuration and additional assessment criteria. This is followed by an initial experimental analysis of the concept. The standard testing campaign showed that temperature rise in mullions can exceed 180 °C after 30 min if limiting measures are not considered in the façade design. However, this can be only detected if framing is in the non-exposed area of the sample, being part of the evaluation surface. Meanwhile, differences are detected between the results from standard and new assessment criteria in the new configuration proposed, including a more rapid temperature rise for framing elements (207 K in a second level mullion at minute 90) than for the common non-exposed assessment surface of the sample (172 K at the same time) in cases where cavities are not protected. Accordingly, the proposed configuration successfully detected vertical temperature transfer within mullions, which can remain undetected in standard EN 1364-4 tests, highlighting the potential for fire spread even in EI120-rated assemblies. Full article

Background: About half of all Major Depressive Disorder (MDD) patients have anxiety disorder. There is a neurologic basis for the comorbidity of balance (vestibular) disorders and anxiety. To detect comorbid anxiety disorder in MDD patients and, importantly, to investigate its relationship with depressive severity, we use Electrovestibulography (EVestG), which is predominantly a measure of vestibular response. Methods: In a population of 42 (26 with anxiety disorder) MDD patients, EVestG signals were measured. Fourteen (eight with anxiety disorder) were not on any anti-depressants, anti-psychotics or mood stabilizers. Using standard questionnaires, participants were depression-wise labelled as reduced symptomatic (MADRS ≤ 19, R) or symptomatic (MADRS > 19, S) as well as with or without anxiety disorder. Analyses were conducted on the whole data set, matched (age/gender/MADRS) subsets and compared with medication free subsets. Low-frequency EVestG firing pattern modulation was measured. Results: The main differences between MDD populations with and without anxiety disorder populations, regardless of being medicated or not, were (1) the presence of an increased 10.8 Hz component in the dynamic movement phase recordings, (2) the presence of asymmetric right versus left 7.6–8.9 Hz and 12.1–13.8 Hz frequency bands in the no motion (static) phase recordings, and (3) these differences were dependent on depressive severity. Conclusions: The EVestG measures are capable of quantifying anxiety in MDD patients. These measures are functions of depressive severity and are hypothesized to be linked to Hippocampal Theta (~4–12 Hz). Full article

The continuous advancement of fire protection technologies has necessitated the development of comprehensive safety standards, leading to an increasingly diversified and specialized regulatory landscape. This has made it difficult for fire protection professionals to quickly and accurately locate the required fire safety standard information. In addition, the lack of effective integration and knowledge organization concerning fire safety standard entities has led to the severe fragmentation of fire safety standard information and the absence of a comprehensive “one map”. To address this challenge, we introduce FT-FLAT, an innovative CNN–Transformer fusion architecture designed specifically for fire safety standard entity extraction. Unlike traditional methods that rely on rules or single-modality deep learning, our approach integrates TextCNN for local feature extraction and combines it with the Flat-Lattice Transformer for global dependency modeling. The key innovations include the following. (1) Relative Position Embedding (RPE) dynamically encodes the positional relationships between spans in fire safety texts, addressing the limitations of absolute positional encoding in hierarchical structures. (2) The Multi-Branch Prediction Head (MBPH) aggregates the outputs of TextCNN and the Transformer using Einstein summation, enhancing the feature learning capabilities and improving the robustness for domain-specific terminology. (3) Experiments conducted on the newly annotated Fire Safety Standard Entity Recognition Dataset (FSSERD) demonstrate state-of-the-art performance (94.24% accuracy, 83.20% precision). This work provides a scalable solution for constructing fire safety knowledge graphs and supports intelligent information retrieval in emergency situations. Full article