Sampling Points Research Articles

Analyzing the spatial relationship between land surface temperature and elevation in three gullies locations in southeastern Nigeria

Gully erosion is a dangerous phenomenon that threatened human existence, plants and animals communities. Families are being separated from their ancestral home and livelihood due to gully erosion. Also, available agricultural lands to combat food insecurity are contently depleted on yearly basis across the globe as a result of gullies menace. Southeastern Nigeria is highly vulnerable to gully erosions due to it geology and studies on spatial relationships between gully LST and elevation has not been carried out, particular by applying remote sensing and GIS technique to investigate it variations. Available studies where limited to mapping and causes of gully erosion in the region. The study applies LST and elevation data to investigate correlation on three gullies locations. LST was derived from Landsat data and was first converted to radiance image while the elevation data was obtained from Google Earth image. Elevation data was used to produce raster surface in that cell values equivalent to LST at every point can be extracted. Three sets of paired sample points of LST and elevation was extracted from the three gullies locations and used to compute correlation coefficient (r). The study observed at Nanka gully a strong negative correlation of -0.665. This implies an increase in LST correspond to decrease in elevation within the gully. At Ekwolobia and Aguluezechukwu gullies sites, positive correlation coefficient of 0.692 and 0.910 were obtained. The positive correlation coefficients implied that LST and elevation increase or decrease within the gullies. This might be due to unequal distribution of radiation within gully surface as a result of three canopies. For further study, it is recommended that improve elevation data source like LiDAR elevation data should be use to investigate spatial correlation between LST and elevation at gully location.

Detailed chemical analysis of soils and identification of priority pollutants on the territory of the state aviation airfield (Krymsk)

In connection with the intensification of military activities, environmental problems have worsened in large-scale territories. The primary task is to create lists of priority pollutants of environmental objects that are mandatory for control at landfills for various purposes, military bases, airfields. The paper presents the data of a detailed geochemical analysis of the soils of the state aviation airfield territory, on the basis of which priority contaminants were identified taking into account their gross content, hazard classes and the frequency of exceeding dangerous concentrations relative to the background. In the Krasnodar Territory (Krymsk), 32 samples from the territory of the state aviation airfield were analyzed over 4 years of research, and background concentrations of 22 metals, petroleum products, formaldehyde and nitrate nitrogen were also established. To determine the hazard classes of metals with unspecified maximum permissible concentrations, a correlation analysis was carried out comparing the hazard classes of substances and their clarks. Based on the results of the study, a list of pollutants recommended for control has been compiled including cesium, arsenic, scandium, niobium, lead (hazard class I); antimony, formaldehyde, nitrate nitrogen (II); petroleum products (III). The variability of the distribution of concentrations of pollutants on the airfield territory depends on the specifics of the operation of aircraft and special equipment for flight support. But at all sampling points (beginning – end, runway perimeter; taxiways; under the glide path), the main soil pollutant is arsenic, which concentrations exceed background concentrations by 16–36 times. The dangerously high content of petroleum products, formaldehyde, nitrate nitrogen, and scandium and caesium in some points in the airfield soils has been also found.

Assessment of Surface Water Quality Using Entropy-WQI, Fuzzy-TOPSIS Analysis, Irrigation Indices and Spatial Interpolation Approaches in Mahanadi River Basin, Odisha, India

