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Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) is a crop of significant interest due to its nutritional value and resilience to drought conditions. However, drought, particularly following flowering, is a major factor contributing to yield reduction. This research employed two distinct Tartary buckwheat genotypes to investigate the effects of post-anthesis drought on growth and physicochemical characteristics. The study aimed to elucidate the response of Tartary buckwheat to drought stress. The findings indicated that post-anthesis drought adversely impacted the growth, morphology, and biomass accumulation of Tartary buckwheat. Drought stress enhanced the maximum photosynthetic capacity (Fv/Fm) and light protection ability (NPQ) of the 'Xiqiao-2' genotype. In response to drought stress, 'Dingku-1' and 'Xiqiao-2' maintained osmotic balance by accumulating soluble sugars and proline, respectively. Notably, 'Xiqiao-2' exhibited elevated levels of flavonoids and polyphenols in its leaves, which helped mitigate oxidative damage caused by drought. Furthermore, rewatering after a brief drought period significantly improved plant height, stem diameter, and biomass accumulation in 'Dingku-1'. Overall, 'Xiqiao-2' demonstrated greater long-term tolerance to post-anthesis drought, while 'Dingku-1' was less adversely affected by short-term post-anthesis drought. [ABSTRACT FROM AUTHOR]

Identifying the weak buses in power system networks is crucial for planning and operation since most generators operate close to their operating limits, resulting in generator failures. This work aims to identify the critical/weak node and reduce the system's power loss. The line stability index ( L m n ) and fast voltage stability index (FVSI) were used to identify the critical node and lines close to instability in the power system networks. Enhanced particle swarm optimization (EPSO) was chosen because of its ability to communicate with better individuals, making it more efficient to obtain a prominent solution. EPSO and other PSO variants minimized the system's actual/real losses. Nodes 8 and 14 were identified as the critical nodes of the IEEE 9 and 14 bus systems, respectively. The power loss of the IEEE 9 bus system was reduced from 9.842 MW to 7.543 MW, and for the IEEE 14 bus system, the loss was reduced from 13.775 MW of the base case to 12.253 MW for EPSO. EPSO gives a better active power loss reduction and improves the node's voltage profile than other PSO variants and algorithms in the literature. This suggests the feasibility and suitability of EPSO to improve the grid voltage quality. [ABSTRACT FROM AUTHOR]

With the rapid developments in electronic commerce and digital payment technologies, credit card transactions have increased significantly. Machine learning (ML) has been vital in analyzing customer data to detect and prevent fraud. However, the presence of redundant and irrelevant features in most real-world credit card data degrades the performance of ML classifiers. This study proposes a hybrid feature-selection technique consisting of filter and wrapper feature-selection steps to ensure that only the most relevant features are used for machine learning. The proposed method uses the information gain (IG) technique to rank the features, and the top-ranked features are fed to a genetic algorithm (GA) wrapper, which uses the extreme learning machine (ELM) as the learning algorithm. Meanwhile, the proposed GA wrapper is optimized for imbalanced classification using the geometric mean (G-mean) as the fitness function instead of the conventional accuracy metric. The proposed approach achieved a sensitivity and specificity of 0.997 and 0.994, respectively, outperforming other baseline techniques and methods in the recent literature. [ABSTRACT FROM AUTHOR]

Part of the widely discussed problem in electrical power systems is the optimal reactive power dispatch (ORPD) due to its reliability and economical operation of electrical power systems. The ORPD is a complex and nonlinear optimization problem. The pathfinder algorithm (PFA) is a newly developed algorithm that inspires the group movement of prey with a leader called a pathfinder when hunting for food. The inertia weight is added to the PFA and is called an improved pathfinder algorithm (IPFA) to support the proper random work of the swarm to avoid the decrease in searchability of the PFA. The IPFA was proposed in this work to diminish the active power loss while improving the voltage profile. The IPFA was validated on the IEEE 30 and 118 bus systems along with particle swarm optimization (PSO) and the teaching–learning-based optimizer (TLBO). The proposed IPFA provides the best result as the losses of the IEEE 30 and 118 test systems were reduced to 16.035 and 115.048 MW from the initial base of 17.89 and 132.86 MW, respectively. The losses of PSO and the TLBO were 16.1568 and 16.1607 MW for the IEEE 30 bus system, respectively, while for the IEEE 118 bus system, the PSO provided 117.9129 MW and the TLBO provided 118.0524 MW. The two test systems' reduction percentages (%) were 10.37% and 13.41%, respectively. The results were compared with those of other algorithms in the literature, and the IPFA provided a superior result, thereby suggesting the superiority of IPFA methods in diminishing the power loss and improving the system's voltage profile. [ABSTRACT FROM AUTHOR]

