Identification study of solar cell/module using recent optimization techniques

This paper proposes the application of a novel metaphor-free population optimization based on the mathematics of the Runge Kutta method (RUN) for parameter extraction of a double-diode model of the unknown solar cell and photovoltaic (PV) module parameters. The RUN optimizer is employed to determine the seven unknown parameters of the two-diode model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. Consequently, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. The suggested objective function achieves the closeness degree between the estimated and experimental data. For getting the generic model, applications of the proposed RUN are carried out on two different commercial PV cells. To assess the proposed algorithm, a comprehensive comparison study is employed and compared with several well-matured optimization algorithms reported in the literature. Numerical simulations prove the high precision and fast response of the proposed RUN algorithm for solving multiple PV models. Added to that, the RUN can be considered as a good alternative optimization method for solving power systems optimization problems.

Generic Model for Adaptive Systems Realization

The permanent acquisition of the technical environment state and the ability to react to changes in this environment as well as to adapt to it are nowadays crucial for any information system. In this article, the authors present a well-defined model to guarantee in a simple way the design and the realization of adaptive information systems. This model is based on the Unified Modeling Language (UML) which is a widely known modeling standard. Its coverage is limited to bringing out the graded parties in the design of adaptive information systems. A future definition of a metamodel less related to UML language is therefore possible. The authors also present a code generator based on a model transformation technique. This generator allows you to partially produce domain-specific code as needed. A more complete code generator will come to ensure automatic generation of the code.

Generic Model for Adaptive Systems Realization

The permanent acquisition of the technical environment state and the ability to react to changes in this environment as well as to adapt to it are nowadays crucial for any information system. In this article, the authors present a well-defined model to guarantee in a simple way the design and the realization of adaptive information systems. This model is based on the Unified Modeling Language (UML) which is a widely known modeling standard. Its coverage is limited to bringing out the graded parties in the design of adaptive information systems. A future definition of a metamodel less related to UML language is therefore possible. The authors also present a code generator based on a model transformation technique. This generator allows you to partially produce domain-specific code as needed. A more complete code generator will come to ensure automatic generation of the code.

Spectrometric Prediction of Nitrogen Content in Different Tissues of Slash Pine Trees

The internal cycling of nitrogen (N) storage and consumption in trees is an important physiological mechanism associated with tree growth. Here, we examined the capability of near-infrared spectroscopy (NIR) to quantify the N concentration across tissue types (needle, trunk, branch, and root) without time and cost-consuming. The NIR spectral data of different tissues from slash pine trees were collected, and the N concentration in each tissue was determined using standard analytical method in laboratory. Partial least squares regression (PLSR) models were performed on a set of training data randomly selected. The full-length spectra and the significant multivariate correlation (sMC) variable selected spectra were used for model calibration. Branch, needle, and trunk PLSR models performed well for the N concentration using both full length and sMC selected NIR spectra. The generic model preformatted a reliable accuracy with R2C and R2CV of 0.62 and 0.66 using the full-length spectra, and 0.61 and 0.65 using sMC-selected spectra, respectively. Individual tissue models did not perform well when being used in other tissues. Five significantly important regions, i.e., 1480, 1650, 1744, 2170, and 2390 nm, were found highly related to the N content in plant tissues. This study evaluates a rapid and efficient method for the estimation of N content in different tissues that can help to serve as a tool for tree N storage and recompilation study.

A dynamical evolution of GRB-afterglows in a new generic model

In this paper, a new model using a general expression of the radiation energy and explaining the dynamics of the afterglows is proposed. It is shown that this model is suitable for the ultra-relativistic and non-relativistic phases as well as the study of radiative and adiabatic fireballs.