The current study is aimed at the assessment of surface water quality in the Mahanadi River, Odisha and its suitability for agricultural and consumption purposes, utilizing various indices of water quality such as Entropy Water Quality Index, Osmotic Pressure, Potential Salinity, Residual Sodium Carbonate, Sodium Adsorption Ratio and Kelly Index. Multi-criteria decision-making (MCDM) mechanisms namely, fuzzy-TOPSIS modeling and GIS tools were used; additionally, the suitability of river water for industrial uses was evaluated using indices such as Ryznar Stability Index and Puckorius scaling index. From 2019 to 2023, samples from 19 sampling stations were collected and 21 physical and chemical parameters were evaluated and compared with the normative recommendations advised by WHO. The dispersion of surface water quality on land use changes was discovered using GIS approaches. Additionally, using ArcGIS software, the spatial variability of hydrological processes was determined using the IDW interpolation approach. The pH levels of certain sampling points were slightly above the acceptable limit. Tests revealed that the Total coliform (TC) and turbidity levels in the water samples exceeded critical limits, particularly in areas of the urban river basin that were irrigated with wastewater. The calculated values of EWQI lie between 14.6 and 1066. Entropy WQI values designated 2 locations (ST-8, 19) out of 19 sampling locations as poor category and another one testing point (ST-9) as extremely poor category. The study found that 84.21% of the water samples were of excellent quality for drinking, while 15.78% of locations had poor or extremely poor water quality. However, some indices were favorable for the usage of the water for irrigation purposes. Magnesium hazard (MH) readings at two sampling sites were above 50%, indicating that they were inappropriate for irrigation. USSL (United States Salinity Laboratory) diagrams categorized the water samples as C1-S1 (low salinity and low sodium) for almost 18 sites and C4-S3 (very high salinity and high sodium) for only ONE sample, respectively, suggesting river water’s irrigation suitability. Further, Piper diagrams revealed that most investigated waters were Ca2+-Mg2+-Cl- water type. In a later stage, from Gibb’s diagram, it was found that most samples fall under the rock-water dominance. Based on the above and all other investigation results, it was concluded that water is suitable for irrigation and drinking purposes in all sites, except for three locations. The reason may be due to long-term use of wastewater, anthropogenic activities, over-extraction of surface water and changes in land use patterns. To sum up, it is advantageous to combine physicochemical properties, EWQI, fuzzy-TOPSIS, and GIS tools to evaluate surface water suitability for consumption and agriculture and their regulating variables. The strategy utilized in this work will aid the water management authorities in ensuring the supply of safe water for stakeholders.

A UAV-Borne Six-Vessel Negative-Pressure Enrichment Device with Filters Designed to Collect Infectious Fungal Spores in Rice Fields

Fungal spores that cause infectious fungal diseases in rice are mainly transmitted through air. The existing fixed, portable or vehicle-mounted fungal spore collection devices used for rice infectious diseases have several disadvantages, such as low efficiency, large volume, low precision and incomplete information. In this study, a mobile fungal spore collection device is designed, consisting of six filters called “Capture-A”, which can collect spores and other airborne particles onto a filter located on a rotating disc of six filters that can be rotated to a position allowing for the capture of six individual samples. They are captured one at a time and designed and validated by capturing spores above the rice field, and the parameters of the key components of the collector are optimized through fluid simulation and verification experiments. The parameter combination of the “Capturer-A” in the best working state is as follows: sampling vessel filter screen with aperture size of 0.150 mm, bent air duct with inner diameter of 20 mm, negative pressure fan with 1500 Pa and spore sampling of cylindrical shape. In the field test, the self-developed “Capturer-A” was compared with the existing “YFBZ3” (mobile spore collection device made by Yunfei Co., Ltd., Zhengzhou, China). The two devices were experimented on at 15 sampling points in three diseased rice fields, and the samples were examined and counted under a microscope in the laboratory. It was found that the spores of rice blast disease and rice flax spot disease of rice were contained in the samples; the number of samples collected by a single sampling vessel of “Capturer-A” was about twice that of the device “YFBZ3”in the test.

Spatial Distribution and Influencing Factors of Daylily Cultivation in the Farming–Pastoral Ecotone of Northern China

The farming–pastoral ecotone in northern China is an ecologically vulnerable area with low-quality arable land, and cash crops are an important economic source for local farmers. Although local governments have introduced supportive policies, there are still several factors that hinder the implementation of the policies: there is a lack of sufficient research on the distribution of specialty crops, and the driving factors for agricultural planting structure adjustment are not yet clear. In this study, the specialty cash crop of the daylily planting industry in Yunzhou District, in the Farming–Pastoral Ecotone in northern China, was selected as the research object. Field surveys were conducted to collect sample points and village-level survey data, which were further combined with Sentinel-1 and Sentinel-2 data, and vegetation indices. Support vector machine (SVM) and random forest (RF) classifiers were utilized to identify daylilies and compare the accuracy using different combinations of input data. Furthermore, the classification results were counted by village, and spatial autocorrelation was used to analyze the spatial distribution pattern of daylilies. Finally, in conjunction with the village-level survey data, Spearman correlation analysis, multiple regression trees (MRT), and random forests were employed to explore the driving factors of daylily cultivation. The results indicate that using an RF classification tree of 300 resulted in the optimal method, as it achieved the highest accuracy for crop classification. The overall accuracy and daylily classification accuracy were 94.6% and 94.75%, respectively. Daylily distributions were mainly concentrated near the Sanggan River, urban areas, and the tourism industry. The distribution area of daylilies in each village was concentrated in 13.4–38.8 hm2. Spatial clustering showed more aggregation of low–low and high–high types. Labor force and daylily yield were identified as the most significant influencing factors. Further analysis of the different regions revealed the importance of industry support policies and technical training. This study provides data to support the distribution of specialty crops in Yunzhou District and a technical basis for adjusting agricultural planting structures.

