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Configuring a carbon substrate with low surface polarity seems very important and significant for accelerating the cathode electrochemical performances of the Lithium-sulfur batteries, which is widely regarded as one of the most promising candidates for the next-generation green energy suppliers. The corn starch-derived carbon with in-situ multi-elements doping, initially carbonized by 6 mol/L H 2 SO 4 and then calcined in nitrogen atmosphere, is employed to configure sulfur cathode for Lithium–Sulfur batteries in this study. As-received carbon with randomly distributed graphical domains serves as charge carriers diffusion channels during discharging/charging cycles, thus endowing the cathode considerable excellent performances. Specifically, the loading amount of sulfur in cathode is around 60.31 wt% and the effective utilization efficiency of active reagent sulfur in cathode is up to 60 %. On activating the cathode at 0.114 A g−1 at 0, 25 and 40 °C, the initial discharge capacities of the Lithium–Sulfur batteries with corn starch-derived carbon-based composite cathode (with ∼1.3 mg cm−2 sulfur) are 278.6, 1023.7 and 976.1 mAh∙g−1, respectively. Accordingly, more uniform, and thinner solid electrolyte interphase membrane formed under room temperature activation, leaving in the dramatically diminished R sur value, contributes to the comparable cycling stability and rate performances of as-designed carbon-based sulfur cathode for Lithium–Sulfur Batteries. [ABSTRACT FROM AUTHOR]

Background and objective: The effectiveness of goal-directed fluid therapy (GDFT) in promoting postoperative recovery remains unclear, the aim of this study was to evaluate the effect of GDFT on length of hospital stay and postoperative recovery of GI function in patients undergoing major abdominal oncologic surgery. Methods: In this randomized, double- blinded, controlled trial, adult patients scheduled for elective major abdominal surgery with general anesthesia, were randomly divided into the GDFT protocol (group G) or conventional fluid therapy group (group C). Patients in group C underwent conventional fluid therapy based on mean arterial pressure (MAP) and central venous pressure (CVP) whereas those in group G received GDFT protocol associated with the SVV less than 12% and the cardiac index (CI) was controlled at a minimum of 2.5 L/min/m2. The primary outcomes were the length of hospital stay and postoperative GI function. Results: One hundred patients completed the study protocol. The length of hospital stay was significantly shorter in group G compared with group C [9.0 ± 5.8 days versus 12.0 ± 4.6 days, P = 0.001]. Postoperative gastrointestinal dysfunction (POGD) occurred in two of 50 patients (4%) in group G and 16 of 50 patients (32%) in the control group (P < 0.001). GDFT significantly also shorten time to first flatus by 11 h (P = 0.009) and time to first tolerate oral diet by 2 days (P < 0.001). Conclusions: Guided by SVV and CI, the application of GDFT has the potential to expedite postoperative recovery of GI function and reduce hospitalization duration after major abdominal surgery. Trial registration: This study was registered on www.clinicaltrials.gov on 07/05/2019 with registration number: NCT03940144. [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]

Objective: This study aimed to investigate the changes in oocytes at the transcriptome level after applying continuous microvibrational mechanical stimulation to human immature oocytes during in vitro maturation. Methods: The discarded germinal-vesicle stage (GV) oocytes with no fertilization value after oocytes retrieval in assisted reproduction cycles were collected. Part of them was stimulated with vibration (n = 6) at 10 Hz for 24 h after obtaining informed consent; the other was cultured in static condition (n = 6). Single-cell transcriptome sequencing was used to detect the differences in oocyte transcriptome compared with the static culture group. Results: The applied 10-Hz continuous microvibrational stimulation altered the expression of 352 genes compared with the static culture. Gene Ontology (GO) analysis suggested that the altered genes were mainly enriched with 31 biological processes. The mechanical stimulation upregulated 155 of these genes and downregulated 197 genes. Among them, the genes related to mechanical signaling, such as protein localization to intercellular adhesion (DSP and DLG-5) and cytoskeleton (DSP, FGD6, DNAJC7, KRT16, KLHL1, HSPB1, MAP2K6), were detected. DLG-5, which was related to protein localization to intercellular adhesion, was selected for immunofluorescence experiments based on the transcriptome sequencing results. The protein expression of DLG-5 in the microvibration-stimulated oocytes was higher than that in the static culture oocytes. Conclusions: Mechanical stimulation affects the transcriptome during oocyte maturation, causing the express changes in intercellular adhesion and cytoskeleton-related genes. We speculate that the mechanical signal may be transmitted to the cell through DLG-5 protein and cytoskeleton-related protein to regulate cellular activities. [ABSTRACT FROM AUTHOR]

Future energy planning relies on understanding how much energy is produced and consumed. In response, this study developed a multihybrid adaptive neuro-fuzzy inference system (ANFIS) for students' residences, using the University of Johannesburg residence, South Africa as a case study. The model input variables are wind speed, temperature, and humidity, with the output being the equivalent energy consumption for the student housing. While the particle swarm optimization (PSO) technique is versatile and widely used, it falls short by exhibiting premature convergence. To address this problem, the velocity update equation of the original PSO algorithm is modified by incorporating a dynamic linear decreasing inertia weight, which improves the PSO algorithm's convergence behaviour and aids both local and global search. Following that, the modified PSO (MPSO) is used to optimize the ANFIS parameters for the best model prediction. A comparative analysis is conducted between the MPSO, the original PSO, and six other hybrid models using a dataset division of 70% for training and 30% for testing. Performance evaluation was carried out using three well-known performance benchmarks: root mean square error (RMSE), mean absolute deviation (MAD), and coefficient of variation (RCoV). The experimental results show that the performance of the proposed MPSO-ANFIS outperformed other methods with the least values of the RMSE (1.8928 KWh), MAD (1.5051 KWh), and RCoV (0.1370), respectively. Furthermore, when compared to the PSO-ANFIS, the MPSO-ANFIS demonstrated improvements in RMSE, MAD, and RCoV with 1.58%, 2.11%, and 5.23%, respectively. Based on the results, it can be concluded that the MPSO-ANFIS provides better prediction accuracy which is vital for strategic energy planning. [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]

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]

Electricity has become one of the most essential components of establishing a quality standard of living in any country. Consequently, considerable work has been focused on designing a sophisticated load frequency control (LFC) system. However, in light of limited resources and real-world challenges, computationally based control algorithms that are more effective and less expensive remain critically needed. Thus, this paper employs a modified honey badger algorithm (HBA) in conjunction with the concepts of Lévy flight and inertia weight to optimize the parameters of a new cascaded two-degree-of-freedom fractional-PID structure coupled with a proportional derivative (2DOF + FOPIDN)-PD controller to solve LFC problems in an interconnected power system (IPS) comprising conventional and renewable energy sources (RES). The proposed control technique is applied to a two-area IPS under diverse load conditions and in the presence of nonlinear elements and electronic devices. The proposed method is evaluated with respect to a range of performance metrics, such as settling time, undershoots, and error criteria values. The collective performance of the established control scheme indicated that the suggested control approach provides excellent reliability under various load condition scenarios, sensitivity tests, and perturbations, proving the system's efficacy and dependability. [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]

