Search Results (97)

Spodoptera frugiperda J.E. Smith (Lepidoptera: Noctuidae) represents a major threat to maize production across Pakistan, with chemical control serving as the predominant management approach. The intensive application of insecticides, particularly diamide compounds such as chlorantraniliprole, has escalated concerns regarding resistance evolution in field populations. This study evaluated the insecticidal efficacy of seven commonly used compounds against geographically diverse field-collected populations of S. frugiperda from major maize-growing regions of Pakistan, revealing significant inter-population variability in susceptibility profiles. Chlorantraniliprole was selected for comprehensive transgenerational screening based on moderate baseline LC₅₀ values and optimal laboratory colony establishment parameters. A representative field strain underwent six consecutive generations of selection pressure at LC₇₀ concentrations, resulting in a 4.48-fold increase in resistance levels with a realized heritability (h²) of 0.198. Predictive modeling using established quantitative genetic frameworks demonstrated that resistance evolution rates are critically dependent on both selection intensity and genetic parameters. Under constant h² = 0.198, increasing selection intensity substantially accelerated resistance development, with 10-fold resistance achievable in approximately 18 generations at 80% selection intensity (slope = 2.696) compared to 36 generations at lower intensities (slope = 4.696). Sensitivity analysis revealed that heritability variations from 0.148 to 0.248 could reduce generation requirements from >40 to ~25 generations when slope was maintained at 3.696. Life table analyses of the chlorantraniliprole-selected strain demonstrated significant fitness costs manifested as extended developmental periods, reduced reproductive output, and decreased intrinsic rate of population increase (r), indicating evolutionary trade-offs associated with resistance acquisition. These findings provide crucial insights for developing sustainable management strategies, highlighting the importance of integrating resistance monitoring, refuge-based approaches, and rotation with insecticides of different modes of action to delay resistance buildup in field populations. Such data-driven management frameworks are vital for maintaining the long-term efficacy of diamides in Pakistan’s maize production systems. Full article

Temperature excursions during refrigerated transport strongly affect the quality and shelf life of perishable food, yet reproducing realistic, time-varying cold-chain temperature histories in the laboratory remains challenging. In this study, we present a compact, portable climate chamber driven by Peltier modules and an identification-guided control architecture designed to reproduce real refrigerated-truck temperature histories with high fidelity. Control is implemented as a cascaded regulator: an outer two-degree-of-freedom PID for air-temperature tracking and faster inner PID loops for module-face regulation, enhanced with derivative filtering, anti-windup back-calculation, a Smith predictor, and hysteresis-based bumpless switching to manage dead time and polarity reversals. The system integrates distributed temperature and humidity sensors to provide real-time feedback for precise thermal control, enabling accurate reproduction of cold-chain conditions. Validation comprised two independent 36-day reproductions of field traces and a focused 24-h comparison against traditional control baselines. Over the long trials, the chamber achieved very low long-run errors ($\mathrm{MAE}\cong 0.19 °\mathrm{C}$, $\mathrm{MedAE}\cong 0.10 °\mathrm{C}$, $\mathrm{RMSE}\cong 0.33 °\mathrm{C}$, $R^2=0.9985$). The 24-h test demonstrated that our optimized controller tracked the reference, improving both transient and steady-state behaviour. The system tolerated realistic humidity transients without loss of closed-loop performance. This portable platform functions as a reproducible physical twin for cold-chain experiments and a reliable data source for training predictive shelf-life and digital-twin models to reduce food waste. Full article

The development of tropical maize populations with high heterosis potential is essential for sustaining genetic progress in hybrid breeding programs, yet accurate selection remains challenging due to genotype–phenotype interactions and inbreeding depression. This study evaluated the efficiency of five selection strategies in recurrent selection programs using F₂ populations derived from commercial maize hybrids: Smith–Hazel Index (SHI), Base Index (BIA), Mulamba–Mock Index (MMI), REML/BLUP for grain yield (BLUP_GY), and REML/BLUP for inbreeding depression (BLUP_ID). Consistency among methods was assessed with a heatmap, and predicted genetic gains were compared with realized field performance. Predicted gains were highest with MMI and BIA for grain yield and ear weight, although realized results revealed discrepancies, particularly for BLUP-based approaches. Notably, BLUP_GY, which had the lowest predicted yield (4025 kg ha⁻¹), achieved a realized yield of 5620 kg ha⁻¹, surpassing BIA and SHI. This indicates that additive potential was underestimated in predictions, likely due to dominance and environmental effects in early F₂ cycles. Overall, BLUP-based methods proved effective in identifying progenies with higher additive value, and their integration with phenotypic indices is recommended to combine short-term realized gains with sustained genetic improvement. Full article