Evaluation of the point sampling method and inter-comparison of remote emission sensing systems for screening real-world car emissions

Emissions from internal combustion vehicles are currently not properly monitored throughout their life cycle. Remote emission sensing (RES) is a technology that can measure emissions under real driving conditions without contact. Current light extinction based RES systems are capable of providing emission factors for various gases, but lack accuracy for particulate matter (PM). Point Sampling (PS) is an extraction-based RES technique that can measure gases as well as various particle metrics such as black carbon or particle number. In this work, we evaluated the performance of a recently developed PS system and the state-of-the-art light extinction based remote sensing devices EDAR (HEAT) and ORSD (OPUS RSE) during co-location measurements. Validation measurements with portable emission measurement systems and emissions screening of several thousand cars in three European cities provide detailed insights into system's performance. Meteorological evaluations showed that the PS capture rate is strongly influenced by wind, but no other weather influences were found. Both light extinction based systems are unable to measure during rain. We found that all three systems tested were capable of screening NOx emissions from pre-Euro 6 diesel cars. Measurement results show the ability of the PS system to quantify high and low PM emitters equally well. The open-path RES systems (EDAR, ORSD) are capable of estimating PM emissions from pre-Euro 5 diesel cars. However, deficiencies of open-path RES systems are evident in the quantification of PM emissions from newer engine technologies (diesel Euro 5 and beyond) and from petrol cars. The PS system has a 2 to 5 times lower capture rate than open-path RES systems, but the PS measurement results are more accurate (more than 5 times for PM and more than 1.35 times for NOx). The good accuracy of individual measurements makes PS a powerful tool for reliable high emitter identification.

Water Quality Modelling with Industrial and Domestic Point Source Pollution : a Study Case of Cikakembang River, Majalaya District

Rapid industrial development is one of the leading causes of environmental degradation. The textile industries and the domestic activities in Majalaya District produce wastewater directly discharged into the Cikakembang River. As a result, the Cikakembang River’s water quality has decreased to the point that the water quality cannot be used for daily needs. This study modeled three main parameters in water quality modelling, namely Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), and Chemical Oxygen Demand (COD). Using MATLAB, the three-water quality governing equations originating from the Advection-Dispersion Equation were solved using the Runge Kutte-4 discretization scheme. The numerical modelling was carried out along 2.36 km of the Cikakembang River. All water quality coefficients, such as the DO Saturation (DOsat), the Reaeration Rate (ka), the Dispersion Coefficient (D), the Deoxygenation Rate (kd), and the Decomposition Rate (kc), for the Cikakembang River were estimated using equations developed by existing studies. The estimation of ka and D coefficients requires hydraulic parameters, which in this study were estimated using the HEC-RAS simulation. Meanwhile, kd and kc values were obtained from the calibration and verification process. The Relative Root Mean Square Error (RRMSE) objective function was used to evaluate the results of water quality modelling at three sampling points. In the calibration process, the resultsof water quality modelling produced RRMSE values for the DO, BOD, and COD parameters of 1.99%, 0.36% and 0.92%, respectively. Meanwhile, for the verification process, the RRMSE values for the DO, BOD, and COD parameters are 1.95%, 1.02% and 1.86%. All water quality parameters produce small RRMSE values in the calibration and verification processes. Hence, the water quality model created has good accuracy and stability.

A Comparison of Greenhouse Gas Emission Patterns in Different Water Levels in Peatlands