Loss of selection pressure in the presence of many objectives is one of the pertinent problems in evolutionary optimization. Therefore, it is difficult for evolutionary algorithms to find the best-fitting candidate solutions for the final Pareto optimal front representing a multi-objective optimization problem, particularly when the solution space changes with time. In this study, we propose a multi-objective algorithm called enhanced dynamic non-dominated sorting genetic algorithm III (E-dyNSGA-III). This evolutionary algorithm is an improvement of the earlier proposed dyNSGA-III, which used principal component analysis and Euclidean distance to maintain selection pressure and integrity of the final Pareto optimal front. E-dyNSGA-III proposes a strategy to select a group of super-performing mutated candidates to improve the selection pressure at high dimensions and with changing time. This strategy is based on an earlier proposed approach on the use of mutated candidates, which are randomly chosen from the mutation and crossover stages of the original NSGA-II algorithm. In our proposed approach, these mutated candidates are used to improve the diversity of the solution space when the rate of change in the objective function space increases with respect to time. The improved algorithm is tested on RPOOT problems and a real-world hydrothermal model, and the results show that the approach is promising. [ABSTRACT FROM AUTHOR]

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has attracted a lot of attention since it can handle multiobjective problems (MOP) with a complicated Pareto front. The procedure involves decomposing a MOP into single subproblems, which are eventually optimized simultaneously based on the MOP neighborhood information. However, the MOEA/D strategy tends to produce a distributed optimization that is not of good quality in some problems with complex Pareto optimal front, such as problems with a long tail and sharp peak, common in real-world situations. This paper proposes an improved MOEA/D to enhance the distributed optimization quality and minimize its complexity while accelerating the optimization to get a better solution. The improved method is achieved by incorporating a Hybrid Differential Evolution/Particle Swarm Optimization algorithm and a hybrid operator based on nondominated sorting and crowding distance algorithm. This incorporation takes place in the mutation generator and initial population part of the original MOEA/D algorithm. Simulations and comparisons are carried out based on some MOP benchmark functions to verify the proposed method's performance. The experimental results show that the proposed method achieves better performance compared to other algorithms. Furthermore, the proposed method is also applied to optimize the multiobjective wave energy converter model to maximize power per year and minimize cost per unit power. [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]

• This paper presents the Survey of AI Methods for Renewable Energy Forecasting. • Taxonomy of AI for solar and wind renewable energy is presented. • Combinations of ML and metaheuristic methods for renewable energy are discussed. • Prospects of improved metaheuristic algorithms for renewable energy forecasting are presented. The efforts to revolutionize electric power generation and produce clean and sustainable electricity have led to the exploration of renewable energy systems (RES). This form of energy is replenished and cost-effective in terms of production and maintenance. However, RES, such as solar and wind energies, is intermittent; this is one of the drawbacks of its usage. In order to overcome this limitation, studies have been undertaken to forecast its availability and power output. The current trending method of forecasting availability and the power generated by the RES is the artificial intelligence (AI) method. However, with all its potential, traditional AI, such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and many more, does not have it all. Because of this, metaheuristic algorithms are being explored as optimization techniques to increase the performance accuracy of these AI forecasting methods and overcome some challenges of traditional AI models. This study presents an insightful survey of AI (traditional AI and metaheuristic) methods explored to forecast the availability of solar and wind renewable energy systems. A survey of the existing surveyed literature was presented. The taxonomy of the explored AI in RES was formulated, and the theoretical backgrounds of some of the traditional AI algorithms were presented. Also, the various forms of metaheuristic algorithms and the improved versions applied to optimize classical AI methods to forecast RES systems' availability and power output were surveyed. A conceptual framework of the hybrid AI application in RES was formulated. Finally, the survey discussion, insight, challenges of the existing models and future directions were presented. [ABSTRACT FROM AUTHOR]

Three-dimensional profilometry tends to be less effective at measuring discontinuous surfaces. To overcome this problem, an optimized profilometry based on fringe projection is proposed in this paper. Due to the limitation of the shooting angle, there are projection blind spots on the surface of discontinuous objects. Since the noises and unwrapping errors are always localized at the projection blind spots, an algorithm is designed to determine the blind spots automatically with the light intensity difference information. Besides, in order to improve the measurement accuracy, a processing scheme is introduced to deal with the local height distortion introduced by the dual-frequency grating profilometry. Lots of measurement tests on various surfaces are carried out to assess the optimized profilometry, and experimental results indicate that the modified profilometry system works more robust with high reliability and accuracy in measuring different kinds of surfaces, especially discontinuous ones. [ABSTRACT FROM AUTHOR]

In this study, high-temperature solid-phase techniques are used to prepare pristine and Mg-modified LiNiO 2 cathode materials. Various characterizations are performed to analyze the morphology, structure, and electrical state of the two materials. Electrochemical tests show that Mg doping effectively improves the electrochemical properties of the materials. In particular, the modified sample exhibits a higher specific discharge capacity of 244.5 mA h g−1 than the pristine sample. Moreover, its maximum capacity retention after 304 cycles at 1 C is 54.1%. In situ X-ray powder diffraction imaging reveals that the modified material has better phase-transition reversibility than the pristine material. Density functional theory calculations indicate that Mg-doped Li sites can accelerate Li+ transport. Atomic force microscopy results demonstrate that Mg-doped Li sites act as supportive skeletons that enhance the mechanical properties of the material. Therefore, the proposed strategy for the Mg doping of Li sites can improve the architecture of LiNiO 2 cathode materials. [ABSTRACT FROM AUTHOR]

The electrical power system (EPS) has been heavily stressed due to high load demand. It operates close to the total capacity limits, resulting in voltage instability that can lead to voltage collapse. In this regard, incorporating flexible alternating current transmission system (FACTS) devices and renewable energy sources (RESs) to obtain the optimum values of the generator voltage, reactive compensation, and transformer tab in optimal reactive power dispatch (ORPD) is essential in increasing the reliability and safety of the system. ORPD involves discrete and continuous variables, which are nonlinear, noncontinuous, non-convex, and complex problems. The objective functions of ORPD are reduction in active power loss (Ploss), voltage deviation, and voltage profile enhancement. This paper presents a recent advancement of the ORPD problem, mathematical formulation of the objectives function, and a summary of various metaheuristic optimization methods (single and hybrid) used to solve the ORPD problems. The hybrid method combines two or more methods to improve the demerits of one method to obtain a quality solution to a problem. This review covered incorporating FACTS devices and RESs used in solving the ORPD problem to reduce the active Ploss and improve the voltage profile in the EPS. The benefits of FACTS devices and RESs are also discussed. Also, various metaheuristic algorithms (single, modified, and hybrid) employed to solve the ORPD problem were discussed. The future direction for researchers in this field was provided to give insight into the applicability and performance. Overall, this research explores different techniques used in solving ORPD problems from the optimization point of view to incorporating RESs and FACTS devices to obtain quality solutions. Some existing methods do not guarantee an optimum solution, but incorporating RESs and FACTS devices will help attain the best solution to the problem for better power system operation to improve system reliability and voltage profile. Based on the review journal, it can be concluded that hybrid techniques offer efficient quality solutions to the ORPD problem. [ABSTRACT FROM AUTHOR]

