MDPI publications

Permanent URI for this collectionhttps://hdl.handle.net/10657/15459

This collection gathers materials published by University of Houston authors in MDPI journals

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Wearable Biosensor Technology in Education: A Systematic Review
(2024-04-11) Hernández-Mustieles, María A.; Lima-Carmona, Yoshua E.; Pacheco-Ramírez, Maxine A.; Mendoza-Armenta, Axel A.; Romero-Gómez, José Esteban; Cruz-Gómez, César F.; Rodríguez-Alvarado, Diana C.; Arceo, Alejandro; Cruz-Garza, Jesús G.; Ramírez-Moreno, Mauricio A.; Lozoya-Santos, Jorge de J.
Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012–2022), highlighting the evolution of this field to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, this review sheds light on the potential implications of WBT on the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with the PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation, with EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.
Hybrid Newton-like Inverse Free Algorithms for Solving Nonlinear Equations
(2024-04-10) Argyros, Ioannis K.; George, Santhosh; Regmi, Samundra; Argyros, Christopher I.
Iterative algorithms requiring the computationally expensive in general inversion of linear operators are difficult to implement. This is the reason why hybrid Newton-like algorithms without inverses are developed in this paper to solve Banach space-valued nonlinear equations. The inverses of the linear operator are exchanged by a finite sum of fixed linear operators. Two types of convergence analysis are presented for these algorithms: the semilocal and the local. The Fréchet derivative of the operator on the equation is controlled by a majorant function. The semi-local analysis also relies on majorizing sequences. The celebrated contraction mapping principle is utilized to study the convergence of the Krasnoselskij-like algorithm. The numerical experimentation demonstrates that the new algorithms are essentially as effective but less expensive to implement. Although the new approach is demonstrated for Newton-like algorithms, it can be applied to other single-step, multistep, or multipoint algorithms using inverses of linear operators along the same lines.
Peripheral Wavefront Sensor with Fixation Target Made by Optical Simulation for Measuring Human Eye Regardless of Spectacle
(2024-04-02) Oh, Su-Keun; Kim, Jung-Min; Yoon, Geun-Young; Yoo, Young-Sik; Kim, Dae Yu
This study proposes a custom-built aberrometer that measures peripheral defocus to evaluate myopia progression in the human eye. This advanced device can measure visual fields in both horizontal (up to 40°) and vertical (up to 30°) orientations. It incorporates a novel fixation target that is meticulously designed using an optical simulation software. Notably, each angular point of this novel fixation target differs considerably from the conventional fixation target. To mitigate the effects of the optical variations introduced by spectacles and the subject’s vision, we incorporated a position-variable lens positioned in front of the eye. This lens compensates for these variations, enhancing the precision of the measurements. To evaluate the performance of the proposed aberrometer, we conducted experiments under three distinct conditions: first, with the naked eye; second, while wearing spectacles; and third, while wearing a multifocal lens.
Exploring the Prospects of Transcranial Electrical Stimulation (tES) as a Therapeutic Intervention for Post-Stroke Motor Recovery: A Narrative Review
(2024-03-27) Meng, Hao; Houston, Michael; Zhang, Yingchun; Li, Sheng
Introduction: Stroke survivors often have motor impairments and related functional deficits. Transcranial Electrical Stimulation (tES) is a rapidly evolving field that offers a wide range of capabilities for modulating brain function, and it is safe and inexpensive. It has the potential for widespread use for post-stroke motor recovery. Transcranial Direct Current Stimulation (tDCS), Transcranial Alternating Current Stimulation (tACS), and Transcranial Random Noise Stimulation (tRNS) are three recognized tES techniques that have gained substantial attention in recent years but have different mechanisms of action. tDCS has been widely used in stroke motor rehabilitation, while applications of tACS and tRNS are very limited. The tDCS protocols could vary significantly, and outcomes are heterogeneous. Purpose: the current review attempted to explore the mechanisms underlying commonly employed tES techniques and evaluate their prospective advantages and challenges for their applications in motor recovery after stroke. Conclusion: tDCS could depolarize and hyperpolarize the potentials of cortical motor neurons, while tACS and tRNS could target specific brain rhythms and entrain neural networks. Despite the extensive use of tDCS, the complexity of neural networks calls for more sophisticated modifications like tACS and tRNS.
