bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of meteorological drought periods based on the Standardized Precipitation Evapotranspiration Index (SPEI) using the Power Law Process approach
Authors: Auliana Nur Hikmah , Sunusi Nurtiti , Herdiani Erna Tri
Published: August 2024

In recent decades, abnormal rainfall and temperature patterns have significantly impacted the environment and human life, particularly in East Nusa Tenggara. The region is known for its low rainfall and high temperatures, making it vulnerable to drought events, which have their own complexities due to being random and changing over time. This study aimed to analyze the trend of short-term meteorological drought intensity in Timor Island, East Nusa Tenggara. The analysis was carried out by utilizing the standardized precipitation evapotranspiration index (SPEI) for a 1-month period to characterize drought in intensity, duration, and severity. A power law process approach was used to model the intensity of the event, which is inversely proportional to the magnitude of the drought event. Intensity parameters of the power law process were estimated using the maximum likelihood estimation (MLE) method to predict an increase in the intensity of drought events in the future. The probability of drought was calculated using the non-homogeneous Poisson process. The analysis showed that "extremely dry" events in Timor Island are less frequent than "very dry" and "dry" events. The power law process model's estimated intensity parameter showed a beta value greater than 1, indicating an increase in future drought events. In the next 12 months, two months of drought are expected in each region of Timor Island, East Nusa Tenggara, with the following probabilities for each region: 0.264 for Kupang City, 0.25 for Kupang, 0.265 for South Central Timor, 0.269 for North Central Timor, 0.265 for Malaka, and 0.266 for Belu. This research provides important insights into drought dynamics in vulnerable regions such as East Nusa Tenggara and its potential impact on future mitigation and adaptation planning.
Empirical likelihood method for detecting change points in network autoregressive models
Authors: Yang Jingjing , Tian Weizhong , Tian Chengliang , Li Sha , Ning Wei
Published: August 2024

The network autoregressive model is a super high-dimensional time series model that can fully explain social relationships. This model can fully reflect the complex relationships in reality. Therefore, it plays a vital role in detecting the inflection point problem of this network autoregressive model for economics and finance. In this paper, we proposed the change-point problem of detecting network autoregressive models using empirical likelihood statistics based on the expected error term of the switching rule being 0, using the empirical likelihood method. Moreover, the asymptotic null distribution of the proposed empirical likelihood statistic was investigated. Simulation studies based on different settings were considered, and the results showed that the power of test statistics is significant. In the end, the Chinese stock market was investigated to demonstrate the significance of the proposed method.
INTEGRATING DUAL FUNDUS IMAGE DEEP FEATURE POOLING BASED ON CUP AND DISC SEGMENTATION FOR EFFICIENT GLAUCOMA DETECTION FRAMEWORK INDULGING HEURISTIC CONCEPT
Authors: Archana E. , Geetha S. , Victo Sudha George G.
Published: August 2024

The permanent damage occurring to the optical nerve, which transmits the visualized images to the brain leading to permanent vision loss, is called glaucoma. The detection and diagnosis of glaucoma at the beginning stage are essential to prevent permanent damage caused to vision as there are no initial symptoms to detect the onset of the damage, and there is no cure while the damage progresses to its extreme stage. Many models utilizing deep learning technology have been executed to identify the presence of glaucoma using digital Fundus Images (FI). However, these methods need specialized hardware and are computationally complicated as there is not much-labeled data available, leading to generalization issues in these models. Therefore, the development of a deep learning-oriented model for Glaucoma Detection (GD) using the dual feature pooling of cup and disc segmentation features with the aid of the FI is the main motive behind this work. At first, the digital FI is congregated from standardized online data sources. Then the gathered images are considered for the image segmentation procedure. The disc is segmented from the input image with the aid of the Transformer based Adaptive ResUNet++ (TA-ResUNet++) framework. The segmented disc image is now provided to hybrid convolution networks (HCN) for extracting the disc-oriented features. At the same time, the cup is segmented from the gathered input image using the executed TA-ResUnet++. Following this, the cup features are extracted using the same HCN model. The obtained features regarding the disc and the cup are concatenated in order to generate a single pool of glaucoma features. Then these features are inputted into the implemented bidirectional long short-term memory with multi-head attention (Bi-LSTM-MHA) mechanism to detect the presence of glaucoma in people. The segmentation and detection outcomes are made better by optimizing the parameters in the TA-ResUNet++ and Bi-LSTM-MHA models with the aid of hybrid sewing training with the Chameleon swarm optimization algorithm (HST-CSA). The experimental analysis is done to analyze the effective performance of the developed GD model. Jaccard analysis performed for the developed HST-CSOA-TA-ResUNet++-based disc segmentation gained better performance as 18.29% than MobileNet, 14.11% efficient than ResNet, 12.79% superior to Bi-LSTM and 5.43% higher than Bi-LSTM-MHA while considering best as the statistical measure.
AI-Powered Clinical Trial Design With Translational Bioinformatics
Authors: Mittal Shashank , Singh Priyank Kumar , Gochhait Saikat , Gaur Nisha , Kumar Shubham
Published: August 2024