Background: Ischemic stroke is a serious cardiovascular disease with high morbidity and mortality rates that affects millions of people worldwide.Currently, the only therapy with proven efficacy for acute ischemic stroke is alteplase, however, it still has many shortcomings and limitations. Therefore,we screen new compounds from traditional Chinese medicine to explore their efficacy against ischemic reperfusion injury. Procyanidins, a natural productextracted from grapes seed, which have been shown can ameliorate cerebral ischemic injury. However, the underlying mechanism is still not very clear. Theaim of this study was to investigate the effect of procyanidins on middle cerebral artery occlusion/reperfusion (MCAO/R)-mediated cerebral ischemic injuryand its underlying possible mechanisms. Methods: SD rats were used to evaluate the effect of procyanidins on MCAO/R induced cerebral ischemic injury in vivo. Histological analysis was used toassess neuronal apoptosis. Cell signaling was assayed by Western blot. Results: In this study, we found that procyanidins can significantly ameliorate the middle cerebral artery occlusion/reperfusion (MCAO/R)-mediatedneurological deficits, and relieved brain edema, cerebral infarction volume, histopathological damage and apoptosis in rats. In addition, procyanidins canalso markedly inhibit MCAO/R and oxygen-glucose deprivation/reoxygenation (OGD/R)-mediated activation of TLR4-p38-NF-κB-NLRP3 signalingpathway in vitro and in vivo. Moreover, procyanidins can inhibit MCAO/R and OGD/R-induced the production of inflammatory cytokines such asinterleukin-1β (IL-1β) in vitro and in vivo. Besides, treatment with TLR4 inhibitor (Cli-095) in BV2 cell also shows the same effect. Conclusion: Altogether, these data suggested that procyanidins exerted a potential neuroprotective effect may by inhibit the TLR4-p38-NF-κB-NLRP3signaling pathway in the brain in MCAO/R rats. [ABSTRACT FROM AUTHOR]

This research presents a robust model predictive controller of two microgrids (MGs) interconnected via tie‐line (TL) power. Power system operations continue to face new challenges due to the large integration of renewable energy generation with an active load demand uncertainty and the management and control of bidirectional power flow between MGs and others uncertainties and system nonlinearities due to the power system operating condition. To handle these uncertainties and system nonlinearities a robust model using predictive control scheme is presented in this paper to achieve the control of the active power between two MGs when considering three disturbances scenario models. In the design process, uncertainties and nonlinearities of the system were taken into account and contributed to the robustness of the closed‐loop system. Robust control scheme development considering power system nonlinearities and uncertainties were used to improve the online close‐loop robustness. The model helped to achieve a desired active power regulation by coordinating the MGs and active power over‐shoot and under‐shoot time were reduced outstandingly. A Comparison was made between open‐loop and close‐loop simulation results, which indicate that the model can handle robustness while settling power deviation of the interconnected system. MATLAB environment was used to solve the optimisation problem. [ABSTRACT FROM AUTHOR]

Summary: Investigating the interaction between environmental heterogeneity and local adaptation is critical for understanding the evolutionary history of a species, providing the premise for studying the response of organisms to rapid climate change. However, for most species how exactly the spatial heterogeneity promotes population divergence and how genomic variations contribute to adaptive evolution remain poorly understood.We examine the contributions of geographical and environmental variables to population divergence of the relictual, alpine herb Circaeaster agrestis, as well as the genetic basis of local adaptation using RAD‐seq and plastome data.We detected significant genetic structure with an extraordinary disequilibrium of genetic diversity among regions, and signals of isolation‐by‐distance along with isolation‐by‐resistance. The populations were estimated to begin diverging in the late Miocene, along with a possible ancestral distribution of the Hengduan Mountains and adjacent regions. Both environmental gradient and redundancy analyses revealed significant association between genetic variation and temperature variables. Genome–environment association analyses identified 16 putatively adaptive loci related mainly to biotic and abiotic stress resistance.Our genome‐wide data provide new insights into the important role of environmental heterogeneity in shaping genetic structure, and access the footprints of local adaptation in an ancient relictual species, informing future conservation efforts. [ABSTRACT FROM AUTHOR]

In a smart home, the nonintrusive load monitoring recognition scheme normally achieves high appliance recognition performance in the case where the appliance signals have widely varying power levels and signature characteristics. However, it becomes more difficult to recognize appliances with equal or very close power specifications, often with almost identical signature characteristics. In literature, complex methods based on transient event detection and multiple classifiers that operate on different hand crafted features of the signal have been proposed to tackle this issue. In this paper, we propose a deep learning approach that dispenses with the complex transient event detection and hand crafting of signal features to provide high performance recognition of close tolerance appliances. The appliance classification is premised on the deep multilayer perceptron having three appliance signal parameters as input to increase the number of trainable samples and hence accuracy. In the case where we have limited data, we implement a transfer learning-based appliance classification strategy. With the view of obtaining an appropriate high performing disaggregation deep learning network for the said problem, we explore individually three deep learning disaggregation algorithms based on the multiple parallel structure convolutional neural networks, the recurrent neural network with parallel dense layers for a shared input, and the hybrid convolutional recurrent neural network. We disaggregate a total of three signal parameters per appliance in each case. To evaluate the performance of the proposed method, some simulations and comparisons have been carried out, and the results show that the proposed method can achieve promising performance. [ABSTRACT FROM AUTHOR]

Background: Plastome-scale data have been prevalent in reconstructing the plant Tree of Life. However, phylogenomic studies currently based on plastomes rely primarily on maximum likelihood inference of concatenated alignments of plastid genes, and thus phylogenetic discordance produced by individual plastid genes has generally been ignored. Moreover, structural and functional characteristics of plastomes indicate that plastid genes may not evolve as a single locus and are experiencing different evolutionary forces, yet the genetic characteristics of plastid genes within a lineage remain poorly studied. Results: We sequenced and annotated 10 plastome sequences of Gentianeae. Phylogenomic analyses yielded robust relationships among genera within Gentianeae. We detected great variation of gene tree topologies and revealed that more than half of the genes, including one (atpB) of the three widely used plastid markers (rbcL, atpB and matK) in phylogenetic inference of Gentianeae, are likely contributing to phylogenetic ambiguity of Gentianeae. Estimation of nucleotide substitution rates showed extensive rate heterogeneity among different plastid genes and among different functional groups of genes. Comparative analysis suggested that the ribosomal protein (RPL and RPS) genes and the RNA polymerase (RPO) genes have higher substitution rates and genetic variations among plastid genes in Gentianeae. Our study revealed that just one (matK) of the three (matK, ndhB and rbcL) widely used markers show high phylogenetic informativeness (PI) value. Due to the high PI and lowest gene-tree discordance, rpoC2 is advocated as a promising plastid DNA barcode for taxonomic studies of Gentianeae. Furthermore, our analyses revealed a positive correlation of evolutionary rates with genetic variation of plastid genes, but a negative correlation with gene-tree discordance under purifying selection. Conclusions: Overall, our results demonstrate the heterogeneity of nucleotide substitution rates and genetic characteristics among plastid genes providing new insights into plastome evolution, while highlighting the necessity of considering gene-tree discordance into phylogenomic studies based on plastome-scale data. [ABSTRACT FROM AUTHOR]

