Standard Hybrid Research Articles

Wildlife Object Detection Method Applying Segmentation Gradient Flow and Feature Dimensionality Reduction

This work suggests an enhanced natural environment animal detection algorithm based on YOLOv5s to address the issues of low detection accuracy and sluggish detection speed when automatically detecting and classifying large animals in natural environments. To increase the detection speed of the model, the algorithm first enhances the SPP by switching the parallel connection of the original maximum pooling layer for a series connection. It then expands the model’s receptive field using the dataset from this paper to enhance the feature fusion network by stacking the feature pyramid network structure as a whole; secondly, it introduces the GSConv module, which combines standard convolution, depth-separable convolution, and hybrid channels to reduce network parameters and computation, making the model lightweight and easier to deploy to endpoints. At the same time, GS bottleneck is used to replace the Bottleneck module in C3, which divides the input feature map into two channels and assigns different weights to them. The two channels are combined and connected in accordance with the number of channels, which enhances the model’s ability to express non-linear functions and resolves the gradient disappearance issue. Wildlife images are obtained from the OpenImages public dataset and real-life shots. The experimental results show that the improved YOLOv5s algorithm proposed in this paper reduces the computational effort of the model compared to the original algorithm, while also providing an improvement in both detection accuracy and speed, and it can be well applied to the real-time detection of animals in natural environments.

Integrated Energy System Powered a Building in Sharjah Emirates in the United Arab Emirates

In this study, a green hydrogen system was studied to provide electricity for an office building in the Sharjah emirate in the United Arab Emirates. Using a solar PV, a fuel cell, a diesel generator, and battery energy storage; a hybrid green hydrogen energy system was compared to a standard hybrid system (Solar PV, a diesel generator, and battery energy storage). The results show that both systems adequately provided the power needed for the load of the office building. The cost of the energy for both the basic and green hydrogen energy systems was 0.305 USD/kWh and 0.313 USD/kWh, respectively. The cost of the energy for both systems is very similar, even though the capital cost of the green hydrogen energy system was the highest value; however, the replacement and operational costs of the basic system were higher in comparison to the green hydrogen energy system. Moreover, the impact of the basic system in terms of the carbon footprint was more significant when compared with the green hydrogen system. The reduction in carbon dioxide was a 4.6 ratio when compared with the basic system.

Growth Attributes of Eco Races of Eri Silkworm, Philosamia Ricini Donovan in the Western Zone Conditions of Tamil Nadu

One of the most economic exploitation, widely cultivated, and financially viable non-mulberry silkworms is the eri silkworm (Philosamia ricini). The feeds that eri silkworms consume have a significant impact on their yield and silk production. A study was conducted to assess the growth characteristics of the eco races of the eri silkworm in the circumstances of western region of Tamil Nadu. The Central Muga and Eri Research Institute in Jorhat, Assam provided the eco races of eri silkworm. Three replications of the Completely Randomized Design (CRD) were used to set up the treatments. By feeding worms with castor leaves in the cellular rearing method, the growth characteristics of eco races of eri silkworm, specifically the larval parameter and cocoon parameter, were examined. Among the eco races of eri silkworm in comparison with standard F1 hybrid, the eco race Jonai recorded the maximum growth attributes such as larval weight (7.09 g), cocoon weight (3.93 g), shell weight (0.66 g), shell ratio, ERR (16.78 %) which was on par with F1 hybrid followed by Khanapara and Titabar. The least was observed in Barpathar and Adogiri. This study reveals that among ten eco races of eri silkworm, the eco race Jonai recorded performed well in western zone condition of Tamil Nadu and it can be commercially reared in alternative to Commercial F1 breed.

ωB97X-3c: A composite range-separated hybrid DFT method with a molecule-optimized polarized valence double-ζ basis set.

