Random Scheme Research Articles

Trade Network Resilience of the Global Photovoltaic Industry Chain

Based on the trade data of the global photovoltaic (PV) industry chain from 2005 to 2021, this paper constructs a global PV industry chain trade network model and analyzes its static resilience and dynamic resilience characteristics, reaching the following conclusions. The static resilience of the trade network is analyzed through aggregation, transmission, hierarchy and matching. The upstream polysilicon trade network is relatively loose, while the midstream and downstream trade networks appear to aggregate significantly. The average path length is short and the average clustering coefficient is high, indicating that the global PV industry chain trade network has high transmission efficiency and small-world characteristics. The global PV industry chain trade network is hierarchical. The degree distribution slope of the upstream polysilicon trade network is the largest and has a high level of hierarchy. Meanwhile, the global PV industry chain trade network is a heterogeneous network, which strengthens the connection between the core countries and edge countries, making the global PV industry chain trade network more resilient. Regarding the dynamic resilience of the trade network, the deterministic disturbance strategies have a greater impact on network performance than the random disturbance strategies, indicating that targeted impacts exert greater impact on the resilience of the global PV industry chain trade network. For the deterministic disturbance strategy, the interference based on intermediate centrality has a higher impact on the overall resilience of the network than the interference based on degree centrality, thus the influence of the country with strong trade radiating ability on the network elasticity is greater than that of the country with strong transit ability.

Coupled optimization of task sequence and hoist scheduling for electroplating production lines based on an improved salp swarm algorithm

Automatic electroplating production lines have been widely used in electronics industries to reduce the labour intensity and improve the production efficiency. In the multi-variety and low-volume electroplating production, it is known that the task loading sequence and hoist scheduling are coupled with each other, and they codetermine the production efficiency, while all the existing scheduling methods consider them separately, and thus the optimal production schemes become unavailable. Therefore, this paper develops a Task sequence-Hoist scheduling Coupled Optimization (THCO) model which simultaneously considers the requirements and practical constrains of task sequence and hoist scheduling, having an optimization objective of minimizing the maximum completion time. For this model, a double-layer code is developed and an Improved Salp Swarm Algorithm (ISSA) is developed by introducing three improvement strategies: the random spare strategy which is used to increase the population diversity, the nonlinear adaptive weight strategy which is used to balance the exploration and exploitation capacities, and a golden sine algorithm which is used to improve the convergence rate. Experiments based on 23 benchmark functions are then conducted. The obtained results show that ISSA has better convergence and solving quality than existing algorithms. Furthermore, several production cases prove that THCO can generate production schemes that better meet the requirements of production lines.

Estimating Brazilian Amazon Canopy Height Using Landsat Reflectance Products in a Random Forest Model with Lidar as Reference Data

Landsat data have been used to derive forest canopy structure, height, and volume using machine learning models, i.e., giving computers the ability to learn from data and make decisions and predictions without being explicitly programmed, with training data provided by ground measurement or airborne lidar. This study explored the potential use of Landsat reflectance and airborne lidar data as training data to estimate canopy heights in the Brazilian Amazon forest and examined the impacts of Landsat reflectance products at different process levels and sample spatial autocorrelation on random forest modeling. Specifically, this study assessed the accuracy of canopy height predictions from random forest regression models impacted by three different Landsat 8 reflectance product inputs (i.e., USGS level 1 top of atmosphere reflectance, USGS level 2 surface reflectance, and NASA nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance (NBAR)), sample sizes, training/test split strategies, and geographic coordinates. In the establishment of random forest regression models, the dependent variable (i.e., the response variable) was the dominant canopy heights at a 90 m resolution derived from airborne lidar data, while the independent variables (i.e., the predictor variables) were the temporal metrics extracted from each Landsat reflectance product. The results indicated that the choice of Landsat reflectance products had an impact on model accuracy, with NBAR data yielding more trustful results than the other products despite having higher RMSE values. Training and test split strategy also affected the derived model accuracy metrics, with the random sample split (randomly distributed training and test samples) showing inflated accuracy compared to the spatial split (training and test samples spatially set apart). Such inflation was induced by the spatial autocorrelation that existed between training and test data in the random split. The inclusion of geographic coordinates as independent variables improved model accuracy in the random split strategy but not in the spatial split, where training and test samples had different geographic coordinate ranges. The study highlighted the importance of data processing levels and the training and test split methods in random forest modeling of canopy height.