Modern power systems are increasingly becoming more complex and thus become vulnerable to voltage collapse due to constant increase in load demand and introduction of new operation enhancement technologies. In this study, an approach which is based on network structural properties of a power system is proposed for the identification of critical nodes that are liable to voltage instability. The proposed Network Structurally Based Closeness Centrality (NSBCC) is formulated based on the admittance matrix between the interconnection of load to load nodes in a power system. The vertex (node) that has the highest value of NSBCC is taken as the critical node of the system. To demonstrate the significance of the concept formulated, the comparative analysis of the proposed NSBCC with the conventional techniques such as Electrical Closeness Centrality (ECC), Closeness Voltage Centrality (CVC) and Modal Analysis is performed. The effectiveness of all the approaches presented is tested on both IEEE 30 bus and the Southern Indian 10-bus power systems. Results of simulation obtained show that the proposed NSBCC could serve as valuable tool for rapid real time voltage stability assessment in a power system. [ABSTRACT FROM AUTHOR]

Numerous potential advantages to the requirements and effectiveness of the supplied electricity can be accomplished by the installation of distributed generation units. In order to take full advantage of these benefits, it is essential to position the Distributed Generation (DG) units in appropriate locations. Otherwise, their installation may have an adverse effect on the quality of energy and system operation. Several optimization techniques have been created over the years to optimize distributed generation integration. Optimization techniques are therefore constantly changing and have been the main attention of many fresh types of research lately. This article evaluates cutting-edge techniques of optimizing the issue of positioning and sizing distributed generation units from renewable energy sources based on recent papers that have already been applied to distribution system optimization. Furthermore, this article pointed out the environmental, economic, technological and regulatory drivers that lead to a rapid interest in the DG system based on renewable sources. A summary of popular meta-heuristic optimization tools discussed in table form with merits and demerits to increase fresh prospective paths to multi-approach that have not yet been studied. [ABSTRACT FROM AUTHOR]

Success-History-Based Parameter Adaptation Multi-Objective Differential Evolution (SHAMODE) has shown excellent performance recently compared to other multi-objective evolutionary algorithms (MOEAs) and therefore caught the interest of researchers lately. The SHAMODE framework employs historical memory of control parameters, which stores set values of control parameters that have performed well in the past generation and produces new set values of the parameter by directly sampling the parameter space close to one of these stored pairs. Since the algorithm's performance depends on the problem to be optimized, this paper proposes an enhanced version of SHAMODE that is compatible with optimizing the multi-objective wave energy converter problem. The improved version of SHAMODE is achieved by incorporating competing machine learning algorithms, Particle Swarm Optimization, and a modified bubble-net attacking method formula of the Whale optimization algorithm from its original form. Simulations and comparisons are used to verify the proposed method's performance based on some famous multi-objective problem (MOP) benchmark functions. The results reveal that the proposed algorithm performs better than other algorithms that have been recently developed. In addition, the suggested technique is used to optimize the multi-objective wave energy converter model in order to maximize the annual production of electric energy while minimizing the per-unit energy cost. • Machine Learning and PSO based Success-History Based Parameter Adaptation Multi-Objective Differential Evolution algorithm was proposed. • The Improved version of SHAMODE optimized the WEC system to maximize the annual power generated at a minimum Levelized cost of energy. • The Improved version of SHAMODE was applied to optimize the multi-objective wave energy converter model to maximize the annual generated power and minimize the per unit energy cost. [ABSTRACT FROM AUTHOR]

This article investigates the use of optimal reference point placement to improve performance of non-dominated sorting genetic algorithm (NSGA). Placement of reference points for many-objective optimization is inspired by wheel and Von Neumann topologies of Particle Swarm Optimization (PSO). Results obtained show that the pattern of reference point placement determines performance efficiency of NSGA. The better-performing wheel topology (called wheel reference point genetic algorithm (wRPGA), is compared to three other many-objective evolutionary algorithms: knee-driven evolutionary algorithm (KnEA), non-dominated sorting genetic algorithm III (NSGAIII) and multi-objective evolutionary algorithm based on dominance and decomposition (MOEAD/D). The selected many-objective benchmark problems are Walking Fish Group 2 (WFG2) and Deb-Thiele-Laumanns-Zitzler 2 (DTLZ2). It is also tested on a 3-objective cost function for a hypothetical model of a stand-alone microgrid. Through the simulations, the wheel configuration performed 88.9% better than the Von Neumann configuration. The wheel topology also achieved better performance with respect to inverted generational distance (IGD) compared to KnEA, NSGAIII and MOEAD/D for 7 out of 15 IEEE Congress on Evolutionary Computation (CEC) 2017 benchmark problems. wRPGA gave a good approximation of the Pareto front for the 3-objective model representing the hypothetical microgrid. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other well-performing evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms' ability to optimize a multi-objective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio. Highlights • Evolutionary algorithms play significant role in demand response implementation. • Aggregator-consumer matching via evolutionary algorithms improves mutual benefits. • Two-archive strategy improves mutation and crossover speed in differential evolution. [ABSTRACT FROM AUTHOR]

The relationships among the genera of the early-diverging eudicot family Berberidaceae have long been controversial. To resolve these relationships and to better understand plastome evolution within the family, we sequenced the complete plastome sequences of ten Berberidaceae genera, combined these with six existing plastomes for the family, and conducted a series of phylogenomic analyses on the resulting data set. Five of the newly sequenced plastomes were found to possess the typical angiosperm plastome complement of 79 protein-coding genes, 4 rRNA genes, and 30 tRNA genes. The infA gene was found to be pseudogenized in Bongardia , Diphylleia , Dysosma and Vancouveria ; rps7 was found to be severely truncated in Diphylleia , Dysosma and Podophyllum ; clpP was found to be highly divergent in Vancouveria ; and a ∼19 kb inversion was detected in Bongardia. Maximum likelihood phylogenetic analyses of a 79-gene, 24-taxon data set including nearly all genera of Berberidaceae recovered four chromosome groups ( x  = 6, 7, 8, 10), resolved the x  = 8 group as the sister to the x  = 10 group, and supported the monophyly of the clade comprising x  = 7, 8, 10. The generic relationships within each group were all resolved with high support. Based on gene presence within the Inverted Repeat (IR), a total of seven plastome IR types were identified within Berberidaceae. Biogeographical analysis indicated the origin and diversification of Berberidaceae has likely been strongly influenced by the distribution of its favored habitat: temperate forests. [ABSTRACT FROM AUTHOR]