A new composite density functional theory (DFT) method is presented. It is based on ωB97X-V as one of the best-performing density functionals for the GMTKN55 thermochemistry database and completes the family of "3c" methods toward range-separated hybrid DFT. This method is consistently available for all elements up to Rn (Z = 1-86). Its further key ingredients are a polarized valence double-ζ (vDZP) Gaussian basis set, which was fully optimized in molecular DFT calculations, in combination with large-core effective core potentials and a specially adapted D4 dispersion correction. Unlike most existing double-ζ atomic orbital sets, vDZP shows only small basis set superposition errors (BSSEs) and can compete with standard sets of triple-ζ quality. Small residual BSSE effects are efficiently absorbed by the D4 damping scheme, which overall eliminates the need for an explicit treatment or empirical corrections for BSSE. Thorough tests on a variety of thermochemistry benchmark sets show that the new composite method, dubbed ωB97X-3c, is on par with or even outperforms standard hybrid DFT methods in a quadruple-zeta basis set at a small fraction of the computational cost. Particular strengths of this method are the description of non-covalent interactions and barrier heights, for which it is among the best-performing density functionals overall.

Terminal Node of Active Distribution Network Correlation Compactness Model and Application Based on Complex Network Topology Graph

Multiple nodes (such as distributed generation (DG), electric vehicles (EV), energy storage (ES), flexible loads (FL), etc.) are connected to the active distribution network (ADN), which changes its original operational mode. According to the bidirectional current and low-voltage transmission mode, this study proposed a multi voltage and multi electricity flat loop network, AC/DC (Alternating Current/ Direct Current) hybrid network, unified interface and flexible self-organizing network based on Complex network theory. First, the ADN complex network topology of various nodes is established based on the actual grid connected terminal nodes and power flow sensitivity algorithm. Second, using the TOPSIS model, the influence factor matrix of weighted directed network is established. The matrix can be used to guide the formulation of the distribution network operation mode, and the robustness and reliability of this paper are verified by using the standard multi voltage level main distribution hybrid model provided by the Panda Power website as the verification method. Finally, using the influence maximization calculation model of the New Creedy algorithm, the node correlation matrix is expanded to form a super family region set of active distribution network. The results show that the seven nodes in this paper have high correlation, while the other nodes have low correlation. In addition, the change of reactive power has little impact on other nodes, for a node with a change rate of 0, it is obviously not in the same power supply family as node 1, and theoretically it may not have a topological relationship, be a power generation node, or be completely independent. Analyzing the relationship between nodes has a guiding significance for power supply recovery and interaction in distribution network reconfiguration.

Structural performance and corrosion resistance of fiber reinforced polymer wrapped steel reinforcing bars

Structural behavior and corrosion resistance of hybrid fiber reinforced polymer (FRP) wrapped steel bars is investigated for use in reinforced concrete structures. Experiments were conducted in the laboratory to assess the axial tensile capacity and corrosion resistance of the composite bars, and flexural behavior of beams reinforced with these hybrid bars. Data from short term and long-term corrosion experiments were used to evaluate the resistance to degradation provided by the FRP, including applied defects to determine any gain in corrosion resistance over standard epoxy-coated bars. Corrosion experiments are performed by placing bars with and without applied defects in an aggressive solution that mimics a concrete pore environment. Corrosion rates of standard epoxy-coated bars and hybrid carbon and glass FRP wrapped steel bars were compared considering the crevice corrosion at applied defects. In addition to their contributions to the strength capacity of steel bars confirmed by the tensile tests, FRP-wrapped steel reinforcing bars have high corrosion resistance. The overall behavior of beams reinforced with hybrid FRP-steel bars was similar to those reinforced with epoxy coated bars. During the experiments, no debonding was observed between the hybrid bars and concrete inside the beams before failure.

Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular Networks

The accurate and reliable predictive estimation of signal attenuation loss is of prime importance in radio resource management. During wireless network design and planning, a reliable path loss model is required for optimal predictive estimation of the received signal strength, coverage, quality, and signal interference-to-noise ratio. A set of trees (100) on the target measured data was employed to determine the most informative and important subset of features, which were in turn employed as input data to the Particle Swarm (PS) model for predictive path loss analysis. The proposed Random Forest (RF-PS) based model exhibited optimal precision performance in the real-time prognostic analysis of measured path loss over operational 4G LTE networks in Nigeria. The relative performance of the proposed RF-PS model was compared to the standard PS and hybrid radial basis function-particle swarm optimization (RBF-PS) algorithm for benchmarking. Generally, results indicate that the proposed RF-PS model gave better prediction accuracy than the standard PS and RBF-PS models across the investigated environments. The projected hybrid model would find useful applications in path loss modeling in related wireless propagation environments.