Comparison of 3 methods characterizing H2S exposure in water and wastewater management work.

This study evaluates the effectiveness of self-assessed exposure (SAE) data collection for characterization of hydrogen sulfide (H2S) risks in water and wastewater management, challenging the adequacy of traditional random or campaign sampling strategies. We compared 3 datasets derived from distinct strategies: expert data with activity metadata (A), SAE without metadata (B), and SAE with logbook metadata (C). The findings reveal that standard practices of random sampling (dataset A) fail to capture the sporadic nature of H2S exposure. Instead, SAE methods enhanced by logbook metadata and supported by reliable detection and calibration infrastructure (datasets B and C) are more effective. When assessing risk, particularly peak exposure risks, it is crucial to adopt measures that capture exposure variability, such as the range and standard deviations. This finer assessment is vital where high H2S peaks occur in confined spaces. Risk assessment should incorporate indices that account for peak exposure, utilizing variability measures like range and standard or geometric standard deviation to reflect the actual risk more accurately. For large datasets, a histogram is just as useful as statistical measures. This approach has revealed that not only wastewater workers but also water distribution network workers, can face unexpectedly high H2S levels when accessing confined underground spaces. Our research underscores the need for continuous monitoring with personal electrochemical gas detector alarm systems, particularly in environments with variable and potentially hazardous exposure levels.

A Modified Sand Cat Swarm Optimization Algorithm Based on Multi-Strategy Fusion and Its Application in Engineering Problems

Addressing the issues of the sand cat swarm optimization algorithm (SCSO), such as its weak global search ability and tendency to fall into local optima, this paper proposes an improved strategy called the multi-strategy integrated sand cat swarm optimization algorithm (MSCSO). The MSCSO algorithm improves upon the SCSO in several ways. Firstly, it employs the good point set strategy instead of a random strategy for population initialization, effectively enhancing the uniformity and diversity of the population distribution. Secondly, a nonlinear adjustment strategy is introduced to dynamically adjust the search range of the sand cats during the exploration and exploitation phases, significantly increasing the likelihood of finding more high-quality solutions. Lastly, the algorithm integrates the early warning mechanism of the sparrow search algorithm, enabling the sand cats to escape from their original positions and rapidly move towards the optimal solution, thus avoiding local optima. Using 29 benchmark functions of 30, 50, and 100 dimensions from CEC 2017 as experimental subjects, this paper further evaluates the MSCSO algorithm through Wilcoxon rank-sum tests and Friedman’s test, verifying its global solid search ability and convergence performance. In practical engineering problems such as reducer and welded beam design, MSCSO also demonstrates superior performance compared to five other intelligent algorithms, showing a remarkable ability to approach the optimal solutions for these engineering problems.

Saudi teachers’ perceptions on pedagogical affordances of digital applications in teaching students with learning disabilities

ABSTRACT Creating a supporting learning environment for students with learning disabilities has been the concern of most teachers. However, digital applications provide numerous pedagogical affordances for teaching students with learning disabilities. This quantitative study examined teachers’ perspectives on the pedagogical affordances of digital applications and challenges when teaching students with learning disabilities in primary schools in Riyadh, Saudi Arabia. Data were collected from 280 teachers of students with learning disabilities through a questionnaire using a random strategy. The results showed that despite teachers’ awareness of the pedagogical affordances of digital applications in developing the academic skills of students with learning disabilities, the use of digital applications was intermediate. Teachers identified a lack of digital tools, time pressure and teachers’ workload as hindering the use of digital applications in their teaching. Findings also reveal statistically significant differences in perceptions for males and females, holding a master’s degree or above qualification, experience category (1–5 years), and training category (1–5 training sessions). These findings highlight the need for ongoing professional development and support that focuses more on real-classroom practices and resources, and how teachers can use digital tools to meet the various learning needs of students with learning disabilities.