The present study aimed to detect the expression of Caspase-1 in the tumor tissues and tumor-adjacent tissues of patients with breast cancer, and to investigate the effects of Caspase-1 on the proliferation, apoptosis and invasion of breast cancer cells. Reverse transcription-quantitative polymerase chain reaction was used to detect Caspase-1 mRNA expression in breast cancer tissues and tumor-adjacent tissues from patients. Additionally, the human breast cancer MDA-MB-231 cell line was treated with the Caspase-1 small molecule inhibitor Ac-YVAD-CMK, following which the changes to Caspase-1 protein expression were detected via western blotting. The MTT method detected the changes to cell proliferation, flow cytometry detected the rate of apoptosis, and a Transwell assay was employed to assess invasion. Caspase-1 mRNA expression was significantly decreased in the breast cancer tissues of patients, compared with in the tumor-adjacent tissues, a difference that was statistically significant (P<0.05). Treatment with the Ac-YVAD-CMK markedly decreased the protein expression of Caspase-1 in MDA-MB-231 cells, and the difference was statistically significant (P<0.05). Following this treatment of Ac-YVAD-CMK cells, the proliferation and invasion abilities markedly increased, while the apoptotic levels significantly decreased (P<0.05). In conclusion, the expression of Caspase-1 is low in breast cancer tissues, which may promote the proliferation and invasion of breast cancer cells and could be closely associated with the occurrence and development of breast cancer. [ABSTRACT FROM AUTHOR]

Magnetic sepiolite was synthesized by chemical precipitation of Fe and Fe at sepiolite suspension. Removal kinetics of U(VI) on sepiolite and magnetic sepiolite can be well fitted by pseudo-second-order and pseudo-first-order kinetic models, respectively. At pH < 5.0, U(VI) removal on sepiolite significantly decreased with increasing ionic strength, whereas U(VI) removal on magnetic sepiolite was independent of ionic strength. The maximum removal capacity of sepiolite and magnetic sepiolite at pH 5.0 and 298 K calculated from Langmuir model were 28.17 and 74.63 mg/g, respectively. XANES spectra suggested that U(VI) was partly reduced to U(IV) at long-term conditions. [ABSTRACT FROM AUTHOR]

In the present study, titanate nanotubes (denoted as TNTs) synthesized through a simple hydrothermal approach was applied for the decontamination of Zn(II) from aqueous solution. Batch experimental results showed that the removal kinetics process was fast and reached equilibrium after 60 min. The sorption of Zn(II) on TNTs was strongly dependent on pH and ionic strength at pH < 6.0, while independent of ionic strength at pH > 6.0. Based on Langmuir model fit, the maximum sorption capacity of Zn(II) at pH 5.5 and 293 K was calculated to be 139.44 mg/g, being higher than a series of currently reported adsorbents. TNTs also exhibited a favorable recycling performance for the removal of Zn(II). According to the desorption experiments, surface complexation modeling, X-ray diffraction (XRD) and extended X-ray absorption fine structure (EXAFS) analysis, cation exchange and/or outer-sphere surface complexation were the predominant removal mechanisms of Zn(II) at lower pH, while inner-sphere surface complexation and/or precipitation were the main driving forces at higher pH. The experimental findings herein demonstrated the application potentiality of TNTs for the highly efficient treatment of Zn(II)-bearing wastewaters. [ABSTRACT FROM AUTHOR]

In recent years, the fate and transport of graphene oxide (GO) has received considerable attention owing to its inevitable release into the natural ecosystem during production and applications, wherein mineral particles and radionuclides are generally co-presented. This paper aimed to clarify the sorption and co-sorption of Eu(III) and GO onto Mg/Al layered double hydroxides (LDH) using batch and extended X-ray absorption fine structure (EXAFS) studies. The results showed that Eu(III) and GO displayed interactively enhancement roles in their co-sorption on LDH. Eu(III) enhanced GO co-sorption on LDH which was mainly due to the formation of “LDH-Eu(III)-GO” ternary surface complexes (i.e., Eu(III)-bridging). While, GO increased Eu(III) co-sorption on LDH which was primarily due to the formation of “LDH-GO-Eu(III)” ternary surface complexes (i.e., GO-bridging). EXAFS fitting results exhibited that the presence of GO could suppress the formation of outer-sphere complexes of Eu(III) on LDH at low pH values, while inhibit the formation of surface precipitates of Eu(III) on LDH at high pH values. The enhanced co-sorption of Eu(III) and GO on LDH as they co-presented might decrease their mobility in the natural environment. These new findings are important to understand the environmental behavior and impact of GO in real ecosystem, and the natural purification of radionuclides in ecological environments. [ABSTRACT FROM AUTHOR]

The grade of early-diverging eudicots includes five major lineages: Ranunculales, Trochodendrales, Buxales, Proteales and Sabiaceae. To examine the evolution of plastome structure in early-diverging eudicots, we determined the complete plastome sequences of eight previously unsequenced early-diverging eudicot taxa, Pachysandra terminalis (Buxaceae), Meliosma aff. cuneifolia (Sabiaceae), Sabia yunnanensis (Sabiaceae), Epimedium sagittatum (Berberidaceae), Euptelea pleiosperma (Eupteleaceae), Akebia trifoliata (Lardizabalaceae), Stephania japonica (Menispermaceae) and Papaver somniferum (Papaveraceae), and compared them to previously published plastomes of the early-diverging eudicots Buxus , Tetracentron , Trochodendron , Nelumbo , Platanus , Nandina , Megaleranthis , Ranunculus , Mahonia and Macadamia . All of the newly sequenced plastomes share the same 79 protein-coding genes, 4 rRNA genes, and 30 tRNA genes, except for that of Epimedium , in which infA is pseudogenized and clpP is highly divergent and possibly a pseudogene. The boundaries of the plastid Inverted Repeat (IR) were found to vary significantly across early-diverging eudicots; IRs ranged from 24.3 to 36.4 kb in length and contained from 18 to 33 genes. Based on gene content, the IR was classified into six types, with shifts among types characterized by high levels of homoplasy. Reconstruction of ancestral IR gene content suggested that 18 genes were likely present in the IR region of the ancestor of eudicots. Maximum likelihood phylogenetic analysis of a 79-gene, 97-taxon data set that included all available early-diverging eudicots and representative sampling of remaining angiosperm diversity largely agreed with previous estimates of early-diverging eudicot relationships, but resolved Trochodendrales rather than Buxales as sister to Gunneridae , albeit with relatively weak bootstrap support, conflicting with what has been found for these three clades in most previous analyses. In addition, Proteales was resolved as sister to Sabiaceae with the highest support (bootstrap >90%) yet observed in plastome-scale phylogenetic analyses. [ABSTRACT FROM AUTHOR]

