Search Results (21)

This paper presents an analytical study of the bit error rate (BER) and signal-to-noise ratio (SNR) performance in simultaneous wireless information and power transfer (SWIPT) amplify-and-forward (AF) relaying systems over Rayleigh fading channels. A power-splitting (PS) protocol is employed at the energy-constrained relay to divide the received signal for concurrent energy harvesting and information processing. Closed-form and asymptotic BER expressions are derived based on exact and bounded moment-generating functions (MGFs), offering insights into how the SNR balance between the source–relay (SR) and relay–destination (RD) links influences system performance. An asymptotic BER expression further reveals that a SWIPT AF relay system can be interpreted as a generalized AF relaying model, sharing the same diversity order as conventional AF systems. Based on this interpretation, an optimization method for the PS factor is proposed, effectively reducing the BER by reinforcing the weaker link. Simulation results confirm the tightness of the derived expressions and the effectiveness of the optimization strategy. Moreover, the analytical framework is extended to multiple SWIPT relaying systems, where multiple relays operate with individually optimized PS ratios. For such configurations, approximations for the system BER, outage probability, and channel capacity are derived and validated. Results demonstrate that increasing the number of relays significantly improves system performance, and the proposed analysis accurately captures these performance gains under varying channel conditions. Full article

The Parameter Space Concept (PSC) is an alternative approach to solving and refining (partial) crystal structures from very few pre-chosen X-ray or neutron diffraction amplitudes without the use of Fourier inversion. PSC interprets those amplitudes as piecewise analytic hyper-surfaces, so-called isosurfaces, in the Parameter Space, which is spanned by the spatial coordinates of all atoms of interest. The intersections of all isosurfaces constitute the (possibly degenerate) structure solution. The present feasibility study investigates the La and Sr split position of the potential high-temperature super-conductor (La_0.5Sr_1.5)MnO₄, $I4$/mmm, with a postulated total displacement between La and Sr of a few $\mathrm{p}$$\mathrm{m}$ by theoretical amplitudes of pre-selected $00l$ reflections ($l=2,4,\dots ,20$). The revision of 15-year-old results with state-of-the-art computing equipment enhances the former simplified model by varying the scattering power ratio $f_{\begin{array}{c}\hfill \mathrm{La}\end{array}}/f_{\begin{array}{c}\hfill \mathrm{Sr}\end{array}}$, as exploitable by means of resonant scattering contrast at synchrotron facilities, and irrevocably reveals one of the two originally proposed solutions as being a “blurred” pseudo-solution. Finally, studying the resolution limits of PSC as a function of intensity errors by means of Monte-Carlo simulations shows both that the split can only be resolved for sufficiently low errors and, particularly for the resonant scattering contrast, a theoretical precision down to $\pm 0.19$ $\mathrm{p}$$\mathrm{m}$ can be achieved for this specific structural problem. Full article