Peatlands store large amounts of carbon in wetland ecosystems. The hydrological conditions within peatlands are important factors that affect the biochemical cycle and patterns of greenhouse gas emissions in these peatlands. This study was carried out in Changbai Mountain Jinchuan peatland to investigate variations in carbon dioxide and methane emissions in peat swamps that have undergone distinct saturation conditions. Three peatland types (high water levels (S1); medium water levels (S2); low water levels (S3)) at different flood depths were selected as specific sampling points. The static box and gas chromatography methods were used at different time periods (6:00; 12:00; and 18:00) from July to September. The discharge flux of CO2 and CH4 slowly increased with the increase in the water level. The results indicate similarity in the fluctuation trends between the fluxes of CO2 and CH4 in S1 and S2 to the fluctuations of water levels. During the entire growth season, the flux range of CO2 and CH4 was −695.329~859.907 mg m2h−1 and 259.981~147.155 mg m2h−1, respectively. Furthermore, there was variation in mutation characteristics between two gases, the CO2 exhibited larger mutation range (−7.08~3.40) than CH4 (−1.79~1.26). In terms of daily flux changes, CO2 showed an upward trend, while CH4 had a downward trend. These results indicate variations in saturation conditions tend to affect discharge of greenhouse gases, with subsequent effects on climate change. This study highlights potential theoretical support to reduce anthropogenic activities on peatlands. This can be achieved by undertaking measures to conserve peatlands and explore mitigation measures to minimize greenhouse gas emissions and hence impacts of climate change.

A Mach-Zehnder-based optical fiber sensor enables multi-directional and wide-range curvature sensing with the assistance of CNN

In this work, wide-range curvature sensing in multiple directions was achieved by combining a Mach-Zehnder-based optical fiber sensor (OFS) with convolutional neural networks (CNNs). The experimental results show that the curvature sensitivity of the proposed sensor is − 8.8320 nm/m−1 in the range of 0–3.1171 m−1. The application of CNNs significantly improves the measurement range to 0–10.4167 m−1, and the sensor can realize measurements over a wide curvature range in four directions. The accuracy of the measurements can still be guaranteed despite a sudden decrease in the number of wavelength sampling points, and curvature direction recognition was realized. Further, OFSs can achieve a wide range of curvature sensing in 12 directions by taking full advantage of the information contained in the transmission spectrum. The proposed technique is highly versatile and conducive to the popularization of OFSs in the field of curvature measurement.

Solver-free reduced order homogenization for nonlinear periodic heterogeneous media

Reduced-order homogenization (ROH) and related methods are important computational tools for simulating the material behavior of composites. These methods generally sacrifice accuracy in exchange for superior computational efficiency, relative to methods such as classical computational homogenization (CCH). In this study, building on the recently developed solver-free CCH, we propose a fine-scale solver-free reduced order homogenization approach that avoids solving the fine-scale equilibrium equations and approximates the phase-average eigenstrains by sampling the fine-scale eigenstrain at a small number of points. The proposed method, which we call solver-free ROH, works by pre-computing history-dependent eigenstrain influence function tensors and sampling point contribution factors based on training data from a small set of CCH simulations. Then, during the online stage of the computation, phase-average eigenstrains are computed from sampling point eigenstrains and used to compute homogenized stresses and strains. Focusing on small-deformation problems, this paper formulates and verifies the solver-free ROH approach for nonlinear periodic heterogeneous media. First, in the formulation, we delineate a sampling point approximation of the phase eigenstrains and describe the use of this approximation within the coarse-scale stress update. Next, we verify the solver-free ROH using loading cases outside the training data set. Finally, we use the proposed method to simulate a multilayer composite plate in three point bending (3pt-bend) and open hole tension (OHT), demonstrating the method’s efficiency and accuracy relative to the CCH.

Sampling Points-Independent Identification of the Fractional Maxwell Model of Viscoelastic Materials Based on Stress Relaxation Experiment Data.

Considerable development has been observed in the area of applying fractional-order rheological models to describe the viscoelastic properties of miscellaneous materials in the last few decades together with the increasingly stronger adoption of fractional calculus. The fractional Maxwell model is the best-known non-integer-order rheological model. A weighted least-square approximation problem of the relaxation modulus by the fractional Maxwell model is considered when only the time measurements of the relaxation modulus corrupted by additive noises are accessible for identification. This study was dedicated to the determination of the model, optimal in the sense of the integral square weighted model quality index, which does not depend on the particular sampling points applied in the stress relaxation experiment. It is proved that even when the real description of the material relaxation modulus is entirely unknown, the optimal fractional Maxwell model parameters can be recovered from the relaxation modulus measurements recorded for sampling time points selected randomly according to respective randomization. The identified model is a strongly consistent estimate of the desired optimal model. The exponential convergence rate is demonstrated both by the stochastic convergence analysis and by the numerical studies. A simple scheme for the optimal model identification is given. Numerical studies are presented for the materials described by the short relaxation times of the unimodal Gauss-like relaxation spectrum and the long relaxation times of the Baumgaertel, Schausberger and Winter spectrum. These studies have shown that the appropriate randomization introduced in the selection of sampling points guarantees that the sequence of the optimal fractional Maxwell model parameters asymptotically converge to parameters independent of these sampling points. The robustness of the identified model to the measurement disturbances was demonstrated by analytical analysis and numerical studies.