The AP2/ERF (APETALA2/Ethylene−Responsive element binding factor) family genes play crucial roles in plant growth and development, and responses to environmental factors; however, this family has not been characterized in Diospyros species. In Diospyros, the diploid Oily persimmon (D. oleifera, 2n = 2x = 30) has been released with complete genome assembly, which makes it possible for genome-wide gene family identification and exploration of molecular function in cultivated persimmon (D. kaki, 2n = 6x = 90). Here, we identified the AP2/ERF family in Oily persimmon for the first time and investigated its classification, main physicochemical properties, structural characteristic, chromosome distribution, gene replication and collinearity, cis-factor binding sites deduction, GO term annotation, and PPI interaction, as well as its expression profiles in different tissue and under the treatment of polyamines. A total of 157 AP2/ERF genes, including four subfamilies (AP2, RAV, Soloist, and ERF), were identified with distribution on all 15 chromosomes. DkAP2/ERF gene expression patterns were extensive and diverse. They were detected expression in every examined tissue, with the highest number of DkAP2/ERF genes expressed in the root. DkAP2/ERF gene expression analysis in adventitious root generation and elongation of polyamines showed their different responses to the action of polyamines, and more pairs of DkAP2/ERF genes with high correlation in gene expression were obtained. In addition, some DkAP2/ERF genes were detected remarkably correlated with genes related to polyamine synthesis and cell metabolism, including S-adenosyl-L-methionine Decarboxyla2 (SAMDC2), D-type cyclin1 (CYCD1), and D-type cyclin2 (CYCD2) genes,. indicating that DkAP2/ERF genes may play a synergistic role in adventitious root development This study was the first to analyze the AP2/ERF gene comprehensively in Diospyros on a genome−wide scale and will provide insights into the application of adventitious root formation in cultivated persimmon. [ABSTRACT FROM AUTHOR]

Asexual lineages are perceived to be short-lived on evolutionary timescales. Hence, reports for exceptional cases of putative 'ancient asexuals' usually raise questions about the persistence of such species. So far, there have been few studies to solve the mystery in plants. The monotypic Kingdonia dating to the early Eocene, contains only K. uniflora that has no known definitive evidence for sexual reproduction nor records for having congeneric sexual species, raising the possibility that the species has persisted under strict asexuality for a long period of time. Here, we analyze whole genome polymorphism and divergence in K. uniflora. Our results show that K. uniflora is characterized by high allelic heterozygosity and elevated πN/πS ratio, in line with theoretical expectations under asexual evolution. Allele frequency spectrum analysis reveals the origin of asexuality in K. uniflora occurred prior to lineage differentiation of the species. Although divergence within K. uniflora individuals exceeds that between populations, the topologies of the two haplotype trees, however, fail to match each other, indicating long-term asexuality is unlikely to account for the high allele divergence and K. uniflora may have a recent hybrid origin. Phi-test shows a statistical probability of recombination for the conflicting phylogenetic signals revealed by the split network, suggesting K. uniflora engages in undetected sexual reproduction. Detection of elevated genetic differentiation and premature stop codons (in some populations) in genes regulating seed development indicates mutational degradation of sexuality-specific genes in K. uniflora. This study unfolds the origin and persistence mechanism of a plant lineage that has been known to reproduce asexually and presents the genomic consequences of lack of sexuality. [ABSTRACT FROM AUTHOR]

This study aimed to understand the intention and correlation of receiving and recommending influenza vaccine (IV) among healthcare workers (HCWs) in China during the 2022/2023 season using the behavior and social drivers (BeSD) tools. A self-administered electronic survey collected 17,832 participants on a media platform. We investigated the willingness of IV and used multivariate logistic regression analysis to explore its associated factors. The average scores of the 3Cs' model were compared by multiple comparisons. We also explored the factors that potentially correlated with recommendation willingness by partial regression. The willingness of IV was 74.89% among HCWs, and 82.58% of the participants were likely to recommend it to others during this season. Thinking and feeling was the strongest domain independently associated with willingness. All domains in BeSD were significantly different between the hesitancy and acceptance groups. Central factors in the 3Cs model were significantly different among groups (p < 0.01). HCWs' willingness to IV recommendation was influenced by their ability to answer related questions (r  =  0.187, p  <  0.001) after controlling for their IV willingness and perceived risk. HCWs' attitudes towards IV affect their vaccination and recommendation. The BeSD framework revealed the drivers during the decision-making process. Further study should classify the causes in detail to refine HCWs' education. [ABSTRACT FROM AUTHOR]