Clinical trial design is undergoing a revolution fueled by artificial intelligence (AI) and translational bioinformatics. This chapter explores how AI techniques like machine learning and deep learning are being harnessed to analyze vast datasets of biological and clinical information. By integrating these insights with translational bioinformatics, researchers can identify promising drug candidates, select patients most likely to benefit from treatment, and design more efficient and targeted clinical trials. Real-world examples showcase the application of AI in immuno-oncology patient selection, drug discovery for rare diseases, predicting Alzheimer's trial outcomes, and virtual patient recruitment for cardiovascular studies. While challenges like data quality and ethical considerations exist, AI and translational bioinformatics hold immense promise for accelerating drug development, bringing life-saving therapies to patients faster.
Detection of Breast Cancer in Mammography Images Using Intelligent Models
Authors: Sharma Vandana , Rawat Yogita , Surve Gauri , Rai Richa Hirendra
Published: August 2024

Amongst the several cancer types, incidence of breast cancer is the highest in women. Breast cancer can be diagnosed and treated effectively through various screening methods and computer-aided detection systems (CADs). However, conventional computer-aided diagnosis (CAD) programs for detecting potential cancers on mammograms are lacking diagnostic accuracy and require upgradation. The advances in machine learning, particularly with the use of deep (multi-layered) convolutional neural networks, have allowed artificial intelligence to create a transformation in CAD that has improved models' prediction quality. The outline of this chapter includes a structured method for predicting presenting breast cancer stages, identification, segmentation and classification of lesions, and breast density assessment using the current technological models which includes artificial intelligence, deep learning, and machine learning.
Speckle Noise Removal From Biomedical MRI Images and Classification by MSVM
Authors: Karthik B. , C. V. Krishna Reddy
Published: August 2024

Medical images are crucial for detecting and predicting sickness, as well as for keeping track of the patient's development, owing to image processing (IP) techniques. There are numerous applications for the IP methods. Image processing is used, for instance, in the field of medicine to evaluate images produced by x-rays and MRI magnetic resonance imaging. The patient is going to incur a high price for this application. Medical images are commonly contaminated with impulsive, multiplicative, and addictive noise as a result of various image processing non-idealities. By replacing some of the original image's pixels with new ones that have luminance values that are less than the allowed dynamic luminance range, noise frequently taints medical images. In this research, the Speckle type noises are removed using the Mean Filter (MF), and the images are classified using a Multi-SVM classifier. The system was created entirely in Python.
Impact of Ecological Constitution on Conservation of Marine Ecosystem
Authors: Kanojia Siddharth
Published: August 2024

The declining abundance of fish stocks and the deteriorating state of marine ecosystems are triggered by a number of variables such as pollution, overfishing, habitat destruction, and climate change. These factors raise serious concerns about our marine ecosystem. In light of these challenges, there is a pressing need to reframe environmental law on managing the marine resources to better address ecological concerns and promote sustainability. Hence, this chapter established the contextual relevance between the need for Ecological Constitution on the protecting the marine ecosystem from overfishing, loss of biodiversity, ocean acidification, and climate change and provide an overview of how the adaptation of Ecological Constitution can restore the deterioration of marine ecosystem.
Enhancing Ecosystem Services
Authors: Madiedo Ana Maria , Ramos Jorge , Leitão Francisco
Published: August 2024

Artificial reefs (AR) are human made structures deployed in the seabed to provide multiple economic, social, and ecological benefits. AR have emerged as a highly effective strategy to provide ecosystem services, such as habitat protection and restoration. Despite their acknowledged benefits, there remains a limited understanding of how these reefs are currently being used by the local communities. Ecosystem services (EC) are tangible and intangible benefits that the ecosystems provide to humans. The present study aims to identify the EC provided by the AR located on the south coast of Portugal. To achieve this, a literature review was done to set a baseline and find out what has been published in this research area.
Analysing the Role of Artificial Reefs in Blue Tourism
Authors: Costa Joana , Ramos Jorge , Leitão Francisco
Published: August 2024

Artificial reefs (AR) are essentially implemented to increase marine biodiversity, which can indirectly benefit the local ecosystem and human communities. Their presence can attract a wide range of marine organisms and promote recreational activities, attracting anglers and scuba divers, improving local tourism. The aim of this work is to analyse how the Vilamoura AR (Southern Portugal) is useful to the sustainable coastal (blue) tourism in the area. Nowadays, with the help of tools such as vessels tracking intelligence and the use of direct georeferentiation, it is possible to infer about the use of artificial reefs by analysing patterns of recreational vessels around the AR area. The application of those tools reenforced the importance of combining methodologies such as not only monitoring artificial reefs but also to have interviews with the stakeholders to better understand the recreational and blue tourism potential of the Vilamoura AR.
English Language Teachers' Experiences of Transitioning to Emergency Remote Teaching in Malta
Authors: Calleja Rebecca , Xerri Daniel
Published: August 2024