Trading strategies and Financial Performances: A simulation approach

This paper presents a comparative analysis of three major approaches to portfolio strategies: the maximization of the Sharpe ratio, the minimization of the Expected Shortfall and “zero–intelligence” trading. Data from financial time series and from a simulated order-book are used to analyse how various strategies affect investors’ portfolio performance and volatility. Results show, firstly, that the superiority of technical and analytical approaches over a random strategy is not obvious. Secondly, that strategies with lower and less risky profits may reveal preferable to those with higher returns and risk. Balancing this trade-off is crucial for stable financial growth.

Hybrid GRU-CNN bilinear parameters initialization for quantum approximate optimization algorithm

The Quantum Approximate Optimization Algorithm (QAOA), a pivotal paradigm in the realm of variational quantum algorithms (VQAs), offers promising computational advantages for tackling combinatorial optimization problems. Well-defined initial circuit parameters, responsible for preparing a parameterized quantum state encoding the solution, play a key role in optimizing QAOA. However, classical optimization techniques encounter challenges in discerning optimal parameters that align with the optimal solution. In this work, we propose a hybrid optimization approach that integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), and a bilinear strategy as an innovative alternative to conventional optimizers for predicting optimal parameters of QAOA circuits. GRU serves to stochastically initialize favorable parameters for depth-1 circuits, while CNN predicts initial parameters for depth-2 circuits based on the optimized parameters of depth-1 circuits. To assess the efficacy of our approach, we conducted a comparative analysis with traditional initialization methods using QAOA on Erdős-Rényi graph instances, revealing superior optimal approximation ratios. We employ the bilinear strategy to initialize QAOA circuit parameters at greater depths, with reference parameters obtained from GRU-CNN optimization. This approach allows us to forecast parameters for a depth-12 QAOA circuit, yielding a remarkable approximation ratio of 0.998 across 10 qubits, which surpasses that of the random initialization strategy and the PPN2 method at a depth of 10. The proposed hybrid GRU-CNN bilinear optimization method significantly improves the effectiveness and accuracy of parameters initialization, offering a promising iterative framework for QAOA that elevates its performance.

P-008 Sperm DNA methylation landscape in infertile men with clinical varicocele before and after treatment

