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Abstract Background Dendrobium officinale Kimura et Migo is a perennial epiphytic herb in traditional Chinese medicine, showing remarkable resistance to salt stress. Water-soluble sugars serve as important osmoprotectants and play crucial roles in plant stress responses. Previous studies have primarily focused on sugar metabolism in individual tissues under stress, resulting in a limited understanding of the regulatory differences between tissues and the relationship between sugar metabolism and transport. Results A variety of salt-responsive genes were identified through transcriptome analysis of D. officinale. GO and KEGG enrichment analyses revealed functional differences among the differentially expressed genes (DEGs) between leaves and roots. Expression analysis indicated that sugar metabolic genes and D. officinale S ugars W ill E ventually be E xported T ransporters (DoSWEETs) displayed distinct expression patterns in leaves and roots under salt stress. Most sugar metabolic genes were up-regulated in the leaves and down-regulated in the roots in response to salt, while DoSWEETs predominantly responded in the roots. Specifically, DoSWEET2a, 6a, 12a, 14, and 16 were confirmed via RT-qPCR. Additionally, positive correlations were observed between certain genes (scrK, INV, SUS) and DoSWEETs, with INV (LOC110096666) showing a strong positive correlation with all detected DoSWEETs in both leaves and roots. Conclusions Our findings not only illustrated the distinct responses of leaves and roots to salt stress, but also highlighted the relationship between sugar metabolic genes and DoSWEETs in adapting to such stress. This enhances our understanding of the differential responses of plant tissues to salt stress and identified candidate genes for salt-resistance breeding in D. officinale.

Abstract Optimization algorithms are essential tools used to address real-world problems through minimization or maximization processes. In the context of photovoltaic (PV) systems, various optimization algorithms have been employed to extract solar cell parameters using minimization techniques, and to identify the maximum power point (MPP) using maximization approaches. However, under partial shading conditions or during rapidly changing irradiance, many of these algorithms tend to get trapped in local minima, failing to locate the global maximum power point (GMPPT). This shortcoming leads to significant energy losses from PV cells. Similarly, the extraction of solar cell parameters is often compromised by the random search behavior and inadequate exploration and exploitation capabilities of many existing algorithms. The Jellyfish Search Optimization (JSO) algorithm is a recent, parameter-free method developed to tackle a wide range of optimization problems. Despite its innovative design, JSO is prone to premature convergence and exhibits a longer convergence time. To address these limitations, this paper proposes a modified version of the JSO algorithm, which integrates the state-of-the-art features of JSO with the Lévy flight characteristic from the cuckoo search algorithm. This combination aims to achieve a better balance between exploration and exploitation. To validate the effectiveness of the proposed Improved Jellyfish Search Optimization (IJSO) algorithm in PV systems, extensive experiments were conducted. These experiments included benchmarking with complex test functions, extracting cell parameters using various solar cell models, and maximizing energy output from PV systems under different environmental conditions. The results demonstrate that the proposed IJSO algorithm effectively enhances parameter extraction and global maximum power point tracking, making it a promising solution for optimizing energy extraction from PV cells in dynamic conditions.

The ongoing transmission of mpox in specific countries and regions necessitates urgent action. It is essential to implement targeted containment strategies that concentrate on high-risk populations and critical locations, such as college campuses, to effectively curb the spread of mpox. This study is dedicated to evaluating the performance of various vaccination and quarantine strategies in curbing the spread of mpox and estimating the outbreak risk. To accomplish this, we constructed a stochastic, agent-based, discrete-time susceptible-latent-infectious-recovered (SLIR) model, to examine mpox transmission on a simulated college campus. Our findings reveal that relying solely on PEP is insufficient in containing mpox effectively. To bolster the population immunity and protect the vulnerable, pre-exposure vaccination among high-risk populations prior to an outbreak is imperative. Our study demonstrates that a pre-exposure vaccination rate of 50% in high-risk populations can led to a remarkable 74.2% reduction of infections. This translated to a mere 1.0% cumulative infection incidence in the overall population. In cases where the desired vaccination coverage is not attainable, enhancing case detection and isolation measures can serve as an effective emergency response to contain mpox outbreaks. For pre-exposure vaccination coverage of 20% or lower, a 40% isolation ratio is necessary to keep the cumulative number of infections in check. However, when the coverage exceeds 30%, a reduced isolation ratio of 20% becomes sufficient to manage the outbreak effectively. These insights underscore the importance of strategic pre-exposure vaccination in conjunction with robust surveillance and isolation protocols to safeguard public health and prevent the escalation of mpox outbreaks.

Abstract Dasiphora fruticosa (Rosaceae), commonly known as shrubby cinquefoil, is a flowering shrub of high ornamental value yet underutilized in East Asian landscapes. Given its broad elevational distribution range, D. fruticosa serves as an ideal model for studying genetic adaptations and speciation along elevation gradients. Here, we present a high-quality chromosome-scale assembly of D. fruticosa with a genome size of 249.23 Mb and a contig N50 length of 14.01 Mb. The genome sequence contains 32,613 protein-coding genes, of which 30,643 (93.96%) were functionally annotated. Compared to the published D. fruticosa genome sequence, our assembly demonstrates higher completeness and continuity. Furthermore, comparative genomic analyses provide insights into the phylogenetic relationship and high-altitude adaptation of D. fruticosa. Overall, our study offers a valuable genetic resource for both molecular and evolutionary research on shrubby cinquefoil.

Artificial Intelligence (AI) techniques are transforming various sectors and hold significant potential to advance sustainable development in Africa. However, their effective integration is constrained by region-specific challenges, limiting widespread deployment. This study reviews the current state of sustainable development in Africa, highlighting the role AI can play in driving progress across key sectors, including healthcare, agriculture, education, environmental protection, and infrastructure. The paper outlines the challenges hindering AI adoption and presents strategic approaches to address these obstacles, specifically targeting Africa’s socio-economic and environmental needs. In addition, the study proposes a comprehensive framework for integrating AI into Africa’s sustainable development efforts, offering tailored AI-driven strategies that align with the continent’s unique context. This framework provides a valuable resource for AI researchers, policymakers, and practitioners working towards sustainable development in Africa.