The COVID-19 pandemic necessitated a rapid transition to emergency remote teaching (ERT) in schools around the world. This small-scale study examines English language teachers' experiences of ERT in Malta and the way they navigated the challenges posed by this unexpected situation. By means of the findings that emerged through the use of a questionnaire, written narratives and interviews, the study highlights the lived experiences and diverse perspectives of teachers during the ERT period. The findings indicate that even though they encountered substantial hurdles in adapting to remote teaching, this experience was also an opportunity for them to develop their technological competencies and knowledge of varied teaching approaches. The study underscores the need for extensive professional development in the use of digital tools and effective remote teaching pedagogy. Furthermore, it emphasizes the importance for schools to be prepared for the possibility of unforeseen future disruptions demanding rapid adaptation.
Modern Technology as a Management Strategy for Gender Equity Achievement
Authors: Ramya R.
Published: August 2024

In the current dynamic corporate environment, attaining gender parity has emerged as a crucial objective for establishments across the globe. This chapter investigates how gender equity in the workplace can be advanced via the strategic use of contemporary technology. It looks at how to use technology to promote diversity and inclusion, overcome structural prejudices, and make the workplace more equal. The first section of the chapter examines the status of gender parity in the workplace today and the obstacles that businesses must overcome to achieve it. The use of contemporary technology to address these issues is then examined, including the application of machine learning, artificial intelligence, and data analytics. The study also looks at the drawbacks and hazards of utilising technology as a managerial tool to promote gender parity, including privacy issues, bias, and the possibility that technology would exacerbate already-existing disparities.
Gender and Organization
Authors: Roy Suparna
Published: August 2024

The concept of naming functions as a political corpus of marking the bodies so that the power of control is piped appropriately by the phallogocentric order. Weaving in Bengal has remained as industries that unmark the women weavers from their basic pay. The gender crisis faced by women weavers in West Bengal, particularly in regions like Shantipur, and Phulia, is a multifaceted issue deeply rooted in socio-economic factors. Women weavers often face unequal pay compared to their male counterparts for similar work. They are sometimes exploited by middlemen who control the market, leading to unfair compensation for their labor. This chapter will examine in depth the government policies, socio-cultural discrimination, and wage gap of the women weavers of Shantipur and Fulia districts of West Bengal under a deconstructive feminist theory with an empirical methodology that will also present the intersectionalities of marginalization that the women weavers of West Bengal face.
Issues and Challenges of Artificial Intelligence Implementation in Healthcare
Authors: Kaushik Tarun Kumar , Ravish  , Singh Anurag , Raghav Anjali , Singh Bhupinder
Published: August 2024

In 1956, the United States of America was the birthplace of artificial intelligence (AI), which at the time consisted of an algorithm that was developed via the process of analyzing previously collected data and learning on its own. This chapter provides a discussion on the present applications of artificial intelligence in the medical field, as well as a summary of the existing challenges that are restricting the widespread acceptance of AI. Therefore, the implementation of technology and artificial intelligence in the healthcare industry may be beneficial to hospitals, particularly in situations where viruses such as COVID diminish the workforce. There is also a dependence on trustworthy sources, as well as worry over artificial intelligence and healthcare. To fulfill its objectives, however, the healthcare industry must contend with a great deal of difficulty.
Generative AI in Qualitative Research
Authors: Laajini Tariq , Tadjousti Hind
Published: August 2024

In today's world, generative artificial intelligence (GenAI) is driving transformative shifts in technological advancements and research paradigms, requiring new methods and ideas to be adopted in qualitative research. This chapter explores the opportunities of using GenAI for advanced data analysis in qualitative methodologies. It focuses on how GenAI integration can reveal previously hidden patterns in the analysis of social phenomena. By reviewing the latest advancements in GenAI applications for qualitative studies from 2022 to 2024, the chapter highlights the significant role of GenAI in improving the quality, validity, and creativity of qualitative research. Furthermore, it discusses how GenAI can open up new pathways for comprehensive exploration, allowing a deeper understanding of complex social dynamics.
Students Wellbeing and Performance in Higher Education Institutions
Authors: Singh Amrik
Published: August 2024

Lack of social support during the COVID-19 pandemic has caused a decline in students' psychological well-being. The students who cannot adapt to online learning demonstrate stress and disruption in psychological well-being. Stress among students in institutions of higher learning is an issue that needs to be paid attention to especially when dealing with the spread of the coronavirus. The present chapter aims to explore student well-being over time, on a university-wide basis. It explores students' perceptions of strengths and weaknesses in the university's academic and service infrastructure and the relationship of these perceptions to student well-being. The focus on student psychological well-being in addition to satisfaction, the longitudinal approach, and the university-wide scope a system of institutional self-evaluation. Moreover, social support from the student's environment such as the encouragement of parents, lecturers, friends, and the surrounding community will help to improve the level of psychological well-being of a student. This matter indirectly affects the student's academic performance and therefore to achieve excellent academic results.
Familial Factors Impacting the Well-Being of Students at Universities
Authors: Okita Dan O.
Published: August 2024