Corn grain moisture (CGM) is critical to estimate grain maturity status and schedule harvest. Traditional methods for determining CGM range from manual scouting, destructive laboratory analyses, and weather-based dry down estimates. Such methods are either time consuming, expensive, spatially inaccurate, or subjective, therefore they are prone to errors or limitations. Realizing that precision harvest management could be critical for extracting the maximum crop value, this study evaluates the estimation of CGM at a pre-harvest stage using high-resolution (1.3 cm/pixel) multispectral imagery and machine learning techniques. Aerial imagery data were collected in the 2022 cropping season over 116 experimental corn planted plots. A total of 24 vegetation indices (VIs) were derived from imagery data along with reflectance (REF) information in the blue, green, red, red-edge, and near-infrared imaging spectrum that was initially evaluated for inter-correlations as well as subject to principal component analysis (PCA). VIs including the Green Normalized Difference Index (GNDVI), Green Chlorophyll Index (GCI), Infrared Percentage Vegetation Index (IPVI), Simple Ratio Index (SR), Normalized Difference Red-Edge Index (NDRE), and Visible Atmospherically Resistant Index (VARI) had the highest correlations with CGM (r: 0.68–0.80). Next, two state-of-the-art statistical and four machine learning (ML) models (Stepwise Linear Regression (SLR), Partial Least Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)), and their 120 derivates (six ML models × two input groups (REFs and REFs+VIs) × 10 train–test data split ratios (starting 50:50)) were formulated and evaluated for CGM estimation. The CGM estimation accuracy was impacted by the ML model and train-test data split ratio. However, the impact was not significant for the input groups. For validation over the train and entire dataset, RF performed the best at a 95:5 split ratio, and REFs+VIs as the input variables (r_train: 0.97, rRMSE_train: 1.17%, r_entire: 0.95, rRMSE_entire: 1.37%). However, when validated for the test dataset, an increase in the train–test split ratio decreased the performances of the other ML models where SVM performed the best at a 50:50 split ratio (r = 0.70, rRMSE = 2.58%) and with REFs+VIs as the input variables. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for relatively smaller or medium-sized datasets. RF was identified to be the most stable and consistent ML model (r: 0.95, rRMSE: 1.37%). Findings in the study indicate that the integration of aerial remote sensing and ML-based data-run techniques could be useful for reliably predicting CGM at the pre-harvest stage, and developing precision corn harvest scheduling and management strategies for the growers. Full article

The combustion instability and ash agglomeration in a wood pellet boiler were investigated in this study. The tests were conducted using the Taguchi method of orthogonal array L₂₇(13³). Several parameters are applied, including grate area (GA), primary to secondary air split ratio (SR), excess air (EA), and fuel power (P). Pine wood pellets were used, and the boiler’s nominal load was 20 kW. The results show that instability during combustion occurs since the fuel bed rises as the accumulation of the unburned wood pellets on the grate causes a slow combustion rate and pressure drop, which creates noise and disturbances. A good combination of the parameters applied to TN9 and TN20 can be useful in obtaining stable combustion. In addition, the ash agglomerations were influenced by the duration of the combustion and the temperature of the fuel bed. The largest size of the ash agglomeration was referred to as test number-TN26 (P: 16 kW, EA: 110%, SR: 30/70, and GA: 115 mm × 75 mm), which is 59 mm, and the duration time is 14,400 s (≈4 h). Full article

Tyre pressure monitoring systems (TPMS) are electronic devices that monitor tyre pressure in vehicles. Existing systems rely on wheel speed sensors or pressure sensors. They rely on batteries and radio transmitters, which add to the expense and complexity. There are two types of basic tyres: non-pneumatic and pneumatic tyres. Non-pneumatic tyres lack air and combine the tyre and wheel into a single unit. When it comes to noise reduction, durability, and shock absorption, pneumatic tyres are more valuable than non-pneumatic tyres. In this study, nitrogen-filled pneumatic tyres were considered due to the uniform pressure management property. Additionally, nitrogen has less of an effect on thermal expansion than regular air-filled tyres. This work aimed to offer a deep learning approach for TPMS. An accelerometer captured vertical vibrations from a moving vehicle’s wheel hub, which were then converted in the form of vibration plots and categorized using pretrained networks. The most popular pretrained networks such as AlexNet, GoogLeNet, ResNet-50 and VGG-16 were employed in this study. From these pretrained networks, the best-performing pretrained network was determined and suggested for TPMS by varying the hyperparameters such as learning rate (LR), batch size (BS), train-test split ratio (TR), and solver (SR). Findings: A higher classification accuracy of 97.20% was obtained while using ResNet-50. Full article