Abstract Study question As varicocele leads to poor semen parameters, does it also affect sperm DNA methylation levels? Does varicocele treatment help in epigenetics restoration in the sperm? Summary answer Varicocele leads to aberrant sperm DNA methylation and varicocele treatment helps in the partial restoration of sperm DNA methylation levels. What is known already Varicocele affects 15% to 20% of the adult male population and 20% of men with varicocele are reported to be infertile. Treatment modalities for varicocele improve semen parameters, yet more than 50% of cases remain infertile. High oxidative stress (OS) has been implicated as a key factor in varicocele-associated infertility. OS during spermatogenesis may lead to aberrant epigenetic modifications in spermatozoa. In view of the crucial role of sperm epigenome in its functionality, this study aims to evaluate sperm genome-wide DNA methylation by whole-genome bisulfite sequencing (WGBS) before and after varicocele treatment in men with varicocele. Study design, size, duration This is a case-control study, which includes (n = 28) healthy fertile men and (n = 35) infertile men with clinical varicocele. Study participants were recruited from ICMR-NIRRCH and, LTMG Hospital, Mumbai. According to the inclusion and exclusion criteria, study participants were recruited from December 2020 to November 2023. Varicocele patients were followed up, and semen samples (n = 18) were recollected after 3-6 months of varicocele treatment. Participants/materials, setting, methods WGBS was used to identify differentially methylated CpG (DMC) sites in sperm of varicocele group compared to controls. 5 samples from each group were pooled for WGBS. DMCs identified with ≥25% methylation change and ≤0.05 q-value. DMCs within genes relevant to sperm function were selected for validation in the study population by pyrosequencing. Differentially methylated Genes (DMGs) were identified based on the location of DMC and used for pathway analysis using KEGG and DAVID tools. Main results and the role of chance Varicocele group had significantly lower semen parameters compared to controls, which were found to be improved after varicocele treatment. WGBS of sperm DNA was done and DMC sites were obtained in varicocele group compared to controls. A total of 6414 DMCs were obtained, of which, 3730 were hypermethylated and 2684 were hypomethylated. Based on genomic context, differential methylation was observed in introns, exons, promoters and intergenic regions. WGBS analysis gave a total of 1484 DMGs. KEGG and DAVID pathway analysis revealed that the PI3K-Akt signaling, MAPK signaling, cAMP signaling, calcium signaling, signal transduction pathway, cell adhesion molecules, and tight junction, were affected in the varicocele group. The DMGs SPATA6L, H2AX, SOD2, BCL2, NF1, PRKN, CDKN1B, BCR, P2RX7, RNF212B obtained are involved in spermatogenesis, regulation of cell cycle, apoptotic signaling pathway, cell differentiation, regulation of ROS metabolic process, response to heat, mitochondria regulation, regulation of gene expression, etc. Pyrosequencing validation of a few selected DMCs reflects the same methylation difference (hypo or hyper) as obtained by the WGBS pooled sample. Significant hypermethylation and hypomethylation were observed in the H2AX DMC and CDKN1B DMC, respectively, in the varicocele population; however, after varicocele treatments, these methylation levels seem to be restored. Limitations, reasons for caution The limitations of this study are the small number of patients and pooled sperm DNA sample WGBS analysis in the study population. However, a pooling sample strategy is considered more efficient than a random sample selection strategy, as pooling minimizes the amount of information lost below the detection threshold. Wider implications of the findings This study identified differentially methylated genes relevant to spermatogenesis and sperm functions in the case of varicocele compared to fertile controls and gave deeper insights into the possibility of epigenetic restoration after varicocele treatment. This study provides insight into the molecular aetiology of varicocele associated infertility. Trial registration number Not Applicable

Veterinary Drug Use Pattern in livestock and Its Public Health Significance, Gondar Town Veterinary Clinic, Northern Ethiopia

A cross-sectional and retrospective study design and simple random sampling strategy was conducted in Gondar town veterinary clinic, northern Ethiopia with the aim of evaluating drug use pattern and show public health significance of irrational veterinary drug use. A total of 2117 drugs were prescribed to 1717 veterinary patients randomly selected from the case registry in this study. The result indicates average number of drugs prescribed per case was 1.23 and the maximum of three drugs was prescribed. The percentages of antimicrobial, anthelmintic, endectocide and other drugs prescribed were 1182 (55.7%), 481 (22.7%), 426 (20.1%), and 28 (1.3%), respectively. All patients were treated without correct laboratory support. As a result, antimicrobials were inappropriately prescribed in 1.4% of viral diseases, 0.2% of external parasitic diseases, 0.8% of internal parasitic diseases, 0.3% of metabolic diseases, 0.1% of fungal diseases, and anthelmintics in 0.1% of bacterial diseases and 0.5% of external parasitic diseases. 2013/2117 (95.1%) the route of administration of the prescribed drugs was not stated. Clinic professionals were 80% animal health assistants and 20% veterinarians. The results reveal problems with correct diagnosis, low level of education of prescribers, few essential drugs, absence of standard veterinary drug lists, and inappropriate drug use; these all principals to public health significance. Therefore, the availability of key essential drugs should improve and available drugs be prescribed with its appropriate doses, routes, and regimens. Veterinarians should be made aware of the irrational use of veterinary drugs and their public health implications.