The well-being of students encompasses their overall state of physical, mental, emotional, and social health and functioning within the context of their academic pursuits and personal development. However, there remains a significant gap in understanding the influence of familial social factors on the well-being of university students. Familial social factors, including family cohesion, communication patterns, parental expectations, and socio-economic status, are likely to significantly influence university students' well-being. Family relationships and support systems can serve as crucial sources of emotional, financial, and practical support during the transition to university life, influencing students' ability to cope with academic stressors and navigate developmental challenges. Thus, there is a pressing need for further research to explore the role of familial social factors in shaping the well-being of university students.
Institutional-Based Factors Influencing the Wellbeing of Students in Universities
Authors: Birech Jeniffer O. , Onyango Pamela A.
Published: August 2024

The well-being of students in the university environment continues to draw global attention. Well-being is not only happiness but also satisfaction; it is a condition of being able to develop oneself as a whole person who contributes to society. More importantly, more research needs to be done to unearth the institutional based factors that affect the wellbeing of the students. This chapter sought to examine the extent to which institutional facilities, student-teacher relationship, orientation influence the wellbeing of the students. The findings of this chapter may assist the universities to prioritize the wellbeing of the students by strengthening the facilities, create awareness and come up with specific targeted programs for sustainable solutions.
Impact of Digital Storytelling on Middle School Students' Attitudes Toward English Language Learning
Authors: Roy Abhipriya
Published: August 2024

The integration of digital storytelling (DST) into teaching has significantly influenced educational development, especially English language acquisition. This study examines the impact of DST-integrated pedagogy on students' attitudes and perceptions toward learning English. In a quantitative study using an experimental design, 200 middle school students were purposively selected and divided into control and experimental groups. The control group received the traditional method of language teaching, while the experimental group received the DST method. Data were collected through a survey and analysed using descriptive and Wilcoxon test. The results suggest that exposure to DST positively influenced students' attitudes and led to better understanding, engagement, and motivation in learning English in the treatment group. This suggests that incorporating DST into English lessons can improve teaching quality and students' overall progress. Further analysis is needed to fully explore the potential of DST-based instruction in developing language acquisition skills.
Design and Implementation of Artificial Intelligence Online Learning Platform Based on Resource Scheduling Technology
Authors: Xu Libo , Yu Wenbo
Published: August 2024

Online learning (OL) is a new platform, which is used as a supplementary resource to improve the teaching and learning process in university education. The open educational resources provide supplementary resources for strengthening online learning platform. In this work, an artificial intelligence-based online learning platform is designed that can manage resources using scheduling technology. The OL platform automatically allocates resources through appropriate cluster-based scheduling technology. The proposed deep learning-based OL platform (DLOL) supports the sequential integration of the model by simplifying and adjusting the model operating environment. Combined with the fuzzy-based convolutional neural networks, the decision tree algorithm is used to implement OL platform using blockchain models. The research results proved that artificial intelligence can help students improve their learning ability and makes the learning process relatively easy and exciting.
Study on the Behavioral Motives of Algorithmic Avoidance in Intelligent Recommendation Systems
Authors: Cheng Xiao , Peng Guochao
Published: August 2024

Through an exploration of the underlying mechanisms driving users' algorithmic avoidance in intelligent recommendation systems, this study aims to facilitate a positive interaction between users and technology, providing theoretical guidance for the efficient operations of enterprises using intelligent recommendation systems. The research integrates the theories of information ecology and psychological resistance, establishing a model of influencing factors on users' algorithmic avoidance in intelligent recommendation systems. Utilizing a structural equation model, the study conducts analysis and validation on data collected from 506 questionnaires. The findings reveal that algorithmic transparency and perceived manipulation significantly impact the users' algorithmic avoidance in intelligent recommendation systems. The sense of being manipulated emerges as a crucial psychological factor leading to algorithmic avoidance, playing a complete mediating role in the influence of information quality, homogeneous recommendation, and algorithmic transparency on algorithmic avoidance.
The Impact of Macroeconomic Variables on Stock Price: An ARDL Approach to Sri
Lanka
Authors: Mathusha S.
Published: August 2024