Additive Manufacturing (AM) has attracted considerable interest over the past three decades, driven by growing industrial demand. Among metal AM techniques, Wire and Arc Additive Manufacturing (WAAM), a Directed Energy Deposition (DED) variant, has emerged as a prominent method for producing large-scale components with high deposition rates and cost efficiency. However, WAAM parts typically exhibit rough surface profiles, which can induce stress concentrations and promote fatigue crack initiation under cyclic loading. This study presents an integrated experimental and numerical investigation into the fatigue performance of as-built WAAM steel. Fatigue specimens extracted from a WAAM-fabricated wall were tested under cyclic loading, followed by fractography to assess the influence of surface irregularities and subsurface defects on fatigue behaviour. Surface topography analysis identified critical stress-concentration regions and key surface roughness parameters. Additionally, 3D scanning was used to reconstruct the specimen topography, enabling detailed 2D and 3D finite element (FE) modelling to analyze stress distribution along the as-built surface and predict fatigue life. A Smith-Watson-Topper (SWT) critical plane-based approach was applied for multiaxial fatigue life estimation. The results reveal a good correlation between experimental fatigue data and numerically predicted results, validating the proposed combined methodology for assessing durability of as-built WAAM components. Full article

This study investigates the structural and fracture behavior of an automotive exhaust manifold with a predefined semi-elliptical surface crack under realistic thermo-mechanical loading. A combined FEM–XFEM workflow was applied; the FEM identified the critical stress concentration zone, where the maximum Von Mises stress reached 165.6 MPa at 700 °C, and the XFEM was used to model crack growth with a refined mesh. The computed Mode I stress intensity factors ranged from 21 to 24 MPa√m, remaining below the temperature-dependent fracture toughness of AISI 321 stainless steel, which confirmed stable crack behavior under service conditions. Fatigue life was assessed using the Smith–Watson–Topper (SWT) parameter. Two scenarios were considered: a quasi-pulsating case, giving a predicted life of 3.8 × 10⁸ cycles, and a fully reversed case, reducing the life to 6.7 × 10⁷ cycles. These results confirm that the manifold operates within the high-cycle fatigue regime, while also demonstrating the strong sensitivity of life predictions to the applied stress ratio. This combined FEM–XFEM methodology provides a reliable numerical framework for assessing crack driving forces and guiding durability-based design of exhaust manifolds. Full article

To implement predictive maintenance of units in the practice of metallurgical manufacturers, computer information and diagnostic systems are being developed to assess the current state of individual units throughout their entire life cycle. This publication presents the results of a study on developing an infrared diagnostic system for predictive maintenance of converter units in the non-ferrous metallurgy industry. A 3D mathematical model of the transient heat transfer in the wall of a real operating unit has been developed and numerically implemented to study, analyze, and diagnose surface temperature fields resulting from wear and local damage. To adjust the operation of the mathematical model, the design parameters and the results for operating and technological parameters from an industrial experiment are taken into consideration. Using the model, a full-factor experiment was simulated to study the surface temperature fields resulting from the erosion wear of the wall and the presence of local damage. Based on the simulation results, the optimal time range for thermographic monitoring is determined. A regression dependence was derived to predict the refractory wall wear as a function of the outer surface temperature of the converter unit. The results are part of a comprehensive investigation aimed at developing thermal imaging techniques for converter units in non-ferrous metallurgy. Full article