A quantitative comparison of atlas parcellations on the human superior temporal sulcus

The superior temporal sulcus (STS) has a functional topography that has been difficult to characterize through traditional approaches. Automated atlas parcellations may be one solution while also being beneficial for both dimensional reduction and standardizing regions of interest, but they yield very different boundary definitions along the STS. Here we evaluate how well machine learning classifiers can correctly identify six social cognitive tasks from STS activation patterns dimensionally reduced using four popular atlases (Glasser et al., 2016; Gordon et al., 2016; Power et al., 2011 as projected onto the surface by Arslan et al., 2018; Schaefer et al., 2018). Functional data was summarized within each STS parcel in one of four ways, then subjected to leave-one-subject-out cross-validation SVM classification. We found that the classifiers could readily label conditions when data was parcellated using any of the four atlases, evidence that dimensional reduction to parcels did not compromise functional fingerprints. Mean activation for the social conditions was the most effective metric for classification in the right STS, whereas all the metrics classified equally well in the left STS. Interestingly, even atlases constructed from random parcellation schemes (null atlases) classified the conditions with high accuracy. We therefore conclude that the complex activation maps on the STS are readily differentiated at a coarse granular level, despite a strict topography having not yet been identified. Further work is required to identify what features have greatest potential to improve the utility of atlases in replacing functional localizers.

Effects of packetization on communication dynamics in brain networks.

Computational studies in network neuroscience build models of communication dynamics in the connectome that help us understand the structure-function relationships of the brain. In these models, the dynamics of cortical signal transmission in brain networks are approximated with simple propagation strategies such as random walks and shortest path routing. Furthermore, the signal transmission dynamics in brain networks can be associated with the switching architectures of engineered communication systems (e.g., message switching and packet switching). However, it has been unclear how propagation strategies and switching architectures are related in models of brain network communication. Here, we investigate the effects of the difference between packet switching and message switching (i.e., whether signals are packetized or not) on the transmission completion time of propagation strategies when simulating signal propagation in mammalian brain networks. The results show that packetization in the connectome with hubs increases the time of the random walk strategy and does not change that of the shortest path strategy, but decreases that of more plausible strategies for brain networks that balance between communication speed and information requirements. This finding suggests an advantage of packet-switched communication in the connectome and provides new insights into modeling the communication dynamics in brain networks.

LEAP: the latent exchangeability prior for borrowing information from historical data.

It is becoming increasingly popular to elicit informative priors on the basis of historical data. Popular existing priors, including the power prior, commensurate prior, and robust meta-analytic predictive prior, provide blanket discounting. Thus, if only a subset of participants in the historical data are exchangeable with the current data, these priors may not be appropriate. In order to combat this issue, propensity score approaches have been proposed. However, these approaches are only concerned with the covariate distribution, whereas exchangeability is typically assessed with parameters pertaining to the outcome. In this paper, we introduce the latent exchangeability prior (LEAP), where observations in the historical data are classified into exchangeable and non-exchangeable groups. The LEAP discounts the historical data by identifying the most relevant subjects from the historical data. We compare our proposed approach against alternative approaches in simulations and present a case study using our proposed prior to augment a control arm in a phase 3 clinical trial in plaque psoriasis with an unbalanced randomization scheme.

A hybrid approach to obesity level determination with decision tree and pelican optimization algorithm

Approximately 2 billion people in the world struggle with "obesity" and factors like eating lifestyle, habits, health conditions and mode of transport affect obesity. In this study, an artificial intelligence and machine learning-based model has been developed to predict obesity levels. It is proposed to create a hybrid model by combining the Decision Tree (DT) algorithm with the Pelican Optimization Algorithm (POA) on the obesity dataset of 2111 patients in SSggle. These models emphasize the critical role of parameters, aiming to achieve high performance. To solve the classification problem of multi-class obesity level determination, fuzzy logic-based parameter optimization is used to achieve high performance. While obesity rates are increasing worldwide, the study, which aims to globalize the parameters with the random discovery strategy of POA, is thought to be helpful for health professionals and decision-makers by successfully predicting obesity levels.