The study investigates the impact of macroeconomic variables on stock prices in
Sri Lanka by analyzing the monthly data from January 2001 – December 2020. ARDL bounds
testing approach is applied to check the long-run relationship between macroeconomic
variables and stock price. Error correction version of the ARDL model was used to
identify the short-run relationship between macroeconomic variables and stock price. The
findings suggest that stock price in long term is significantly affected by the consumer
price index and Treasury bill rate. In the short run, all variables are insignificant
except the Treasury bill rate which is negatively related to the stock price. The
government should consider the stock price and these macroeconomic variables when
implementing government policies such as privatization, foreign exchange control and
monetary policy.
Unsteady Loads of a Scaled Rotor on and near the Landing Deck of the NATO Generic Destroyer
Authors:   Georgia Institute of Technology, Chen Wei-Han , Rauleder Juergen
Published: August 2024

Rotorcraft shipboard operations are risky and demand high piloting skills. Computational simulations are invaluable for pilot training and understanding of the complicated aerodynamic environment. Yet, simulations may not capture a realistic response of the rotorcraft due to simplified modeling of the interactional aerodynamics. Thus, improvements to modeling and simulation are required, and experimental data are needed that unveil the interactional aerodynamics and dynamics between the rotor and ship for computational validation efforts. In this study, an extensive experimental investigation of the ship-rotor dynamic interface problem was conducted to gain a general understanding of the interactional aerodynamics between a 1:100 wind-tunnel-scale NATO Generic Destroyer and a representative single main rotor, operating both stationary and dynamically in the vicinity of the landing deck. Data obtained through simultaneous measurements of rotor hub loads, ship deck surface pressures, and stereoscopic particle image velocimetry flow fields gave valuable insight into the highly coupled aerodynamic phenomena. Results showed that the rotor hub loads exhibited a high dependency on both the wind direction and also the position of the rotor relative to the landing deck. A bifurcation in the regions of high unsteady thrust was observed under certain wind conditions due to the ship airwake impacting different portions of the rotor disk. The inflow angles across the rotor disk were estimated and assessed through flow field measurement, revealing how the ship airwake altered the rotor inflow and wake structure that contributed to the unique change in rotor loads under various hovering conditions.
Efficient Hybrid Wake Modelling for Advanced Configuration Design and Analysis
Authors:   Continuum Dynamics Inc., Whitehouse Glen , Wachspress Daniel , Boschitsch Alexander
Published: August 2024

Advanced and Urban Air Mobility aircraft development is revolutionizing the aerospace industry by providing a realizable path towards affordable on-demand passenger-carrying operations in metropolitan areas. Many aircraft concepts are in development, and as they start to mature, there is a need for higher fidelity design and analysis tools that have improved modeling of component-wake interactions. Unfortunately, the current generation of CFD-based high fidelity tools is unsuitable for many daily design and analysis applications due to computational cost, expertise and setup requirements. Conversely, current design tools often rely on empirical relationships to represent many interactional effects, or become increasingly inaccurate in regions where the wakes trailed and shed from aerodynamic component becomes highly distorted. This paper describes recent work by Continuum Dynamics, Inc. to develop a mid-fidelity analysis tool, embedded within the CHARM analysis package, which brings CFD-class interactional aerodynamics analysis capabilities to aspects of the design cycle without the cost and expertise requirements of contemporary CFD-based analysis.
A Meta-model for Comprehensive Pilot Modeling
Authors:   Politecnico di Milano, Marchesoli Davide , Zanoni Andrea , Masarati Pierangelo
Published: August 2024

Piloted flight involves the participation of both an air vehicle and a pilot. Modeling humans is becoming more and more important due to the growing integration of automatic systems interacting with the pilot at an ever deeper cognitive level. This research wants to offer a systematic approach to analyzing pilot behavior during a mission. The development of the pilot behavior meta-model was preceded by a careful definition of the required terminology, starting from the important assumption that human behavior is inherently teleological and therefore directed towards either explicit or implicit goals. A custom test performed with two pilots showed how humans respond to an unexpected event with a perceptible variation in muscle activation; the mechanism falls within the pilot's voluntary behavior as it aims at achieving a goal that changed following the event itself. The proposed meta-model can be adapted and used to analyze complex pilot behavior, possibly leading to new paradigms in the design of training procedures or novel cockpit layouts.
Computational Analysis of Behavior and Structures of Coaxial Rotor Hub and Sail Fairing Wake
Authors:   The Pennsylvania State University, Deore Neal , Coder James
Published: August 2024

A computational study is conducted on a coaxial rotor hub and sail fairing configuration to analyze hub surface forces and the characteristics of its downstream wake. The flow conditions and grids are based on experimental tests performed at the Penn State Applied Research Lab (ARL) Water Tunnel at a baseline Reynolds number. Grid development for the rotor hubs and sail fairing is done using Pointwise v18.04R1 and Chimera Grid Tools (version 2.2). Simulations are performed using NASA's OVERFLOW2.4b Reynolds Averaged Navier-Stokes solver. The drag forces on the rotor hubs are computed and compared to standalone drag data to analyze the effects of interactional aerodynamics. Flow features, frequency content and Reynolds stresses of the wake are analyzed. Frequency content and Reynolds stresses show clear spatial bias. The anisotropy of the Reynolds stresses is computed and used to determine the character of the wake turbulence.
Full Vehicle Helios Model Performance Correlation with SB>1 Defiant® Flight Test
Authors:   Sikorsky a Lockheed Martin Company, Min Byung-Young , Kim Jeewoong , Klimchenko Vera , Neiswonger Jacob , Griffiths Daniel , Wake Brian
Published: August 2024

