Model Approach Research Articles

Estimation of Groundwater River Availability in Leang Lonrong Cave Using ARIMA Model and Econophysics Valuation Approach

This quantitative descriptive study uses the ARIMA model approach to predict groundwater river water availability. It assesses its valuation with an econophysics orientation in the Leang Lonrong Cave area of TN Bantimurung Bulusaraung. Secondary data from 2010-2023, including Water Discharge and Water Level data from the River Basin Center of Pompengan Jeneberang (BBWS) and primary data on the Valuation of Willingness to Pay, were utilized. The analysis reveals that water discharge predictions (2024-2030) in the Gua Leang Lonrong area indicate consistent annual water availability, with the highest discharge projected in December 2030 (5.08 m3/s) and the lowest in September 2024 (1.12 m3/s). Valuation of utilizing Leang Lonrong River water through willingness to pay showed residents willing to pay Rp 42,142.85/month for clean water management. At the same time, visitors are willing to contribute Rp 21,596.77/visit to enhance water quality management. Elasticity analysis modeling water discharge with willingness to pay suggests a relatively elastic relationship, with a minor impact, indicating unresponsiveness to changes in water discharge percentages. Statistical and economic methods were employed to analyze daily water discharge patterns and evaluate residents' willingness to pay for underground river water utilization. The ARIMA model (0,1,1) forecasted future water discharge patterns with a mean absolute percentage error (MAPE) of 33.7%. The study surveyed 62 respondents, finding an average willingness to pay approximately IDR 21,596.77 per visit to maintain Leang Lonrong River water quality management.

Exploring fission and quasi-fission in $^{31}$Al + $^{197}$Au: Probing energies near and beyond Coulomb Barrier

Abstract An idea to understand the fragmentation of a formed nuclear entity (i.e. compound nucleus), induced via heavy-ion reactions, is an interesting task especially at incident energies near, above and far away from the Coulomb barrier. In the present work, we are studying the decay dynamics of a formed nuclear entity i.e. $^{228}$U$^*$ compound nucleus (CN), via the radioactive beam of Aluminium-31 ($^{31}$Al) striking on the stable Gold-197 ($^{197}$Au) target nucleus, for incident energies $E_{\textrm{\textrm{c.m.}}}$=106.2-163.2 MeV. A comprehensive knowledge regarding the probable decaying fragments from $^{228}$U$^*$ has been accumulated using the collective clusterization approach of the Dynamical Cluster-decay Model (DCM) in terms of mass and charge dispersion, which built up on the Quantum Mechanical Fragmentation Theory (QMFT). Through both of these dispersion cases, we have identified the decaying fragments in the fission and heavy-mass regions, at different scale of incident energies. Further, the fission cross-sections ($\sigma_{\textrm{fis}}^{\textrm{Theo}}$) have been obtained for $^{31}$Al+$^{197}$Au$\rightarrow^{228}$U$^*$ reaction at the above said $E_{\textrm{c.m.}}$ (MeV) and compared with the experimental data. This study has been carried out using two different sets of Skyrme force parameters (SIII and SSk), which are employed within the semiclassical approach of Skyrme Energy Density Formalism. Unlike for energies below and near the barrier, a significant difference has been observed between calculated ($\sigma_{\textrm{fis}}^{\textrm{Theo}}$) and experimental ($\sigma_{\textrm{fis}}^{\textrm{Expt}}$) cross-sections for above barrier energies, with independent choice of Skyrme forces. Such discrepancy noticed has been addressed with the presence of quasi-fission (\emph{QF}) event. Similar phenomena has also been seen for $^{19}$F-induced reaction forming the same CN ($^{228}$U$^*$), for excitation energy $E_{\textrm{CN}}^*>$ 42 MeV.

Penerapan Arsitektur Model View Controller dan Framework Zend dalam Pengembangan Aplikasi Category Online di PT XYZ

This research discusses the implementation of the Model View Controller (MVC) architecture and Zend Framework in developing the Category Online application at PT XYZ, a leading retail company in Indonesia. The IT team at PT XYZ faced challenges in developing applications using PHP Native, and the adoption of the Zend Framework improved development efficiency. The application handles manual changes in item status across numerous branches, which is inefficient. The study focuses on developing the Transformation Class Product (TCP) module. The research method involves five stages, from requirements analysis to implementation, using a prototype model approach. System testing, utilizing black box testing during the User Acceptance Test (UAT) phase, was successful, indicating the effectiveness of the application's core functionalities. Thus, the implementation of MVC and Zend Framework in the Category Online application at PT XYZ has proven effective in enhancing the company's operational efficiency.

CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone

Abstract Accurate prediction of phenology is the most critical aspect for the development of models aimed at estimating seed yield, particularly in species that exhibit variable sensitivity to environmental factors throughout the cycle and among genotypes. With this purpose, we evaluated the phenology of 34 soybean varieties in field experiments located in Argentina, Uruguay, and Paraguay. Experiments covered a broad range of maturity groups (2.2-6.8), sowing dates (from spring to summer), and latitude range (24.9-35.6 °S), thus ensuring a wide range of thermo-photoperiodic conditions during the growing season. Based on the observed data, daily time-step models were developed and tested, first for each genotype, and then across maturity groups. We identified base temperatures specific for different developmental phases and an extra parameter for calculating the photoperiod effect after the R1 stage (flowering). Also, an optimum photoperiod length for each maturity group was found. Model selection showed that the determinants of phenology across maturity groups were mainly affecting the duration of vegetative and early reproductive phases. Even so, early phases of development were better predicted than later ones, particularly in locations with cool growing seasons, where the model tended to overestimate their duration. In summary, we have constructed a soybean phenology model that simulates phenology accurately across various geographic locations and sowing dates. The model's process-based approach has resulted in root mean square errors ranging from 5.8 to 9.5 days for different developmental stages. The final model was made available at http://cronosoja.agro.uba.ar.

Multi-market P2P trading of cooling–heating-power-hydrogen integrated energy systems: An equilibrium-heuristic online prediction optimization approach

In this paper, an equilibrium-heuristic online prediction optimization approach is proposed for multi-market peer-to-peer (P2P) electricity–hydrogen trading of integrated energy systems (IESs) with uncertainties. First, the IES, consisting of a hydrogen energy storage subsystem and a combined cooling, heating, and power subsystem, is constructed in the distribution network to improve energy utilization and market efficiency. Then, a bi-level optimization model for IESs, participating in the P2P electricity–hydrogen energy trading, the real-time electricity, and the ancillary service markets, is proposed, in which the top-level model can be formulated as a P2P electricity–hydrogen trading pricing model through the social welfare maximization problem, and the lower-level model is used to maximize the IES operating profit. To effectively solve the bi-level model, the game equilibrium-based ADMM distributed algorithm is used to obtain the P2P trading volume and prices of the top-level model, and a new hybrid heuristic algorithm, called hybrid sand cat swarm optimization and improved honey badger algorithm (SCIHB), is proposed to solve the lower level model. SCIHB utilizes a hybrid mechanism and an adaptive learning rate parameter to balance local exploitation and global exploration. Furthermore, a new online learning probabilistic prediction method based on the hidden Markov model and wavelet transform is introduced to describe the uncertainties of wind power, electricity prices, and frequency regulation prices. Finally, case studies are conducted on an IEEE 33-bus test system, and the numerical results verify the effectiveness of the proposed P2P trading model and solution approach.

Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients

AimsThe population pharmacokinetic (PPK) model-based machine learning (ML) approach offers a novel perspective on individual concentration prediction. This study aimed to establish a PPK-based ML model for predicting tacrolimus (TAC) concentrations in Chinese renal transplant recipients.MethodsConventional TAC monitoring data from 127 Chinese renal transplant patients were divided into training (80%) and testing (20%) datasets. A PPK model was developed using the training group data. ML models were then established based on individual pharmacokinetic data derived from the PPK basic model. The prediction performances of the PPK-based ML model and Bayesian forecasting approach were compared using data from the test group.ResultsThe final PPK model, incorporating hematocrit and CYP3A5 genotypes as covariates, was successfully established. Individual predictions of TAC using the PPK basic model, postoperative date, CYP3A5 genotype, and hematocrit showed improved rankings in ML model construction. XGBoost, based on the TAC PPK, exhibited the best prediction performance.ConclusionThe PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.

Unveiling the relationships between visibility, information technologies and innovation management for sustainability performance: an empirical study

PurposeThe paper investigates how visibility, information technology and innovation management impact sustainability performance. It proposes a framework explaining the role of visibility in driving firms' sustainable performance and the relevance of innovation management and information technologies in enhancing organisational visibility. This study intends to add to the discussion within the management literature about the potential of innovation management to drive sustainability. It seeks to provide insight into the practices that small and medium-sized enterprises (SMEs) can adopt to improve their sustainable performance.Design/methodology/approachUsing empirical methods, the study investigates SMEs in central Mexico. The demographic information in the dataset includes 15 years as an average length of service from firms. Of the surveyed firms, 70% were from the manufacturing sector and 30% were from the service sector, as these are the most representative sectors of the productive region. A variance-based structural equation model approach was used to test the hypotheses, processed with the partial least squares (PLS) regression method.FindingsThe research results show that visibility significantly impacts sustainability performance. Innovation management has a higher influence on visibility than information technologies, emphasising the need to improve the quality of information in firms, not just the tools. The findings support managers in comprehending the crucial importance of visibility in aiding firms to achieve higher sustainability performance.Research limitations/implicationsThe study only examined a sample of Mexican SMEs; therefore, the findings' generalizability must be considered within this context. Secondly, the survey only focused on services and manufacturing firms and a more detailed analysis of the sector could provide further clarity on the relationships between variables. As a result, future research should consider these limitations and explore additional contexts to improve the overall understanding of the topic. Moreover, the scale used to measure the variables was adapted from other researchers with similar context research and reflective variables.Practical implicationsThe results provide helpful information for SME managers about the importance of focusing on innovation management processes and employing information technologies as crucial managerial strategies. This will aid in increasing visibility and supporting the development of sustainability performance in firms.Social implicationsThe world red-code, among others, with climate change and social gaps, has generated the need to contribute to sustainable development, and it has mobilised people on all levels all over the world for the simple purpose of preserving life. Therefore, society, as a crucial group that affects and is affected by this red-code situation, should act in favour of visibility, the use of high-quality information (e.g. transparent, accessible and relevant) and information technologies to promote sustainable practices. This could mean that society should be prepared to incorporate new capabilities and spaces to interchange knowledge as a participatory community that can contribute to better sustainable dynamics that could expand its participation in public decisions. Also, the government should encourage digital democracy (e.g. develop social participation platforms), opening and harmonising rules and mechanisms combining high-quality information with IT to provide flexible and adequate services that support sustainable development, such as efforts towards constructing sustainable and smart cities.Originality/valueThis study explores how innovation management can drive firms' sustainability performance, which is crucial for improving competitiveness. The question of how to enhance sustainability performance through managerial drivers is a critical one. This study empirically investigates the nexus of visibility and sustainability performance, innovation management and information technology with visibility.

Exploring potential mechanisms linking urban landscapes to instream stressors and macroinvertebrates in the Southeast Piedmont: Implications for watershed management

Urbanization has greatly degraded stream health, particularly in the rapidly urbanizing southeastern United States. Recent studies identified primary instream stressors affecting biological communities along urban gradients in the Southeast Piedmont. However, a comprehensive understanding of the complex mechanisms is still lacking, which impedes effective management actions. To improve our understanding, we investigated the potential pathways by which urbanization, as represented by the landscape development intensity index (LDII), and important environmental characteristics affected instream stressors and macroinvertebrate communities using structural equation modeling (SEM) and the U.S. Geological Survey Southeast Stream Quality Assessment dataset. We investigated the direct, indirect, and total effects of LDII and environmental characteristics on instream stressors and macroinvertebrate communities as well as the interactions of instream stressors. Moreover, we explored alternative reasonable relationships between variables by using an alternative model approach. We found that LDII had moderate to strong total effects on all instream stressors and macroinvertebrates with complex impact pathways revealed in SEM models, demonstrating the dominant impact of urban landscape development on stream ecosystems in the Southeast Piedmont. Flow disturbance and pesticides were major mediators transmitting most of LDII’s effects on macroinvertebrates. Furthermore, we found potential interactions among instream stressors, which were not widely discussed in previous studies. Compared with agricultural watersheds, urban watersheds represented different land use impact mechanisms. Our findings contributed to an improved understanding of urbanization’s impact on stream ecosystems and demonstrated the usefulness of an alternative model approach that provided insights into the underlying mechanisms. In addition, we demonstrated the utility of LDII in characterizing landscape development and improving SEM models. Finally, we explored the potential of SEM in global watershed management activities by discussing the practical implications of impact pathways and the innovative use of SEM as a decision-making tool.

Clustering techniques performance comparison for predicting the battery state of charge: A hybrid model approach

Abstract Batteries are a fundamental storage component due to its various applications in mobility, renewable energies and consumer electronics among others. Regardless of the battery typology, one key variable from a user’s perspective is the remaining energy in the battery. It is usually presented as the percentage of remaining energy compared to the total energy that can be stored and is labeled State Of Charge (SOC). This work addresses the development of a hybrid model based on a Lithium Iron Phosphate (LiFePO4) power cell, due to its broad implementation. The proposed model calculates the SOC, by means of voltage and electric current as inputs and the latter as the output. Therefore, four models based on k-Means, Agglomerative Clustering, Gaussian Mixture and Spectral Clustering techniques have been tested in order to obtain an optimal solution.

Global Energy Transition and the Efficiency of the Largest Oil and Gas Companies

The challenges posed by climate change and global warming loom large, necessitating a critical initial step towards the long-term growth and the enhancement of both environmental and operational efficiency. Within the energy sector, renewable energy sources are gaining increasing prominence. Consequently, traditional oil and gas companies (OGC) are undergoing a gradual transformation into comprehensive energy corporations, aligning themselves with energy transition policies. This paper examines two types of efficiency measures—operational and environmental—for the 20 largest OGC during the period of 2010–2019. Secondly, this research aims to explore the effect of the global energy transition on both environmental and operational efficiency. Based on three estimation methods, two estimation steps are used in this research. In the first step, the True Fixed Effect (TFE) model and the Battese and coelli (1995) SFA model are applied to evaluate, measure and compare the environmental and operational efficiency scores. In the second step, the TFE model and GMM approach for the dynamic panel data model are used to explore, evaluate and verify the effect of global energy transition on the environmental and operational efficiency of the largest 20 OGC in the world. The results reveal that the average operational efficiency of major OGC measured using the BC.95 model and TFE model is 66% and 85%, respectively, and the overall average level of environmental efficiency for OGC over a 10-year period is 31% (based to B.C.95 model) and 13% (based to TFE model). Our findings reveal that biofuels, solar and hydropower contribute to promote the operational and environmental efficiency of the largest 20 OGC. However, the analysis suggests that while the global energy transition significantly influences and bolsters environmental efficiency, its effect on operational efficiency among these major OGC remains less pronounced and insufficient.

A Short Note on Generating a Random Sample from Finite Mixture Distributions

Computational statistics is a critical skill for professionals in fields such as data science, statistics, and related disciplines. One essential aspect of computational statistics is the ability to simulate random variables from specified probability distributions. Commonly employed techniques for sampling random variables include the inverse transform method, acceptance–rejection method, and Box–Muller transformation, all of which rely on sampling from the uniform (0,1) distribution. A significant concept in statistics is the finite mixture model, characterized by a convex combination of multiple probability density functions. In this paper, we introduce a modified version of the composition method, a standard approach for sampling finite mixture models. Our modification offers the advantage of relying on sampling from the uniform (0,1) distribution, aligning with prevalent methods in computational statistics. This alignment simplifies teaching computational statistics courses, as well as having other benefits. We offer several examples to illustrate the approach.

The global effects of digestive system cancers worldwide on the COVID-19 pandemic by negative binomial (mixed) regression models

In this study, the main goal is to determine statistically significant relationships between the biggest epidemic problem of last years as the COVID-19 pandemic and digestive system cancers belonging to 168 countries worldwide using generalized linear mixed model (GLMM) and its special case as generalized linear model (GLM) approaches, and to obtain global inferences that will shed light on this pandemic. For this goal, the response variable is “total cases of the COVID-19 pandemic per 100,000 people” until January 14, 2022. The explanatory variables are total number of people suffering from colon and rectum, stomach, lip and oral cavity, esophageal, and nasopharynx cancers belonging to 2019, respectively. In this study, the negative binomial (NB) regression model in GLM using iteratively reweighted least squares (IRLS) algorithm and the NB mixed regression model in GLMM with “countries” taken as “random effects” using Adaptive Gauss-Hermite Quadrature (AGHQ) approximation method at 1, 2, 10, and 20 quadrature points are used for modelling the COVID-19 pandemic and digestive systems cancer data. In this study, by using information criteria, the NB mixed regression model in GLMM under log-link function with 168 countries taken as random effects using AGHQ method at 20 quadrature point is detected as the most appropriate model for the global COVID-19 pandemic and digestive system cancers data.

Environmental Management Technologies, Environmental Policy Stringency, Energy Productivity and Pollution Emission: Fresh Evidence from Indonesia under STIRPAT Framework

The rapid pace of climate change and global warming has become an ever growing challenge all over the world. Global warming and carbon concentrations can be mitigated by adopting strict environmental policies and effective management of the environment. Therefore, the study aims to empirically investigate the effect of environmental policy stringency (EPS), energy productivity and environmental management technologies (EMT) on pollution emission in Indonesia- one of the most polluting countries of the world. To this end, the study collects data over the 1990 to 2020 period. The study uses Autoregressive Distributed Lag Model (ARDL) approach to carry out empirical estimation. The findings reveal that environmental management technologies, energy productivity and environmental policy stringency had negative impact on CO2 emission. However, economic growth (GDP) and urbanization had positive role in increasing CO2 emission in the long run and the short run. On the basis of the outcomes, the study recommends that Indonesia should adopt strict environmental policies and legislations to mitigate environmental deterioration. Indonesian government is recommended to promote the adoption of environmental management technologies by providing substantial incentives and investment opportunities for businesses.

Adaptive Workload Scheduling Design in Parallel Computational Framework

In the recent years, many researchers have showed a great deal of interest to improve the scheduling of workload in the cloud platforms. On the other hand, to carry out the execution of the scientific workloads in the cloud environment, it consumes much time and is expensive; hence, it is neither time efficient nor cost-efficient. Due to this reason, many research studies have been carried, by which the researchers tend to reduce the processing time and make a cost-efficient method as the users are charged based on the usage. Very few studies have been done to optimize the cost with processing time and energy parameters together in order to meet the Service Level Agreement (SLA) and Quality of Service (QoS) of the workload task. Hence, in this paper, we present an Adaptive Workload Scheduling (AWS) model that ensures the Task Level SLA prerequisites in a heterogeneous distributed-computing environment. This AWS- model approach reduces the amount of energy and time needed to execute a given workloads. Cybershake scientific workload has been utilized for the studying proposed AWS model. When model was compared with the standard workload scheduling approach, our model reduced the consumption of cost, energy and time.

Enhancing multiple myeloma staging: a novel cell death risk model approach

The prognostication of survival trajectories in multiple myeloma (MM) patients presents a substantial clinical challenge. Leveraging transcriptomic and clinical profiles from an expansive cohort of 2,088 MM patients, sourced from the Gene Expression Omnibus and The Cancer Genome Atlas repositories, we applied a sophisticated nested lasso regression technique to construct a prognostic model predicated on 28 gene pairings intrinsic to cell death pathways, thereby deriving a quantifiable risk stratification metric. Employing a threshold of 0.15, we dichotomized the MM samples into discrete high-risk and low-risk categories. Notably, the delineated high-risk cohort exhibited a statistically significant diminution in survival duration, a finding which consistently replicated across both training and external validation datasets. The prognostic acumen of our cell death signature was further corroborated by TIME ROC analyses, with the model demonstrating robust performance, evidenced by AUC metrics consistently surpassing the 0.6 benchmark across the evaluated arrays. Further analytical rigor was applied through multivariate COX regression analyses, which ratified the cell death risk model as an independent prognostic determinant. In an innovative stratagem, we amalgamated this risk stratification with the established International Staging System (ISS), culminating in the genesis of a novel, refined ISS categorization. This tripartite classification system was subjected to comparative analysis against extant prognostic models, whereupon it manifested superior predictive precision, as reflected by an elevated C-index. In summation, our endeavors have yielded a clinically viable gene pairing model predicated on cellular mortality, which, when synthesized with the ISS, engenders an augmented prognostic tool that exhibits pronounced predictive prowess in the context of multiple myeloma.