Increasing economic and population growth has led to a rise in electricity consumption. Consequently, electrical utility firms must have a proper energy management strategy in place to improve citizens' quality of life and ensure an organization's seamless operation, particularly amid unanticipated circumstances such as coronavirus disease (COVID-19). There is a growing interest in the application of artificial intelligence models to electricity prediction during the COVID-19 pandemic, but the impacts of clustering methods and parameter selection have not been explored. Consequently, this study investigates the impacts of clustering techniques and different significant parameters of the adaptive neuro-fuzzy inference systems (ANFIS) model for predicting electricity consumption during the COVID-19 pandemic using districts of Lagos, Nigeria as a case study. The energy prediction of the dataset was examined in relation to three clustering techniques: grid partitioning (GP), subtractive clustering (SC), fuzzy c-means (FCM), and other key parameters such as clustering radius (CR), input and output membership functions, and the number of clusters. Using renowned statistical metrics, the best sub-models for each clustering technique were selected. The outcome showed that the ANFIS-based FCM technique produced the best results with five clusters, with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Variation (RCoV), Coefficient of Variation of the Root Mean Square Error (CVRMSE), and Mean Absolute Percentage Error (MAPE) being 1137.6024, 898.5070, 0.0586, 11.5727, and 9.3122, respectively. The FCM clustering technique is recommended for usage in ANFIS models that employ similar time series data due to its accuracy and speed. [ABSTRACT FROM AUTHOR]

Quadrature phase shift keying (QPSK) is a digital modulation technique that transmits data at a constant frequency whilst varying the phases of the carrier signal. QPSK is one of the fundamental modulation schemes for orthogonal frequency division multiplexing systems (OFDM). It is a stable modulation technique with good spectral efficiency. However, during transmission, the carrier signal can undergo numerous phase changes. This creates phase ambiguity problems at the receiver end. This results in inter-symbol interference (ISI) and a high bit error rate (BER). In this paper, the wind-driven optimization was incorporated into the genetic algorithm (GA) as its population selection function. This hybrid algorithm was used to determine the phase assignments for the QPSK. The developed QPSK was implemented on an OFDM network and the message signal was recovered at more than 92% accuracy in a noisy Rayleigh fading channel and 100% accuracy in a noiseless channel. The enhancements greatly mitigated phase ambiguity and bit errors. [ABSTRACT FROM AUTHOR]

Ni-rich layered cathode materials for lithium-ion batteries have received widespread attention due to their large capacity and low cost; however, the structural stability of the material needs to be improved. Herein, F-doped and undoped cathode materials prepared with an advanced co-precipitation method were used to measure the effect of F doping on the material. Compared to the undoped sample, the F-doped cathode materials exhibited an improved rate performance, because the porous structure of F-doped cathode materials is favorable for the infiltration of the electrolyte and the material, and the F-doped cathode material has a larger (003) crystal plane and a smaller Li+ migration barrier energy. This simple F-doping treatment strategy provides a promising way to improve the performance of Ni-rich layered cathode materials for lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) plays a vital role in electrical power networks. However, power loss reduction, voltage profile improvement, friendly environment, and reliability are all benefits of DG units. In this research work, a worthwhile methodology is recommended for optimal allocation of traditional (gas turbine) and renewable energy sources that are based on distributed generators which include solar and wind in the distribution system. The major objective of the research paper is the minimization of real, reactive power losses and emissions produced during the application of these conventional sources. Originally, the best locations to place this DG are identified using the concept of water, energy, and food algorithm (WEFA). The number and sizes of these renewable energy sources selected (wind and solar) are determined by applying the concepts of the Dragonfly Algorithm. The Weibull and beta distribution functions are modeled to extract the exact position to fix our DGs to minimize losses within the distribution network. To assess the performance of WEF five different cases scenario considered are DG capacity, Location of Bus, voltage profile, maximum power loss as well as utilization rate. The proposed WEF Algorithm is tested on the IEEE standard 33-bus system. The simulated results were compared with others found in literature and found to be better in terms of power loss reductions. [ABSTRACT FROM AUTHOR]