Uncovering Novel Protein Partners of Inducible Nitric Oxide Synthase in Human Testis
(2024-03-24) Prabhakara, Karthik S.; Ganapathy, Kavya; Islam, Kazi N.; Thyagarajan, Hiran M.; Tiwari, Kirti K.; Parimi, Ramya L.; Rashid, Mohammad B.
Peroxidative damage to human spermatozoa has been shown to be the primary cause of male infertility. The possible role of nitric oxide (NO) in affecting sperm motility, capacitation, and acrosome reaction has been reported, too. The overproduction of NO by the enzyme inducible nitric oxide synthase (iNOS) could be responsible as it has been implicated in the pathogenesis of many diseases. There have been many studies on regulating iNOS function in various tissues, especially by protein–protein interaction; however, no study has looked for iNOS-interacting proteins in the human testis. Here, we have reported the identification of two proteins that interact with iNOS. We initially undertook a popular yeast two-hybrid assay to screen a human testis cDNA library in yeast using an iNOS-peptide fragment (amino acids 181–335) as bait. We verified our data using the mammalian chemiluminescent co-IP method; first, employing the same peptide and, then, a full-length protein co-expressed in HEK293 cells in addition to the candidate protein. In both cases, these two protein partners of iNOS were revealed: (a) sperm acrosome-associated 7 protein and (b) retinoblastoma tumor-suppressor binding protein.
Hope Mediates Stress to Reduce Burden in Family Caregivers of Persons with Alzheimer’s Disease
(2024-03-18) McGee, Jocelyn Shealy; Polson, Edward C.; Myers, Dennis R.; McClellan, Angela; Meraz, Rebecca; Ke, Weiming; Zhao, Holly Carlson
The experience of burden among family caregivers of persons with Alzheimer’s disease and other forms of dementia may be deleterious for their health and well-being. Little is known, however, about the degree to which internal positive psychological resources, such as hope, influence burden perceptions in this population. The current study is novel in that it examined how multiple dimensions of hope, hope–agency and hope–pathway, influenced burden in a sample of one-hundred and fifty-five family caregivers of persons with Alzheimer’s disease. The stress process model was used as the theoretical framework for variable specification in this study. Hope was conceptualized using Snyder and colleagues’ hope theory. Supporting our first hypothesis, we found that burden was negatively associated with hope–agency, r = −0.33, p < 0.001 and hope–pathway, r = −0.24, p < 0.01. Multiple regression was used to determine if hope–agency and hope–pathway independently contributed to burden. Analysis revealed that hope–agency but not hope–pathway influenced burden when other key variables were taken into consideration. Findings from mediation analysis affirmed that hope–agency had a small but significant mediation effect between stress and burden in this sample. This study provides evidence for the relevance of assessing multiple dimensions of hope when working with caregivers of persons with Alzheimer’s. Although replication studies are warranted, the current study confirms a need for further development and refinement of hope-bolstering behavioral interventions which may mediate stress and burden in this population. These interventions should be systematically assessed for efficacy and effectiveness via implementation studies in real-world settings.
Dense Time Series Generation of Surface Water Extents through Optical–SAR Sensor Fusion and Gap Filling
(2024-04-03) Markert, Kel N.; Williams, Gustavious P.; Nelson, E. James; Ames, Daniel P.; Lee, Hyongki; Griffin, Robert E.
Surface water is a vital component of the Earth’s water cycle and characterizing its dynamics is essential for understanding and managing our water resources. Satellite-based remote sensing has been used to monitor surface water dynamics, but cloud cover can obscure surface observations, particularly during flood events, hindering water identification. The fusion of optical and synthetic aperture radar (SAR) data leverages the advantages of both sensors to provide accurate surface water maps while increasing the temporal density of unobstructed observations for monitoring surface water spatial dynamics. This paper presents a method for generating dense time series of surface water observations using optical–SAR sensor fusion and gap filling. We applied this method to data from the Copernicus Sentinel-1 and Landsat 8 satellite data from 2019 over six regions spanning different ecological and climatological conditions. We validated the resulting surface water maps using an independent, hand-labeled dataset and found an overall accuracy of 0.9025, with an accuracy range of 0.8656–0.9212 between the different regions. The validation showed an overall false alarm ratio (FAR) of 0.0631, a probability of detection (POD) of 0.8394, and a critical success index (CSI) of 0.8073, indicating that the method generally performs well at identifying water areas. However, it slightly underpredicts water areas with more false negatives. We found that fusing optical and SAR data for surface water mapping increased, on average, the number of observations for the regions and months validated in 2019 from 11.46 for optical and 55.35 for SAR to 64.90 using both, a 466% and 17% increase, respectively. The results show that the method can effectively fill in gaps in optical data caused by cloud cover and produce a dense time series of surface water maps. The method has the potential to improve the monitoring of surface water dynamics and support sustainable water management.