Integrating photovoltaic (PV) and battery energy storage systems (BESS) in modern power distribution networks presents opportunities and challenges, particularly in maintaining voltage stability and optimizing energy resources. This systematic review and bibliometric analysis investigates the coordination of smart inverter-enabled distributed energy resources (DERs) for enhancing PV-BESS integration and ensuring voltage stability. The study synthesizes recent advancements in smart inverter technologies, which provide grid support functions such as Volt/VAr control, and their applications in DER coordination. A comprehensive review of the literature is conducted to identify prevailing trends, research gaps, and emerging techniques in the field. Bibliometric analysis is employed to quantify the research landscape, highlighting key publications, citations, publications per country, and collaborative networks. The findings reveal that smart inverters play a crucial role in mitigating voltage violations and improving the hosting capacity of PV systems in distribution networks. Furthermore, optimal inverter settings, strategic placement of PV-BESS, and advanced control algorithms are identified as critical factors for effective DER integration. The study concludes by proposing future research directions, including the exploration of smart inverter interactions with legacy grid management systems and the development of robust algorithms for dynamic and adaptive DER coordination. This review serves as a valuable resource for researchers and practitioners aiming to enhance the stability and efficiency of power distribution networks through advanced DER management strategies.

Voltage instability is a major problem facing power utility companies due to a lack of maintenance and financial support to maintain the existing power system networks (PSN). Lack of maintenance occurs when PS fails to replace the aging equipment and services and upgrades existing networks. Also, the lack of financial support occurs when there is insufficient financial capital to carry out the above maintenance activities, which has incurred greater power loss in the systems. There is a need to eliminate the loss incurred in the system to avoid voltage collapse. The best way to increase the lifespan of a PSN and improve voltage stability is the optimum allocation of distributed generation (DG). The most common DG are solar photovoltaic (PV) and wind turbines. This review discusses the economic, environmental, and technical benefits of non-traditional DG technologies over a traditional system. However, this work reviewed different DG technologies that can be incorporated into PSN for better loss reduction. Detailed descriptions of energy loss, power loss, and multi-objectives for optimal DG planning were also discussed, along with comprehensive methods used in the sizing and placement of DG for voltage profile improvement (VPI). The findings recommend that the hybrid meta-heuristic algorithm be used for DG placement has it significantly improves voltage profile and lowers power loss. The present study concluded that with proper DG placement, the advantage of DG can be maximally accomplished, the voltage profile is improved, and losses are minimized.

BackgroundThere has been a global decrease in seasonal influenza activity since the onset of the COVID-19 pandemic. ObjectiveWe aimed to describe influenza activity during the 2021/2022 season and compare it to the trends from 2012 to 2023. We also explored the influence of social and public health prevention measures during the COVID-19 pandemic on influenza activity. MethodsWe obtained influenza data from January 1, 2012, to February 5, 2023, from publicly available platforms for China, the United States, and Australia. Mitigation measures were evaluated per the stringency index, a composite index with 9 measures. A general additive model was used to assess the stringency index and the influenza positivity rate correlation, and the deviance explained was calculated. ResultsWe used over 200,000 influenza surveillance data. Influenza activity remained low in the United States and Australia during the 2021/2022 season. However, it increased in the United States with a positive rate of 26.2% in the 49th week of 2022. During the 2021/2022 season, influenza activity significantly increased compared with the previous year in southern and northern China, with peak positivity rates of 28.1% and 35.1% in the second week of 2022, respectively. After the COVID-19 pandemic, the dominant influenza virus genotype in China was type B/Victoria, during the 2021/2022 season, and accounted for >98% (24,541/24,908 in the South and 20,543/20,634 in the North) of all cases. Influenza virus type B/Yamagata was not detected in all these areas after the COVID-19 pandemic. Several measures individually significantly influence local influenza activity, except for influenza type B in Australia. When combined with all the measures, the deviance explained values for influenza A and B were 87.4% (P

Abstract Credit risk prediction is a crucial task for financial institutions. The technological advancements in machine learning, coupled with the availability of data and computing power, has given rise to more credit risk prediction models in financial institutions. In this paper, we propose a stacked classifier approach coupled with a filter-based feature selection (FS) technique to achieve efficient credit risk prediction using multiple datasets. The proposed stacked model includes the following base estimators: Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB). Furthermore, the estimators in the Stacked architecture were linked sequentially to extract the best performance. The filter- based FS method that is used in this research is based on information gain (IG) theory. The proposed algorithm was evaluated using the accuracy, the F1-Score and the Area Under the Curve (AUC). Furthermore, the Stacked algorithm was compared to the following methods: Artificial Neural Network (ANN), Decision Tree (DT), and k-Nearest Neighbour (KNN). The experimental results show that stacked model obtained AUCs of 0.934, 0.944 and 0.870 on the Australian, German and Taiwan datasets, respectively. These results, in conjunction with the accuracy and F1-score metrics, demonstrated that the proposed stacked classifier outperforms the individual estimators and other existing methods.

The rising volume of online transactions has concurrently increased the incidence of credit card fraud, presenting severe challenges to financial institutions and consumers alike. Traditional fraud detection techniques often fail to adapt to the sophisticated and evolving strategies employed by fraudsters, resulting in significant financial losses. This study introduces a novel hybrid deep learning ensemble model to enhance credit card fraud detection. The proposed model integrates Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and Transformers as base learners, with Extreme Gradient Boosting (XGBoost) serving as the meta-learner. The experimental results demonstrate that the hybrid ensemble significantly outperforms individual base learners and traditional methods, with a sensitivity of 0.961, specificity of 0.999, and area under the receiver operating characteristic curve (AUC-ROC) of 0.972 using the European Credit Card Dataset. This research demonstrates the effectiveness of hybrid deep learning techniques in addressing the complexities of credit card fraud detection, thereby contributing to the development of more secure and reliable financial systems.

Advanced control approaches are needed for prosumer-centric residential microgrids to manage power optimally due to the shift to sustainable energy systems In residential contexts where prosumers are important, this study investigates novel approaches to improve renewable energy sources’ efficiency, dependability, and integration. Prosumers who produce and consume energy introduce unique challenges and opportunities for managing power flows and maintaining grid stability. This study proposes a smart Home Energy Management System (HEMS) designed to reduce daily energy costs for prosumers while offering ancillary services to the grid. The prosumer’s residence includes an electric vehicle (EV), a home battery pack, a photovoltaic (PV) system, a mix of flexible and critical electrical loads, and heating and cooling loads. The HEMS can operate independently by disconnecting from the main grid. Additionally, it supports grid frequency management by considering the operational flexibility of the controlled power sources and loads. To solve the multi-objective power management optimization problem, the Teaching-Learning-Based Optimization (TLBO) algorithm was implemented. This algorithm optimizes day-ahead power scheduling by considering temperature forecasts, energy prices, solar irradiance, system constraints, and user preferences. A residential load forecasting system, which combines k-means clustering and artificial neural networks (ANNs), was developed and implemented to predict residential loads efficiently. Various simulated scenarios illustrate how the HEMS may save costs while guaranteeing that all power component operating limitations are reached, and user comfort and satisfaction are maintained. To demonstrate the capabilities and cost-effectiveness of the suggested HEMS, several scenarios are simulated and the outcomes of the day-ahead optimization carried out by the HEMS for the prosumer residence are examined. Furthermore, it is verified that the system can sustain frequencies while preserving the flexibility of each module.