Full vehicle CREATE-AV™ Helios model has been developed for SB>1 Defiant®, a Joint-Multi-Role Technology Demonstrator (JMRTD) designed by the Sikorsky-Boeing team utilizing compound design with coaxial rotor and propulsor. The full vehicle model includes coupling with RCAS for elastic blade deformation as well as full vehicle trim in steady level flight condition. The purpose of current study is to assess performance prediction capability of the developed Helios modeling approach for such a complex non-traditional design by correlating with flight test data. To minimize uncertainty in flight test data reduction, correlations were made with the data that were directly measured or requiring minimal derivation. The Helios model showed generally very good correlation in power, component forces, rotor and propulsor efficiencies for wide range of flight test conditions. The model also showed very good correlation in performance sensitivity to trim state and rotor RPM, which demonstrates the modeling approach can be used to find optimal flight trim condition. Significance of geometric details and its aerodynamic interference such as shaft modeling has been demonstrated. Impact of measurement uncertainty to data correlation was also demonstrated.
Verification of Radially Continuous Actuator-Based Models for Rotors in Hover
Authors:   Rensselaer Polytechnic Institute, Hebbar Ullhas ,   North Carolina State University, Gandhi Farhan , Niemiec Robert
Published: August 2024

This study models flow around isolated and side-by-side three-bladed propellers in (IGE) and out of ground effect (OGE) using actuator-based techniques of varying fidelity. Actuator techniques model propellers using momentum sources distributed over the disk in actuator disk method (ADM) or distributed over moving lines in actuator line method (ALM) to reduce computational cost compared to blade-resolved DDES simulations. The lowest fidelity ADM method is observed to reasonably predict thrust with the use of a tip loss model to control runaway thrust at the tip while not resolving flow features such as blade-bound vortices and helical tip vortices at a fraction of the cost of BR-DDES (1/100). The coarser ALM model resolves these features but still requires a tip loss model to control runaway thrust at 1/10th the cost of BR-DDES. Finally, the finer ALM model used in this study accurately captures blade-related features and further predicts the tip loss trend from first principles at 1/3rd the cost of BRDDES. Demonstrating the efficacy of these techniques for a commonly encountered flow scenario - side-by-side rotors at 2.5R hub separation are simulated where turbulent fountaining flow is observed between the rotors which eliminates the thrust increase normally seen IGE, a feature captured by both ADM and ALM techniques. However, only ALM captures the impulsive 3-per-rev thrust loading seen in BR-DDES.
Therapists’ defense use impacts their patients’ defensive functioning: a systematic case study
Authors: Di Giuseppe Mariagrazia , Aafjes-van Doorn Katie , Békés Vera , Gorman Bernard S. , Stukenberg Karl , Waldron Sherwood
Published: August 2024

Research into defensive functioning in psychotherapy has thus far focused on patients’ defense use. However, also the defensive functioning of therapists might be significant because of its potential in promoting changes in the patient’s overall defensive functioning by sharing their higher-level understanding of a given situation and letting the patient have the opportunity to learn how to cope more successfully. This exploratory case study is the first to examine therapist’s defense mechanisms and their relationship to changes in the patient’s defensive functioning evaluated at different times throughout psychoanalytic treatment. We assessed the use of defense mechanisms with the Defense Mechanisms Rating Scales in 20 sessions collected at three phases (early, middle and late) of the psychoanalytic treatment. For each session, we identified therapist’s and patient’s defenses, defense levels and overall defensive functioning, with particular attention to the sequence of consecutively activated defenses within the therapeutic dyad. Results showed that the patient’s defensive functioning tended to gradually improve over the course of the treatment, with a slight decrease at the end. Therapists’ overall defensive functioning remained stable throughout the treatment with values in the range of high-neurotic and mature defenses. Assessment of the dyadic interaction between therapist and patient’s use of defenses showed that within-session, the patient tended to use the same individual defenses that the therapist used, which was especially pronounced in the initial phases of the treatment. Towards the end of the treatment, once there was a stable shared knowledge, the patient started to explore using new, higher-level defenses on her own, independent from what defenses the therapist used. Our findings emphasized the analyst’s role in encouraging the development of more effective ways of coping in the patient, confirming previous theoretical and empirical research regarding the improvement of patient’s defensive functioning in psychotherapy. The alterations in these coping strategies, also called high-adaptive defenses, as part of the therapist-patient interaction demonstrate the importance of studying defenses as an excellent process-based outcome measure. The measurement of the degree to which the analyst models and illustrates these superior coping methods to the patient is a prime vehicle for supporting internalization of these skills by the patient.
Healthcare practitioners’ experiences in managing HIV among young people in Namibia
Authors: Kamangu Jacques W.N. , Mboweni Sheillah H.
Published: August 2024