RESULTS OF THE STUDY OF SETS OF SWEET PEPPER VARIETY SAMPLES AND F1 HYBRIDS IN THE REPEATED TERM

The article describes the results of research on phenological indicators and productivity of 28 varieties of sweet pepper and 10 F1 hybrids, which were repeatedly studied in the conditions of the central region of Uzbekistan. The total productivity of the high-yielding varieties was 110.6-124.8% higher than the standard variety. The varieties Tong, Podarok Moldovy, Bolgarsky 79, Pamir, Kaliforniyskoe chudo, PP10674, PP10678, D11000, D11520, S7103, whose productivity is almost equal to the standard variety, were also isolated. Their yield was 27,7-29,0 t/ha. In the hybrids of the first generation, the yield was slightly higher. Standard F1 Jaihun hybrid yield was 40,9 t/ha. This is a 145% higher result compared to the standard Dar Tashkenta variety.

Theoretical Model of Polarization Effects on Third-Order NLO Properties of the Stilbazolium Derivative Crystal.

The linear and nonlinear properties of the stilbazolium derivative, 2-[2-(3-hydroxy-4-methoxy-phenyl)-vinyl]-1-methyl-pyridinium naphthalene-2-sulfonate dihydrate crystal (VSNS), were investigated using an iterative electrostatic embedding scheme and density functional theory (DFT). The dipole moment and second hyperpolarizability of the VSNS molecule are sharply influenced by its crystalline phase. Standard DFT global hybrids such as B3LYP and M06 are strongly benefited by the effects of crystalline polarization. The performance can be further improved by making small changes in the amount of Hartree-Fock exchange included in the hybrid, delivering good hyperpolarizability and spectroscopy.

Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images

Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers’ trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. In addition, two publicly accessible datasets termed COVID-Xray-5 k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2 s-12c-2 s and i-8c-2 s-16c-2 s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), andMatched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarmrate of less than 0.89%. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.

Limiting Behavior of Hybrid Time-Varying Systems

Checking uniform attractivity of a time-varying dynamic system without a strict Lyapunov function is challenging as it requires the characterization of the limiting behavior of a set of trajectories. In the context of hybrid nonlinear time-varying systems, characterizing such limiting or convergent behaviors is even harder due to the complexity stemming from both continuous-time variations as well as discrete-time jumps. In this article, an extension of the standard hybrid time domain is introduced to define limiting behaviors, using set convergence, when time approaches either positive infinity or negative infinity. In particular, it is shown how to characterize limiting behaviors under the condition that an output signal approaches zero. Such limiting behaviors and their associated limiting systems can be used to verify uniform global attractivity. Particularly, a generalization of the classic Krasovskii–LaSalle theorem is obtained for hybrid time-varying systems. Two examples are used to demonstrate the effectiveness of the results.

REAP-HP survey 2020: Comparing the real-world practice and expectation in Helicobacter pylori eradication of the Taiwanese gastroenterologists in 2015 and 2020.