Cervical cancer is a common and deadly disease that affects women worldwide. Early diagnosis and treatment can improve the survival and quality of life of patients. Machine learning techniques can help to analyze complex and high-dimensional data related to cervical cancer and provide accurate and reliable predictions. However, selecting the most relevant and informative features from the data is a challenging task that affects the performance and interpretability of machine learning models. This paper proposes a novel method that uses particle swarm optimization (PSO) to perform feature selection and optimization for cervical cancer prediction. PSO is a bio-inspired algorithm that mimics the social behavior of a swarm of particles that search for the optimal solution in the feature space. The use of PSO to select the best subset of features that maximize the classification accuracy of eight machine learning models: Support Vector Machines (SVM), Gaussian Naive Bayes (GNB), Random Forests (RF), Decision Trees (DT), Extreme Gradient Boosting (XGB), Linear Regression (LR), Adaptive Boosting (AdaBoost), and K-nearest neighbor (KNN).To evaluate the method, a publicly available dataset was used, the Cervical Cancer Risk Factors Dataset (CCRFD). Then, compare the results with several state-of-the-art methods that use different feature selection techniques and ML algorithms. The experimental results show that the method achieves superior performance in terms of feature reduction rate, accuracy, precision, and AUC. Specifically, the Adaboost-PSO model performed best in terms of feature reduction rate with a reduction of rate 100% while the RF-PSO model performed best in terms of accuracy and precision, with an accuracy of 98% and precision of 100%.

Online card transactions have become more frequent due to the growth of e-commerce and financial technology apps. However, this also means more opportunities for credit card fraud, which affects banks, retailers, and card issuers. Therefore, we need systems that can protect the security and integrity of credit card transactions. In this study, we use the Adaptive Synthetic Minority Oversampling Technique to balance an imbalanced dataset; then we combine that with the Recursive Feature Elimination with the validation technique to enhance the performance of credit card fraud detection systems. We compare the results of several models, which are Decision Tree, Random Forests, Extreme Gradient Boosting, Light Gradient Boosting Machines, and Linear Regression, on the original imbalanced dataset and the resampled dataset using the Adaptive Synthetic Minority Oversampling Technique before finally applying the Recursive Feature Elimination with Cross Validation technique. We aim to find the best model and method for detecting credit card fraud. During training, k-fold cross-validation is applied to both sets of models in order to prevent overfitting and improve the classification. Our results show that the Adaptive Synthetic Minority Oversampling Technique and Recursive Feature Elimination with Cross Validation modified dataset improved overall classification errors over the baseline dataset. Specifically, the best performing models were the Extreme Gradient Boosting and Random Forests with a Matthew’s Correlation Coefficient of 0.8794 and 0.8622, respectively, when used with the baseline dataset, and when used with Adaptive Synthetic Minority Oversampling Technique and Recursive Feature Elimination with Cross Validation dataset, we recorded a Matthew’s Correlation Coefficient of 0.9994 and 0.9991 for the Extreme Gradient Boosting and Random Forest, respectively. Furthermore, our results show that the Light Gradient Boosting Machines model recorded the most improvement in Matthew’s Correlation Coefficient from 0.3394 to 0.9980 when used with baseline and Adaptive Synthetic Minority Oversampling Technique modified datasets, respectively. This represents an increase of 194% in Matthew’s Correlation Coefficient.

Credit risk prediction is a critical task in the financial industry, allowing lenders to assess the likelihood of a borrower defaulting on a loan. Traditional machine learning (ML) classifiers have been widely used for this purpose, and they often struggle with imbalanced data and lack interpretability, making it challenging for financial institutions to make informed decisions. This article explores the use of ensemble classifiers and Synthetic minority over-sampling Edited nearest neighbor (SMOTE-ENN) technique in credit risk prediction, aiming to improve the classification performance. The ensemble classifiers include Random Forest, adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The study addresses the class imbalance issue by leveraging ensemble classifiers and the SMOTE-ENN technique while employing Shapley additive exPlanations (SHAP) for model interpretability. The experimental results showed that the proposed approach resulted in improved classification performance. Specifically, on the German credit dataset, XGBoost outperformed the other models with a Recall of 0.930 and a Specificity of 0.846, while Random Forest obtained the best performance on the Australian dataset, achieving a Recall of 0.907 and Specificity of 0.922. Additionally, the integration of SHAP enhanced the models’ transparency by providing valuable insights into the contribution of individual features, which is crucial for informed financial decision-making.

Abstract 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

Abstract The underlying mechanism of chronic hepatitis B virus (HBV) functional cure by interferon (IFN), especially in patients with low HBsAg and/or young ages, is still unresolved due to the lack of surrogate models. Here, we generate a type I interferon receptor humanized mouse (huIFNAR mouse) through a CRISPR/Cas9-based knock-in strategy. Then, we demonstrate that human IFN stimulates gene expression profiles in huIFNAR peripheral blood mononuclear cells (PBMCs) are similar to those in human PBMCs, supporting the representativeness of this mouse model for functionally analyzing human IFN in vivo. Next, we reveal the tissue-specific gene expression atlas across multiple organs in response to human IFN treatment; this pattern has not been reported in healthy humans in vivo. Finally, by using the AAV-HBV model, we test the antiviral effects of human interferon. Fifteen weeks of human PEG-IFNα2 treatment significantly reduces HBsAg and HBeAg and even achieves HBsAg seroconversion. We observe that activation of intrahepatic monocytes and effector memory CD8 T cells by human interferon may be critical for HBsAg suppression. Our huIFNAR mouse can authentically respond to human interferon stimulation, providing a platform to study interferon function in vivo. PEG-IFNα2 treatment successfully suppresses intrahepatic HBV replication and achieves HBsAg seroconversion.

This study uses JAYA optimized model predictive control to address a three-area load frequency regulation problem that actively involves stochastic renewable energy sources. Some physical constraints employed in the system model, such as the generation rate constraint, dead band, and time delay by governor-turbine, negatively impact the control performance of the classical MPC technique. As such, a more robust control scheme is required to get rid of this flaw. The JAYA algorithm is adopted to dynamically tune the parameters of MPC, which is a parameter-driven controller for optimal system performance. The performance indices such as the integral of square error (ISE), integral of time multiplied by square error (ITSE), integral of the absolute value of error (IAE), and integral of time multiplied by the absolute value of error (ITAE) have been used to compare the effectiveness of the proposed controller with the classical MPC with constant tuning parameters. The suggested controller is superior to the conventional MPC, as demonstrated by the results from the MATLAB/Simulink environment.