Deep neural network-based modeling and optimization methodology of fuel cell electric vehicles considering power sources and electric motors

This study proposes a modeling and optimization methodology for fuel cell electric vehicles (FCEVs). Among FCEV components, the traction motor, lithium-ion battery, fuel cell stack, and air supply system are mainly investigated. The FCEV modeling is performed based on the vehicle specifications, electromagnetic finite element analysis, and experimental data. To conduct design optimization, deep neural networks (DNNs) are adopted and trained to predict vehicle performance considering the fluctuation of applied direct current voltage. At this stage, the adaptive layering and sampling algorithm was suggested, which enables efficient DNN construction. To confirm the feasibility of the suggested training algorithm, the number of hidden layers and sampling points of constructed DNNs are investigated. Finally, DNN-based fuel economy optimization is performed considering the driving performance. The effectiveness of the proposed optimization methodology is validated by additional optimization results.

Position Checking-Based Sampling Approach Combined with Attraction Point Local Optimization for Safe Flight of UAVs.

Trading off the allocation of limited computational resources between front-end path generation and back-end trajectory optimization plays a key role in improving the efficiency of unmanned aerial vehicle (UAV) motion planning. In this paper, a sampling-based kinodynamic planning method that can reduce the computational cost as well as the risks of UAV flight is proposed. Firstly, an initial trajectory connecting the start and end points without considering obstacles is generated. Then, a spherical space is constructed around the topological vertices of the environment, based on the intersections of the trajectory with the obstacles. Next, some unnecessary sampling points, as well as node rewiring, are discarded by the designed position-checking strategy to minimize the computational cost and reduce the risks of UAV flight. Finally, in order to make the planning framework adaptable to complex scenarios, the strategies for selecting different attraction points according to the environment are designed, which further ensures the safe flight of the UAV while improving the success rate of the front-end trajectory. Simulations and real-world experiment comparisons are conducted on a vision-based platform to verify the performance of the proposed method.

Adaptive locally weighted support vector algorithm with asymmetrically parametric insensitive/margin model

In many fields such as finance and electrical load, locally weighted support vector regression (LWSVR) method can achieve better prediction results by constructing different sub-models for each prediction target. However, it selected a fixed and single nearest neighbor parameter of kglobal for each query, and adopted the traditional k-nearest neighbor (KNN) algorithm, which is suboptimal. The weights in LWSVR assigned to the neighboring sample points also affected the accuracy of the model predictions. In this paper, we propose an adaptive locally weighted support vector regression with asymmetrically parametric insensitive/margin model, called Asy-par-v-ALWSVR. Since the selection of similar sample for query points is significant. We propose a novel locally q-neighbour selection algorithm that adaptively selects the most suitable number of neighbours for each query point based on Mahalanobis distance, rather than using a fixed kglobal. This significantly improves prediction accuracy. The weights of neighbours for each query point in Asy-par-v-ALWSVR is based on the distance within the kernel-induced feature space. This effectively discerns the importance of samples during the training process. In particular, Asy-par-v-ALWSVR can handle skewed noise with heteroscedastic structure in regression problems. Finally, we evaluate the proposed model on a synthetic sinc dataset and 14 real datasets. The results demonstrate that Asy-par-v-ALWSVR outperforms six state-of-the-art SVR-based models.

Study on the Neuroprotective Effects of Eight Iridoid Components Using Cell Metabolomics.