Featured Application: This work can be applied to the recovery of precious metals in WEEE and waste catalyst. The extraction of precious metals is of significant importance for recycling valuable precious metal resources, however, most of the current methods for extraction are inefficient, energy-intensive, or environmentally unfriendly. Herein, we report a new chemical dissolution system for precious metals employing the trichloroisocyanuric acid (TCCA) as an oxidant and tributyl monomethyl ammonium chloride (N4441Cl) as a complexant. The leaching yield was achieving 100 wt% for the metals of Au, Pd, Cu, and Ag at 25 °C, and the average dissolution rate of gold was reaching 375 mg/h, 107.1 times higher than that of traditional cyanide method. Based on the analysis of UV-Vis and XPS results, a possible reaction mechanism was proposed: the precious metals were probably oxidized by TCCA, then the formed precious metal ions coordinate with N4441Cl strongly to promote the dissolution process. Applied to gold dissolution from ores, waste electronic and electrical equipment (WEEE) and waste catalysts, the leaching yield of the TCCA-N4441Cl coordinative composite reached 97, 100, and 100 wt%, respectively, demonstrating that this method is not only efficient and environmentally friendly, but also with great adaptability and high potential for real applications. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of selected strategies of distributed energy resources (DER) on an energy cost function that optimizes the distribution of distributed energy resources for a mid-rise apartment building. This is achieved through comparing parameter optimization results for both a high-level and low-level optimizer, respectively. The optimization process is carried out using the following approach: (1) a two-objective function is constructed with one objective function similar to that of the high-level optimizer (DER-CAM); (2) an evolutionary algorithm (EA) with modified selection capability is used to optimize the two-objective function problem in (1) for four selected cases of DER utilization that were previously optimized in DER-CAM; and (3) the optimization results of the low-level optimizer are compared with the outcome of DER-CAM optimization for the four selected cases. This is done to establish the capability of DER-CAM as an effective tool for optimal distributed energy resource allocation. Results obtained reveal the effect of load shifting and solar photovoltaic (PV) panels with power exporting capability on the optimization of the cost function. The Pareto-based MOEA approach has also proved to be effective in observing the interactions between objective function parameters. Mean inverted generational distance (MIGD) values obtained over 10 runs for each of the four cases that were considered show that a DER combination of PV panel, battery storage, heat pump, and load shifting outperforms the other strategies in 70% of the total simulation runs. [ABSTRACT FROM AUTHOR]

In recent times, Microgrids (MG) have emerged as solution approach to establishing resilient power systems. However, the integration of Renewable Energy Resources (RERs) comes with a high degree of uncertainties due to heavy dependency on weather conditions. Hence, improper modeling of these uncertainties can have adverse effects on the performance of the microgrid operations. Due to this effect, more advanced algorithms need to be explored to create stability in MGs'. The Model Predictive Control (MPC) technique has gained sound recognition due to its flexibility in executing controls and speed of processors. Thus, in this review paper, the superiority of MPC to several techniques used to model uncertainties is presented for both grid-connected and islanded system. It highlights the features, strengths and incompetencies of several modeling methods for MPCs and some of its variants regarding handling of uncertainties in MGs. This survey article will help researchers and model developers to come up with more robust model predictive control algorithms and techniques to cope with the changing nature of modern energy systems, especially with the increasing level of RERs penetration. [ABSTRACT FROM AUTHOR]

Heart disease is the leading cause of death globally. The most common type of heart disease is coronary heart disease, which occurs when there is a build-up of plaque inside the arteries that supply blood to the heart, making blood circulation difficult. The prediction of heart disease is a challenge in clinical machine learning. Early detection of people at risk of the disease is vital in preventing its progression. This paper proposes a deep learning approach to achieve improved prediction of heart disease. An enhanced stacked sparse autoencoder network (SSAE) is developed to achieve efficient feature learning. The network consists of multiple sparse autoencoders and a softmax classifier. Additionally, in deep learning models, the algorithm's parameters need to be optimized appropriately to obtain efficient performance. Hence, we propose a particle swarm optimization (PSO) based technique to tune the parameters of the stacked sparse autoencoder. The optimization by the PSO improves the feature learning and classification performance of the SSAE. Meanwhile, the multilayer architecture of autoencoders usually leads to internal covariate shift, a problem that affects the generalization ability of the network; hence, batch normalization is introduced to prevent this problem. The experimental results show that the proposed method effectively predicts heart disease by obtaining a classification accuracy of 0.973 and 0.961 on the Framingham and Cleveland heart disease datasets, respectively, thereby outperforming other machine learning methods and similar studies. [ABSTRACT FROM AUTHOR]