EXAMINING THE ROLE OF SERVICE QUALITY DIMENSIONS OF MOBILE PHONE SERVICE PROVIDERS; AN EMPIRICAL STUDY

Service quality has become a critical factor for the competitive advantage of mobile phone service providers in the telecommunication industry. This study explores the key factors influencing service quality in mobile phone service providers and their impact on customer satisfaction and loyalty. The analysis focuses on dimensions such as network quality, customer support, pricing, service reliability, and value-added services. The research aims to identify how these factors contribute to the overall perception of service quality and determine the most significant determinants of customer satisfaction. The non-probability convenience sampling technique is the most suitable method when it is not possible to obtain information from every member of the sample population. Additionally, the precise population frame of mobile phone service consumers in the study area could not be determined, as the number of users could not be ascertained. The interview schedule was employed to acquire the primary data from the respondents. The data that has been collected is coded and inserted into a primary table. The data are analysed in accordance with the nature of the data and the intent of the analysis (nature of the problem) using the SPSS software. For the purpose of the research, a sample size of 100 respondents is selected from an infinite population. A random sample strategy was used by the researcher in order to choose the objects that would be used for sampling. The findings highlight the need for service providers to continuously adapt and enhance their service offerings to meet evolving customer expectations and maintain competitiveness in a rapidly changing market

Taste Preference for Salt Predicts Salt Intake in a Chinese Population.

This study describes the association between taste preference for salt and actual salt intake, thus guiding and refining personal and public health campaigns designed to lower salt intake in China. A cross-sectional survey of 1489 residents aged 18 to 69 years was conducted in 2017 in China. A multistage random sampling strategy was used, and a combination of questionnaires and physical and laboratory measurements were conducted to collect baseline characteristics and knowledge, attitudes, and behavior (KAB) related to salt. A 24 h urine collection was obtained for sodium and potassium excretion analysis. Participants were divided into two groups, light taste preference and salty taste preference, according to their answer to the question "Compared to others, how do you think your taste preference is for salt?". The mean age of the 1489 participants was 46.26 years, 48.9% were males, over 1/3 (35.7%) were identified as hypertensive, and 317 (21.3%) self-reported a salty taste preference. The mean of 24 h urinary sodium excretion was 167.32 mmol/24 h, corresponding to 9.79 g salt/d intake, and the sodium-to-potassium ratio (Na/K) was 4.90. The 24 h urinary sodium excretion of salty taste preference (177.06 mmol/24 h) was significantly higher than that of light taste preference (164.69 mmol/24 h). The multiple logistic regression analysis showed that the salty taste preference group had significantly higher 24 h urinary sodium (ORa(95%CI) = 1.004(1.002-1.006)), diastolic blood pressure (DBP), proportion of greasy food preference, and drinking levels, but lower potassium excretion, response levels to most KAB questions, and regular physical activity compared to the light taste preference group. Self-reported taste preference for salt predicted actual salt intake, which was verified by 24 h urinary sodium monitoring. Taste preference for salt could be used as a proxy for intake in terms of targeted salt intake, nutrition, and health education.

A Comparative Study of Household Water, Sanitation and Hygiene (WASH) Covariates Across Socioeconomic Strata

Background: Water, sanitation, and hygiene (WASH) are essential for a healthy and productive life. High-quality access to these services reduces disease occurrence and spread in communities. Developing countries, including Pakistan, face significant challenges in providing these facilities. This study aims to highlight WASH-related service disparities in Pakistani households based on socioeconomic strata. Methodology: A stratified random sampling strategy was used, with a sample size of 1200 households based on the PDHS 2019 special survey. Descriptive analysis and chi-square tests were conducted to assess the association between wealth quintiles and WASH access. Results: The study found that 82.4% of households had access to improved sources of drinking water. Only 4.8% of households in the lowest quintile had access to improved drinking water sources. Water unavailability for at least one day per week was 13% higher among the lower quintiles. Forty-four percent of households using improved sanitation facilities belonged to the two highest strata, while poorer households predominantly used unimproved sanitation facilities. Conclusion: Lower-quality WASH access is more prevalent among lower wealth quintiles, increasing the risk of disease and infection. Further research is needed to inform policymakers and develop plans to reduce inequalities in access to WASH services.

Corticosteroid burst therapy in patients with acute heart failure: Design of the CORTAHF pilot study.