Background: Low viral load suppression rates among older adolescents and young adults with HIV are a global challenge, including in Namibia. Healthcare providers struggle with managing these age groups due to their unique demographic characteristics. Monitoring viral load suppression is vital for evaluating antiretroviral treatment effectiveness, making it essential to identify and address existing gaps.Objectives: This study aimed to explore and describe healthcare practitioners’ understanding and experiences in managing older adolescents and younger adults living with HIV in seven high-burden districts of Namibia.Method: Qualitative descriptive phenomenological research was followed in this study. Healthcare practitioners directly managing older adolescents and younger adults living with HIV were purposively recruited. Telephonic individual interviews were conducted, and data saturation was achieved with the 29th participant. Colaizzi’s seven-step analysis was used to analyse the data.Results: Two themes emerged from the study: (1) healthcare practitioners’ knowledge of viral load management and (2) the strategies employed to manage high viral load in these age groups. These strategies included implementing differentiated service delivery, adopting interprofessional and Ubuntu approaches, psychosocial support, community engagement, enhancing adherence counselling, and support from implementing partners.Conclusion: The findings revealed inadequate knowledge among healthcare practitioners regarding viral load management, which negatively impacts the provision of quality care and an effective HIV response within the spirit of Ubuntu.Contribution: This study enhances healthcare practitioners’ capacity in viral load management and guides policy makers in supporting this unique population, thus improving their health outcomes.
Commodification of Zimbabwe’s higher education amid funding decline: Impact on universal access
Authors: Tsverukayi Barbara H. , Poshai Leon
Published: August 2024

The volatile economic environment in Zimbabwe, which is characterised by persistent hyperinflation has exposed the education access inequalities in Zimbabwe’s public Higher Education Institutions (HEIs). The persisting harsh economic conditions in Zimbabwe have further worsened the decline in higher education funding in addition to the budgetary cuts and the removal of student financial support systems in the late 1980s. This study aims to examine the impact of these neoliberal approaches on inclusive education access in Zimbabwe’s HEIs. The study used the qualitative research approach. The study participants included purposefully selected university managers and students at a state university in Zimbabwe. Data were collected from university managers through interviews while a combination of interviews and focus group discussions was used to collect data from the students. The study revealed that the adoption of neoliberal approaches such as austerity measures in Zimbabwe created a domino effect in the higher education sector as HEIs started to introduce market-based approaches. The adoption of market-oriented approaches in HEIs denigrated universal access to higher education and incapacitated students financially as shown by the declining enrolments and soaring student debts in the studied HEI. Based on the study results, this article concludes that there is a glaring need for restoring financial support towards empowering Zimbabwe’s public HEIs to be responsive to the financial needs of students. This will contribute towards achieving inclusive and equitable education, which promotes universal lifelong learning opportunities as embedded in Sustainable Development Goal 4 and Sustainable Development Goal 10.Contribution: Through an in-depth account of the experiences of students and managers in HEIs, this study provides an empirical understanding of the challenges associated with the adoption of neoliberal approaches in Zimbabwe’s higher education institutions.
Antimicrobial Effectiveness of Endodontic Irrigants Against E. Faecalis: An In vitro Study
Authors: Chhabra Ajay , Prathap B. Saravana , Ramya KP , Yadav Priyanka , Mehra Himani , Parvathy Sona J. , Nayak Gayatree
Published: August 2024

ABSTRACT

Aim:

This in vitro study compared the antimicrobial efficacy of 5.25% sodium hypochlorite, 10% citric acid, and 3% hydrogen peroxide against Enterococcus faecalis (E. faecalis), using normal saline as a control.

Method:

E. faecalis isolates were exposed to different irrigants, with bacterial colony counts measured at various intervals using the spread plate method.

Results:

Sodium hypochlorite and citric acid effectively inhibited E. faecalis, while hydrogen peroxide was less effective. Sodium hypochlorite dissolved tissues and acted germicidally; citric acid disrupted bacterial structures through low pH. Hydrogen peroxide’s effect was due to free radical formation and oxygen release.