In order to address the load frequency issue in a three-area power generating system, this research suggests a teaching learning-based optimization-linear matrix, inequalities-tuned proportional–integral controller. The suggested TLBO-LMI approach synchronizes TLBO with LMI control toolbox optimization to acquire the ideal control parameters of the traditional PI controller that adhere to the robust H∞constraints. Based on the suggested control’s simplicity of implementation, adaptability to changes in the system’s characteristics, and resilience to tuning, it is contrasted with the robust H∞controller. Several performance indicators were also employed to evaluate and contrast the efficiency and performance of the H∞controller and the suggested control techniques. Based on the simulation results used to study the performance and robustness of the suggested controller, it is clear that the proposed control approach outperforms the H∞controller in terms of both performance and applicability for the LFC problem.

The conservation biology field underscores the importance of understanding genetic diversity and gene flow within plant populations and the factors that influence them. This study employs Simple Sequence Repeat (SSR) molecular markers to investigate the genetic diversity of the endangered plant species Saussurea involucrata, offering a theoretical foundation for its conservation efforts. Utilizing sequencing results to screen SSR loci, we designed and scrutinized 18 polymorphic microsatellite primers across 112 samples from 11 populations in the Bayinbuluke region. Our findings reveal high genetic diversity (I = 0.837, He = 0.470) and substantial gene flow (Nm = 1.390) among S. involucrata populations (China, Xinjiang), potentially attributed to efficient pollen and seed dispersal mechanisms. Principal Coordinate Analysis (PCoA) indicates a lack of distinct genetic structuring within the Bayinbuluke populations. The cluster analysis using STRUCTURE reflected the genetic structure of S. involucrata to a certain extent compared with PCoA. The results showed that all samples were divided into four groups. To safeguard this species, we advocate for the in situ conservation of all S. involucrata populations in the area. The SSR markers developed in this study provide a valuable resource for future genetic research on S. involucrata.

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.

The number of coronavirus disease 2019 (COVID-19) cases continues to surge, overwhelming healthcare systems and causing excess mortality in many countries. Testing of infectious populations remains a key strategy to contain the COVID-19 outbreak, delay the exponential spread of the disease, and flatten the epidemic curve. Using the Omicron variant outbreak as a background, this study aimed to evaluate the effectiveness of testing strategies with different test combinations and frequencies, analyze the factors associated with testing effectiveness, and optimize testing strategies based on these influencing factors. We developed a stochastic, agent-based, discrete-time susceptible–latent–infectious–recovered model simulating a community to estimate the association between three levels of testing strategies and COVID-19 transmission. Antigen testing and its combination strategies were more efficient than polymerase chain reaction (PCR)-related strategies. Antigen testing also showed better performance in reducing the demand for hospital beds and intensive care unit beds. The delay in the turnaround time of test results had a more significant impact on the efficiency of the testing strategy compared to the detection limit of viral load and detection-related contacts. The main advantage of antigen testing strategies is the short turnaround time, which is also a critical factor to be optimized to improve PCR strategies. After modifying the turnaround time, the strategies with less frequent testing were comparable to daily testing. The choice of testing strategy requires consideration of containment goals, test efficacy, community prevalence, and economic factors. This study provides evidence for the selection and optimization of testing strategies in the post-pandemic era and provides guidance for optimizing healthcare resources.

The economic and health significance of quinoa is steadily growing on a global scale. Nevertheless, the primary obstacle to achieving high yields in quinoa cultivation is pre-harvest sprouting (PHS), which is intricately linked to seed dormancy. However, there exists a dearth of research concerning the regulatory mechanisms governing PHS. The regulation of seed germination by various plant hormones has been extensively studied. Consequently, understanding the mechanisms underlying the role of endogenous hormones in the germination process of quinoa seeds and developing strategies to mitigate PHS in quinoa cultivation are of significant research importance. This study employed the HPLC-ESI-MS/MS internal standard and ELISA method to quantify 8 endogenous hormones. The investigation of gene expression changes before and after germination was conducted using RNA-seq analysis, leading to the discovery of 280 differentially expressed genes associated with the regulatory pathway of endogenous hormones. Additionally, a correlation analysis of 99 genes with significant differences identified 14 potential genes that may act as crucial “transportation hubs” in hormonal interactions. Through the performance of an analysis on the modifications in hormone composition and the expression of associated regulatory genes, we posit a prediction that implies the presence of a negative feedback regulatory mechanism of endogenous hormones during the germination of quinoa seeds. This mechanism is potentially influenced by the unique structure of quinoa seeds. To shed light on the involvement of endogenous hormones in the process of quinoa seed germination, we have established a regulatory network. This study aims to offer innovative perspectives on the breeding of quinoa varieties that exhibit resistance to PHS, as well as strategies for preventing PHS.

Abstract Energy forecasting is crucial for efficient energy management and planning for future energy needs. Previous studies have employed hybrid modeling techniques, but insufficient attention has been given to hyper-parameter tuning and parameter selection. In this study, we present a hybrid model, which combines fuzzy c-means clustered adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA), named GA–ANFIS–FCM, to model electricity consumption in Lagos districts, Nigeria. The model is simulated using the algorithms’ control settings, and the best model is identified after assessing their performance using renowned statistical indicators. To further narrow down the best viable model, the impact of the core parameter of the GA on the GA–ANFIS–FCM optimal model is examined by varying the crossover percentage in the range of 0.2–0.6. Firstly, the results reveal the better performance of the hybridized ANFIS model than the standalone ANFIS model. Additionally, the best model is obtained with the GA–ANFIS–FCM model with four clusters at a crossover percentage of 0.4, with mean absolute percentage error (MAPE), mean absolute error (MAE), coefficient of root mean square error (CVRMSE), root mean square error (RMSE) values of 7.6345 (signifying a forecast accuracy of 92.4%), 706.0547, 9.4913, and 918.6518 during the testing phase, respectively. The study demonstrates the potential of the proposed model as a reliable tool for energy forecasting.