This work proposes a design technique to facilitate the design and optimization of a highperformance power amplifier (PA) in an automated manner. The proposed optimizationoriented strategy consists of the implementation of four deep neural networks (DNNs), sequentially. Firstly, a bidirectional long short-term memory (BiLSTM)-based DNN is trained based on the X-parameters for which the hyperparameters are optimized through the multi-objective ant lion optimizer (MOALO) algorithm. This step is significant since it conforms to the hidden-layer construction of DNNs that will be trained in the following steps. Afterward, a generative adversarial network (GAN) is employed for forecasting the load–pull contours on the Smith chart, such as gate and drain impedances that are employed for the topology construction of the PA. In the third phase, the classification the BiLSTM-based DNN is trained for the employed high-electron-mobility transistor (HEMT), leading to the selection of the optimal configuration of the PA. Finally, a regression BiLSTMbased DNN is executed, leading to optimizing the PA in terms of power gain, efficiency, and output power by predicting the optimal design parameters. The proposed method is fully automated and leads to generating a valid PA configuration for the determined transistor model with much more precision in comparison with long short-term memory (LSTM)-based networks. To validate the effectiveness of the proposed method, it is employed for designing and optimizing a PA operating from 1.8 GHz up to 2.2 GHz at 40 dBm output power. Full article

Breeding programs often overlook the use of root traits. Therefore, we investigated the relevance of sorghum root traits in explaining its adaptation to combined drought and heat stress (CDHS). Six (i.e., three pre-release lines + three checks) sorghum genotypes were established at two low-altitude (i.e., <600 masl) locations with a long-term history of averagely very high temperatures in the beginning of the summer season, under two management (i.e., CDHS and well-watered (WW)) regimes. At each location, the genotypes were laid out in the field using a randomized complete block design (RCBD) replicated two times. Root trait data, namely root diameter (RD), number of roots (NR), number of root tips (NRT), total root length (TRL), root depth (RDP), root width (RW), width–depth ratio (WDR), root network area (RNA), root solidity (RS), lower root area (LRA), root perimeter (RP), root volume (RV), surface area (SA), root holes (RH) and root angle (RA) were gathered using the RhizoVision Explorer software during the pre- and post-flowering stage of growth. RSA traits differentially showed significant (p < 0.05) correlations with grain yield (GY) at pre- and post-flowering growth stages and under CDHS and WW conditions also revealing genotypic variation estimates exceeding 50% for all the traits. Regression models varied between pre-flowering (p = 0.013, R² = 47.15%, R² Predicted = 29.32%) and post-flowering (p = 0.000, R² = 85.64%, R² Predicted = 73.30%) growth stages, indicating post-flowering as the optimal stage to relate root traits to yield performance. RD contributed most to the regression model at post-flowering, explaining 51.79% of the 85.64% total variation. The Smith–Hazel index identified ICSV111IN and ASAREACA12-3-1 as superior pre-release lines, suitable for commercialization as new varieties. The study demonstrated that root traits (in particular, RD, RW, and RP) are linked to crop performance under CDHS conditions and should be incorporated in breeding programs. This approach may accelerate genetic gains not only in sorghum breeding programs, but for other crops, while offering a nature-based breeding strategy for stress adaptation in crops. Full article

To evaluate the characteristics of Smith–Purcell radiation, we modified a surface current model to consider the geometrical shading effect of a grating, which was ignored in the original one, and compared it with measurements for a grating with a shallow blaze angle. According to the numerical calculations based on the surface current model with and without the shading effect, it was found that the azimuthal angular distribution, polarization components and the variation in radiation intensity with the blaze angle of the grating are predicted to show significantly different behaviors under our experimental conditions. Generating the coherent Smith–Purcell radiation using the very short electron bunch in the test accelerator, t-ACTS at the Research Center for Accelerator and Radioisotope Science, Tohoku University, we measured polarization and the angular distribution of radiation for the gratings with different blaze angles. This study supports the validity of the modified surface current model with the shading effect and will provide new insights into the evaluation of the characteristics of Smith–Purcell radiation. Full article

Terahertz (THz) metamaterials based on phase-change materials (PCMs) offer promising approaches to the dynamic modulation of electromagnetic responses. In this study, we design and experimentally demonstrate a tunable THz metamaterial composed of a symmetric split-ring resonator (SRR) pair, with the left halves covered by a 35 nm thick epitaxial vanadium dioxide (VO₂) film, enabling the simultaneous exploitation of both permittivity- and conductivity-induced modulation mechanisms. During the metal–insulator transition (MIT) of VO₂, cooperative changes in permittivity and conductivity lead to the excitation, redshift, and eventual disappearance of a quasi-bound state in the continuum (QBIC) resonance. Finite element simulations, using optical parameters of VO₂ film defined by the Drude–Smith model, predict the evolution of the transmission spectra well. These results indicate that the permittivity change originating from mesoscopic carrier confinement is a non-negligible factor in THz metamaterials hybridized with VO₂ film and also reveal the potential for developing reconfigurable THz metamaterials based on the dielectric modulation effects of VO₂ film. Full article