Peralkaline Sm³⁺-doped aluminosilicate glasses with different network modifier ions (Mg²⁺, Ca²⁺, Sr²⁺, Ba²⁺, Zn²⁺) were investigated to clarify the effect of glass composition and glass structure on the optical properties of the doped Sm³⁺ ions. For this purpose, the Sm³⁺ luminescence emission spectra were correlated with the molecular structure of the glasses derived by molecular dynamics (MD) simulations. The different network modifier ions have a clear and systematic effect on the peak area ratio of the Sm³⁺ emission peaks which correlates with the average rare earth site symmetry in the glasses. The highest site symmetry is found for the calcium aluminosilicate glass. Glasses with network modifier ions of lower and higher ionic radii show a notably lower average site symmetry. The symmetry could be correlated to the rare earth coordination number with oxygen atoms derived by MD simulations. A coordination number of 6 seems to offer the highest average site symmetry. Higher rare earth coordination probabilities with non-bridging oxygen result in an increased splitting of the emission peaks and a notable broadening of the peaks. The zinc containing glass seems to play a special role. The Zn²⁺ ions notably modify the glass structure and especially the rare earth coordination in comparison to the other network modifier ions in the other investigated glasses. The knowledge on how glass structure affects the optical properties of doped rare earth ions can be used to tailor the rare earth absorption and emission spectra for specific applications. Full article

This paper presents a novel linear hybrid excited flux switching permanent magnet machine (LHEFSPMM) with a crooked tooth modular stator. Conventional stators are made up of a pure iron core, which results in high manufacturing costs and increased iron core losses. Using a modular stator lowers the iron volume by up to 18% compared to a conventional stator, which minimizes the core losses and reduces the machine’s overall cost. A crooked angle is introduced to improve the flux linkage between the stator pole and the mover slot. Ferrite magnets are used with parallel magnetization to reduce the cost of the machine. Two-dimensional FEA is performed to analyze and evaluate various performance parameters of the proposed machine. Geometric optimization is used to optimize the split ratio (S.R) and winding slot area ($Slo{t}_{area}$). Genetic algorithm (GA) is applied and is used to optimize stator tooth width ($S_{TW}$), space between the modules (SS), crooked angle ($\alpha$), and starting angle ($\theta$). The proposed model has a high thrust density (306.61 kN/m³), lower detent force (8.4 N), and a simpler design with higher efficiency (86%). The linear modular structure makes it a good candidate for railway transportation and electric trains. Thermal analysis of the machine is performed by FEA and then the results are validated by an LPMEC model. Overall, a very good agreement is observed between both the analyses, and relative percentage error of less than 3% is achieved, which is considerable since the FEA is in 3D while 2D temperature flow is considered in the LPMEC model. Full article

Rice cultivation is one of the most significant human-created sources of methane gas. How to accurately measure the methane concentration produced by rice cultivation has become a major problem. The price of the automatic gas sampler used as a national standard for methane detection (HJ 38-2017) is higher than that of gas chromatography, which greatly increases the difficulty of methane detection in the laboratory. This study established a novel methane detection method based on manual injection and split pattern by changing the parameters of the national standard method without adding any additional automatic gas samplers. The standard curve and correlation coefficient obtained from the parallel determination of methane standard gas were y = 2.4192x + 0.1294 and 0.9998, respectively. Relative standard deviation (RSD, <2.82%), recycle rate (99.67–102.02%), limit of detection (LOD, 0.0567 ppm) and limit of quantification (LOQ, 0.189 ppm) of this manual injection method are satisfying, demonstrating that a gas chromatography-flame ionization detector (GC-FID), based on manual injection at a split ratio (SR) of 5:1, could be an effective and accurate method for methane detection. Methane gases produced by three kinds of low-methane rice treated with oxantel pamoate acid, fumaric acid and alcohol, were also collected and detected using the proposed manual injection approach Good peak shapes were obtained, indicating that this approach could also be used for quantification of methane concentration. Full article