Mathematical Model for a Lithium-Ion Battery with a SiO/Graphite Blended Electrode Based on a Reduced Order Model Derived Using Perturbation Theory

Abstract Silicon oxide (SiO) is a promising anode material for high-energy lithium-ion batteries, as it is made from low-cost precursors, has a potential close to that of Li, and has high theoretical specific capacity. However, the applications of SiO are limited by the intrinsic low electrical conductivity, large volume change, and low coulombic efficiency, which often lead to poor cycling performance. A common strategy to address these shortcomings is to blend SiO with graphite active materials to form a composite anode for better capacity retention. In this work, we derive a reduced order model (ROM1) using perturbation theory. We employ the multi-site, multi-reaction (MSMR) framework of a composite porous electrode blend consisting of two lithium-host materials, SiO and graphite. The ROM1 model employs a single-particle model (SPM) approach as the leading-order solution and involves the numerical analysis of a single, nonlinear partial differential equation for each host material that describes diffusion by means of irreversible thermodynamics, wherein chemical-potential gradients are the driving forces for the diffusion. The first-order correction treats losses other than that of the SPM.

Adding External Artificial Intelligence (AI) into Internal Firm-Wide Smart Dynamic Warehousing Solutions

This study advances knowledge in the AI field. It provides deep insight into current industry generative AI inclusion systems. It shows both literature and practical leading industry operations can link, overlap, and complement each other when it comes to AI and understanding its complexities. It shows how to structurally model and link AI inclusions towards delivering a suitable sustainability positioning. It shows approaches to integrate external AI contributions from one firm into another firm’s intelligences developments. It shows how to track, and maybe benchmark, the progress of such AI inclusions from either an external or an integrated internal software developer perspective. It shows how to understand and create a more sustainable, AI-integrated business positioning. This study considers firm artificial intelligence (AI) and the inclusion of additional external software developer engineering as another AI related pathway to future firm or industry advancement. Several substantive industrial warehousing throughput areas are discussed. Amazon’s ‘smart dynamic warehousing’ necessitates both digital and generative ongoing AI system prowess. Amazon and other substantive, digitally focused industry warehousing operations also likely benefit from astute ongoing external software developer firm inclusions. This study causally, and stagewise, models significant global software development firms involved in generative AI systems developments—specifically ones designed to beneficially enhance both warehouse operational productivity and its ongoing sustainability. A structural equation model (SEM) approach offers unique perspectives through which substantive firms already using AI can now model and track/benchmark the relevance of their prospective or existing external software developer firms, and so create rapid internal ‘net-AI’ competencies incorporations and AI capabilities developments through to sustainable operational and performance outcomes solutions.

From awareness to behaviour: understanding cybersecurity compliance in Vietnam

From awareness to behaviour: understanding cybersecurity compliance in Vietnam

Cyberfraud, usage intention, and cybersecurity awareness among e-banking users in India: an integrated model approach

Cyberfraud, usage intention, and cybersecurity awareness among e-banking users in India: an integrated model approach

Share of food group expenditure in Mexican households according to the gender of the head of household and size of the locality.

To evaluate differences in the percentage of expenditure on food groups in Mexican households according to the gender of the household head and the size of the locality. Analysis of secondary data from the National Household Income and Expenditure Survey (ENIGH) 2018. We estimated the percentage of expenditure on 15 food groups according to the gender of the head of household and locality size and evaluated the differences using a two-part model approach. Mexico, 2018. A nationally representative sample of 74,647 Mexican households. Female-headed households allocated a lower share of expenditure to the purchase of sweetened beverages, and alcoholic beverages, and higher percentages to milk and dairy, fruits, and water. In comparison with metropolitan households, households in rural and urban localities spent more on cereals and tubers, sugar and honey, oil and fat, and less on food away from home. Households allocate different percentages of expenditure to diverse food groups according to the gender of the head of the household and the size of the locality where they are located. Future research should focus on understanding the economic and social disparities related to differences in food expenditure, including the gender perspective.