Empirical equations were developed by many investigators to determine the reaeration coefficients (Ka) required for predicting dissolved oxygen concentrations (DO) in surface waters, especially rivers, lakes, and reservoirs. However, these equations yield a wide range of Ka values. In this paper, an integrated numerical-modeling-based technique was developed to check the validity of the equations before using them in water quality modeling for rivers, lakes, and reservoirs. Depending on direct field measurements at the Hilla River headwater (Saddat Al-Hindiyah Reservoir, Iraq), the temporal oxygen mass transport at the water surface was estimated numerically by solving the one-dimensional advection diffusion equation and then using each Ka empirical equation separately in the numerical model obtained the best specific-waterbody equation. The DO modeling results showed that using a reservoir reaeration coefficient of 0.1 day⁻¹ at 20 °C predicts the best DO simulation with low MAEs of 0.4987 and 0.7880 mg/L during the study years 2021 and 2022, respectively, compared to the field data. However, using the Ka empirical equations simulates the DO with wide-ranging statistical errors even though the temporal Ka values have a similar trend during the year. It was noticed that the empirical equations produced maximum Ka values of (0.0080–0.0967 day⁻¹) and minimum Ka values of (0.00052–0.0267 day⁻¹) in 2021 and maximum Ka values of (0.0079 to 0.0951 day⁻¹) and minimum Ka values of (0.00012 and 0.0231 day⁻¹) in 2022. The present equation selection technique revealed that Broecker et al.’s equation followed by Smith’s equation, developed in 1978, are the best selection for water quality modeling at the Hilla River headwater (MAEs: 0.1347 and 0.1686 mg/L in 2021, respectively; and MAEs: 0.1400 and 0.1744 mg/L in 2022, respectively). Hence, it is necessary to find good agreement for the equation-based prediction of DO, DO source–sink, and Ka values compared to the validated model before making selection. Full article

Servo systems are important actuators of aeroengines. The repetitive, reciprocating motion of the servo system leads to significant changes in its time delay and gain characteristics, and degradation increases the uncertainty of these changes. These characteristic variations may have an adverse effect on the dynamic performance of the aeroengine. Therefore, a neural network-based parameter estimation and a multi-loop neural network-based predictive control (ML-NNPC) method for aeroengine inlet guide vane (IGV) servo systems (SVS) were proposed. In this study, the time delay estimation of the servo system was treated as a classification problem, and an SE (squeeze-and-excitation)-GRU (gated recurrent unit) network was proposed to estimate the time delay by using the selected dynamic data of the servo system. The estimated delay was embedded into an online sequential extreme learning machine, and a nonlinear model predictive controller was designed to obtain an optimal control sequence. The compensation control loop was designed to reduce the impact of the model and delay mismatch problems of the control system. The proposed method was applied to the IGV SVS control of a turboshaft engine. The simulation results demonstrate that the time delay is estimated accurately and compensated effectively. Compared to the existing PI and PI with Smith predictor methods, the ML-NNPC method achieves better control performance in the control of both the SVS and the engine rotor speed system. The stability and robustness of the ML-NNPC also show superiority. The results verify the effectiveness of the proposed time delay estimation method and the ML-NNPC method. Full article

The multi-parameter and nonlinear characteristics of the Smith Watson Topper (SWT) equation present considerable challenges for predicting the fatigue life of 2024-T3 clad Al alloy. To overcome these challenges, a novel model integrating traditional fatigue analysis methods with machine learning algorithms is introduced. An improved SWT fatigue life prediction equation is developed by incorporating key factors such as the mean stress effect, stress concentration factor, and surface roughness coefficient. Extreme gradient boosting, Random Forest, and their derived models are used to construct the fatigue life prediction model. The L-BFGS algorithm was then integrated with the established machine learning model to solve for the multi-parameter of the improved SWT equation. Thus, an accurate modified SWT prediction equation for 2024-T3 clad Al alloy was obtained. To further optimize the solution, the deep deterministic policy gradient and deep reinforcement learning algorithms are introduced to dynamically optimize the nonlinear equation, achieving a more efficient and accurate solution. The improved SWT fatigue life prediction equation and its solution method proposed in this study provide new insights for fatigue life prediction of clad metallic materials. Full article

