Search Results (1,021)

Erosion of the leading edge of blades in windy and sandy environments can cause wind turbines to lose up to 25% of their annual power generation. Traditional studies have mostly focused on the impact of single factors on erosion rates, but the effects of multiple parameters on erosion rates within the framework of the Stokes number (Stk) of dust particles have not yet been clarified. This study employs a numerical approach based on the Euler–Lagrange framework, integrating the SST k-ω turbulence model with a discrete phase model (DPM) to simulate the unsteady gas–solid two-phase flow around a NACA 0012 airfoil. The computational model was rigorously validated through grid independence tests and comparison with experimental aerodynamic data from the database, showing strong agreement under steady conditions. Systematic simulations were conducted with particle diameters ranging from 10 to 360 μm, densities from 2650 to 3580 kg/m³, and inflow velocities from 1.5 to 21 m/s, comprehensively covering Stokes number regimes from Stk << 1 to Stk >> 1. Through parametric analysis, we quantify the control effect of Stk on erosion rate and erosion hot spots. Simulation results indicate that Stk has a zone-specific control effect on airfoil erosion: erosion hot spots in low-Stk zones migrate from the mid-to-rear edge to the leading edge. Erosion rate peaks when Stk ≈ 0.8. Inertial impact in the high-Stk zone dominates surface damage propagation. Based on the simulation results, an erosion model with an error of ≤3.6% was established for the E = K∙Stk^a∙d_p^b∙v^c zone, providing a quantitative physical basis to inform wind turbine blade protection strategies. Full article

Granite wet edge finishing is widely adopted to improve surface durability and aesthetics while reducing dust dispersion compared to dry processes. However, even under flooded lubrication, fine particles (FP, 0.5–20 µm) and ultrafine particles (UFP, <100 nm) containing crystalline silica are emitted, posing health risks such as silicosis and pulmonary or cardiovascular diseases. This study investigates particle emissions during CNC edge finishing of black (containing 0% quartz) and white granites (containing 41% quartz) using two industrially relevant profile tools: Half-Beveled (HB) and Ogee (OG). A full factorial design evaluated the effects of granite type, tool geometry, abrasive grit size, spindle speed, and feed rate. Particle concentrations were measured with Aerodynamic and Scanning Mobility Particle Sizers. Results show that spindle speed (N) is the dominant factor, explaining up to 92% of variance in emissions, whereas feed rate (V_f) played a minor role. Tool geometry had a pronounced effect on UFP release: sharp-edged geometries (HB) promoted localized micro-fracturing and higher emissions, while curved geometries (OG) distributed stresses and reduced particle detachment. White granite generated higher mass emissions due to its high quartz content, while black granite exhibited more stable emission behavior. These findings highlight the dual necessity of optimizing cutting kinematics and selecting appropriate tool profiles to balance surface quality and occupational health in granite processing. Full article

This study investigates particle size distribution and fine dust generation from sanding six tropical wood species (Red Meranti, Iroko, Zebrano, Bubinga, Ipe, and Wenge) using sieve analysis and laser diffraction. The wood species produced different dust particles, primarily influenced by wood density. Bubinga, Zebrano, and Wenge generated the highest proportion of particles in the 125–250 μm range, while Ipe and Iroko produced more dust in the 63–125 μm fraction. Low-density Red Meranti formed the greatest share of coarse particles (10.54% over 549.5 μm), whereas high-density Ipe generated the largest proportion of respirable dust, including PM₁₀ (8.80%), PM_2.5 (2.93%), and PM₁ (0.88%). Statistical analysis confirmed a significant effect of density on both coarse and fine dust fractions, with finer particles increasing consistently as density increased. Laser diffraction showed ultrafine particles down to approximately 0.7 μm in all species except Red Meranti. Microscopy confirmed elongated fibrous fragments, particularly in Wenge and Red Meranti. Overall, denser tropical hardwoods exhibited greater potential to produce hazardous fine dust during sanding, posing health risks and explosion hazards. These findings emphasize the need for effective dust extraction and high-efficiency respiratory protection and contribute to improved understanding of dust formation mechanisms in tropical wood processing. Full article