Iridoid components have been reported to have significant neuroprotective effects. However, it is not yet clear whether the efficacy and mechanisms of iridoid components with similar structures are also similar. This study aimed to compare the neuroprotective effects and mechanisms of eight iridoid components (catalpol (CAT), genipin (GE), geniposide (GEN), geniposidic acid (GPA), aucubin (AU), ajugol (AJU), rehmannioside C (RC), and rehmannioside D (RD)) based on corticosterone (CORT)-induced injury in PC12 cells. PC12 cells were randomly divided into a normal control group (NC), model group (M), positive drug group (FLX), and eight iridoid administration groups. Firstly, PC12 cells were induced with CORT to simulate neuronal injury. Then, the MTT method and flow cytometry were applied to evaluate the protective effects of eight iridoid components on PC12 cell damage. Thirdly, a cell metabolomics study based on ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q/TOF-MS) was performed to explore changes in relevant biomarkers and metabolic pathways following the intervention of administration. The MTT assay and flow cytometry analysis showed that the eight iridoid components can improve cell viability, inhibit cell apoptosis, reduce intracellular ROS levels, and elevate MMP levels. In the PCA score plots, the sample points of the treatment groups showed a trend towards approaching the NC group. Among them, AU, AJU, and RC had a weaker effect. There were 38 metabolites (19 metabolites each in positive and negative ion modes, respectively) identified as potential biomarkers during the experiment, among which 23 metabolites were common biomarkers of the eight iridoid groups. Pathway enrichment analysis revealed that the eight iridoid components regulated the metabolism mainly in relation to D-glutamine and D-glutamate metabolism, arginine biosynthesis, the TCA cycle, purine metabolism, and glutathione metabolism. In conclusion, the eight iridoid components could reverse an imbalanced metabolic state by regulating amino acid neurotransmitters, interfering with amino acid metabolism and energy metabolism, and harmonizing the level of oxidized substances to exhibit neuroprotective effects.

The impact of net cleaning on fecal cortisol metabolites in farmed Atlantic Salmon

Stress assessment is crucial in aquaculture to ensure and improve the health and well-being of production animals including fish. The primary objective of this study was to utilize the assessment of fecal cortisol metabolites (FCMs) as a low-invasive stress determination method for farmed Atlantic salmon (Salmo salar L.) and to suggest an appropriate sampling timeframe for this measurement.Blood, mid-intestinal contents, and feces samples were collected before, during, and at 2 and 20 h after a stress-inducing event, namely, net cleaning. Plasma cortisol (PC) and its metabolite levels in the mid-intestinal contents (MICMs) and FCMs were assessed by an enzyme-linked immunosorbent assay (ELISA). The research reveals a distinctive temporal profile, with a sequential elevation in levels of PC, MICMs and FCMs in response to the effects of net cleaning. Peak FCMs levels were observed approximately 20 h after net cleaning and were significantly higher than peak PC levels (P < 0.05). The findings suggest that FCMs are a promising indicator for acute stress assessment. To enhance the understanding and improve the applicability of FCMs as stress indicators in fish farming practices the critical challenges regarding feces sampling and FCMs analyses are further discussed. Future research aimed at developing practical protocols and industry standards will emphasize method validation and include a greater number of sampling points as well as an expanded experimental period.This study signifies a crucial step toward understanding the role of FCMs as a reliable stress indicator in fish. It underscores the importance of accounting for the delayed and extended sampling patterns when assessing stress in aquatic settings. Moreover, it offers a less invasive and more labor-efficient approach for monitoring the overall health and welfare of salmonids than PC analysis, thereby promoting sustainability in this vital industry.

Optimization Design of Medium-Voltage High-Frequency Transformers Based on Parallel Sampling Kriging Surrogate Model

In the design process of medium-voltage high-frequency transformers, numerical simulation methods often result in long design cycles and significant computational resource consumption. To achieve rapid and high-precision design for medium-voltage high-frequency transformers, a parallel kriging surrogate model is proposed based on the maximum expected improvement criterion. Initially, Latin hypercube sampling is employed to establish the initial sampling points, and corresponding three-dimensional models of medium-voltage high-frequency transformers are constructed for each sampling point configuration. Finite element simulation software is then used to compute the transformer loss and volume for each model. Subsequently, a kriging surrogate model is developed based on the initial sampling point configuration, and an enhanced parallel sampling strategy is implemented to enhance the fitting accuracy of the kriging surrogate model. Finally, the high-precision kriging surrogate model is utilized as the fitness function for a particle swarm optimization algorithm, with the objectives of minimizing transformer loss and volume. The Pareto optimal solution is sought under multi-objective optimization. The effectiveness of the proposed optimization method is verified through experimental measurements of the loss in a prototype of a medium-voltage high-frequency transformer.