Automatic and fast segmentation of retinal vessels in fundus images is a prerequisite in clinical ophthalmic diseases; however, the high model complexity and low segmentation accuracy still limit its application. This paper proposes a lightweight dual-path cascaded network (LDPC-Net) for automatic and fast vessel segmentation. We designed a dual-path cascaded network via two U-shaped structures. Firstly, we employed a structured discarding (SD) convolution module to alleviate the over-fitting problem in both codec parts. Secondly, we introduced the depthwise separable convolution (DSC) technique to reduce the parameter amount of the model. Thirdly, a residual atrous spatial pyramid pooling (ResASPP) model is constructed in the connection layer to aggregate multi-scale information effectively. Finally, we performed comparative experiments on three public datasets. Experimental results show that the proposed method achieved superior performance on the accuracy, connectivity, and parameter quantity, thus proving that it can be a promising lightweight assisted tool for ophthalmic diseases.

Objectives: Global trade and travel have facilitated infectious disease transmission. In 2022, over a short time, cross-border Mpox (monkeypox) outbreaks were reported. Since, most countries are at risk of cross-border Mpox transmissions, in this study, we developed a real-time risk assessment model for the cross-border transmission of Mpox. Methods: This model includes priori indicators related to the source area before the Mpox outbreak and posterior indicators derived from the quantitative data evaluation afterward. Based on transportation, this model can also be used to assess the global import risk of Mpox for specific countries and cities. Results: European risk values displayed high levels between May and July 2022 and gradually decreased after July. After September 2022, risk values elevated in most countries and regions in the Americas. As for China, high importation risk cities were highly exposed to the United States and moderately exposed to Australia and Germany. Some cities were exposed to the potential risks from only one source country. Conclusions: Dynamic surveillance of the cross-border spread of infectious diseases is essential. Importation risks vary widely across cities and regions, and developing risk prevention and control strategies specific to the traffic flow, medical care capabilities, and risk levels in the main source countries are essential.

AbstractVoltage instability is a major challenge facing power system (PS) that has affected some organizations in achieving their desired goals. Therefore, voltage instability is the incapability of the PS to maintain the voltage standard under no disturbance and after subjecting to disruption. This paper describes the voltage instability phenomena; voltage stability indices include Line Stability Index ([Formula: see text]), Line voltage stability index, Fast Voltage Stability Index (FVSI), line stability factor (LQP), Bus voltage collapse prediction index (BVCPI), L index, voltage stability index (VCP-1), and so on. This review focuses on some stability indices that could identify the weak bus in the electrical PS network. The application of particle swarm optimization (PSO) to minimize losses that cause voltage instability is discussed. It started a detailed understanding of the power blackouts and the detrimental effects on the global economy. This was followed by a thorough understanding of the voltage instability/stability phenomenon, classification in power systems, and the corresponding formulations. The study presents an overview of voltage assessment techniques prior to applying PSO in discrete and multi-objective optimization and the corresponding advantages over others. These are followed by the progress and advances in voltage stability using PSO involving single and hybrid optimization methods. Lastly, to bridge the research gaps, the present study highlighted challenges and future prospects to foster further advancement in the field.

Abstract: Quinoa has been recognized as the sole “comprehensive nutritional crop”; however, it is susceptible to pre-harvest sprouting (PHS). While quantitative reverse transcription polymerase chain reaction (RT-qPCR) has been extensively employed for gene expression level detection, the selection of suitable reference genes is imperative to ensure precise gene expression quantification across diverse conditions. This study aims to identify stable reference genes in quinoa seeds under ABA and GA, in order to provide a basis for subsequent research on PHS. Seeds were subjected to different concentrations of ABA and GA (10 μM, 50 μM, 100 μM, and 200 μM). The most suitable treatment concentration was determined based on seed viability. Here, MON1, GAPDH, EIF3, EF1α, ACT, TUB1, and TUB6 were selected as candidate genes. The suitability of these reference genes under different conditions was assessed using various methods including Ct values, geNorm, NormFinder, BestKeeper, Delta Ct, and RefFinder. Based on the results obtained from the hormone experiments, it was observed that the application of 100 μM ABA and 200 μM GA yielded the most advantageous outcomes. Additionally, the most appropriate reference genes for different treatments are ACT and TUB1 (H2O treatment), EIF3 and MON1 (ABA, GA treatment and also for the combined data set of the three groups). However, GAPDH exhibited the least stability across all treatments. In summary, ACT is recommended as the reference gene for natural quinoa germination, while EIF3 and MON1 should be used for ABA and GA treatments.

Storage systems are needed to boost the reliability of intermittent solar and wind resources in power networks. Rather than focus on one storage system or one hybrid energy storage system (HESS), this work models the operation of six HESS configurations in a Renewable Energy (RE) based grid-tied network. The objective is to minimise the daily operational costs of the microgrid while prolonging the storage lifetime by considering storage degradation costs. The influence of fixed tariffs and time-of-use (TOU) tariffs on the optimal operational of the HESS configurations have also been investigated; as well as deferrable demand satisfaction, charge-discharge pattern of different HESS and availability of the power-dense storage system within the microgrid. Results show that the lead-acid battery and hydrogen fuel cell (HFC) HESS incurs the highest operational costs, while the supercapacitor-lead-acid battery HESS incurs the lowest operational costs. The supercapacitor-lead acid battery and the supercapacitor-HFC HESS incur the highest annual storage degradation costs. The grid expenses were seen to be the same for all HESS under each tariff scheme. Lastly, decreasing the minimum storage level further by 10% from the 30% in the base case, led to an increase in the number of hours of availability of the power-dense storage system of five of the six HESS. These results have given a deeper understanding to the operation of HESS systems and can inform better decision making of the suitable HESS to be deployed in different operating scenarios.

Background: The impact of regional anesthesia and analgesia (RAA) on long-term survival following cancer surgery is a topic of debate. The aim of this study was to investigate the effects of perioperative RAA on long-term oncological outcomes in patients undergoing major abdominal cancer surgery. Methods: The authors searched computerized databases and reference lists from inception to December 20, 2022. All studies that investigated the effects of perioperative RAA on long-term oncological outcomes following major abdominal cancer surgery were included. Using the inverse variance method with a random-effects model, hazard ratios (HR) and 95% confidence intervals (CI) were calculated. Results: The systematic review included 51 retrospective studies, one prospective study, and three randomized controlled trials (RCTs), with a total of 95,046 patients. The results showed that perioperative RAA may improve long-term overall survival (HR: 0.85, 95% CI: 0.80 to 0.91, P = 0.00, I2 = 60.2%). However, there was no significant association between perioperative RAA and reduced cancer recurrence (HR: 0.98, 95% CI: 0.90 to 1.03, P = 0.31, I2 = 52.3%). When performing a pooled analysis of the data from the three RCTs, no statistically significant effect of RAA was found in either case. Conclusion: The systematic review suggests perioperative RAA may improve long-term overall survival but does not appear to reduce cancer recurrence in patients undergoing major abdominal cancer surgery. The limited number of RCTs included in this study did not confirm this finding, highlighting the need for further RCTs to corroborate these results.

BackgroundInfluenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. ObjectiveThis study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. MethodsWe collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. ResultsThis study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. ConclusionsComplementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.

Purpose: This study aimed to conduct a bibliometric analysis of the data acquired and clarified the current research status of sexual health problems in patients with cancer, to provide a comprehensive visual perspective suitable as a reference for subsequent research. Methods: We searched the Web of Science Core Collection (WoSCC) up to April 30, 2023 to identify studies associated with sexual health problems in patients with cancer. CiteSpace was used to create visualization networks for countries, institutions, authors, and journals. Results: A total of 3183 publications related to sexual health problems in patients with cancer were collected from the WoSCC. In terms of volume, the USA (1259 papers) was the leading country, the Memorial Sloan Kettering Cancer Center (119 papers) was the leading institution, and Carter (39 papers) was the author with the most publications. The top-cited references and keywords were related to quality of life. The top five clusters of reference cocitation were ‘brachytherapy’, ‘prostate cancer’, ‘radical prostatectomy’, ‘hypogonadism’, and ‘breast cancer’. Meanwhile, the top five clusters of keyword cocitation were ‘breast cancer’, ‘prostate cancer’, ‘rectal cancer’, ‘testicular cancer’, and ‘sexual function’. The analysis of the top 25 references and keywords with the strongest citation bursts of published papers on sexual health problems in patients with cancer to reveal the research hotspots and trends. Conclusions: Research on sexual health among patients with cancer is constantly developing. The current research focuses on the impact of different treatment options for sexual health and quality of life of patients with breast, rectal, and genitourinary neoplasms. Exploring the long-term changing regularities of sexual function among cancer survivors and formulating sexual health interventions toward patient-reported outcomes and needs are key research directions.

Electricity is undeniably one of the most crucial building blocks of high-quality life all over the world. Like many other African countries, Nigeria is still grappling with the challenge of the energy crisis. However, accurate prediction of electricity consumption is vital for the operation of electric utility companies and policymakers. In response, this study underlines the application of hybrid modelling techniques for the accurate prediction of electricity consumption, using Lagos districts, Nigeria, as a case study. To begin with, this research investigates the performance of three evolutionary algorithms — Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) — to optimize the parameters of adaptive network-based fuzzy inference systems (ANFIS). In addition, the impact of renowned clustering techniques such as grid partitioning (GP), fuzzy c-means (FCM), and subtractive clustering (SC) on other pivotal key hyperparameters of the ANFIS was examined and analyzed. Furthermore, the robustness of the optimal sub-model was evaluated by comparing it with other hybrid models that are based on six different variants of PSO. The efficacy of the proposed model was evaluated using four standard statistical measures. Finally, the results showed that the combination of the ANFIS approach and PSO under an SC approach and clustering radius of 0.6 delivered the best forecast scheme with the highest accuracy of the MAPE (8.8418%), the MAE (872.1784), the CVRMSE (10.7895), and the RMSE (1.0945E+03). The simulation results were analyzed and compared to other approaches, revealing that the suggested model is better.

Credit cards play an essential role in today’s digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. In order to solve this problem, this paper proposes a robust deep-learning approach that consists of long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks as base learners in a stacking ensemble framework, with a multilayer perceptron (MLP) as the meta-learner. Meanwhile, the hybrid synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) method is employed to balance the class distribution in the dataset. The experimental results showed that combining the proposed deep learning ensemble with the SMOTE-ENN method achieved a sensitivity and specificity of 1.000 and 0.997, respectively, which is superior to other widely used ML classifiers and methods in the literature.

Optimization of power in Photovoltaic (PV) systems and extraction of cell parameters in PV cells using well-known metaheuristic techniques have been implemented by different researchers. However, under fast-changing irradiances and complex partial shading conditions, these algorithms underperform by getting trapped at the local maxima and with a poor convergence exploration, which in turn leads to a loss in energy supplied from a PV system. This paper proposes an innovative use of improved firefly algorithm (IFA) technique suitable for effective extraction of both the unknown cell parameters in PV cells and locating the global maximum power point (GMPP) that yields optimum power in PV array systems within the shortest possible time and with minimal loss in power when operated under different environmental conditions. To validate the effectiveness of the proposed IFA technique, six experimental analyses were conducted using four optimization techniques comprising of particle swarm optimization, cuckoo search algorithm, flower pollination algorithm, and the proposed IFA technique as case studies. Results achieved confirmed that IFA technique can be used to solve real-world problems in PV systems.

Transactive energy trading, control, and management have gained much importance, receiving significant interest from both industry and academia. As a result, new strategies for its development and implementation are emerging. Prosumage, which combines household loads with solar PV and battery storage systems, has been presented as an effective technique for facilitating the integration of renewable energy sources while reducing distribution grid stress. Therefore, this paper proposes a grid-connected prosumer-centric residential smart community that uses cooperative game theory to exchange, regulate, and plan surplus energy generated by distributed energy resources (DERs) with the other neighbouring prosumers with energy deficits. The energy community considered in this work comprises ten identical prosumers (residential load, solar PV, and battery storage system), an energy community manager, and an energy retailer, all of whom are linked as a group to a distribution grid. The proposed market model seeks to examine the economic benefits of such renewable sources participating in centralized transactive energy trading. A centralized approach is adopted in the transactive energy trading among the ten proactive prosumers and the power grid through an energy community manager that administers trading activities inside the energy community. An optimization model is proposed in the centralized transactive energy trading market to optimize the financial benefits of rooftop solar PV-battery systems. The proposed model will not only optimally exchange, plan, and regulate the community domestic load, but it will also minimize total energy costs, increase system operation efficiency, and reduce system operating stress and carbon emissions.

This paper investigates energy management systems in micro-grid using an optimization-based approach, optimizing the operating cost related to the energy purchased from the utility grid, the operation cost of the energy storage system, and revenue from the selling of energy to the utility grid. This research uses a constrained Particle Swarm Optimization-Based Model Predictive Control (CPSO-MPC) and a Linear Program-Based Optimization approach to solve the constrained optimization problem formulated in micro-grid energy management. Due to the absence of constraint management strategies in the traditional PSO algorithm, it is incapable of solving constrained optimization problems. Hence, to overcome this drawback, an intuitive approach known as Deb’s rule is applied to handle the constraints. The simulation results show the modified particle swarm optimization’s effective performance embedded in the model predictive control algorithm compared to the linear programming algorithm.