Plastic production and subsequent environmental contamination have increased substantially in recent decades, resulting in pervasive human exposure to microplastics (MPs), nanoplastics (NPs), and plastic-associated additives such as bisphenols and phthalates. These substances are known to induce toxic effects via multiple biological mechanisms, including oxidative stress, inflammation, apoptosis, immune system disruption, and genotoxicity. While exceptions exist, current research generally indicates that these exposures may adversely affect fertility. Notably, children constitute the most vulnerable demographic due to behavioral tendencies, higher intake-to-body-weight ratios, underdeveloped detoxification systems, and critical developmental periods of susceptibility. Evidence demonstrates that exposure commences in utero, with MPs, NPs, and additives identified in placental tissue, amniotic fluid, cord blood, and meconium—factors associated with impaired fetal growth and reduced gestational duration. After birth, additional exposure occurs through diet, inhalation, household dust, feeding equipment, toys, and consumer products. Experimental and epidemiological studies suggest that plastics may adversely affect multiple physiological systems. Reported outcomes include altered pubertal development, reduced fertility, neurodevelopmental abnormalities, respiratory diseases such as asthma, and increased risks of metabolic disorders, including obesity and insulin resistance. However, substantial knowledge gaps remain: the relative toxicity of different polymers and additives, dose–response relationships, critical exposure periods, and long-term consequences are not yet fully defined. Given growing concern and mounting evidence of harm, precautionary measures are warranted. Reducing nonessential plastic use, strengthening regulatory actions, improving product labeling, and promoting public awareness are urgent priorities, particularly in vulnerable and resource-limited communities. Further mechanistic studies and longitudinal human research are essential to clarify health risks, guide safer material substitutions, and inform evidence-based policies aimed at protecting children from avoidable plastic-related toxicity. Full article

We develop predictive models for OH maser occurrence in Galactic star-forming regions by integrating dense-clump physical properties from the APEX Telescope Large Area Survey of the Galaxy (ATLASGAL) and Herschel Infrared Galactic Plane Survey (Hi-GAL) 360° catalogs with maser detections and non-detections compiled in the MaserDB.net database. We compare two predictive modeling approaches for Galactic OH maser incidence: a Generalized Linear Model (GLM; logistic regression) and a compact Keras-based binary neural network (BNN). For the 1665/1667 MHz lines, both models achieve recall of 90% with a precision of approximately 50%, while for the excited-state 6031/6035 MHz lines, precision reaches roughly 20% at the same recall. We found no statistically significant difference between the BNN and GLM in out-of-sample performance. This implies that maser occurrence may be expressed as a monotonic trend without requiring nonlinear interactions. Across different catalogs and transition lines, luminosity, luminosity-to-mass ratio ($L/M$), dust temperature, and ${\mathrm{H}}_2$ column, surface, and volume densities are the most influential features for maser prediction. These variables support a physical picture in which radiative pumping favors warm, luminous, and compact clump environments. We provide an accessible online tool that allows users to predict the likelihood of OH maser emission toward ATLASGAL or Hi-GAL sources based on coordinate lists. Full article