REAP-HP study (Real-world practice and Expectation of Asia-Pacific physicians and patients in Helicobacter Pylori eradication) was the pioneer study investigating the expectation and preference of physicians across Asia-Pacific in H.pylori eradication in 2015. This study is the first follow-up study of REAP-HP in Taiwan. (1) To investigate the preference in regimens for the first-line anti-H.pylori therapy of Taiwanese gastroenterologist in 2020, (2) To survey the factor that cause the most concern when prescribing anti-H.pylori regimens in clinical practice, and (3) to compare REAP-HP survey data in 2020 and those surveyed in 2015 regarding the abovementioned end-points. A questionnaire for H.pylori eradication survey of physicians was distributed to the gastroenterologists who attended the Taiwan Digestive Disease Week 2020. Data of most commonly used first-line anti-H.pylori regimens and concerned factors when prescribing anti-H.pylori regimens between 2015 and 2020 were compared. A total of 258 physicians from different districts of Taiwan participated in the REAP-HP Survey in 2020. The top three most commonly used anti-H.pylori regimens in Taiwan in 2020 were 14-day standard triple therapy (36.8%; 95% confidence interval [CI]: 30.9%-42.7%), 7-day standard triple therapy (17.8%; 95% CI: 13.1%-22.5%) and 14-day reverse hybrid therapy (14.7%; 95% CI: 10.4%-19.0%) respectively. The top two factors that cause the most concern during prescribing anti-H.pylori therapy were eradication rate (82.3%; 95% CI: 77.6%-87.0%) and side effect (10.4%; 95% CI: 6.7%-15.1%). In 2015, the top three most commonly used regimens in Taiwan were 7-day standard triple therapy (62%; 95% CI: 56.2%-67.8%), 14-day standard triple therapy (21%; 95% CI: 16.1%-25.9%) and 10-day sequential therapy (7%; 95% CI: 4%-10%). A remarkable difference of the most commonly used anti-H.pylori regimens between 2015 and 2020 existed (p < .001). The top two factors that cause the most concern during prescribing anti-H.pylori therapy in 2015 were eradication rate (84.1%) and side effect (7.0%). There were no differences in the factors that cause the most concern during prescribing anti-H.pylori regimens between 2015 and 2020. 14-day standard triple therapy has replaced 7-day standard triple therapy as the most commonly used first-line anti-H.pylori therapy among Taiwanese gastroenterologists in 2020. 14-day reverse hybrid therapy is on rise to the third place as the most commonly used anti-H.pylori regimen in Taiwan.

Delay-Phase Precoding for Wideband THz Massive MIMO

Benefiting from tens of GHz of bandwidth, terahertz (THz) communication has become a promising technology for future 6G network. To deal with the serious propagation loss of THz signals, massive multiple-input multiple-output (MIMO) with hybrid precoding is utilized to generate directional beams with high array gains. However, the standard hybrid precoding architecture based on frequency-independent phase-shifters cannot cope with the beam split effect in THz massive MIMO caused by the large bandwidth and the large number of antennas, where the beams split into different physical directions at different frequencies. The beam split effect will result in a serious array gain loss across the entire bandwidth, which has not been well investigated in THz massive MIMO. In this paper, we first quantify the seriousness of the beam split effect in THz massive MIMO by analyzing the array gain loss it causes. Then, we propose a new precoding architecture called delay-phase precoding (DPP) to mitigate this effect. Specifically, the proposed DPP introduces a time delay network composed of a small number of time delay elements between radio-frequency chains and phase-shifters in the standard hybrid precoding architecture. Unlike <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">frequency-independent</i> phase shifts, the time delay network introduced in the DPP can realize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">frequency-dependent</i> phase shifts, which can be designed to generate frequency-dependent beams towards the target physical direction across the entire bandwidth. Due to the joint control of delay and phase, the proposed DPP can alleviate the array gain loss caused by the beam split effect. Furthermore, we propose a hardware structure by using true-time-delayers to realize frequency-dependent phase shifts for realizing the concept of DPP. A corresponding precoding algorithm is proposed to realize the precoding design. Theoretical analysis and simulations show that the proposed DPP can mitigate the beam split effect and achieve near-optimal rate with higher energy efficiency.