Understanding how species adapt to extreme environments is an extension of the main goals of evolutionary biology. While alpine plants are an ideal system for investigating the genetic basis of high-altitude adaptation, genomic resources in these species are still limited. In the present study, we generated reference-level transcriptomic data of five Saussurea species through high-throughput sequencing and de novo assembly. Three of them are located in the highland of the Qinghai-Tibet Plateau (QTP), and the other two are close relatives distributed in the lowland. A series of comparative and evolutionary genomics analyses were conducted to explore the genetic signatures of adaptive evolution to high-altitude environments. Estimation of divergence time using single-copy orthologs revealed that Saussurea species diversified during the Miocene, a period with extensive tectonic movement and climatic fluctuation on the QTP. We characterized gene families specific to the alpine species, including genes involved in oxidoreductase activity, pectin catabolic process, lipid transport, and polysaccharide metabolic process, which may play important roles in defense of hypoxia and freezing temperatures of the QTP. Furthermore, in a phylogenetic context with the branch model, we identified hundreds of genes with signatures of positive selection. These genes are involved in DNA repair, membrane transport, response to UV-B and hypoxia, and reproductive processes, as well as some metabolic processes associated with nutrient intake, potentially responsible for Saussurea adaptation to the harsh environments of high altitude. Overall, our study provides valuable genomic resources for alpine species and gained helpful insights into the genomic basis of plants adapting to extreme environments. [ABSTRACT FROM AUTHOR]

Gold as a precious metal resource has high recycling significance. However, the current extraction methods cannot achieve the both efficiency and environmental friendliness. In this paper, we propose a new gold leaching agent, which can leach gold under light condition by mixing iodoform (CHI3) with 1-butyl-3-methylimidazolium dicyanamide (BmimN(CN)2) ionic liquid. Under 25 °C and 13 W incandescent lamp irradiation, the leaching yield of gold can achieve 100 wt%, and the average leaching rate is 945 mg Au/(h·mol·CHI3) (18.9 times of that of the cyanidation method). Through the analysis of the results of radical inhibition experiment, UV-Vis and XPS, a possible leaching mechanism is proposed: the iodine radical generated by light oxidizes Au0 to Au+, and then forms AuN(CN)2 by coordinating with N(CN)2−. Subsequently, the ionic liquid and Au N(CN)2 form a stable [Bmim]·[Au(N(CN)2)2] ion pair structure, further promoting the dissolution reaction. The leaching yield of gold can reach 81.9 wt% and 100 wt%, respectively, when applied to ore and waste electrical and electronic equipment (WEEE); the leaching yield of gold can also reach 100 wt% when applied to a waste catalyst by adding a Soxhlet extraction. The results show that this method is not only efficient, mild, and environmentally friendly, but also has strong adaptability and wide application prospects. [ABSTRACT FROM AUTHOR]

Photosynthesis is the basis for plant productivity, and improvement of photosynthetic efficiency is an important way to improve crop yield. However, the relationship between photosynthetic parameters and the yield of Tartary buckwheat (Fagopyrum tataricum) under rainfed conditions is unclear. A two-year field trial was conducted during 2016 and 2017 to assess the photosynthetic capacity of different leaves, dry matter accumulation, and yield of four Tartary buckwheat cultivars from flowering to maturity. The leaves of all cultivars aged gradually after flowering, and the leaf chlorophyll (Chl) and soluble protein (SP) contents, net photosynthetic rates (Pn), transpiration rates (Tr), and stomatal conductance (Gs) tended to decline. The Chl, SP, Pn, Tr, and Gs of cultivars (cvs.) XiQiao2 and QianKu3 were significantly higher than those of LiuKu3 and JiuJiang at each sampling time from 18 days after anthesis to maturity, but the intercellular CO2 content (Ci) showed the opposite trend. Cultivars XiQiao2 and QianKu3 produced more total dry matter (mean 17.1% higher), had higher harvest index (HI, mean 16.4% higher), and yield (mean 29.0% higher) than cvs. LiuKu3 and JiuJiang at maturity, and the difference was remarkably consistent. The yield of all the cultivars was positively correlated with leaf Chl, SP, Pn, Tr, and Gs, but negatively correlated with Ci. At late growth stages, the high-yielding cultivars maintained higher Chl, SP contents, Pn, Tr, and Gs, and showed higher dry matter accumulation and lower Ci than the low-yielding cultivars, consistent with their higher leaf photosynthetic capacity. The important factors determining the yield of Tartary buckwheat were maintaining higher leaf Chl and SP content and photosynthetic capacity and delaying aging during the grain formation stage. Enhanced rates of photosynthesis and dry matter accumulation led to higher post-anthesis accumulation of biomass with a positive impact on grain number and higher yield. [ABSTRACT FROM AUTHOR]