This study proposes a primal–dual interior-point algorithm, which is used by the prosumers to efficiently manage the operation of their generating and consuming devices, to maximize their own profits while contributing to the stability of the overall electricity grid. The energy management problem investigated in this study is formulated as a convex optimization problem and the primal–dual interior-point algorithm is leveraged to acquire the optimal solution which minimizes the energy cost for prosumers in a smart grid environment. The proposed algorithm considers the cost of energy generation by each available device or the charge of a utility company, as well as the predicted consumption and production levels of the facility, and develops an operating plan that maximizes the prosumer’s profitability. The cost function for energy generation is convex, as are the constraints that must be satisfied. The primal–dual interior-point algorithm is an efficient algorithm and has attracted attention in the optimization community. The proposed algorithm is evaluated on three prosumer profiles that have different generation and storage characteristics. A variable pricing structure for power supplied or bought by the utility company is considered in this study and a scenario in which prosumers are not allowed to sell to the utility company. It is evident from the simulation results of the MATLAB environment that scenario 1 generates the minimum cost operation profiles compared to scenario 2. The effectiveness and the robustness of the proposed algorithm are demonstrated in the optimal solution obtained in solving the energy management problem.

The role of solar PV in the global energy mix is becoming more significant as the cost of solar modules continues to decline. The African continent has a good degree of solar irradiation. Therefore, it is imperative that the benefits of alternative energy generation through solar PV are maximized. In this paper, we present an optimization model of a case study in Sierra Leone which can be adapted for the Nigerian distribution system. This is because both countries are located in the same region (West Africa) and power systems are similar. We consider two demand response (DR) scenarios: price-based and incentive-based. The various DR schemes are compared to a situation in which there is no DR utilization to examine the impact of DR on peak load reduction and financial benefit to participants, and impact of renewable energy dispatch during peak and off-peak periods on financial benefits to participants. Simulation results show that the time- and incentive-based DR strategies reduce peak load demand by 11% and 14% respectively.

This paper presents a novel nature-inspired meta-heuristic optimization algorithm known as the Enhanced Whale Optimization Algorithm (EWOA), which imitates humpback whales' social behavior to solve the optimization of multi-area automatic load frequency control (LFC) problems of a stochastic renewable energy-based power system with superconducting magnetic energy storage (SMES). An EWOA algorithm is presented in response to the limitations of the conventional WOA algorithm, including its sluggish convergence time, low accuracy, and propensity to easily enter local optimum. The system model investigated includes some physical constraints such as the time delay (TD), generation rate constraint (GRC), reheat turbine (RT), and the dead band (DB). The impacts of these physical constraints on the dynamic performance of the proposed controller were investigated. The EWOA algorithm is utilized to dynamically optimize the parameters of the PID controller for optimal system performance. The effectiveness and dynamic performance of the proposed controller are compared with the conventional WOA using some performance metrics. The system model also includes superconducting magnetic energy storage (SMES) units in both areas and their impacts on the system performances are also investigated. The effects of the changes of two different parameters of the system (frequency bias parameter, B, and the governor speed regulation, R) on the frequency deviation responses and the controller's robustness are examined. It is evident from the results that the dynamic performance of the proposed controller is better than that of the conventional WOA and it is more robust and stable to changes in system loading, parameters, and step load perturbation.

The aftermath of including new technologies in a modern electric system in conjunction with the incessant rise in power demand could pose a risk to the optimal operation of the system. Therefore, it becomes imperative to identify the most influential and critical nodes of such a system to avert future problems in network operation. In this paper, to identify most significant nodes of the system, the authors propose two measures of centrality in accordance with the network structural properties of a power system, namely, degree centrality (DC) and eigenvector centrality (EC). These are expressed considering the admittance matrix that exists among the interconnection of load to load nodes in an electrical power network. A critical node closeness centrality (CNCC) method is also proposed to identify critical nodes of the system. This is done by modifying the conventional closeness centrality (CC) to include the influence of interconnection that exists between network load to load nodes as captured by the admittance matrix between them. A comparative analysis of the proposed techniques with other conventional methods is also carried out. The result of the simulation shows that the proposed methods could serve as alternative tools in the identification of influential and weak nodes in a power system.

BackgroundSeasonal influenza activity showed a sharp decline in activity at the beginning of the emergence of COVID-19. Whether there is an epidemiological correlation between the dynamic of these 2 respiratory infectious diseases and their future trends needs to be explored. ObjectiveWe aimed to assess the correlation between COVID-19 and influenza activity and estimate later epidemiological trends. MethodsWe retrospectively described the dynamics of COVID-19 and influenza in 6 World Health Organization (WHO) regions from January 2020 to March 2023 and used the long short-term memory machine learning model to learn potential patterns in previously observed activity and predict trends for the following 16 weeks. Finally, we used Spearman correlation coefficients to assess the past and future epidemiological correlation between these 2 respiratory infectious diseases. ResultsWith the emergence of the original strain of SARS-CoV-2 and other variants, influenza activity stayed below 10% for more than 1 year in the 6 WHO regions. Subsequently, it gradually rose as Delta activity dropped, but still peaked below Delta. During the Omicron pandemic and the following period, the activity of each disease increased as the other decreased, alternating in dominance more than once, with each alternation lasting for 3 to 4 months. Correlation analysis showed that COVID-19 and influenza activity presented a predominantly negative correlation, with coefficients above –0.3 in WHO regions, especially during the Omicron pandemic and the following estimated period. The diseases had a transient positive correlation in the European region of the WHO and the Western Pacific region of the WHO when multiple dominant strains created a mixed pandemic. ConclusionsInfluenza activity and past seasonal epidemiological patterns were shaken by the COVID-19 pandemic. The activity of these diseases was moderately or greater than moderately inversely correlated, and they suppressed and competed with each other, showing a seesaw effect. In the postpandemic era, this seesaw trend may be more prominent, suggesting the possibility of using one disease as an early warning signal for the other when making future estimates and conducting optimized annual vaccine campaigns.