ВИЗНАЧЕННЯ ОПТИМАЛЬНОЇ ГЕТЕРОЗИСНОЇ МОДЕЛІ СЕРЕДНЬОСТИГЛИХ ГІБРИДІВ КУКУРУДЗИ В УМОВАХ ПІВНІЧНОГО СТЕПУ

Topicality. Currently, we can use more effectively the initial material of the working collection due to targeting certain heterosis models. Information about the advantages of different heterotic models in certain cultivation areas makes it easier to search for successful combinations and the selection of promising initial material. Issues. Тo identify of the most promising heterotic models of maize hybrids developed with using lines of different genetic plasms, which characterized by high yields and low grain moisture content compared to standard hybrids adapted to cultivation in the Northern Steppe. Aim. To determine the economic value of maize hybrids developed with using different germplasm lines and the most promising heterotic models for each genoplasm group. To investigate how the plant growing conditions influence on economic valuable indicators of hybrids depending on their genetic origin. Materials and Methods. Visual Method was used for phenological observations; Field and Laboratory Method was used to determine of morphobiological characteristics of plants; Measuring Weight Method – to determine the plant yield and metric characteristics; Mathematical and Statistical Method – to determine the reliability of results, the trait variability indicators, correlation dependence of traits; Analysis of Variance (ANOVA); comprehensive assessment of morphobiological and economically valuable characteristics of inbred lines and hybrids based on different genetic plasms. Results. It was found that testcrosses based on the Lancaster x Iodent model were the most stable by grain yield. However, in the average of three years of research, the highest average level (6.31 t/ha) was recorded in Mixed x Lancaster testcrosses. It was selected testcrosses with a higher grain yield compared to standard hybrids, such as DK3044xDK315, DK3151xDK4454, DK3151xDK3155 and DK365xDK6356 which had a yield of 8.0; 7.59; 7.18; 7.17 t/ha, respectively. We observed the best ratio of yield to grain moisture content (harvest index RH/M) in hybrid combinations of Lancaster x Mixed. Conclusions. It was determined the best testcrosses according to the ratio of yield to grain moisture content: DK315CxDK3821 (Mixed x BSSS) – 0.51; DK633/325xDK365 (Mixed x Iodent) – 0.51; DK365xDK6356 (Iodent x Lancaster) – 0.51; DK3044xDK315 (Lancaster x Mixed) – 0.56; DK3151xDK3155 (BSSS x Mixed) – 0.55; DK633/325xDK3155 (Lancaster x Mixed), based on which are developing the mid-ripening maize hybrids adapted to Steppe zone of Ukraine. Key words: maize, hybrid, line, germplasm, heterotic model, testcross, grain yield and moisture content at harvesting.

Radiomic Signatures Derived from Hybrid Contrast-Enhanced Ultrasound Images (CEUS) for the Assessment of Histological Characteristics of Breast Cancer: A Pilot Study.

Simple SummaryThe hybrid images obtained at the CEUS present an overlap of the tumor in grayscale over the enhancement pattern, which allows an appropriate evaluation of the entire lesion without including in the analysis the peritumoral area. We hypothesized that the spatial heterogeneity of tissue enhancement differs depending on histological characteristics. Therefore, the aim of this study was to evaluate the diagnostic performance of radiomic features extracted from standardized hybrid CEUS data for the assessment of hormone receptor status, HER2 status, tumor grade and Ki-67 in patients with primary breast cancer. The clinical benefit of this study would be to use CEUS and bioinformatics tools for improved biopsy planning or even guiding treatment decisions.The purpose of this study was to evaluate the diagnostic performance of radiomic features extracted from standardized hybrid contrast-enhanced ultrasound (CEUS) data for the assessment of hormone receptor status, human epidermal growth factor receptor 2 (HER2) status, tumor grade and Ki-67 in patients with primary breast cancer. Methods: This prospective study included 72 patients with biopsy-proven breast cancer who underwent CEUS examinations between October 2020 and September 2021. Results: A radiomic analysis found the WavEnHH_s_4 parameter as an independent predictor associated with the HER2+ status with 76.92% sensitivity, and 64.41% specificity and a prediction model that could differentiate between the HER2 entities with 76.92% sensitivity and 84.75% specificity. The RWavEnLH_s-4 parameter was an independent predictor for estrogen receptor (ER) status with 55.93% sensitivity and 84.62% specificity, while a prediction model (RPerc01, RPerc10 and RWavEnLH_s_4) could differentiate between the progesterone receptor (PR) status with 44.74% sensitivity and 88.24% specificity. No texture parameter showed statistically significant results at the univariate analysis when comparing the Nottingham grade and the Ki-67 status. Conclusion: Our preliminary data indicate a potential that hybrid CEUS radiomic features allow the discrimination between breast cancers of different receptor and HER2 statuses with high specificity. Hybrid CEUS radiomic features might have the potential to provide a noninvasive, easily accessible and contrast-agent-safe method to assess tumor biology before and during treatment.