Total leaf area per shoot (A_T) can reflect the photosynthetic capacity of a shoot. A prior study hypothesized that A_T is proportional to the product of the sum of the individual leaf widths per shoot (L_KS) and the maximum individual leaf length per shoot (W_KS), referred to as the Montgomery–Koyama–Smith equation (MKSE). However, empirical evidence does not support such a proportional relationship hypothesis, as A_T was found to allometrically scale with L_KSW_KS, i.e., $A_{\mathrm{T}}\propto {\left(L_{\mathrm{K}\mathrm{S}}{W}_{\mathrm{K}\mathrm{S}}\right)}^{\mathsf{\alpha }}$, where $\mathsf{\alpha }\ne 1$, referred to as the power law equation (PLE). Given that there is variation in the total number of leaves per shoot (n), little is known about whether the leaf area distribution has an explicit mathematical link with the sorted leaf area sequence per shoot, and it is unknown whether the mathematical link can affect the prediction accuracy of the MKSE and PLE. In the present study, the leaves of 500 shoots of a dwarf bamboo (Sasaella kongosanensis ‘Aureostriatus’) were scanned, and the leaf area, length, and width values were obtained by digitizing the leaf images. We selected the shoots with n ranging from 3 to 10, which accounted for 76.6% of the totally sampled shoots (388 out of 500 shoots). We used the formula for the sum of the first j terms (j ranging from 1 to n) of a geometric series (GS), with the mean of the quotients of any adjacent two terms (denoted as ${\overline{q}}_A$) per shoot as the common ratio of the GS, to fit the cumulative leaf area observations. Mean absolute percentage error (MAPE) was used to measure the goodness of fit of the GS. We found that there were 367 out of 388 shoots (94.6%) where 1 < ${\overline{q}}_A$ < 1.618 and MAPE < 15%, and these 367 shoots were defined as valid samples. The GS hypothesis for leaf area distribution was supported by the result that the MAPE values for most valid samples (349 out of 367, i.e., 95.1%) were smaller than 5%. Here, we provide a theoretical basis using the GS hypothesis to demonstrate the validity of the MKSE and PLE. The MAPE values for the two equations to predict A_T were smaller than 5%. This work demonstrates that the leaf area sequence per shoot follows a GS and provides a useful tool for the calculation of total leaf area per shoot, which is helpful to assess the photosynthetic capacity of plants. Full article

Background/Objectives: The increasing prevalence of video gaming has raised concerns about its potential impact on musculoskeletal health, particularly temporomandibular disorders (TMDs). This study aims to compare TMD symptoms, mandibular function, and dental wear between gamers and non-gamers among university students. Methods: An observational study included 108 students aged 20 to 23 years, divided into gamers (n = 48) and non-gamers (n = 60). Participants completed questionnaires assessing TMD symptoms, gaming habits, and screen time. Clinical examinations measured mandibular movements, palpation-induced pain, and dental wear using the Smith and Knight Tooth Wear Index. Statistical analyses included independent t-tests, chi-square tests, Pearson’s correlations, and logistic regression. Seven comprehensive tables present the findings with p-values. Results: Gamers reported significantly higher screen time (Mean = 6.5 h/day) compared to non-gamers (Mean = 4.0 h/day; p < 0.001). Maximum unassisted mouth opening was greater in gamers (Mean = 48.31 mm) than in non-gamers (Mean = 46.33 mm; p = 0.04). Gamers exhibited a higher prevalence of pain on palpation of the masseter muscle (45.8% vs. 30.0%; p = 0.05). Dental wear scores were significantly higher in gamers for teeth 2.3 (upper left canine) and 3.3 (lower left canine) (p < 0.05). Positive correlations were found between hours spent gaming and maximum mouth opening (r = 0.25; p = 0.01) and dental wear (r = 0.30; p = 0.002). Logistic regression showed that gaming status significantly predicted the presence of TMD symptoms (Odds Ratio = 2.5; p = 0.03). Conclusions: Gamers exhibit greater mandibular opening, increased dental wear, and a higher prevalence of masticatory muscle pain compared to non-gamers. Prolonged gaming may contribute to altered mandibular function and increased risk of TMD symptoms. Further research is needed to explore underlying mechanisms and develop preventive strategies. Full article