Free-space optical (FSO) communication systems offer fiber-like bandwidth, high security, and rapid deployment; however, their performance is highly susceptible to atmospheric impairments, such as dust storms, which can cause fading that degrades link reliability. In this study, we analyze the performance of FSO links under a dust-induced fading channel modeled as a Beta distribution channel. We derive an expression for the instantaneous signal-to-noise ratio (SNR) distribution. Using the SNR expression, we construct a general framework that yields closed-form formulas for fundamental performance measures such as outage probability, average bit-error rate (BER), and ergodic capacity. The analysis considers both intensity modulation/direct detection (IM/DD) and coherent detection techniques, encompassing typical modulation schemes including modulation formats such as on–off keying (OOK), M-ary phase-shift keying (M-PSK), and M-ary quadrature amplitude modulation (M-QAM). The results show that dust-induced fading penalizes all modulations, though coherent detection achieves better error performance than IM/DD at equivalent SNR. For example, a coherent receiver requires approximately 4.4 dB lower average SNR than an IM/DD system to achieve the same outage probability. Overall, the proposed unified framework shows that dust-induced fading can severely degrade the performance of FSO links, while also quantifying how network operators can trade off complexity and performance when choosing between coherent and IM/DD detection under realistic dust-storm conditions. Full article

This study investigated spruce wood (Picea abies Karst. L.) dust generated during sanding in a woodworking company, focusing on its health, explosion, and fire hazards. Microscopic analyses revealed that dust particles ranged from 2.38 μm × 1.69 μm to 499.71 μm × 403.30 μm, with an average size of 73.2 μm × 37.98 μm. Smaller particles exhibited a spherical morphology, while larger ones were elongated and fibrous. Sieve analysis confirmed that particles sized 63–75 μm formed the largest fraction (46.74%), with 71% of the total dust being airborne (<100 μm), including 5% PM₁₀ and 1% PM_2.5. Explosion tests identified a lower explosion limit (LEL) of 80 g·m⁻³, with dust classified as highly explosive (ST 2). Smaller particles were found to significantly reduce the LEL, increasing explosion susceptibility. These findings highlight the dual risk of inhalation exposure and explosion potential. Practical safety recommendations include ensuring efficient local dust extraction, mandatory use of respiratory protection, and restricted worker movement near sanders. Furthermore, organizational measures aligned with ATEX standards—such as daily cleaning, removal of settled dust layers, use of explosion-safe industrial vacuum cleaners, and installation of automatic explosion suppression systems in extraction units—are essential. Full article

To address the heat transfer degradation caused by fouling and dust accumulation on the stator windings of marine diesel generators, this study proposes a health condition assessment method based on the convective heat transfer coefficient. A numerical analysis model was developed using the Ansys Fluent platform to systematically investigate the effects of ambient temperature, load power, and fouling layer thickness on the stator winding temperature and convective heat transfer coefficient. The results demonstrate that the convective heat transfer coefficient is highly sensitive to variations in fouling layer thickness. On this basis, a health assessment model centered on the convective heat transfer coefficient was established and validated using experimental data from diesel generator tests. The results show that the proposed model accurately captures the performance degradation process and enables quantitative classification of operating states, including healthy, sub-healthy, degraded, and abnormal conditions. This research provides a feasible theoretical foundation and technical approach for the intelligent monitoring and condition evaluation of marine diesel generators, offering significant engineering value for enhancing the efficiency and reliability of marine power systems. Full article

Maximum power point tracking (MPPT) control is a key technology for increasing the power generation of photovoltaic arrays under varying light and temperature conditions. Traditional perturb and observe methods and incremental conductance methods can achieve good tracking performance for single-peak characteristics. However, under complex conditions such as partial shading or dust accumulation, the power-voltage curve of a photovoltaic array exhibits multi-peak characteristics. In such cases, traditional methods may get trapped in local optima, preventing the photovoltaic array from operating at the maximum power point. Swarm intelligence algorithms perform well when solving multi-extremum functions and can be used for MPPT control of photovoltaic arrays in complex environments. Therefore, this paper focuses on the fireworks algorithm (FWA). To improve the computational speed and global optimization capability of the FWA, the characteristics of each stage of the algorithm are analyzed, a comprehensive improved fireworks algorithm (CIFWA) is proposed, and it is applied to the MPPT control of photovoltaic systems. The improved algorithm introduces an adaptive resource allocation and selection strategy with community inheritance features and applies tent chaos mapping to the algorithm’s explosion behavior. Multiple sets of test functions are used to compare the performance metrics of the optimization algorithm, demonstrating improvements in computational speed and global search capability of CIFWA. Finally, a control strategy for the MPPT of photovoltaic arrays based on CIFWA is presented, and a simulation experimental platform is built to analyze and verify the control performance. Full article