This paper presents a Hybrid Excited Double-Sided Linear Flux Switching Machine (HEDSLFSM) with a crooked tooth modular stator. Generally, the conventional stators are made of a full-length iron core, increasing manufacturing costs and iron losses. Higher iron losses result in lower efficiency and lower overall performance. A U-shaped modular stator with a crooked tooth is used to lower iron consumption and increase the machine’s efficiency. Ferrite magnets are used to replace rare earth magnets, which also reduces the machine cost. Two DC excitation windings are used above and below the ferrite magnet to reduce the PM volume. 2D electromagnetic performance analysis is done to observe the key performance indices. Geometric optimization is used to optimize the Split Ratio (S.R), DC winding slot area (DCw), and AC winding slot area (ACw). Stator Tooth Width (STW), space between the modules (S.S.), and crooked angle ($\alpha$) are optimized through JMAG in-built Genetic Algorithm (G.A.) optimization. High thrust force density and modular stator make it a good candidate for long-stroke applications like railway transits. The thermal analysis of the machine is performed by FEA analysis and then validated by 2D LPMC (Lumped Parametric Magnetic Equivalent Circuit) model. Both analyses are compared, and an error percentage of less than 4% is achieved. Full article

The combustion of wood pellets in a fixed bed combustor of a 20 kW capacity domestic pellet boiler was tested according to several factors including Power, Excess Air (EA), Primary/Secondary air Split Ratio (SR) and Grate Area (GA). The Taguchi method was applied to program the experimental design. Several parameters were measured, including gas emissions (CO), fuel bed temperature (measured at 4 different heights), and efficiency. The experimental results show that the lower CO emission and the higher efficiency were obtained at medium thermal loads and the highest temperature on the fuel bed was obtained at about ¼ of its height (15 mm). The results obtained from the analysis of variance (ANOVA) show that the SR and the Power are the most important factors contributing to the CO reduction and also increase the fuel bed temperature. Full article

Catalytic performance of La_0.3Sr_0.7Co_0.7Fe_0.3O₃ (LSCF3773 or LSCF) catalyst for syngas production via two step thermochemical cycles of H₂O and CO₂ co-splitting was investigated. Oxygen storage capacity (OSC) was found to depend on reduction temperature, rather than the oxidation temperature. The highest oxygen vacancy (Δδ) was achieved when the reduction and oxidation temperature were both fixed at 900 °C with the feed ratio (H₂O to CO₂) of 3 to 1, with an increasing amount of CO₂ in the feed mixture. CO productivity reached its plateau at high ratios of H₂O to CO₂ (1:1, 1:2, and 1:2.5), while the total productivities were reduced with the same ratios. This indicated the existence of a CO₂ blockage, which was the result of either high E_a of CO₂ dissociation or high E_a of CO desorption, resulting in the loss in active species. From the results, it can be concluded that H₂O and CO₂ splitting reactions were competitive reactions. E_a of H₂O and CO₂ splitting was estimated at 31.01 kJ/mol and 48.05 kJ/mol, respectively, which agreed with the results obtained from the experimentation of the effect of the oxidation temperature. A dual-reactors system was applied to provide a continuous product stream, where the operation mode was switched between the reduction and oxidation step. The isothermal thermochemical cycles process, where the reduction and oxidation were performed at the same temperature, was also carried out in order to increase the overall efficiency of the process. The optimal time for the reduction and oxidation step was found to be 30 min for each step, giving total productivity of the syngas mixture at 28,000 μmol/g, approximately. Full article