This study aimed to explore the 1-year temporal change in prevalence, variety, and potential risk factors of long COVID symptoms and to further predict the prognostic trends of long COVID. We searched electronic databases for related studies published from January 2020 to February 2022 and conducted 1-group meta-analysis and locally weighted regression to explore the monthly temporal change in the prevalence of each long COVID symptom in 1-year follow-up period. A total of 137 studies were included in meta-analysis, including 134 093 participants. The temporal change of any long COVID symptom showed a steep decrease initially (from 92% at acute phase to 55% at 1-month follow-up), followed by stabilization at approximately 50% during 1-year follow-up. Six months or more after the acute phase, the odds ratio of population characteristic–related factors increased, such as female (from 1.62 to 1.82), whereas the odds ratio value of acute phase–related factors (severe or critical cases and hospitalization) decreased. As for specific symptoms, approximately two-thirds of the symptoms did not significantly reduce during the 1-year follow-up, and the neuropsychiatric symptoms showed a higher long-term prevalence (approximately 25%) and longer persistence than physical symptoms. The temporal changes in the prevalence and characteristics speculate that long COVID may persist longer than expected. In particular, we should pay more attention to neuropsychiatric symptoms and other symptoms for which there is no significant downward trend in prevalence. The influence of acute phase–related factors for long COVID gradually decreases over time, whereas the influence of population characteristic–related factors gradually increases. [ABSTRACT FROM AUTHOR]

Lithium-rich manganese-based oxides are included in high-capacity cathodes. Herein we introduce a certain amount of Mg and Na into lithium-rich manganese-based oxide microspheres to overcome the weak cycling performance and poor rate capacity. This work uses the hydrothermal method combined with the high-temperature solid-phase method and CH 3 COONa and C 4 H 14 MgO 8 as dopants. Transmission electron microscopy (TEM), X-ray diffraction (XRD), focused ion beam scanning electron microscopy (FIB-SEM), energy dispersive spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and electrochemical characterization are performed to systematically assess the effect of Na and Mg element doping on the morphology, structure and electrochemical characteristics of Li 1.2 Ni 0.13 Co 0.13 Mn 0.54 O 2. The results show that the material has excellent electrochemical performance. These improvements could be related to the presence of Na in Li layers and Mg in TM layers, which can speed up Li-ion diffusion kinetics and improve ionic and electronic conductivity. DFT calculations demonstrate that the injection of Na and Mg reduces the lithium-ion migration barrier energy. This dual-doped modification technique may help researchers better understand how to build high-capacity and stable cathode materials for LIBs. [ABSTRACT FROM AUTHOR]

A series of porous carbon/CoO (NZMC-18(6)-700-3/CoO) composite materials with different cobalt contents were synthesized at low reaction temperature using a facile chemical precipitation method. The morphology and crystal structure of the materials were evaluated on field emission scanning electron microscope and X-ray diffraction, respectively. The results show that composite materials possess incompact lamellar structure with some irregular agglomerated CoO particles adhering. The calcination temperature has a distinct influence on the morphology and electrochemical properties of porous carbon/CoO composites. The electrochemical performances were measured by constant current charge/discharge tests, cyclic voltammetry, and electrochemical impedance spectroscopy tests. It is illustrated that the composite materials have outstanding charge storage ability and the specific capacitance can reach up to 462 F g when the calcination temperature and cobalt content are 250 °C and 95 wt.%. With the increase of scan rate, the response current increases, and the anodic peaks and cathodic peaks shifted to higher potential and lower potential, respectively, which both illustrate that the material has excellent reversibility. The combination impedance of the electrode is only 0.674 Ω cm at a calcination temperature of 250 °C and is much less than those at other calcination temperatures which means that the electrode material has good conductivity. [ABSTRACT FROM AUTHOR]

Aim A phylogeographical study of the widespread but phylogenetically isolated East Asian endemic tree species Tetracentron sinense (Trochodendraceae) was performed to evaluate whether and how Pleistocene and pre-Pleistocene climate changes helped to influence current phylogeographical patterns, and to describe the current patterns of genetic diversity and their implications for conservation. Location Southwestern and central subtropical China. Methods Sequences of four chloroplast spacer regions were obtained from 157 individuals of T. sinense. A haplotype network was constructed using tcs. Genetic diversity and differentiation, spatial analysis of molecular variance ( SAMOVA) and analysis of molecular variance ( AMOVA) were used to test for genetic structure. beast was used to estimate the divergence times between haplotypes. Historical demographic expansion was tested using pairwise mismatch distribution analysis. Results Of the 21 recovered haplotypes, three were widely distributed, but most were restricted to particular regions. Populations with high haplotype diversity were located in western Hubei, southern Sichuan and southern Chongqing. The two earliest-diverging haplotypes were found in southwestern China. The haplotype distribution of T. sinense demonstrated significant phylogeographical structure ( NST > GST; P < 0.05). The best partitioning of genetic diversity by SAMOVA ( K = 5) produced groups that matched the main tcs-derived clades. Two independent range expansions within SAMOVA-derived groups 2 and 3 were dated to approximately 399 and 311 ka, respectively. The time to the most recent common ancestor of all haplotypes was 9.6 (95% highest posterior density: 27.0-2.2) Ma, but most of the haplotype diversity appeared during the Quaternary. Main conclusions The extant distribution of T. sinense is likely to have been shaped by both pre-Quaternary and Pleistocene climate changes. Southwestern China may have served as an important refugium for T. sinense throughout the Neogene, while the species also occupied multiple refugia during the late Pleistocene glacial periods. Populations of T. sinense were resolved into five allopatric groups, between which there is apparently no seed movement. [ABSTRACT FROM AUTHOR]

Modulation and suppression of the immune response of the host by nematode parasites have been reported extensively and the cysteine protease inhibitor ( CPI or cystatin) is identified as one of the major immunomodulators. In the present study, we cloned and produced recombinant CPI protein from the murine nematode parasite Heligmosomoides polygyrus (r Hp- CPI) and investigated its immunomodulatory effects on dendritic cell ( DC) function and immune responses in mice. Bone-marrow-derived CD11c+ DC ( BMDC) that were exposed to r Hp- CPI during the differentiation stage showed reduced MHC- II molecule expression compared with BMDC that were generated in normal culture conditions. The BMDC generated in the presence of r Hp- CPI also exhibited reduced expression of CD40, CD86 and MHC- II molecules and reduced interleukin-6 and tumour necrosis factor-α cytokine production when stimulated with Toll-like receptor ligand Cp G. Activation of BMDC generated in normal conditions induced by lipopolysaccharide and Cp G was also suppressed by r Hp- CPI, as shown by reduced co-stimulatory molecule expression and cytokine production. Furthermore, BMDC treated with r Hp- CPI before ovalbumin ( OVA) antigen pulsing induced a weaker proliferation response and less interferon-γ production of OVA-specific CD4+ T cells compared with BMDC without r Hp- CPI pre-treatment. Adoptive transfer of r Hp- CPI-treated and OVA-loaded BMDC to mice induced significantly lower levels of antigen-specific antibody response than the BMDC loaded with antigen alone. These results demonstrated that the CPI from nematode parasites is able to modulate differentiation and activation stages of BMDC. It also interferes with antigen and MHC- II molecule processing and Toll-like receptor signalling pathway, resulting in functionally deficient DC that induce a suboptimum immune response. [ABSTRACT FROM AUTHOR]