Cutting fluids are widely used in mechanical engineering to reduce friction and heat generation during metal machining. However, during operation, these fluids become contaminated with metal particles, dust, and microorganisms, leading to degradation of their functional properties and environmental concerns. This study investigates the ultrasonic cleaning and regeneration of contaminated cutting fluids. A rheological model of the elastic–viscous medium was analyzed, and a physical model describing the ultrasonic cleaning mechanism was proposed. Experimental investigations were conducted to validate the theoretical assumptions. The results confirmed that ultrasonic treatment promotes dispersion and phase separation of the fluid, removes putrefactive odor, and partially destroys microorganisms. The regenerated fluid exhibited enhanced clarity and stability compared with the contaminated samples. The findings contribute to a deeper understanding of the physicochemical processes occurring during ultrasonic treatment and demonstrate the potential of this method for sustainable reuse of cutting fluids in industrial applications. Full article

With the vigorous development of contemporary clean energy, the participation rate of photovoltaic (PV) power generation in the whole power system is increasing day by day, and accurate PV power prediction technology is crucial for the optimal scheduling of the power system. However, the frequent occurrence of extreme climate in recent years has caused greater disturbance to PV power generation, which greatly increases the degree of difficulty in accurately predicting PV power generation and thus affects the security, economy, reliability and stability of grid system operation. In order to predict PV power under extreme climatic conditions, we firstly elaborate the PV power prediction methods and their respective advantages and disadvantages for sand, dust, rainstorm and snowfall in existing studies, and on this basis, we propose the Gray Wolf Optimization for Short-Term Forecasting Models of the Long and Short-Term Memory Model based on K-Means clustering, which ensures the accuracy of PV power prediction under extreme climatic conditions. power prediction accuracy under extreme climate conditions. Firstly, the K-means clustering algorithm is utilized to perform weather typing, which is divided into four weather categories, namely, dusty weather, heavy rain, heavy snow and normal weather. Then, for the weather typing results, the prediction effects of the Gray Wolf Optimization Long Short-Term Memory Network (GWO-LSTM) Model, Random Forest (RF) Model, Multilayer Feedforward Neural Network (BP) Model, and Long and Short-Term Memory Network (LSTM) Model are compared, respectively. The prediction results indicate that GWO-LSTM achieves the highest forecasting accuracy, with a mean root mean square error (RMSE) of 0.6235 across all four weather scenarios. Its prediction accuracy reaches approximately 95%, providing effective data support for the safe and stable operation of new power systems featuring high proportions of grid-connected photovoltaic generation. Full article

A major issue in industrial production is the generation of post-production wastes that are not biodegradable. The article presents an innovative solution for the management of industrial waste, which includes, among others, metal dust generated during the grinding of castings. The results of research on a concrete composite modified with metallic dust, a by-product from cast iron product manufacturing, were presented. The study analyzed the effect of using metallic dust as a partial replacement for fine aggregate at levels of 10%, 20%, 30%, 40%, and 50% on selected concrete properties. Tests included concrete mix consistency, compressive strength after 28 days and 6 months, density after 28 days of curing, bending strength, abrasion resistance using the Boehme disk method, durability in a salt chamber, and air content in hardened concrete. The research results indicate the possibility of using waste metal dust in concrete composites as a substitute for sand as a fine aggregate. An innovative waste processing solution allows the creation of a product with better abrasion resistance and compressive strength parameters while also having a good impact on the environment. Full article