Due to growing activities of avian influenza, more attention should be paid to avian influenza virus infections. Global summaries or national reports lack data on epidemiological patterns of avian influenza. A descriptive epidemiological analysis of avian influenza outbreaks from 2018 to 2022 was conducted, particularly fowl infections, human infections, and sequence alterations. The number of fowl infection outbreaks in the first half of 2022 was the highest level in the five-year period. Countries or regions could reliably be classified into three clusters according to fowl infection activity scores, with 60.0% of countries or regions in C1 in Europe. Additionally, two host infection patterns of countries were noted, led by the Taiwan (China) region and Germany. Human infections also increased, with 88.1% of cases being in China with an increasing risk of cases in northern China. Sequences that were furin cleavage motif present spread from Asia to Europe and North America over the five-year period. Continuous changes in the global activities of avian influenza highlight the need for sustained global surveillance, including strengthening monitoring capacity for vulnerable population and dynamically detecting new cases or genetic variations of the avian influenza virus under the One Health framework.

Abstract This paper proposes a new hybridised approach comprising the flower pollination algorithm and pathfinder algorithm (FPAPFA), in order to address optimisation problems and for load frequency control system. Although the FPA is a popular algorithm that has been widely used in diverse applications, its implementation is met with the tendency to be trapped in local optimal due to an imbalance between the exploration and exploitation process. Consequently, the FPA's exploration functionality can be enhanced by using the PFA features to shift certain pollens to moderately enhanced locations rather than leading them to random positions. Furthermore, a modified fluctuation rate is incorporated into the PFA to reinforce the exploitative competence of the FPAPFA. Compared to other popular techniques, the proposed algorithm's performance was evaluated against 23 standard mathematical optimisation functions and statistically tested using the Wilcoxon rank‐sum and Friedman rank tests. Moreover, the FPAPFA is applied to regulate two unequal multi‐area interconnected power systems with different generating units (thermal, hydro, diesel, and wind power plants) while also integrating redox flow batteries (RFBs) and interline power flow controller (IPFC). Simulation results show that the proposed FPAPFA delivered better results than other algorithms with improved convergence speed, stability, and robustness.

Abstract The recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using the genetic algorithm (GA) for feature selection. After the optimized features are chosen, the proposed detection engine uses the following ML classifiers: Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), and Naive Bayes (NB). To validate the performance, the proposed credit card fraud detection engine is evaluated using a dataset generated from European cardholders. The result demonstrated that our proposed approach outperforms existing systems.

Abstract This study investigates aberrant DNA methylations as potential diagnosis and prognosis markers for esophageal squamous-cell carcinoma (ESCC), which if diagnosed at advanced stages has

Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering the three main ensemble methods: bagging, boosting, and stacking, their early development to the recent state-of-the-art algorithms. The study focuses on the widely used ensemble algorithms, including random forest, adaptive boosting (AdaBoost), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). An attempt is made to concisely cover their mathematical and algorithmic representations, which is lacking in the existing literature and would be beneficial to machine learning researchers and practitioners.

$\alpha $ BB is an elegant deterministic branch and bound global optimisation that guarantees global optimum convergence with minimal parameter tuning. However, the method suffers from a slow convergence speed calling for computational improvements in several areas. The current paper proposes hybridising the branch and bound process with particle swarm optimisation to improve its global convergence speed when solving twice differentiable ( $C^{2}$ ) box-constrained multimodal functions. This hybridisation complemented with interval analysis leads to an early discovery of the global optimum, quicker pruning of suboptimal regions in the problem space, thus improving global convergence. Also, when used as a heuristic search algorithm, the hybrid algorithm yields superior solution accuracy owing to the combined search capabilities of PSO and the branch and bound framework. Computational experiments have been conducted on CEC 2017/2019 test sets and on n-dimensional classical test sets yielding improved convergence speed in the complete search configuration and superior solution accuracy in the heuristic search configuration.

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.

Edible oil is prone to be contaminated by Benzo[a]pyrene (BaP) during production and processing. Adsorption is an important technology for the removal of BaP from edible oil, and biochar as a new type of adsorbent for the treatment BaP has received extensive attention. In this study, we prepared unmodified biochar and ZnCl2-modified biochar from sesame straw cellulose (SSB and SSZ) and sesame capsule cellulose (SCB and SCZ), carried out structure characterization by Fourier Transform Infrared (FT-IR), Brunauer-Emmett-Teller (BET) and Scanning Electron Microscope (SEM), and finally evaluated their potential effectiveness in BaP removal from sesame oil. FT-IR results revealed that SSZ and SCZ showed higher peak intensities, indicating that they could provide more active sites for adsorption. The results of XRD presented that SSZ and SCZ had a higher graphitization degree than the unmodified biochar. SEM results showed that the SSZ and SCZ had more complex pore structure, which is beneficial to adsorption. BET analysis results showed that SSZ and SCZ had a higher specific surface area of 1622.83–1797.39 m2/g and a better developed porous structure (the total pore volume Vtotal was 0.9434–1.0023 cm3/g) than the SSB and SCB (specific surface area was 350.55–352.59 m2/g, and Vtotal was 0.1907–0.1918 cm3/g), indicating that modification promoted the formation of porous structure and thus improved the adsorption capacity of biochar. Adsorption experiments confirmed once again that the BaP removal rates of SSZ and SCZ (92.67%–96.13%) were much higher than that of unmodified biochar (3.34%–4.28%), and the BaP content in the sesame oil treated by SSZ and SCZ were far below the current EU maximum levels (2 μg/kg). Therefore, the modified biochar from cellulose is a promising adsorbent for BaP removal. In future studies, the influences of bichar on the nutritional quality of edible oil should be taken into consideration so as to find an ideal active carbon substitute for BaP removal in edible oil.

Electric vehicles (EVs) with voltage-to-grid (V2G) capability are useful in augmenting grid capability to handle high energy demand of end users during peak periods. We propose a hybrid state-of-charge (SOC) battery model with aggregator to optimize battery charging and maintain grid stability during peak periods. The proposed SOC model leverages the advantages of three well-known previously proposed battery models namely: Shepherd, Unnewehr and Nernst models. The proposed hybrid model is a combination of the merits of the three specified empirical Lithium-ion battery models to optimize slow charging. This will enhance battery performance by improving its depth-of-discharge profile. This results in enhanced V2G capability and longer driving time for EV owners. Battery parameters used in the simulation are for Nissan Leaf 2019 EV. The proposed SOC model parameters are used to optimize a two-objective function which is used by the aggregator to maximize benefits to both EV owners and DSO. Multi-objective genetic algorithm (MOGA) is used to optimize the objective function because of its ability to obtain non-dominated solutions while still maintaining diversity of the solutions. From simulation results, proposed OCV model improves battery SOC by 10% after V2G operating period (2 p.m.) compared to a case without the model. Also, proposed model earns aggregator $445 and $45 more for voltage and frequency regulation services, respectively. Voltage stability of all 5 considered grid buses of the IEEE 33-node system remains at 0.9–1.0 p.u.