It is well known that no single histological feature is diagnostic for malignant pheochromocytomas (PCCs). So we developed a logistic model based on a series of clinical and pathological features to predict malignance in PCCs, and evaluated its diagnostic performance. In all 130 cases with malignant or benign PCCs, 15 predictive variables were observed and entered in the logistic regression analysis in a backward stepwise way. The diagnostic performance of this logistic model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve. In logistic analysis, of the 15 variables entered in the logistic regression analysis, 9 were retained in the model. High cellularity had the highest odds ratio (OR), followed by spindle cell (>10% of tumor volume), atypical mitotic figure, periadrenal adipose tissue invasion, mitotic figures [>3/10 high-power field (HPF)], cellular monotony, capsular invasion, vascular invasion, and central or confluent tumor necrosis. High cellularity, spindle cell (>10% of tumor volume) and atypical mitotic figure were selected to built a logistic model. This model had the area under the ROC curve of 0.927 (95% confidence interval 0.883–0.971). The application of the model can benefit the clinical management decision for patients with PCCs. We still emphasize, however, that a clinical prospective evaluation is needed to confirm its actual value. [ABSTRACT FROM AUTHOR]

In the real applications, we found that it is difficult to achieve good control performance through manually tuning proportional–integral (PI) parameters of phase locked loop (PLL) and speed-loop of Luenberger observer (LO) for the PMSM sensorless control system. Therefore, this paper is to use the particle swarm optimization (PSO) algorithm to optimize the PI parameters of PLL and speed-loop of Luenberger observer of the system. Firstly, the ranges of PLL parameters are obtained by analyzing the PLL subsystem stability. Then, the ranges of PI parameters of PLL and speed-loop are set based on theoretical estimation and empirical values. The control system model is realized in MATLAB/Simulink that considers the constraints such as the saturation. The integral time absolute error is the objective function, and the PSO with different topologies is used to optimize the PI parameters. The simulation and experimental results show that the proposed method is feasible, and the optimized parameters can effectively improve the precision of position estimation and speed estimation. Moreover, the simulations and experiments are carried out to verify the robustness of the proposed method, and the results show that the optimized system can achieve good performance when there are uncertainties or disturbances. [ABSTRACT FROM AUTHOR]

The flora of northern China forms the main part of the Sino‐Japanese floristic region and is located in a south–north vegetative transect in East Asia. Phylogeographic studies have demonstrated that an arid belt in this region has promoted divergence of plants in East Asia. However, little is known about how plants that are restricted to the arid belt of flora in northern China respond to climatic oscillation and environmental change. Here, we used genomic‐level data of Myripnois dioica across its distribution as a representative of northern China flora to reconstruct plant demographic history, examine local adaptation related to environmental disequilibrium, and investigate the factors related to effective population size change. Our results indicate M. dioica originated from the northern area and expanded to the southern area, with the Taihang Mountains serving as a physical barrier promoting population divergence. Genome‐wide evidence found strong correlation between genomic variation and environmental factors, specifically signatures associated with local adaptation to drought stress in heterogeneous environments. Multiple linear regression analyses revealed joint effects of population age, mean temperature of coldest quarter, and precipitation of wettest month on effective population size (Ne). Our current study uses M. dioica as a case for providing new insights into the evolutionary history and local adaptation of northern China flora and provides qualitative strategies for plant conservation. [ABSTRACT FROM AUTHOR]

Recent times have seen the emergence of prosumers with undispatchable renewable onsite generators, which can complicate operational planning of grids. The complication can be exacerbated when prosumers have the leeway to export excess generation to the grid, which may necessitate the development of a new paradigm for the operational planning of prosumer grids. In this paper, a computationally tractable robust microgrid operational dispatch model that uses diesel generators, a battery, and interruptible loads to handle uncertainty in prosumer generation is proposed. Using the modified version of a microgrid in Guangdong Province, China, the CPLEX solver in the Advanced Interactive Multidimensional Modeling System environment is used to validate the effectiveness of the proposed model. The proposed robust model yields a higher objective function value than its deterministic counterpart; however, it guarantees system reliability under any realization of prosumer generation within specified bounds, which the deterministic model cannot guarantee. Further analysis shows that the optimal objective function value increases with the uncertainty level of prosumer generation. [ABSTRACT FROM AUTHOR]

Operational planning of prosumer microgrids with solar and wind energy sources is quite a complex task considering the intermittency of these sources and energy import/export from prosumers. Reserve capacities which can be reliably provided by dispatchable sources like conventional generators (CGs) may be needed to ensure reliability of the grid. However, these sources produce emissions which have adverse effects on the environment. Hence, emission curtailment should be incorporated in the operational planning of microgrids with these generators. In this paper, a mathematical formulation for the joint economic and emission dispatch of a renewable energy-assisted prosumer microgrid is presented and solved using the CPLEX Solver in Advanced Interactive Multidimensional Modelling System (AIMMS). A modified microgrid test system is used as a case study in this work. Results show that incorporating an emission function in the objective of the operational dispatch formulation not only reduces emissions, but could be of advantage to customers as larger capacities of their behind-the-meter resources get the chance to provide grid ancillary services; however, it also puts a restriction on the profit that could be made from selling energy to the main grid during periods when energy prices are high. [ABSTRACT FROM AUTHOR]

In composite expansion planning, improving the efficiency and power quality of power supplied from Renewable Energy Sources (RES) is highly essential. Therefore, this paper focuses on developing an optimal power flow optimization model for minimizing power loss and harmonics emanating from RES with emphasis on reducing their impact on the Locational Marginal Prices. In order to assess the magnitude of harmonics, grid modelling and simulation with RES such as wind and solar energy were carried out using the Electrical Transient Analyzer Program 12.6.0 software. The proposed mixed integer nonlinear programming mathematical model was solved using the Outer Approximation Algorithm solver in the advanced interactive multidimensional modelling system, and the efficiency of the model was tested on the modified IEEE 6 bus and 24 bus system. The results obtained from simulations reveal the efficiency of the proposed model in terms of effective cost reduction, nodal marginal price reduction, minimization of harmonic losses, and additional lines in the power system network. [ABSTRACT FROM AUTHOR]

The uncertainty introduced by intermittent renewable energy generation and prosumer energy imports makes operational planning of renewable energy-assisted prosumer microgrids challenging. This is due to the difficulty in obtaining accurate forecasts of the energy expected from these renewable energy sources and prosumers. Operators of such microgrids therefore require additional grid-balancing tools to maintain power supply and demand balance during grid operation. In this paper, the impact of demand response aggregators (DRAs) in a prosumer microgrid is investigated. This is achieved by developing and solving a deterministic mathematical formulation for the operational planning of the grid. Also, taking a cue from CAISO's proposed tariff revision which allows the state-of-charge of non-generator resources (like storage units) to be submitted as a bid parameter in the day-ahead market and permits scheduling coordinators of these resources to self-manage their energy limits and state-of-charge, the proposed formulation permits prosumers to submit the battery energy content as a bid parameter and self-manage their battery energy limits. Furthermore, a robust counterpart of the model is developed. Both formulations are constrained mixed integer optimization problems which are solved using the CPLEX solver in the Advanced Interactive Multidimensional Modelling System environment. The results obtained from tests carried out on a hypothetical prosumer microgrid show that the operating cost of the microgrid reduces in the presence of DRAs. In addition, the storage facility owner may benefit from self-managing its energy limits, but this may cut the amount of grid-balancing resource available to the microgrid operator, thereby increasing the operating cost of the microgrid. [ABSTRACT FROM AUTHOR]