Standard Heterosis Estimation for Quality Protein Maize Hybrids Over Best Released and Commercialized Hybrids

Maize plays an indispensable role in meeting high food demand. It is globally one of the most widely adopted and cultivated crops. Hybrid development from fixed inbred lines is one of the tactics to boost maize production. The national average maize yield in Ethiopia is low and thus, selection of promising germplasm, knowledge of combining ability, and heterotic grouping are prerequisites to develop high-yielding maize varieties. Forty-two Quality Protein Maize (QPM) single crosses (21 inbred lines each crossed with two testers) along with three popular standard hybrid checks were evaluated in two replications using alpha lattice design during the 2017 cropping season at Ambo, Arsi-Negele, and Kulumsa. The objective of this study was to estimate standard heterosis for grain yield (GY), and other agronomic and morphological characters. Significant difference among crosses was observed for 19 traits at Ambo, 14 traits at Arsi-Negele, and 19 traits at Kulumsa in the hybrid trial. For GY, at Ambo, almost all crosses showed negative heterosis against the best check (AMH853). At Arsi-Negele 14 crosses had positive standard heterosis, from these only three crosses: L8xT1 (50.8%), L8xT2 (46.6%), and L7xT1 (33.9%) showed significant difference against Jibat but at Kulumsa, the difference for standard heterosis was positive but non-significant only by two crosses: (L7xT1 (6.6%) and L19xT1 (4.7%). Based on mean grain yield and standard heterosis, L8xT2, L7xT1, L8xT1, L19xT1, L6xT2, and L18xT1 are promising. The study of the results highlighted that the breeding program was successful in generating superior QPM hybrids. Based on the finding we suggest that it is better to use the parents of theses hybrids as potential source materials in the breeding program through to form different crosses formation.

Hybrid random utility-random regret model in the presence of preference heterogeneity, modeling drivers’ actions

Despite the importance of drivers’ actions and behaviors, the underlying factors to those actions have not received adequate attention. Understanding the factors contributing to various drivers’ actions before crashes could help policy makers to take appropriate actions to tackle those behaviors before crashes occur. One of the first steps could be to identify contributing factors to drivers’ actions by using a reliable statistical technique. It is reasonable to assume that drivers vary in their decision-making processes. Thus, in this study, in addition to the random utility maximization (RUM), the random regret minimization (RRM), as a psychological representation of the choice-making process, was considered. While most of the past studies, in the context of traffic safety, focused on either the RRM or RUM, both models’ frameworks as hybrid models might be needed to account for the heterogeneity of drivers’ decision-making behaviors. In addition, we accounted for the additional dimensions of preference heterogeneity in the latent class (LC) that the model might not capture. The results showed a significant improvement in the model fit of the mixed hybrid LC model compared with the standard hybrid and simple mixed RRM and RUM models. The emotional conditions of drivers, distraction, environmental conditions, and gender are some of the factors found to impact drivers’ choices. The results suggest that while the majority of attributes are processed according to the RUM, a significant portion of attributes are processed by the RRM. The hybrid model provides a richer understanding regarding factors to drivers’ actions before crashes based on different paradigms.

Estimation of General and Specific Combining Ability Effects for Quality Protein Maize Inbred Lines

Background: Maize (Zea mays L.) plays a critical role in meeting high food demand. It is globally one of the most widely adapted and cultivated crops. Hybrid development from fixed inbred lines is one of the strategies for the improvement of maize production. The national average maize yield in Ethiopia is low and thus, selection of promising germplasm and knowledge of combining ability are prerequisites to developing high yielding maize varieties. Forty-two Quality Protein Maize (QPM) crosses (21 inbred lines each crossed with two testers) along with three popular standard hybrids were evaluated in two replications using alpha lattice during the 2017 cropping season at Ambo, Arsi-Negele, and Kulumsa. The objectives of this study were to identify lines with high GCA and estimate the SCA of crosses for grain yield, and other agronomic and morphological characters.&#x0D; Results: Significant difference among crosses was observed for 19 traits at Ambo, 14 traits at Arsi-Negele, and 19 traits at Kulumsa in the hybrid trial. Regarding the GCA effect, L8 had a significant difference to the positive side with the highest magnitude of GCA effect at three locations (3.40, 2.03, and 1.88 GCA effect values at Arsi-Negele, Ambo, and Kulumsa, respectively) which is followed by L7 for GY. All crosses did not show a significant SCA effect for GY in both directions at Ambo and Arsi-Negele but at Kulumsa, five crosses: L2xT1(1.89), L13xT2(1.88), L7xT1(1.86), L4xT2(1.49) and L19xT1(1.41) showed significant difference. In the combined analysis for six traits, Additive gene action was more important which was manifested by a higher sum square contribution of DS (79.6) EPP (79.3%), EL (80.0%), KPR (80.1%), ED (78.5%) and TSW (79.1%).&#x0D; Conclusion: Based on mean grain yield, and combining ability, L8xT2, L7xT1, L8xT1, L19xT1, L6xT2, and L18xT1 are promising crosses that could be forwarded for further use in maize breeding programs for further works.