Unveiling the Air Quality Impacts of Municipal Solid Waste Disposal: An Integrative Study of On-Site Measurements and Community Perceptions

This study examines air quality conditions in and around a classroom located in the Sarıçam/Adana region of Türkiye, near the campus of Adana Alparslan Türkeş Science and Technology University and the Sofulu municipal solid waste (MSW) facility. This academic setting was strategically chosen due to its proximity to the waste facility. The study aims to provide a comprehensive view of the environmental and social impacts of solid waste management through a methodological approach that combines quantitative on-site measurements and qualitative survey studies. Findings from measurements and surveys underline the significant and measurable impacts of MSW facilities on the ambient air quality of university residents. The analysis revealed a marked increase in concentrations of key pollutants, including carbon monoxide (CO), hydrogen sulfide (H2S), dust, and methane (CH4). At sampling point N1, H2S levels rose from 0 ppm in July to 13 ppm in November. Methane increased from 0.2% to 2.5% of the Lower Explosive Limit (LEL) at the same point, although it remained within safety limits. Additionally, CO levels showed a 40% increase, and dust concentration levels rose from 0.21 mg/m3 to 2.36 mg/m3 from summer to winter, indicating a seasonal variation likely influenced by the landfill’s operational dynamics, as well as changes in temperature and relative humidity. In particular, the results indicate high concentrations of CO, H2S and dust, which are directly related to air quality degradation. The study also sheds light on the impacts of these waste disposal facilities on the general well-being and health of the university community, particularly on students and staff. In addition to these findings, the study highlights a general lack of awareness in the university community about the impacts of MSW facilities on air quality. This highlights the need for increased education and information dissemination. The results support the development of comprehensive and effective strategies, including technical solutions and public awareness initiatives, to mitigate the impacts of these facilities on residential areas. In conclusion, the impacts of MSW facilities on air quality should be seen as a multidimensional issue that requires a holistic approach addressing environmental, health, social, and educational dimensions.

A Study on the Ultimate Span of a Concrete-Filled Steel Tube Arch Bridge

In order to study the ultimate span of a concrete-filled steel tube (CFST) arch bridge, taking the structural strength, stiffness, and stability as the limiting conditions, the finite element analysis method is adopted to carry out research on the influence law of a single parameter of the pipe diameter, wall thickness, and cross-section height on the ultimate span of the arch axial shape. The result is used as a sample point to determine the ultimate span of the CFST arch bridge under multifactor coupling based on the response surface method. The finite element method is used to check the strength, stiffness, stability, number of segments and maximum lifting weight, steel content rate, and steel pipe concrete constraint effect coefficient of the CFST arch bridge under the ultimate span diameter. The results show that, when analyzed using a single parameter, the ultimate span diameter of the CFST arch bridge increases with the increase in the steel pipe diameter and the cross-section height, and then decreases. Moreover, it increases with the increase in the wall thickness of the steel pipe, and the CFST arch bridge reaches the ultimate span with the increase in the steel pipe wall thickness. When the pipe diameter is 1.38 m, the CFST arch bridge reaches the ultimate span; according to a multi-parameter coupling analysis, when the pipe diameter is 1.49 m, wall thickness is 37 mm, and cross-section height is 17 m, the CFST arch bridge reaches the ultimate span of 821 m, which meets all of the limiting conditions, and, at this point, the arch axial coefficient is 1.2. The results of the finite element calculation show that the structural strength, prior to the stiffness, stability, and other limitations, just reaches the critical value of the limiting conditions.

A Low-Power Fully Differential Level-Crossing ADC Based on Single-Reference Comparator for Wireless Medical Implantable Devices

This paper introduces a low-power fully differential fixed window level-crossing analog-to-digital converter (LC-ADC) for wireless medical implantable devices. The LC-ADC could be an excellent candidate for low-power systems due to the reduction of sampling points for bio-potential signals. Different from existing fixed window LC-ADCs, which use a 1-bit DAC or scaler and two reference levels to move the input signal to the comparison window, a simplified scheme is proposed in which the DAC or scaler is removed and a single-reference level is used to create the comparison window. The proposed LC-ADC utilizes a single-reference comparator, which leads to a simplified implementation and significant reduction in power consumption and circuit area. In addition, using a single reference level and removing DAC, leads to a decrease in the complexity of the controlling logic. The proposed LC-ADC is simulated in 0.18[Formula: see text][Formula: see text]m CMOS technology. The simulation results achieve an effective number of bits (ENOB) of up to 6.5 bits with about 59–141 nW power consumption under 0.8[Formula: see text]V supply and input signal bandwidth from 5[Formula: see text]Hz to 4[Formula: see text]kHz.