The heading face is one of the zones most severely affected by dust pollution in underground coal mines, and dust control becomes even more challenging during roadway excavation with continuous miners. To improve dust mitigation in environments characterized by intense dust generation, high ventilation demand, and large cross-sectional areas, this study integrates numerical simulations, laboratory experiments, and field tests to investigate the physicochemical properties of dust, airflow distribution, dust migration behavior, and a comprehensive dust control strategy combining airflow regulation, foam suppression, and dust extraction fan systems. The results show that dust dispersion patterns differ markedly between the left-side advancement and right-side advancement of the roadway; however, the wind return side of the continuous miner consistently exhibits the highest dust concentrations. The most effective purification of dust-laden airflow is achieved when the dust extraction fan delivers an airflow rate of 500 m³/min and is positioned behind the continuous miner on the return side. After optimization of foam flow rate and coverage based on the cutting head structure of the continuous miner, the dust suppression efficiency reached 78%. With coordinated optimization and on-site implementation of wall-mounted ducted airflow control, foam suppression, and dust extraction fan systems, the total dust reduction rate at the heading face reached 95.2%. These measures substantially enhance dust control effectiveness, improving mine safety and protecting worker health. The resulting reduction in dust concentration also improves visibility for underground intelligent equipment and provides practical guidance for industrial application. Full article

Sandstone quarrying in Lekokoaneng contributes to both local and national economic development, yet it raises concerns about environmental degradation and community livelihoods. Using a mixed-methods design framed by the Sustainable Livelihood Framework (SLF) and the Sustainable Development Theory (SDT), 203 households were surveyed across five buffer zones (0–1000 m) around the formal quarry site in Lekokoaneng, Berea District, Lesotho. Quantitative data were analysed descriptively, while qualitative responses underwent thematic analysis and were transformed into quantifiable categories. Quarrying generated employment and small-business opportunities concentrated within 0–600 m of the site, alongside elevated reports of dust, soil degradation and water contamination that undermined agriculture and health. Households nearest the quarry reported the highest income benefits (e.g., 35% via employment) but also the greatest environmental burdens. Households furthest away reported fewer risks but also limited economic gain. Thematic analysis yielded four domains: Socio-Economic Empowerment, Livelihood Vulnerability, Health and Safety Risks, and Environmental Degradation and Control. Integrating SLF and SDT shows quarrying as a double-edged livelihood system with short-term financial gains that coincide with erosion of natural, human and social capitals. Targeted environmental safeguards, labour formalisation and community-inclusive governance are essential to realign quarrying with resilience and sustainability goals. Full article

Background/Objectives: Mechanical alloknesis (m-alloknesis), the sensation of itch evoked by normally non-pruritic mechanical stimuli, is commonly observed in dry skin-associated conditions, such as xerosis, atopic dermatitis (AD), and psoriasis. Janus kinase (JAK) inhibitors are currently used to treat AD and suppress inflammation and itch. However, their specific roles in the modulation of m-alloknesis remain unclear. Therefore, in this study, we investigated the effects of various oral JAK inhibitors on m-alloknesis using a murine model of AD. Methods: An AD-like phenotype was induced in mice through the repeated topical application of an ointment containing Dermatophagoides farinae (house dust mite) extract. The mice were then orally treated with one of three JAK inhibitors: the JAK1/2 inhibitor baricitinib, the JAK1-selective inhibitor abrocitinib, or the JAK2-selective inhibitor AZ960. M-alloknesis was evaluated by quantifying scratching behavior in response to 30 controlled mechanical stimuli applied to lesional skin. Results: The JAK inhibitor treatments did not affect skin barrier integrity, dermatitis severity, or spontaneous scratching behavior. However, baricitinib and abrocitinib significantly reduced m-alloknesis scores, whereas AZ960 had no effect. Conclusions: These results suggest that JAK1 signaling plays a critical role in the induction of m-alloknesis in AD. Selective JAK1 inhibition is a promising therapeutic strategy for attenuating m-alloknesis and improving quality of life for patients with AD, independent of general skin inflammation and barrier function. Full article