Design optimization and control of dividing wall column for purification of trichlorosilane

• TA C appr . is proposed as a novel objective function for DWC parameter optimization. • Optimal energy control structure can be achieved by manipulating liquid split ratio. • MPC-PI hybrid structure balances both control performance and safety performance. • Simulated annealing algorithm is employed to optimize the weights of MPC controller. Polysilicon quality and energy consumption are directly affected by the purification process of trichlorosilane. In this work, the dividing wall column (DWC) as a promising energy-saving technology is utilized for trichlorosilane purification, with the design optimization carried out using steady-state simulation. Compared with conventional distillation process, DWC can reduce total annual cost (TAC), CO 2 emissions and exergy loss ( El ) by 35.81%, 53.56% and 54.10% on average, respectively. Established on the characteristics of superfractionator, four control structures are proposed in this work, two of which are multi-loop proportional-integral (PI) control schemes including condenser duty ( C ), distillate flow rate ( D ), bottom flow rate ( B )/reflux flow rate ( R ), side flow rate ( S ), reboiler duty ( V ) control structure and optimal energy control structure. The other two are model predictive control (MPC) schemes including standard MPC structure and MPC-PI hybrid structure. The weights of MPC controller are optimized using the simulated annealing (SA) algorithm. Dynamic simulation results demonstrate that MPC schemes achieve improved closed-loop performance in terms of minor overshoot, shorter transition time and reduced oscillation than PI control schemes. The integral absolute error (IAE) is introduced to further quantitatively evaluate MPC schemes. The simulation results demonstrate that the standard MPC structure achieves the best control performance and the MPC-PI hybrid structure enhances process safety while maintaining the desired control performance.

Performance Evaluation and Identification of Highland Quality Protein Maize Hybrids in Ethiopia

Background: Maize plays an indispensable role in meeting high food demand. It is globally one of the most widely adopted and cultivated crops. Hybrid development from fixed inbred lines is one of the strategies for the improvement of maize production. The national average maize yield is low in East Africa; thus, the selection of promising germplasm has a great role to meet the high food demand of growing the population. Forty-two Quality Protein Maize (QPM) crosses (21 inbred lines each crossed with two testers) along with three popular standard hybrids checks were evaluated in two replications using alpha lattice during the 2017 cropping season at Ambo, Arsi-Negele, and Kulumsa. The objective of this study was to evaluate and select the best new QPM hybrids for grain yield, and other agronomic and morphological characteristics.&#x0D; Results: Significant difference among crosses was observed for 19 traits at Ambo, 14 traits at Arsi-Negele, and 19 traits at Kulumsa. Out of 28 traits studied, six of them did not show genotype by location interaction but they showed a significant genotypic effect. These traits were: Days to Silking (DS), Number of Ears per plant (EPP), Ear Length (EL), Kernels Per Row (KPR), Ear Diameter (ED), and Thousand Seed Weight (TSW). Based on the mean performance in the combined analysis, from 42 new QPM crosse, six of them (L8xT2, L7xT1, L8xT1, L19xT1, L6xT2, and L18xT1) were scored higher grain yield compared with the mean of the three standard checks and best conventional maize (CM) check (AMH853). L8xT2, L7xT1, L8xT1 crosses showed a higher grain yield advantage over the best check (AMH853) by 20.87, 14.13, and 13.63%, respectively.&#x0D; Conclusion: The study implicated the existence of a difference between the newly developed hybrids and the standard check varieties. In general, the study enabled us to identify promising crosses that could be forwarded for further use in maize breeding programs in future work.