Modeling Techniques Research Articles

Influence Of Good Governance on Employee Satisfaction in Faith-Based Higher Educational Institutions: The Mediating Role of Policy Management

The ever-increasing role of employee satisfaction in the survival and success of higher educational institutions points towards a strong emphasis on good governance. This study examined the mediating role of policy management on the relationship between good governance and employee satisfaction. Two hundred and sixty-eight respondents participated in the study, representing the population of Faith-Based Higher Educational Institutions in the (AHEI's) Philippines. The descriptive, correlational, regression, t-test, and structural equation modeling techniques were used to analyze the data. The results exhibit strong implementation of good governance, high levels of employee satisfaction, and effective policy management as perceived by the employees. Moreover, the study found a positive correlation between good governance, employee satisfaction, and policy management. It was also discovered that the sex, marital status, educational attainment, and salary of the respondents do not moderate the influence of good governance on employee satisfaction. Finally, the findings also revealed that policy management partially mediates the relationship between good governance and employee satisfaction.

Review of global burden of disease studies: trends, challenges, and future directions

Global burden of disease (GBD) studies plays an important role in assessing the variability of risk factors, injuries, and diseases worldwide, providing essential evidence for policy-making and healthcare planning. This study presents a review of current literature on GBD studies, aiming to analyze the trends, challenges, and future directions in this field. We conducted a review of published GBD studies from inception to date, utilizing major scientific databases and relevant sources. Our findings reveal several noteworthy trends in GBD studies. Over the years, GBD studies have expanded to cover a wider range of diseases and risk factors, providing a more comprehensive understanding of global health. Advances in data availability, technology, and modeling techniques have improved the accuracy of disease burden estimates. The inclusion of disability-adjusted life years (DALYs) has enabled comparisons as well as prioritization of interventions. However, challenges remain, such as limited data in middle and low-income countries and methodological complexities. The COVID-19 pandemic has emphasized the need for responsive methodologies. Future directions include strengthening data collection, utilizing machine learning, big data analytics, ethical use of artificial intelligence and promoting collaboration for consistent GBD studies.

Fast synchronization with enhanced switching control for grid-tied single-phase square wave inverter using FPGA

Research on grid synchronization has been conducted worldwide by researchers in conjunction with the development of innovative technologies, such as dedicated short-range communication (DSRC) and cellular vehicle-to-everything (C-V2X). However, grid-connected inverters face several challenges, mainly the mismatch in voltage amplitude, frequency, and phase angle, as well as grid voltage disturbance and grid faults. Thus, the control algorithm of this research mainly focused on a half-cycle algorithm to design an enhanced digital switching control for fast synchronization using an FPGA. The control algorithm was developed based on zero-crossing detection (ZCD) and digital phase-locked loop (PLL) modeling techniques using the hardware description language (HDL) and a combination of digital logic blocks in Quartus II software, where the proposed switching was applied using the square-wave switching technique through a 300-watt full-bridge experimental prototype. The performance of the proposed technique was studied, where the total harmonic distortion (THD) for voltage and current resulted in a percentage reduction of 89.29% and 78.05% for voltage and current, respectively, after filter implementation. Also, the resulting signal synchronized in every half cycle and matched the voltage amplitude, frequency, and phase angle of the grid signal in 10 ms.

Harnessing Computational Modeling for Efficient Drug Design Strategies

Abstract: Computational modeling has become a crucial tool in drug design, offering efficiency and cost-effectiveness. This paper discusses the various computational modeling techniques used in drug design and their role in enabling efficient drug discovery strategies. Molecular docking predicts the binding affinity of a small molecule to a target protein, allowing the researchers to identify potential lead compounds and optimize their interactions. Molecular dynamics simulations provide insights into protein-ligand complexes, enabling the exploration of conformational changes, binding free energies, and fundamental protein-ligand interactions. Integrating computational modeling with machine learning algorithms, such as QSAR modeling and virtual screening, enables the prediction of compound properties and prioritizes potential drug candidates. High-performance computing resources and advanced algorithms are essential for accelerating drug design workflows, with parallel computing, cloud computing, and GPU acceleration reducing computational time. The paper also addresses the challenges and limitations of computational modeling in drug design, such as the accuracy of scoring functions, protein flexibility representation, and validation of predictive models. It emphasizes the need for experimental validation and iterative refinement of computational predictions to ensure the reliability and efficacy of designed drugs.

A Structure-Based Drug Design Approach for the Identification of Antiviral Compounds Targeting the Chikungunya Virus RdRp Protein

There are an estimated 3 million Chikungunya virus (CHIKV) infections per year, but no vaccines or specific pharmaceutical treatments are available. RNA-dependent RNA polymerase (RdRp) of CHIKV, encoded by nonstructural protein 4 (nsP4), was targeted to identify potential antiviral compounds in this study. A 3D model of the nsP4 was generated by homology modeling technique and Pockdrug-Server was used to predict ligand binding sites. The pharmacophore hypothesis was developed followed by virtual screening. 300 most potential ligands were selected from a library of 3000 compounds, after filtering for the molecules satisfying Lipinski's Rule of Five. Using molecular docking followed by ADMET analysis, 27 leads were then selected. We used MD simulations of the three best compounds that bind to the cataltic, palm, and thumb domains to find out how the ligand-protein interactions change over time. RMSD, RMSF, radius of gyration, free energy landscapes, and principal component analyses revealed that complexes nsP4-ZINC12820763 and nsP4-ZINC33280972 were more stable. Simulated trajectories were further used to compute MMGBSA binding energies of PubChem135638918 (-22.33 kcal/mol), ZINC12820763 (-50.19 kcal/mol) and ZINC33280972 (-32.69 kcal/mol) bound to catalytic, palm and thumb domains, respectively. HOMO and LUMO of potential molecules were also investigated using density functional theory computations. With the support of computational simulations, three compounds were shown to have the potential to inhibit RdRp of CHIKV and need further evaluation using in-vitro and in-vivo analysis.

Enhancing Criminal Analysis through Multi-Model Integration: Addressing Challenges and Ensuring Ethical Implementation

Abstract: Law enforcement agencies face numerous challenges in criminal analysis, from data quality issues to ethical concerns. To address these challenges, this research proposes a novel approach: integrating multiple modelling techniques into a unified framework. By combining regression analysis, decision trees, support vector machines, neural networks, and ensemble methods, this approach aims to provide more robust and accurate solutions. The research seeks to develop a framework for integration, evaluate effectiveness using real-world data, and explore ethical implications. Ultimately, the goal is to advance criminal analysis, promote multi-model integration in law enforcement, and ensure ethical implementation for justice.

Predictive Modeling for Lung Cancer Detection: Unveiling Insights through Machine Learning Techniques

Abstract: Lung cancer stands as a formidable global health challenge, characterized by its widespread prevalence and high mortality rates, placing significant burdens on both public health systems and clinical practitioners. Despite notable advancements in diagnostic and therapeutic modalities, early detection remains paramount in improving patient outcomes and enhancing survival rates. In response to this imperative, the integration of predictive modelling techniques within healthcare has garnered substantial attention in recent years, offering promising avenues for bolstering early detection strategies and optimizing patient care pathways.

Computational modelling of tyre behavior on concrete and sandy soil surfaces using solid works software

Tyres are integral components of vehicles, crucial for controlling movement, supporting vehicle weight, and providing traction. Consequently, there's a growing need for accurate tyre modelling to optimize performance under varying conditions. While extensive literature exists on tyre-soil interaction, information is scarce regarding tyre behaviour across different soil types. This study aims to address this gap by employing computational modelling techniques to analyse tyre behaviour on two distinct surfaces: concrete and sandy soil. Utilizing SolidWorks software, a comprehensive investigation was conducted to evaluate tyre performance under varying road conditions. The study focused on analysing shear forces, soil resistances, and partial soil resistant forces exerted on the tyres. The findings indicate that tyres exhibit superior performance on concrete surfaces compared to sandy soil. The computational simulations predicted shear forces of 0.6 kN, soil resistances of 0.2 kN, and partial soil resistant forces of 0.25 kN for the concrete surfaces. These results were notably higher than those observed on sandy soil surfaces. This study sheds light on the intricate dynamics of tyre behaviour under different road conditions and underscores the importance of computational modelling in understanding and optimizing tyre performance. The insights gained from this research have significant implications for vehicle design, road engineering, and the development of more efficient and durable tyres tailored to specific environmental conditions.

Data-driven strategies for business expansion: Utilizing predictive analytics for enhanced profitability and opportunity identification

In today's hyper-competitive business landscape, leveraging data-driven strategies is paramount for sustainable growth and profitability. This review presents an overview of the imperative role of predictive analytics in facilitating business expansion by enhancing profitability and identifying opportunities. Predictive analytics harnesses historical data and advanced modeling techniques to forecast future trends, enabling businesses to make informed decisions with precision. By understanding predictive analytics, businesses can effectively identify expansion opportunities by analyzing market trends, segmenting customer bases, and uncovering new markets and niches. Moreover, predictive analytics empowers organizations to enhance profitability through optimized pricing strategies, demand forecasting, and personalized marketing initiatives tailored to customer preferences and behaviors. Implementing data-driven strategies for business expansion requires building a robust data infrastructure, selecting appropriate tools and technologies, and fostering a data-driven culture within the organization. Real-world case studies illustrate the transformative impact of predictive analytics on business expansion, offering insights into best practices and lessons learned. Despite the undeniable benefits of predictive analytics, challenges such as data privacy concerns, ensuring data quality, and organizational resistance must be addressed. However, the rewards of embracing data-driven decision-making far outweigh the challenges, positioning businesses for sustainable growth and competitive advantage in the dynamic marketplace. In conclusion, this review emphasizes the critical role of predictive analytics in driving business expansion by enhancing profitability and opportunity identification. By harnessing the power of data, businesses can unlock new avenues for growth, mitigate risks, and stay ahead of the curve in today's data-driven economy.

Effect of printing parameters on impact energy absorption of additively manufactured hierarchical structures

Purpose This study aims to examine how the thickness of layers and printing speed impact the energy absorption capacity of honeycomb structures through drop-weight experiments. In addition, the effect of printing orientation on the resulting microstructure and mechanical performance was targeted to be examined. Design/methodology/approach In this paper, after manufacturing test specimens using fused deposition modeling technique with three distinct layer thicknesses (0.16 mm, 0.20 mm and 0.28 mm) and printing speeds (40 mm/min, 50 mm/min and 70 mm/min), drop weight tests were carried out. Then to see the effect of printing orientation on mechanical performance, three-point-bending tests were performed and damage mechanisms were comparatively examined through scanning electron microscopy. Findings An increase in layer thickness from 0.16 mm to 0.28 mm resulted in a notable 37% decrease in the impact resistance of the printed part. In addition, increasing the printing speed from 50 to 70 mm/min reduced the energy absorption capacity of the printed part by approximately 36.5%. Moreover, in terms of printing direction, transversely printed specimens showed 10% lower flexural strength than longitudinally printed specimens. Finally, scanning electron microscopy (SEM) observation showed that internal defects were more prominent in transversely printed specimens, resulting in premature failure. Furthermore, delamination was also detected in transversely printed specimens as another damage mechanism accelerating material failure. Originality/value It is seen that the effect of printing parameters on the fundamental mechanical properties including tensile strength, strain at break, ductility and elastic modulus were studied by various researchers. However, to the best of authors’ knowledge, the effect of printing speed and layer thickness on the energy absorption of polylactic acid based hexagonal honeycomb was not encountered. In addition, in-depth SEM analysis to discover the influence of printing direction significantly contributes to the literature.

Future directions in geological research impacting renewable energy and carbon capture: A synthesis of sustainable management techniques

Geological research plays a pivotal role in shaping the future of renewable energy and carbon capture initiatives, offering insights into sustainable management techniques. This review synthesizes the future directions in geological research that impact renewable energy and carbon capture, focusing on sustainable management techniques. Future geological research will increasingly focus on enhancing the integration of renewable energy sources into existing energy systems. This includes the development of innovative geological mapping techniques to identify and characterize renewable energy resources with greater precision, aiding in the selection of optimal sites for energy production. Additionally, there will be a growing emphasis on utilizing geological data to assess the feasibility of carbon capture and storage (CCS) projects, particularly in volcanic regions, where the unique geological characteristics offer potential for efficient carbon sequestration. Another key aspect of future geological research is the advancement of monitoring and modeling techniques to evaluate the long-term performance and environmental impact of renewable energy and CCS projects. This includes the use of advanced geophysical and geochemical methods to monitor subsurface changes associated with energy extraction and carbon storage, ensuring the effectiveness and safety of these practices. Furthermore, future research will explore the potential of geological formations, such as deep saline aquifers and depleted oil and gas reservoirs, for large-scale carbon storage. This will involve developing strategies to enhance storage capacity and mitigate the risk of CO2 leakage, contributing to the sustainable management of carbon emissions. In conclusion, future geological research will play a critical role in advancing renewable energy and carbon capture technologies, offering sustainable management techniques that are essential for addressing climate change. By focusing on innovative mapping, monitoring, and modeling approaches, researchers can pave the way for a more sustainable energy future.

A Simulation-Based Investigation on Thermal Responses of Suspension Bridge Tower Under Different Fire Scenarios

AbstractRecords of bridge fire incidents illustrate that bridge fires can have catastrophic consequences. The severity of these fires can be influenced by various factors such as bridge type, vehicle size, and wind. Contrary to building fires that have been extensively studied, scant attention has been paid to bridge fires and more specifically fire exposure to the suspension bridges. In addition, existing bridge fire literature is mostly concentrated on fire exposure to girders or cables of suspension bridges. Therefore, this study focused on the post-fire condition of a fire-exposed suspension bridge tower using computational fluid dynamics (CFD) modeling techniques and finite element analysis (FEA). The impacts of the main bridge fire parameters including vehicle size, exposure duration, distance between the fire source and tower, and wind effects were also evaluated. Initially, fire dynamic simulator (FDS) was used to simulate 12 different fire scenarios. The time–temperature histories obtained from each scenario were transferred to the ABAQUS finite element (FE) software to conduct transient thermal analysis and obtain the temperature development within the steel tower of the bridge. The post-fire evaluation was performed with respect to the temperature-induced reduction in the yield strength of steel. The results show that fire exposure from a fuel truck in the proximity of a steel tower could significantly reduce the strength of the tower and lead to severe damage. Early control of the fuel truck fire is crucial in reducing the severity of the damage and preventing temperature development in higher areas of the tower. Although a wind toward the tower can significantly increase the fire-induced damage to the bottom parts of the tower, it considerably reduces the temperature exposure to the higher parts of the tower. Fire exposure from a normal vehicle does not put the tower at risk of failure, and an unprotected steel tower can withstand it. However, a bus fire may lead to minor damage. The thermal strengthening of the first 20 m of the tower can help in preventing the potential fire damage.

Multi-source connectivity as the driver of solar wind variability in the heliosphere

AbstractThe ambient solar wind that fills the heliosphere originates from multiple sources in the solar corona and is highly structured. It is often described as high-speed, relatively homogeneous, plasma streams from coronal holes and slow-speed, highly variable, streams whose source regions are under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify solar wind sources and understand what drives the complexity seen in the heliosphere. By combining magnetic field modelling and spectroscopic techniques with high-resolution observations and measurements, we show that the solar wind variability detected in situ by Solar Orbiter in March 2022 is driven by spatio-temporal changes in the magnetic connectivity to multiple sources in the solar atmosphere. The magnetic field footpoints connected to the spacecraft moved from the boundaries of a coronal hole to one active region (12961) and then across to another region (12957). This is reflected in the in situ measurements, which show the transition from fast to highly Alfvénic then to slow solar wind that is disrupted by the arrival of a coronal mass ejection. Our results describe solar wind variability at 0.5 au but are applicable to near-Earth observatories.

Business analytics, corporate entrepreneurship, and open innovation

PurposeThis research investigates the impact of business analytics (BA) on corporate entrepreneurship (CE) and open innovation (OI), considering the moderated mediation analysis in the context of Iran as a developing country. The study was conducted in various industries, including food, chemicals, agriculture, automobile, and service industries, with 207 observations.Design/methodology/approachThrough an in-depth review of the extant literature a conceptual model was developed and the proposed hypotheses were tested using Structural Equation Modeling technique (PLS-SEM).FindingsThe results indicate that business analytics has significant effects on corporate entrepreneurship and open innovation. Open innovation has a significant effect on corporate entrepreneurship, with open innovation serving as a suitable mediator. Furthermore, the moderated mediation analysis shows the positive impact of Business Analytics on Open Innovation-Corporate Entrepreneurship relationship.Research limitations/implicationsAs this study was conducted in Iran, one of the main limitations can be attributed to the specific characteristics of the country which may affect how and how much the variables influence each other.Practical implicationsThe study highlights the importance of promoting Open Innovation in organizations and utilizing Business Analytics to make strategic decisions and foster innovation in entrepreneurial activities.Originality/valueThis study fills the gap in the literature by exploring how BA contributes to corporate entrepreneurship of the Iranian organizations in various industries, given open innovation as a mediator under dynamic market conditions.

Data-driven modeling techniques for prediction of settled water turbidity in drinking water treatment

Drinking water treatment is a complex system of chemical, physical, and biological processes that is highly dependent on water quality and the design of the treatment process. To create decision-support tools, the prediction of key performance indicators, such as settled water turbidity, is needed. A variety of data-driven modeling techniques is available to formulate such predictions. Data-driven models provide valuable tools for formulating predictions where there is a lack of mechanistic models or the mechanisms are not fully understood, as in surface water treatment. The objective of this paper is to evaluate and compare the effectiveness of various data-driven techniques for this important, but difficult, problem. Recognizing that the size and quality of the dataset are most critical in this kind of analysis, this work uses one of the largest datasets used in this context consisting of 2,527 vectors of water quality and operational data (2,527 X nine data frame) from a full-scale water treatment plant. The paper constructs and compares the performance of the several data-driven models including k-nearest neighbor (KNN) regression, polynomial regression, and artificial neural networks (ANN). Based on test scaled root mean square error (RMSE), the ANN model was the most predictive (0.124). Similarly, the ANN model had the best predictive performance based on total scaled RMSE (0.086). These results show that ANNs have a high potential for the development of a future decision support system in selecting appropriate coagulant doses based on settled water turbidity.

ESAT-6 undergoes self-association at phagosomal pH and an ESAT-6-specific nanobody restricts M. tuberculosis growth in macrophages.

Mycobacterium tuberculosis (Mtb) is known to survive within macrophages by compromising the integrity of the phagosomal compartment in which it resides. This activity primarily relies on the ESX-1 secretion system, predominantly involving the protein duo ESAT-6 and CFP-10. CFP-10 likely acts as a chaperone, while ESAT-6 likely disrupts phagosomal membrane stability via a largely unknown mechanism. we employ a series of biochemical analyses, protein modeling techniques, and a novel ESAT-6-specific nanobody to gain insight into the ESAT-6's mode of action. First, we measure the binding kinetics of the tight 1:1 complex formed by ESAT-6 and CFP-10 at neutral pH. Subsequently, we demonstrate a rapid self-association of ESAT-6 into large complexes under acidic conditions, leading to the identification of a stable tetrameric ESAT-6 species. Using molecular dynamics simulations, we pinpoint the most probable interaction interface. Furthermore, we show that cytoplasmic expression of an anti-ESAT-6 nanobody blocks Mtb replication, thereby underlining the pivotal role of ESAT-6 in intracellular survival. Together, these data suggest that ESAT-6 acts by a pH-dependent mechanism to establish two-way communication between the cytoplasm and the Mtb-containing phagosome.