Conclusion:

Both 5.25% sodium hypochlorite and 10% citric acid showed equal efficacy against E. faecalis, while hydrogen peroxide was less effective. Further research should explore clinical applications and polymicrobial biofilms.
Blockchain Technology for Manufacturing Sector
Authors: K Lakshminarayana , Kulkarni Praveen , Dandannavar Padma S , S. Tigadi Basavaraj , Gokhale Prayag , Naik Shreekant
Published: August 2024

With technology advancing rapidly, organizations must continuously develop to remain competitive. They invest in technologies such as blockchain, artificial intelligence, machine learning, and cloud computing. This study focuses on the challenges of implementing blockchain technology in the manufacturing sector. Data was collected through structured interviews with production and design managers, as well as employees of organizations using new technologies. The snowball sampling method was employed, and analysis was conducted using the large group decision method. The findings will have significant implications for leveraging blockchain in manufacturing. The study focuses on exploring factors related to opportunities and challenges within the technology organisation's environment, addressing existing research gaps. The findings are constrained by the scope of the data series, presenting longitudinal facts. To tackle the prospects and complications highlighted in the study, organizations should make use of this technology to enhance their manufacturing processes.
Advancing Climate Modeling through High-Performance Computing: Towards More Accurate and Efficient Simulations
Authors: Kulkarni Prasad , Manoharan Sendhilkumar , Gaddi Alok
Published: August 2024

A crucial branch of science called climate modeling uses mathematical equations and computer simulations to study and forecast the Earth's climate sys- tem. The main elements of climate modeling, such as general circulation models (GCMs), data assimilation methods, and numerical formulations, are outlined in this paper. GCMs, which include grid point and spectral models, are effective instruments for examining the behavior of the climate. Four-Dimensional Data Assimilation (4D-Var) is one example of a data assimilation technique that in- corporates observational data into models to improve their correctness. Numeri cal methods, ocean dynamics, heat transport, radiative transfer, and atmospheric dynamics are all included in numerical formulations. The simulation of different climate processes is possible because to these mathematical representations. Fur thermore, the detection of precipitation patterns within climate modeling is using machine learning techniques like Random Forest more frequently. This paper highlights the importance of high-performance computing (HPC) in climate modeling, boosting efficiency and simulations, in the context of research technique. Advanced data assimilation and validation techniques are also examined, as well as the influence of high-resolution modeling on small-scale climatic processes. On HPC platforms, accessibility to climate modeling is addressed. It is shown how climate modeling crosses physics, mathematics, computer science, and engineering to be interdisciplinary. A comprehensive understanding of the Earth's intricate climate system gains from the integration of all its parts, from atmospheric dynamics to data assimilation. We explore the consequences of these research approaches, their contribution to enhancing climate prediction models, and the influence of various factors on climatic variables in the debate. Climate modeling becomes an essential tool for studying precipitation patterns and climate change, ultimately improving our comprehension of the complex cli- mate system on Earth.
Distal Femur MRI Rotational Anatomy in a Black African Population: A Pilot Study
Authors: Atinga Mordicai , Loyal Poonamjeet , Oroko Parmenas Kibagendi , Chikophe Idris , Lutomia Mark Lumbasi , Baraza Njalalle
Published: August 2024

Background: Correct distal femoral rotation, which varies across populations, is one important factor in total knee arthroplasty (TKA)  surgical success. This descriptive pilot study aims to present the magnetic resonance imaging (MRI) findings in a black population using  recognized measures that have been utilized in different populations and to compare these findings with published data.
Materials and  methods: MRI images of 58 knees taken over a 6-month period were retrospectively reviewed to measure the condylar twist angle (CTA),  Whiteside-posterior condylar angle (WPC), and Whiteside-epicondylar angle (W-EP). The correlation between the angles and age and  gender was assessed.
Results: The mean CTA was 4.4° (4.7° in males and 4.2° in females), while the mean W-PC and W-EP were 87.1°  (87.2° in males and 87.1° in females) and 86.5° (86.5° in males and 86.4° in females), respectively. There was no correlation between the rotational measurements and the age or gender of the study subjects.
Conclusion: The mean CTA was 4.4 in this study which differs from  published data showing 6° in Indian and 3° in Caucasian populations. A larger study is required to review the distal femoral and proximal tibial anatomy in the black population group to assess whether significant variations exist that may impact the outcomes in TKA  using standard implants.
Seventh Annual Research Meeting of the Noguchi Memorial Institute for Medical Research: Health Systems Research
Authors: Amoakoh-Coleman Mary , Kyei-Baafour Eric , Asamoah Kusi Kwadwo , Ofori Michael , K. de Souza Dziedzom , Yeboah-Manu Dorothy
Published: August 2024

Health system research is crucial for identifying problems and evaluating solutions, ensuring the robustness, safety, effectiveness, and accessibility of health systems. It incorporates people-centred approaches, integrating individual and community perspectives into co-designed interventions. Community engagement in health services promotes equity and sustainability. Several papers focused on strengthening health systems and community engagement during the seventh Annual Research Meeting (ARM) of the Noguchi Memorial Institute for Medical Research (NMIMR) at the University of Ghana. Topics included improving child welfare clinic (CWC) attendance beyond immunisation periods, understanding neglected tropical diseases (NTDs), adolescent girls' knowledge of diet and hormonal imbalance, and public engagement activities regarding antimicrobial use (AMU) among rural livestock farmers.