The reduction of nitrogen (N) fertilizer during the rice growing season is an important practice in rice production and is ecologically beneficial. Will different N reduction stages affect rice yields and NUE? The timing of the reduction in N-efficient varieties (NEVs) is yet to be identified, especially under moderate N rate applications. We investigated the effectiveness of various N reduction stages (NRSs) on grain yield and N-use efficiency (NUE) in NEVs in the lower reaches of the Yangtze River in China. Two NEVs were grown in the field, and five N reduction treatments, including basal N reduction (BR) at pre-transplanting (PT), tillering N reduction (TR) at early tillering (ET), promoting-spikelet N reduction (PR) at panicle initiation (PI), keeping-spikelet N reduction (KR) at spikelet differentiation (SD), and N split reduction (SR) at all four stages, were adopted, with no N reduction (CK) and no N application (N0) as controls. The results showed that grain yield and NUE varied substantially with the NRSs. Yield decreases were observed in descending order of magnitude in BR, PR, SR, TR, and KR when compared to CK. For both NEVs, BR and PR were the most effective treatments in decreasing yield and NUE at the same N reduction rate. BR and PR markedly decreased the panicles per unit area or spikelets per panicle, root biomass, root length, root length density, and root oxidation activity and exhibited simultaneously decreased leaf area index, grain leaf ratio, shoot biomass, and crop growth rate from joining to the heading and from heading to maturity. According to the results, PT and PI were considered to be N reduction sensitive stages, and ET and SD were considered to be N reduction insensitive stages. According to the results, an N reduction strategy was suggested as follows: N reduction at SD and ET, with increased N proportions at PT and PI for NEVs when adopting moderate N application rates. Full article

Cassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of plant performance would be useful means for monitoring the health of plants for crop-management decisions. We investigated whether the red–green–blue (RGB) and multispectral images could be used to detect the previsual effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images. Pot experiments were conducted with cassava cuttings. The experimental design was a split-plot arranged in a completely randomized design. Treatments were three irrigation doses split into various K rates. Plant images were captured beginning 30 days after planting (DAP) and ended at 90 DAP when plants were harvested. Results show that biomass, chlorophyll, and net photosynthesis were estimated with the highest accuracy (R² = 0.90), followed by leaf area (R² = 0.76). Starch, energy, carotenoid, and cyanide were also estimated satisfactorily (R² > 0.80), although cyanide showed negative regression coefficients. All mineral elements showed lower estimation accuracy (R² = 0.14–0.48) and exhibited weak associations with the spectral indices. Use of the normalized difference vegetation index (NDVI), green area (GA), and simple ratio (SR) indices allowed better estimation of growth and key nutritional traits. Irrigation dose 30% of pot capacity enriched with 0.01 mM K reduced most index values but increased the crop senescence index (CSI). Increasing K to 16 mM over the irrigation doses resulted in high index values, but low CSI. The findings indicate that RGB and multispectral imaging can provide indirect measurements of growth and key nutritional traits in cassava. Hence, they can be used as a tool in various breeding programs to facilitate cultivar evaluation and support management decisions to avert stress, such as the decision to irrigate or apply fertilizers. Full article

Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha⁻¹ increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/−1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (ΔTemp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models. Full article

In the synthesis method of a BiVO₄ photoanode via BiOI flakes, a BiOI film is formed by electrochemical deposition in Step 1, and a vanadium (V) source solution is placed by drop-casting on the BiOI film in Step 2. Following this, BiVO₄ particles are converted from the BiOI–(V species) precursors by annealing. However, it is challenging to evenly distribute vanadium species among the BiOI flakes. As a result, the conversion reaction to form BiVO₄ does not proceed simultaneously and uniformly. To address this limitation, in Step 2, we developed a new electrochemical deposition method that allowed the even distribution of V₂O₅ among Bi–O–I flakes to enhance the conversion reaction uniformly. Furthermore, when lactic acid was added to the electrodeposition bath solution, BiVO₄ crystals with an increased (040) peak intensity of the X-ray diffractometer (XRD) pattern were obtained. The photocurrent of the BiVO₄ photoanode was 2.2 mA/cm² at 1.23 V vs. reversible hydrogen electrode (RHE) under solar simulated light of 100 mW/cm² illumination. The Faradaic efficiency of oxygen evolution was close to 100%. In addition, overall water splitting was performed using a Ru/SrTiO₃:Rh–BiVO₄ photocatalyst sheet prepared by the BiVO₄ synthesis method. The corresponding hydrogen and oxygen were produced in a 2:1 stoichiometric ratio under visible light irradiation. Full article