Asymptotically Newton-Type Methods without Inverses for Solving Equations
(2024-04-02) Argyros, Ioannis K.; George, Santhosh; Shakhno, Stepan; Regmi, Samundra; Havdiak, Mykhailo; Argyros, Michael I.
The implementation of Newton’s method for solving nonlinear equations in abstract domains requires the inversion of a linear operator at each step. Such an inversion may be computationally very expensive or impossible to find. That is why alternative iterative methods are developed in this article that require no inversion or only one inversion of a linear operator at each step. The inverse of the operator is replaced by a frozen sum of linear operators depending on the Fréchet derivative of an operator. The numerical examples illustrate that for all practical purposes, the new methods are as effective as Newton’s but much cheaper to implement. The same methodology can be used to create similar alternatives to other methods using inversions of linear operators such as divided differences or other linear operators.
Laminin Alpha 2 Enhances the Protective Effect of Exosomes on Human iPSC-Derived Cardiomyocytes in an In Vitro Ischemia-Reoxygenation Model
(2024-03-28) Mesquita, Fernanda C. P.; King, Madelyn; da Costa Lopez, Patricia Luciana; Thevasagayampillai, Shiyanth; Gunaratne, Preethi H.; Hochman-Mendez, Camila
Ischemic heart disease, a leading cause of death worldwide, manifests clinically as myocardial infarction. Contemporary therapies using mesenchymal stromal cells (MSCs) and their derivative (exosomes, EXOs) were developed to decrease the progression of cell damage during ischemic injury. Laminin alpha 2 (LAMA2) is an important extracellular matrix protein of the heart. Here, we generated MSC-derived exosomes cultivated under LAMA2 coating to enhance human-induced pluripotent stem cell (hiPSC)-cardiomyocyte recognition of LAMA2-EXOs, thus, increasing cell protection during ischemia reoxygenation. We mapped the mRNA content of LAMA2 and gelatin-EXOs and identified 798 genes that were differentially expressed, including genes associated with cardiac muscle development and extracellular matrix organization. Cells were treated with LAMA2-EXOs 2 h before a 4 h ischemia period (1% O₂, 5% CO₂, glucose-free media). LAMA2-EXOs had a two-fold protective effect compared to non-treatment on plasma membrane integrity and the apoptosis activation pathway; after a 1.5 h recovery period (20% O₂, 5% CO₂, cardiomyocyte-enriched media), cardiomyocytes treated with LAMA2-EXOs showed faster recovery than did the control group. Although EXOs had a protective effect on endothelial cells, there was no LAMA2-enhanced protection on these cells. This is the first report of LAMA2-EXOs used to treat cardiomyocytes that underwent ischemia-reoxygenation injury. Overall, we showed that membrane-specific EXOs may help improve cardiomyocyte survival in treating ischemic cardiovascular disease.
Towards Uncovering the Role of Incomplete Penetrance in Maculopathies through Sequencing of 105 Disease-Associated Genes
(2024-03-19) Hitti-Malin, Rebekkah J.; Panneman, Daan M.; Corradi, Zelia; Boonen, Erica G. M.; Astuti, Galuh; Dhaenens, Claire-Marie; Stöhr, Heidi; Weber, Bernhard H. F.; Sharon, Dror; Banin, Eyal; Karali, Marianthi; Banfi, Sandro; Ben-Yosef, Tamar; Glavač, Damjan; Farrar, G. Jane; Ayuso, Carmen; Liskova, Petra; Dudakova, Lubica; Vajter, Marie; Ołdak, Monika; Szaflik, Jacek P.; Matynia, Anna; Gorin, Michael B.; Kämpjärvi, Kati; Bauwens, Miriam; De Baere, Elfride; Hoyng, Carel B.; Li, Catherina H. Z.; Klaver, Caroline C. W.; Inglehearn, Chris F.; Fujinami, Kaoru; Rivolta, Carlo; Allikmets, Rando; