Search Results (3,383)

Efficient crop management requires intelligent control strategies capable of handling uncertainty, nonlinear environmental interactions and dynamic crop growth conditions. This study presents a multisensor data fusion-based intelligent crop management framework for Capsicum cultivation using both a Mamdani fuzzy inference system (MFIS) and an adaptive Mamdani fuzzy inference system (AMFIS). The Capsicum dataset from the SmartFasal platform includes temperature, humidity and soil moisture at three depths, recorded over a four-month period (March–June 2020) with a total of 7188 samples. The proposed MFIS and AMFIS models are implemented and evaluated in the simulation environment. A Capsicum yield of 60–63 t/ha (3.6–3.8 kg/plant) is predicted via a regression model built on raw sensor inputs under conventional environmental management. An expert-rule MFIS with triangular memberships improves the regulation of agricultural parameters, increasing yield to 70–73 t/ha (4.2–4.4 kg/plant), a 15–18% increase. To improve adaptability, the AMFIS model incorporates fuzzy C-means (FCM) clustering for the automatic tuning of Gaussian membership functions and enables the controller to adjust dynamically to sensor data distributions. The adaptive system achieves a predicted productivity range of 82–87 t/ha (4.9–5.2 kg/plant), a 30–35% increase over the baseline. The regression model validation metrics R² = 0.86, RMSE = 2.1 t/ha, and MAE = 1.7 t/ha confirm the reliability of the yield estimation within the simulation framework rather than experimentally measuring crop performance. A correlation analysis, histograms, scatter plots, and Bland–Altman assessments reveal that compared with the MFIS, the AMFIS results in smoother control transitions, lower variability, and higher resource-use efficiency. This study represents a data-driven simulation framework, and future work will focus on real-time implementation and experimental validation under actual greenhouse conditions. Full article

Recent studies have shown that common household activities produce characteristic patterns in indoor air pollutants, enabling activity inference using environmental measurements alone. However, pollutant-based approaches are usually formulated as flat multi-class classification problems, even though indoor environments are dominated by long baseline periods with no emission-generating activity, leading to false alarms and unstable predictions. This work proposes a gated hierarchical inference framework for recognizing activities from indoor air quality data. A first-stage gate detects whether a time window contains activity-induced pollutant dynamics, while a second-stage classifier conditionally identifies the specific activity only when activity relevance is detected. Multivariate time-series measurements of particulate matter, volatile organic compounds, nitrogen oxides, carbon dioxide, temperature and relative humidity were collected using a portable monitoring system during controlled household cooking and cleaning experiments. Temporal windows were processed using recurrent neural network models in both stages. By separating activity detection from activity identification, the proposed method aligns inference with the physical generation of indoor pollutant signals and improves robustness in baseline-dominated monitoring scenarios while maintaining reliable discrimination among activities. The framework supports unobtrusive activity recognition and enables applications in exposure-aware monitoring and intelligent indoor environmental management. Full article

Formalin, a commercial aqueous solution typically containing 37% formaldehyde, often includes a few percent methanol to inhibit polymerization. Nevertheless, formaldehyde readily forms polymerization products such as glycols, dimethoxy (acetal), and methoxyalcohol (hemiacetal) derivatives, making their analysis important. In this work, we employ ion mobility spectrometry (IMS) for qualitative and quantitative detection of these species and demonstrate that analysis is not feasible using the standard IMS reactant ion, H₃O⁺(H₂O)_n. Protonation by H₃O⁺(H₂O)_n induces loss of water or methanol, preventing stable detection of the intact derivatives. Hence, ammonia was introduced as a dopant to replace H₃O⁺(H₂O)_n with NH₄⁺(H₂O)_n in the ionization region, thereby shifting the ionization mechanism from proton transfer to ammonium attachment. A high-temperature injection port was also designed to enable the analysis of both liquid samples and their corresponding headspace. Using the developed method, we identified both acetal and hemiacetal derivatives in commercial formaldehyde solution, while only the more volatile acetal species were detected in the headspace. Quantitative analysis yielded a limit of detection (LOD) of 1.9 ppm and a linear range of 5.5–120 ppm for solution measurements. Importantly, the method provides reliable detection in the presence of substantial humidity, an environment in which many polymer-based sensors fail due to severe moisture interference. Overall, ammonia-doped IMS offers a robust and humidity-tolerant platform for characterizing formaldehyde polymerization products in both the gas and liquid phases. Full article

The indoor microclimate of museums plays an essential role in preserving priceless cultural heritage for future generations and in ensuring visitors’ comfort and health. In this context, the present study aimed to evaluate indoor air quality, the degree of fungal contamination, and visitors’ perceptions in a museum environment through an integrated, interdependent approach. Measurements of the physicochemical parameters of air quality (temperature, relative humidity, CO₂, TVOC, HCHO, PM_2.5 and PM₁₀, negative and positive ions and brightness) were carried out in three exhibition halls within a museum in Oradea, Romania, during the period January–August 2024. Fungal contamination was assessed using surface and air samples, with classical isolation and microscopic identification methods. Visitors’ perceptions were analysed using a standardised questionnaire that focused on perceived comfort and visit duration. The results showed that the parameters defining indoor air quality generally fell within the limits set by the international standards in force, with occasional exceedances. These conditions are associated with the presence of fungi of the genera Cladosporium, Penicillium, and Aspergillus in the air and on museum exhibits, which pose risks to human health and the deterioration of the exhibited materials. The statistical decision-making model determined the critical thresholds above which visitor behaviour changed visibly. The results highlighted the importance of maintaining a stable microclimate in museum spaces, not only for the protection of exhibits, but also for optimising the cultural experience. Indoor air quality indicators and fungal microflora can only affect vulnerable people or those with pre-existing conditions. Occasional visitors do not present a significant risk of developing new conditions, considering the limited duration of exposure. Full article

To address the pronounced degradation of skid resistance in steel bridge deck pavements exposed to hot, humid, and rainy environments, this study investigates an EA-10 epoxy–asphalt mixture and proposes a gradation design method with skid resistance as the primary performance objective. An orthogonal experimental design was employed to systematically analyze different combinations of sieve passing rates, and after determining an optimum asphalt–aggregate ratio of 6.25%, the skid resistance of the mixtures under various service conditions was evaluated using macrotexture depth, dry friction coefficient, and water-film friction coefficient. The results demonstrate that the formation of skid resistance follows a mechanism in which the macroscopic framework and microscopic pores interact synergistically. The passing rate of the 4.75 mm sieve is the dominant factor governing macrotexture depth, while the 0.3 mm sieve plays a critical regulating role in texture development; meanwhile, the passing rates of the 2.36 mm and 0.6 mm sieves exert a decisive influence on both dry and water-film friction coefficients. When the passing rates of the 4.75 mm, 0.3 mm, 2.36 mm, and 0.6 mm sieves are approximately 70%, 26.5%, 58–61%, and 34%, respectively, the mixture exhibits superior overall skid-resistance performance. Based on the evaluation results of the International Friction Index (IFI), the optimized gradation shows a more stable level of skid resistance under wet and slippery conditions. These findings provide quantitative evidence and engineering guidance for the skid-resistance-oriented gradation design of epoxy–asphalt mixtures used in steel bridge deck pavements in hot and humid regions. Full article

Upper Northern Thailand continues to face a protracted structural crisis from fine-particulate matter (PM_2.5), primarily driven by biomass burning and wildfires. Conventional mechanical capture systems, such as cyclones, often suffer a drastic efficiency drop when treating sub-micron particles. This study introduces an innovative Evaporative Condensation Growth Scrubber (ECGS) designed to bridge this technological gap by promoting the growth of fine particles through heterogeneous nucleation. Experimental testing across 10 different nozzle configurations was conducted to optimize the system’s performance. The results revealed that the ECGS system significantly outperformed the dry cyclone (Baseline) across all nine testing configurations. While the Baseline showed inherent limitations in capturing sub-micron particles, the ECGS demonstrated relative efficiency improvements ranging from 39.53% to 83.23% for PM_2.5, and 26.10% to 61.50% for PM₁₀ compared to the baseline. Optimal performance was achieved using a 90-degree injection angle and a 10 cm distance, which created a complete spray curtain and maximized collision probability. Under these conditions, the outlet PM_2.5 concentration stabilized at 11.81 µg/m³ within 180 s of water injection. Crucially, despite sensor interference caused by high relative humidity, the system’s effectiveness was confirmed by a significant difference in performance in PM₁₀ and PM_2.5 removal. The PM₁₀ collection efficiency outperformed that of PM_2.5 by 28.82%, providing empirical evidence that PM_2.5 particles successfully acted as nuclei for condensation and grew into the larger PM₁₀ size range. This particle growth enabled more effective centrifugal separation, demonstrating that the ECGS system offers a viable and efficient solution for fine particle removal in highly polluted environments. Full article

Lilium longiflorum is a diploid lily species valued for its tolerance to humid–hot environments and pleasant fragrance. However, its poor cold hardiness and low bulb-forming capacity limit its cultivation. To overcome these deficiencies, autotetraploids were previously generated in our laboratory via somatic doubling. In order to expand the reproductive efficiency of the two, this study optimized in vitro regeneration and bulblet enlargement protocols. We analyzed the effects of various plant growth regulators and sucrose concentrations, alongside the expression of genes related to carbohydrate metabolism and hormone signaling. Results revealed divergent regenerative pathways: diploids favored direct organogenesis (optimal medium: MS + 30 g/L sucrose + 0.5 mg/L 6-BA + 0.2 mg/L NAA + 1.0 mg/L glyphosate), whereas tetraploids thrived via a TDZ-induced callus pathway (1/2 MS + 30 g/L sucrose + 1.0 mg/L NAA + 0.2 mg/L TDZ). During bulblet enlargement, diploids were predominantly regulated by IBA and prone to proliferation (optimal enlargement medium: MS + 60 g/L sucrose + 2.0 mg/L IBA), while tetraploids were sucrose-sensitive and prioritized single-bulb hypertrophy (MS + 60 g/L sucrose + 0.5 mg/L IBA + 0.1 mg/L 6-BA + 0.1 mg/L CPPU). qRT-PCR indicated that LlAGPS1, LlGBSSI, LlSWEET15, LlMYC2, and LlSAUR32 were highly expressed in tetraploids during rapid enlargement (24–36 d), suggesting a role in bulb hypertrophy, whereas upregulated LlSUS4 and LlCWIN3 in diploids correlated with proliferation. The study provides a practical technical reference for the industrialized propagation of high-quality L.longiflorum bulbs and provide a theoretical foundation for understanding ploidy-dependent development in Lilium. Full article

Observing pig behavior, such as feed intake, water intake, and resting behavior, is essential for improving the well-being of these animals. However, monitoring such behaviors by traditional methods can be exhausting for both humans and animals, interfering with their development. The research aimed to identify behavioral patterns of pigs in an air-conditioned environment through computer vision. Microcameras were installed in the animals’ stalls to generate videos over an experimental period of 92 days and the temperature and humidity of the air were simultaneously recorded. The physiological variables of the animals were collected to identify whether they were under heat stress. To recognize the drinking, eating, standing and lying behavior of pigs, YOLOv5 was trained and then the model was used to detect the animals. Regions in the images corresponding to the feeders and drinkers were established. To identify feeding behavior and water intake, criteria based on the occupation of the feeding zone by pigs detected in the standing position were established. The results showed that the trained model achieved an average accuracy rate of 97.3% and an average recall of 96.1% in animal detection. The model exhibited 97.5% accuracy and 97.0% recall rates in recognizing the feeding behavior and water consumption of pigs. The proposed method can be used in videos or images and minimizes the need for manual intervention, offering an efficient means of monitoring pig behavior in agricultural environments and contributing to the productivity of pig farming operations. Full article

Island reef road construction faces a complex marine service environment characterized by high salinity and high humidity. Meanwhile, rapid construction and prompt subgrade repair are urgently required, creating a strong demand for novel calcareous-sand-based stabilization materials that combine excellent mechanical performance with resistance to seawater erosion. To this end, this study developed an early-strength cemented calcareous-sand reinforcement material for road base construction. Sulfoaluminate cement (SAC) and ferrite-aluminate cement (FAC), both featuring rapid setting/early strength development and superior corrosion resistance, were used to cement calcareous sand (CS) and to investigate its mechanical and microstructural characteristics under different water environments. Unconfined compressive strength tests (UCS) showed that SC-CS and FC-CS could meet subgrade requirements at 1 d and 7 d, with SC-CS and FC-CS reaching 3.12 MPa and 3.44 MPa at 1 d, and 3.26 MPa and 3.67 MPa at 7 d, respectively, under seawater SS conditions. Seawater mixing and immersion were found to promote the early strength and stiffness development of both SC-CS and FC-CS, with a more pronounced effect observed for FC-CS. Based on experimental results, a damage model for the stabilized specimens was established with a fitting accuracy of R² > 0.97. This constitutive model accurately describes the stress–strain relationship of the material and quantitatively characterizes its damage evolution. Microscopic XRD and SEM analyses indicated that the main hydration product in freshwater-cured specimens was ettringite, and the interparticle connection of CS was dominated by bridging through rod-like ettringite. In contrast, under seawater conditions, the ettringite content decreased, while hydrotalcite and calcium aluminate hydrate increased, forming massive and lamellar bridging products. Compared with SC-CS, the bridging structure in FC-CS was denser. Moreover, the compactness of the bridging structure not only affected its mechanical properties but also governed the movement mode of CS particles, thereby influencing the damage evolution and failure mode of the specimens. The findings provide theoretical support for the construction needs of island road. Full article

Understanding the spatial distribution patterns and environmental drivers of plant communities is fundamental for biodiversity conservation and ecosystem management. Bombax ceiba is a widely distributed tree species that occurs in both humid tropical rainforests and drought-prone dry-hot valleys, representing two strongly contrasting ecological environments. However, the spatial patterns and environmental drivers of plant communities associated with B. ceiba across these habitats remain poorly understood. In this study, we investigated B. ceiba-associated plant communities in two representative habitats in Yunnan Province, Southwest China: a tropical rainforest in Mengla and a dry-hot valley in Yuanjiang. The species composition, community structure, and spatial coordinates of associated plants were recorded in replicated 20 m × 20 m plots. Spatial distribution patterns were analyzed using the pair-correlation function g(r), while environmental drivers were examined using Pearson correlation analysis and redundancy analysis (RDA). Species richness was substantially higher in the tropical rainforest (41 species from 33 families) than in the dry-hot valley (19 species from 14 families). Both communities contained a substantial proportion of tropical Asian floristic elements. Most dominant species exhibited aggregated spatial distributions at small spatial scales (0–7 m), indicating strong dispersal limitation and microhabitat heterogeneity. Spatial associations varied across scales: in the dry-hot valley, species associations alternated between positive and negative correlations at small scales (0–5 m) and shifted toward positive correlations at larger distances, whereas in the tropical rainforest negative associations were more common at small scales and positive associations increased at larger spatial scales. Environmental drivers differed markedly between habitats. In the dry-hot valley, community attributes were positively associated with slope, precipitation, and soil ammonium nitrogen, suggesting that community assembly is influenced by interactions between topography and water availability. In contrast, tropical rainforest communities were more strongly associated with soil phosphorus availability and temperature-related variables. These findings highlight distinct community assembly mechanisms in contrasting habitats and provide ecological insights for vegetation restoration in dry-hot valleys and biodiversity conservation in tropical rainforests. Full article

Puerto Rico has experienced increasingly frequent and intense extreme heat conditions in recent years, with the 2023–2024 warm seasons standing out for prolonged periods of dangerously high heat index values and widespread spatial exposure. These conditions are particularly concerning in tropical island environments, where high humidity limits physiological cooling and amplifies heat-related health risks. The main objective of this study is to identify and characterize extreme heat zones and events across Puerto Rico using NOAA-modeled heat index (apparent temperature) data, as well as to examine their spatial and temporal variability during the 2021–2024 period. Hourly modeled apparent temperature data between 2 and 4 pm, representing the warmest time of day, were analyzed for each day from June through October. Mean maximum and maximum heat index surfaces were generated for each month and warm season, and extreme heat zones were identified using the 103 °F (39.4 °C) danger threshold. Results show a persistent concentration of extreme heat in low-elevation coastal regions, particularly across the northern coastal plains from San Juan to Hatillo, with floodplain areas in Arecibo and Manatí exhibiting the highest and most consistent exposure. August was identified as the month with the highest mean maximum heat index across all study years, followed by September. The warm seasons of 2023 and 2024 exhibited the highest magnitudes and spatial extents of extreme heat, with some regions experiencing apparent temperatures exceeding 110 °F and up to 141 extreme heat days during peak afternoon hours. The findings indicate a transition from localized heat hotspots to widespread and sustained extreme heat exposure across Puerto Rico’s coastal regions. This study provides an island-scale assessment of extreme heat patterns with direct implications for public health, infrastructure planning, and heat-risk management in a warming tropical climate. Full article

