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To orient the commercial development of cowpeas, we identify the determinants of the value of cowpea grain sold by traders in Senegal’s local markets. We test whether the determinants differ between men and women traders and explore seasonal patterns.

We employ ordinary least squares and seemingly unrelated regressions using a nationally representative dataset of 973 traders, of whom 380 sell cowpea grain, in 99 urban and rural markets across the 14 regions of Senegal.

The value of cowpea grain sold is influenced by vendor and market characteristics but not by cowpea type. Women and men traders represent statistically distinct groups. The sales value was eight times higher during the survey season among men. Most women grain sellers are retailers, whereas men are involved in both retailing and wholesaling. The picture that emerges is that men traders are able to respond more to economic signals, such as purchase cost, credit and labor payments, perhaps because they operate on a larger scale. Sales were significantly correlated across seasons.

To support cowpea commercialization, researchers should explore the characteristics of enterprises led by women and men traders in greater depth. Sampling grain sold in markets to test genetic relationships with improved varieties would enable researchers to link market-based incentives directly to cowpea breeding.

Previous economics research about cowpea grain markets emphasized the hedonic analysis of grain characteristics to guide crop improvement. This study reveals differentiation among traders by gender and the importance of trader and market characteristics in sales value.

The purpose of this study focused on a global longitudinal bibliometric mapping of research in the field of health biotechnology between 1990 and 2023 to determine who is leading this field of knowledge and to estimate the sub-disciplines that are emerging and project those that will prevail in the future.

The study identified the most relevant countries, institutions and researchers, as well as the type of scientific collaborations. The applied steps applied in the study were the following: identification and selection of keyword terms by a panel of experts; design and application of an algorithm to identify these selected keywords in titles, abstracts and keywords using Web of Science terms to contrast them; performance of JCR data processing during 2023 using R, Python and VOSviewer.

Among the most relevant conclusions of the study are the following exponential growth has been observed in the study period; new branches of knowledge have emerged in which the subjects have been acquiring their own autonomous capabilities; and R&D in this field is still concentrated in a small group of core countries, and the trend is for it to remain so due to the capacity needs required.

This contribution seeks to systematize the existing scientific knowledge in the field of biotechnology, specifically in the area of health, using the technique of scientific mapping based on a logical model of indicators that aims to determine potential thematic ramifications.

This paper aims to develop a method for tuning the parameters of the active disturbance rejection controller (ADRC) for fixed-wing unmanned aerial vehicles (UAVs). The bald eagle search (BES) algorithm has been improved, and a cost function has been designed to enhance the optimization efficiency of ADRC parameters.

A six-degree-of-freedom nonlinear model for a fixed-wing UAV has been developed, and its attitude controller has been formulated using the active disturbance rejection control method. The parameters of the disturbance rejection controller have been fine-tuned using the collaborative mutual promotion bald eagle search (CMP-BES) algorithm. The pitch and roll controllers for the UAV have been individually optimized to obtain the most effective controller parameters.

Inspired by the salp swarm algorithm (SSA), the interaction among individual eagles has been incorporated into the CMP-BES algorithm, thereby enhancing the algorithm's exploration capability. The efficient and accurate optimization ability of the proposed algorithm has been demonstrated through comparative experiments with genetic algorithm, particle swarm optimization, Harris hawks optimization HHO, BES and modified bald eagle search algorithms. The algorithm's capability to solve complex optimization problems has been further proven by testing on the CEC2017 test function suite. A transitional function for fitness calculation has been introduced to accelerate the ability of the algorithm to find the optimal parameters for the ADRC controller. The tuned ADRC controller has been compared with the classical proportional-integral-derivative (PID) controller, with gust disturbances introduced to the UAV body axis. The results have shown that the tuned ADRC controller has faster response times and stronger disturbance rejection capabilities than the PID controller.

The proposed CMP-BES algorithm, combined with a fitness function composed of transition functions, can be used to optimize the ADRC controller parameters for fixed-wing UAVs more quickly and effectively. The tuned ADRC controller has exhibited excellent robustness and disturbance rejection capabilities.

The CMP-BES algorithm and transitional function have been proposed for the parameter optimization of the active disturbance rejection controller for fixed-wing UAVs.

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Considering the restricted and enclosed nature of prisons and closed environments and the prolonged and close contact between individuals, COVID-19 is more likely to have a higher incidence in these settings. This study aims to assess the prevalence of COVID-19 among prisoners.

Papers published in English from 2019 to July 7, 2023, were identified using relevant keywords such as prevalence, COVID-19 and prisoner in the following databases: PubMed/MEDLINE, Scopus and Google Scholar. For the meta-analysis of the prevalence, Cochrane’s Q statistics were calculated. A random effect model was used due to the heterogeneity in COVID-19 prevalence across included studies in the meta-analysis. All analyses were performed in STATA-13.

The pooled data presented a COVID-19 prevalence of 20% [95%CI: 0.13, 0.26] and 24% [95%CI: 0.07, 0.41], respectively, in studies that used PCR and antibody tests. Furthermore, two study designs, cross-sectional and cohort, were used. The results of the meta-analysis showed studies with cross-sectional and cohort designs reported 20% [95%CI: 0.11, 0.29] and 25% [95%CI: 0.13, 0.38], respectively.

Through more meticulous planning, it is feasible to reduce the number of individuals in prison cells, thereby preventing the further spread of COVID-19.

The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems.

Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners.

The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes.

The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process.

E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status.

A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

This study assesses postreplanting oil palm farming risks, analyzes seed procurement parameters, investigates seed institutions' performance factors and develops a framework for improved sustainability.

Incorporating data from 219 smallholder farmers in designated replanting areas, our study comprehensively evaluates seed supply performance, examining the roles of stakeholders and identifying potential risks in seed management. We assess these risks using the Risk Priority Number (RPN) methodology and Multidimensional Scaling (MDS) techniques.

The results show that the timing and quantity of oil palm seed supply have a relatively small impact on postreplanting failure risk. To mitigate this risk, focus on monitoring seed purity using high-quality Tenera oil palm-type seeds and early detection technology. Encourage seed-producing cooperatives to become legal seed producers for an inclusive system and consider smallholders' variety preferences.

This study’s significance lies in its comprehensive assessment of the risks associated with oil palm replanting on smallholder plantations, detailed analysis of critical parameters in seed procurement, investigation into the performance of palm oil seed institutions across various dimensions and development of a strategic framework to strengthen inclusive seed institutions for sustainable oil palm farming. This strategy holds valuable potential for the development of oil palm in Indonesia, particularly in expediting the smallholders' replanting program.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2023-0811

In an age of educational reform which incentivises increased digitisation and standardisation, teachers are expected to embrace the rise of ‘new’ tools and pedagogies with limited agency to inform, question or direct what ‘newness’ must be brought into their classrooms. Drawing on my research with English as a Foreign Language (EFL) educators in South Africa and using an ‘excessive entitlement’ lens, I showcase how teachers' lack of agency can result in ‘defensive’ and ‘coercive’ practices in the classroom which are a far cry from the education transformation imagined according to either global and local imaginaries for teaching and learning. If we are interested in an educational revolution, I argue that a fundamental reorientation in education recognising teachers' agency in informing change is necessary. To do so requires theoretically driven intervention methodologies which view the competing demands placed on teachers as entry points to developing their agency and volition to find practices which work for them and their students in the classroom. To that end, I illustrate how Cultural-Historical Activity Theory (CHAT) informed interventions like Change Laboratories could aid in this fundamental repositioning for teachers regarding transformational efforts and their far-reaching potential for educational revolution becoming conscious of and overcoming their feelings of excessive entitlement.