On Developing Generic Models for Predicting Student Outcomes in Educational Data Mining

Poor academic performance of students is a concern in the educational sector, especially if it leads to students being unable to meet minimum course requirements. However, with timely prediction of students’ performance, educators can detect at-risk students, thereby enabling early interventions for supporting these students in overcoming their learning difficulties. However, the majority of studies have taken the approach of developing individual models that target a single course while developing prediction models. These models are tailored to specific attributes of each course amongst a very diverse set of possibilities. While this approach can yield accurate models in some instances, this strategy is associated with limitations. In many cases, overfitting can take place when course data is small or when new courses are devised. Additionally, maintaining a large suite of models per course is a significant overhead. This issue can be tackled by developing a generic and course-agnostic predictive model that captures more abstract patterns and is able to operate across all courses, irrespective of their differences. This study demonstrates how a generic predictive model can be developed that identifies at-risk students across a wide variety of courses. Experiments were conducted using a range of algorithms, with the generic model producing an effective accuracy. The findings showed that the CatBoost algorithm performed the best on our dataset across the F-measure, ROC (receiver operating characteristic) curve and AUC scores; therefore, it is an excellent candidate algorithm for providing solutions on this domain given its capabilities to seamlessly handle categorical and missing data, which is frequently a feature in educational datasets.

Seizure Prediction With HIVE-CODAs: The Hierarchical Vote Collective of Domain Adaptation Methods

Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional methods are usually trained and tested on the same patient due to the interindividual variability. However, the challenging problem of the domain shift between different subjects remains unsolved, resulting in low prevalence of clinical application. In this study, a generic model based on the domain adaptation (DA) technique is proposed to alleviate such problems. Ensemble learning is employed by developing a hierarchical vote collective of seven DA modules over multi-modality data, such that the predictive performance is improved by training multiple models. Moreover, to increase the feasibility of its implementation, this study mimics the data distribution of clinical sampling and tests the model under this simulated realistic condition. Based on the performance of seven subnetworks, the applicability of each DA algorithm for seizure prediction is evaluated, which is the first study that provides the assessment. Experimental results on both intracranial and scalp EEG databases demonstrate that this method can reduce the domain gap effectively compared with previous studies.

Adoption and Use of Human Information System Digital Technology for Organizational Competitiveness

Staying competitive in the current digitized workplace era requires, among other things, an adequate and efficient use of modern technology. Human resource information system (HRIS) is one of several tools that helps organizations remain sustainable by providing technology that can help to acquire, store, generate, analyze, and disseminate timely and accurate employee information and activities. Of late, HRIS is slowly gaining prominence in Nepal. A generic model for conditions that are necessary for successful adoption and use of HRIS in Nepali organizations is designed as the models proposed by earlier researchers in a developed context may not work well in a developing context. This sets fertile ground to carry out scholarly inquiry into the domain of HRIS in the Nepalese context. The limitations of present study are mentioned and practical/research implications of the same are discussed towards the end. Researchers are of the opinion that the findings of this preliminary study can be taken up to the next level for carrying out quantitative research in HRIS domain in Nepal.

AGR4BS: A Generic Multi-Agent Organizational Model for Blockchain Systems

Blockchain is a very attractive technology since it maintains a public, append-only, immutable and ordered log of transactions which guarantees an auditable ledger accessible by anyone. Blockchain systems are inherently interdisciplinary since they combine various fields such as cryptography, multi-agent systems, distributed systems, social systems, economy, and finance. Furthermore, they have a very active and dynamic ecosystem where new blockchain platforms and algorithms are developed continuously due to the interest of the public and the industries to the technology. Consequently, we anticipate a challenging and interdisciplinary research agenda in blockchain systems, built upon a methodology that strives to capture the rich process resulting from the interplay between the behavior of agents and the dynamic interactions among them. To be effective, however, modeling studies providing insights into blockchain systems, and appropriate description of agents paired with a generic understanding of their components are needed. Such studies will create a more unified field of blockchain systems that advances our understanding and leads to further insight. According to this perspective, in this study, we propose using a generic multi-agent organizational modeling for studying blockchain systems, namely AGR4BS. Concretely, we use the Agent/Group/Role (AGR) organizational modeling approach to identify and represent the generic entities which are common to blockchain systems. We show through four real case studies how this generic model can be used to model different blockchain systems. We also show briefly how it can be used for modeling three well-known attacks on blockchain systems.