ESAT-6 undergoes self-association at phagosomal pH and an ESAT-6-specific nanobody restricts M. tuberculosis growth in macrophages

Mycobacterium tuberculosis (Mtb) is known to survive within macrophages by compromising the integrity of the phagosomal compartment in which it resides. This activity primarily relies on the ESX-1 secretion system, predominantly involving the protein duo ESAT-6 and CFP-10. CFP-10 likely acts as a chaperone, while ESAT-6 likely disrupts phagosomal membrane stability via a largely unknown mechanism. we employ a series of biochemical analyses, protein modeling techniques, and a novel ESAT-6-specific nanobody to gain insight into the ESAT-6’s mode of action. First, we measure the binding kinetics of the tight 1:1 complex formed by ESAT-6 and CFP-10 at neutral pH. Subsequently, we demonstrate a rapid self-association of ESAT-6 into large complexes under acidic conditions, leading to the identification of a stable tetrameric ESAT-6 species. Using molecular dynamics simulations, we pinpoint the most probable interaction interface. Furthermore, we show that cytoplasmic expression of an anti-ESAT-6 nanobody blocks Mtb replication, thereby underlining the pivotal role of ESAT-6 in intracellular survival. Together, these data suggest that ESAT-6 acts by a pH-dependent mechanism to establish two-way communication between the cytoplasm and the Mtb-containing phagosome.

Collapse behavior of corrugated stiffened pressure shells under external hydrostatic pressure considering actual fabrication imperfections: A reverse modeling technique

The pressure shell is the most critical load-bearing component in submersible vehicles. It is mainly used to withstand the enormous seawater pressure. In this study, a corrugated stiffened pressure shell was proposed to address the problem of low compression performance of the traditional cylindrical pressure shell. Nine corrugated shells made of aluminum alloy with varying corrugation parameters were created using CNC machining. After welding and sealing treatment, hydrostatic pressure tests were conducted on the shells to investigate the impact of parameters such as wall thickness, number of corrugations, and corrugation height on their compression performance. The shells were measured using a 3D scanner, and the resulting data was used for reverse modeling. The analysis focused on the failure behavior of corrugated shells with geometric imperfections under uniform external pressure using nonlinear finite element analysis. The results indicate that increasing the wall thickness or corrugation height can significantly improve the compression performance of the shells. The position of collapse remains consistent with the location of the maximum Mises stress. Furthermore, it was discovered that the corrugated structure also has an additional advantage in retarding the generation and expansion of diamond-shaped defects on the shell surface. The equilibrium paths of all corrugated shells maintained a smooth and continuous state, demonstrating stable buckling behavior. The numerical calculations were in excellent agreement with the experimental results, highlighting the advantages of the reverse modeling technique. This study provides new insights for the structural design of submarine pressure shells and explores more reasonable and feasible stiffening methods.

Smart Experience and Green Health Tourism: The Moderating Role of Content Marketing

The purpose of this research is to evaluate smart experience in green health tourism with the mediating role of content marketing by using the two models of technology acceptance and hedonism. This study utilizes quantitative and causal research methods. A total of 332 respondents completed a survey conducted at two Iranian tourism hubs. Also, purposeful sampling was used for data collection. Respondents rated their perceptions of each item on a five-point Likert scale. Using the partial least squares path modeling technique, the results reveal the direct effects of smart experience on green health tourism. The findings suggest that content marketing interacts with smart experience to influence green health tourism, such that the use of smart experience in health tourism can reduce the annual costs of developing environmental protection methods. This paper thus contributes to the hospitality management theory, green tourism, and wellness literature.

Integrating Financial and Economic Justice Content into Social Work Education

ABSTRACT Teaching financial and economic justice content is an important feature of social work education. Such content helps students effectively address financial challenges with their clients, transform communities, and advocate for appropriate economic policies. Despite initial efforts by CSWE and others, many social work educators still do not teach financial and economic justice content in their courses. Using the diffusion of innovation theory, this paper assesses what sociodemographic, personal, and education related factors impact the odds of teaching financial and economic justice content. The study used original survey data (n = 163) from social work educators and binary logistic regression modeling techniques. Results suggest that the social work educator’s highest achieved education level, as well as the type of course taught, play a meaningful role in determining the odds of teaching the content. Furthermore, perceived relevance has a strong, positive association with the odds of teaching the content. These findings suggest that those seeking to increase the prevalence of financial and economic justice content in social work education can target their efforts on a few key intervention points, and that policy measures supporting the integration of the content have yet to be implemented by educators.