Inflammation has emerged as a potential key pathophysiological mechanism in heart failure (HF) in general and acute HF (AHF) specifically, with inflammatory biomarkers shown to be highly predictive of adverse outcomes in these patients. The CORTAHF study builds on both these data and the fact that steroid burst therapy has been shown to be effective in the treatment of respiratory diseases and COVID-19. Our hypothesis is that in patients with AHF and elevated C-reactive protein (CRP) levels without symptoms or signs of infection, a 7-day course of steroid therapy will lead to reduced inflammation and short-term improvement in quality of life and a reduced risk of worsening HF (WHF) events. The study, which is currently ongoing, will include 100 patients with AHF ages 18-85, regardless of ejection fraction, screened within 12h of presentation. Patients will be included who have NT-proBNP>1500pg/mL and CRP>20mg/L at screening. Exclusion criteria include haemodynamic instability and symptoms and signs of infection. After signed consent, eligible patients will be randomized according to a central randomization scheme stratified by centre 1:1 to either treatment once daily for 7days with 40mg prednisone orally or to standard care. Patients will be assessed at study day 2, day 4 or at discharge if earlier, and at days 7 and 31 at the hospital; and at day 91 through a telephone follow-up. The primary endpoint is the change in CRP level from baseline to day 7, estimated from a mixed model for repeated measures (MMRM) including all measured timepoints, in patients without a major protocol violation. Secondary endpoints include the time to the first event of WHF adverse event, readmission for HF, or death through day 91; and changes to day 7 in EQ-5D visual analogue scale score and utility index. Additional clinical and laboratory measures will be assessed. The results of the study will add to the knowledge of the role of inflammation in AHF and potentially inform the design of larger studies with possibly longer duration of anti-inflammatory therapies in AHF.

Grey Wolf Optimization algorithm with random local optimal regulation and first-element dominance

Due to the classical Grey Wolf algorithm GWO does not consider the characteristics of the local information of individual in population, a novel local random optimization strategy is proposed to make up for the defect of GWO. In this method, several points in the neighborhood of the current location of each individual are selected at random in the axial direction as candidates, and the best points are selected to participate in the renewal decision of the individual. Furthermore, in our experiments, a special first-element dominance characteristic is found and can greatly improve the combination effect of global and local information. In order to ensure that all constraints are not violated in the process of constraint optimization in industrial design, the random mixed population initialization method is proposed to generate population individuals that meet the constraint requirements and contain boundary values randomly. In addition, a treatment method of shrinking in a specific direction is proposed for dealing with individuals who cross the boundary. Experimental results on several test function sets show that compared with recent improved algorithms for GWO, the proposed algorithm has obvious advantages in fitness value, convergence speed and stability.

A Multi-Strategy Collaborative Grey Wolf Optimization Algorithm for UAV Path Planning

The Grey Wolf Optimization Algorithm (GWO) is a member of the swarm intelligence algorithm family, which possesses the highlights of easy realization, simple parameter settings and wide applicability. However, in some large-scale application problems, the grey wolf optimization algorithm easily gets trapped in local optima, exhibits poor global exploration ability and suffers from premature convergence. Since grey wolf’s update is guided only by the best three wolves, it leads to low population multiplicity and poor global exploration capacity. In response to the above issues, we design a multi-strategy collaborative grey wolf optimization algorithm (NOGWO). Firstly, we use a random walk strategy to extend the exploration scope and enhance the algorithm’s global exploration capacity. Secondly, we add an opposition-based learning model influenced by refraction principle to generate an opposite solution for each population, thereby improving population multiplicity and preventing the algorithm from being attracted to local optima. Finally, to balance local exploration and global exploration and elevate the convergence effect, we introduce a novel convergent factor. We conduct experimental testing on NOGWO by using 30 CEC2017 test functions. The experimental outcomes indicate that compared with GWO and some swarm intelligence algorithms, NOGWO has better global exploration capacity and convergence accuracy. In addition, we also apply NOGWO to three engineering problems and an unmanned aerial vehicle path planning problem. The outcomes of the experiment suggest that NOGWO performs well in solving these practical problems.