Evolutionary convergence is one of the most striking examples of adaptation driven by natural selection. However, genomic evidence for convergent adaptation to extreme environments remains scarce. Here, we assembled reference genomes of two alpine plants, Saussurea obvallata (Asteraceae) and Rheum alexandrae (Polygonaceae), with 37,938 and 61,463 annotated protein‐coding genes. By integrating an additional five alpine genomes, we elucidated genomic convergence underlying high‐altitude adaptation in alpine plants. Our results detected convergent contractions of disease‐resistance genes in alpine genomes, which might be an energy‐saving strategy for surviving in hostile environments with only a few pathogens present. We identified signatures of positive selection on a set of genes involved in reproduction and respiration (e.g., MMD1, NBS1, and HPR), and revealed signatures of molecular convergence on genes involved in self‐incompatibility, cell wall modification, DNA repair and stress resistance, which may underlie adaptation to extreme cold, high ultraviolet radiation and hypoxia environments. Incorporating transcriptomic data, we further demonstrated that genes associated with cuticular wax and flavonoid biosynthetic pathways exhibit higher expression levels in leafy bracts, shedding light on the genetic mechanisms of the adaptive "greenhouse" morphology. Our integrative data provide novel insights into convergent evolution at a high‐taxonomic level, aiding in a deep understanding of genetic adaptation to complex environments. [ABSTRACT FROM AUTHOR]

Understanding how the morphology and microstructure of secondary particles are affected by pH is essential to design spherical and dense hydroxide aggregates as precursors of high-performance cathode materials for lithium-ion batteries. To investigate the interplay between thermodynamic reaction conditions and the physicochemical properties of precursors, a chemical equilibrium model is used to describe the evolution of metal ammonia complexes. Furthermore, the formation and growth of aggregates are studied by monitoring the changes in surface morphologies and crystal structures of precursors at different reaction times. As the pH value varied, two notable phenomena occurred during crystal growth: the fast growth of grains in the early stage as the pH decreased and the layered growth of grains in the later stage with increased pH values. Such findings could be explained from the following two perspectives. First, the decrease in pH causes a significant increase in the complex ion concentration, thereby facilitating the growth of crystals along the [010] direction. Second, an increase in pH generally accelerates deprotonation of the crystal surface, thus strengthening the adsorption of complex ions on the crystal surface and promoting layer-by-layer growth of primary grains along the [001] direction. [Display omitted] • The effect of pH on crystal growth is investigated by modeling and experimental. • The weak hydrogen bonding dominates the adsorption of complexes on (001) facet. • The deprotonation strengthens the adsorption of complexes on crystal surface. [ABSTRACT FROM AUTHOR]

This study develops a new integrated measurement and control system for intermixed biogas and photovoltaic systems to achieve safe and optimal energy usage. Literature and field studies show that existing control methods on small- to medium-scale systems fall short of comprehensive system optimization and fault diagnosis, hence the need to revisit these control methods. The control strategy developed in this study is intelligent as it is wholly based on fuzzy logic algorithms. Fuzzy logic controllers due to their superior nonlinear problem solving capabilities to classical controllers considerably simplify controller design. The mathematical models that define classical controllers are difficult or impossible to realize in biogas and photovoltaic generation process. A microcontroller centered fuzzy logic measurement and control embedded system is designed and developed on the existing hybrid biogas and photovoltaic installations. The designed system is able to accurately predict digester stability, quantify biogas output, and carry out biogas fault detection and control. Optimized battery charging and photovoltaic fault detection and control are also successfully implemented. The system is able to optimize the operation and performance of biogas and photovoltaic energy generation. [ABSTRACT FROM AUTHOR]

Scanning polarization force microscopy (SPFM) is a useful surface characterization technique to visually characterize and distinguish nanomaterial with different local dielectric properties at nanometer scale. In this paper, taking the individual one-atom-thick graphene oxide (GO) and reduced graphene oxide (rGO) sheets on mica as examples, we described the influences of environmental humidity on SPFM imaging. We found that the apparent heights (AHs) or contrast of SPFM imaging was influenced significantly by relative humidity (RH) at a response time of a few seconds. And this influence rooted in the sensitive dielectric constant of mica surface to the RH change. While dielectric properties of GO and rGO sheets were almost immune to the humidity change. In addition, we gave the method to determine the critical humidity at which the contrast conversion happened under different conditions. And this is important to the contrast control and repeatable imaging of SPFM through RH adjusting. These findings suggest a strategy of controllable and repeatable imaging the local dielectric properties of nanomaterials with SPFM, which is critically important for further distinguishment, manipulation, electronic applications, etc. [ABSTRACT FROM AUTHOR]

Tartary buckwheat (Fagopyrum tataricum Garetn.), a dicotyledonous herbaceous crop, has good adaptation to low nitrogen (LN) condition. The plasticity of roots drives the adaption of Tartary buckwheat under LN, but the detailed mechanism behind the response of TB roots to LN remains unclear. In this study, the molecular mechanism of two Tartary buckwheat genotypes' roots with contrasting sensitivity in response to LN was investigated by integrating physiological, transcriptome and whole-genome re-sequencing analysis. LN improved primary and lateral root growth of LN-sensitive genotype, whereas the roots of LN-insensitive genotype showed no response to LN. 2, 661 LN-responsive differentially expressed genes (DEGs) were identified by transcriptome analysis. Of these genes, 17 N transport and assimilation-related and 29 hormone biosynthesis and signaling genes showed response to LN, and they may play important role in Tartary buckwheat root development under LN. The flavonoid biosynthetic genes' expression was improved by LN, and their transcriptional regulations mediated by MYB and bHLH were analyzed. 78 transcription factors, 124 small secreted peptides and 38 receptor-like protein kinases encoding genes involved in LN response. 438 genes were differentially expressed between LN-sensitive and LN-insensitive genotypes by comparing their transcriptome, including 176 LN-responsive DEGs. Furthermore, nine key LN-responsive genes with sequence variation were identified, including FtNRT2.4 , FtNPF2.6 and FtMYB1R1. This paper provided useful information on the response and adaptation of Tartary buckwheat root to LN, and the candidate genes for breeding Tartary buckwheat with high N use efficiency were identified. • Nitrogen deficiency improved root growth of low nitrogen (LN)-sensitive genotype. • Flavonoid biosynthetic genes' expression was improved by LN, and their transcriptional regulations were analyzed. • Nine key LN-responsive genes with sequence variation were identified, such as FtNRT2.4 , FtNPF2.6 and FtMYB1R1. [ABSTRACT FROM AUTHOR]