Crop cultivation involves a series of procedures from sowing till harvesting, making it a time-consuming activity. The crop cycle typically spans four to six months, during which cultivation outcomes are influenced by dynamic environmental and management factors such as water availability, temperature, and humidity. These parameters are collectively referred to as Optimum Cultivation Factors (OCFs). Once the cultivation process starts, poor OCFs may lead to reduced crop growth, leading to heavy economic loss. Historically, lessons learned from previous cultivation cycles have been a primary source for improving agricultural practices. Developing simulators that mimic agricultural environments in a controlled setting can support the analysis of cultivation factors while reducing time and resource requirements. In this study, a multifunctional agricultural simulator with a network of actuators is developed in the MATLAB/Simulink environment. The designed simulator mimics the agricultural field’s real-time environment while maintaining the temperature, humidity, and moisture content with appropriate water provision. Based on real field environmental data, the fuzzy-based membership functions are designed to emulate outdoor agricultural conditions at the laboratory scale. The designed system monitors and controls the actuators, such as water pumps for moisture, a heater for temperature, and a sun simulator for solar irradiation control. The cascaded fuzzy logic controller enables multi-factor environmental assessment by analyzing actuator responses under varying operating conditions, supporting pre-cultivation decision making. Full article

The corrosion behavior of high-entropy alloys under cyclic wet–dry conditions simulating the salt-lake atmosphere was investigated. The composition, morphology, and electrochemical properties of the corrosion products formed on the alloy surface after corrosion were systematically analyzed. The results show that in a chloride-containing environment with alternating temperature and humidity, the Cr-containing oxide passive film formed on the alloy surface effectively inhibits the corrosion process in the early stages. In addition, electrochemical results show that the charge transfer resistance in the MgCl₂ system reaches 4.96 × 10⁵ Ω·cm² at prolonged exposure, which is significantly higher than that in the NaCl system, indicating a lower corrosion rate. However, over time, the passive film undergoes localized rupture due to chloride ion attack and stress, leading to pitting corrosion and expansion toward the substrate. This study reveals the corrosion mechanism of high-entropy alloys in high-altitude salt-lake atmospheric environments and provides crucial insights for material design and performance optimization for their engineering applications in salt-lake scenarios. Full article

Carbon monoxide (CO) is a highly toxic, colorless, and odorless gas posing significant risks to human health and the environment. Catalytic oxidation offers a promising, economically viable solution by converting CO into nontoxic CO₂ under mild conditions without energy-intensive regeneration. However, existing MnO₂-based catalysts often exhibit poor activity and stability in harsh environments, particularly at low temperatures and high humidity. In this study, we propose a Cu²⁺ ion-exchange modification strategy to activate lattice oxygen species in δ-MnO₂, thereby significantly enhancing its low-temperature CO oxidation performance. Structural characterization by XRD and SEM confirms that Cu-doped δ-MnO₂ retains its original birnessite-type structure and porous morphology. ICP-OES and XPS analyses verify that Cu²⁺ ions effectively replace interlayer K⁺ ions. The resulting MnO₂-150Cu catalyst demonstrates exceptional activity, achieving 100% CO conversion at 40 °C in dry air and maintaining full conversion at 80 °C in the presence of 1.3 vol.% H₂O at a weight hourly space velocity of 150 L/g·h. H₂-TPR and O₂-TPD results confirm that Cu doping enhances the reducibility and mobility of lattice oxygen. Furthermore, in situ DRIFTS analysis reveals that the migration of active oxygen species is the rate-limiting step at low temperatures. This work provides a novel and effective strategy for activating lattice oxygen in MnO₂-based catalysts, offering a promising pathway for developing high-performance materials for low-temperature CO oxidation under practical environmental conditions. Full article

Coated ferromagnetic conductors (CFCs) are widely used in the engineering field, such as transportation, petrochemicals, energy, etc. Owing to long-term exposure to harsh and corrosive environments, involving large temperature differences, cyclic loading and humidity, hidden corrosion occurring under the coatings of CFCs has been found to be one of the most critical defects posing a severe threat to the structural strength and safety of CFCs. Therefore, it is important to conduct rapid imaging and quantitative evaluation of this hidden corrosion via Non-Destructive Evaluation (NDE) techniques. A magnetic field-viewing film (MFVF) characterizes magnetic fields by displaying corresponding color shifts, offering a direct visual representation of the magnetic field intensity. In light of this, this paper proposes an MFVF-based probe composed of multiple micro-sensor units for fast imaging of hidden corrosion in CFCs. An image-processing technique based on the modified Canny algorithm is subsequently proposed for identification of corrosion opening profiles in MFVF images. Based on the identification results, an assessment of hidden corrosion parameters is conducted. It is inferred from the experimental results that the opening area, depth and volume of hidden corrosion can be quantitatively evaluated, with an average accuracy of 86.1%. Full article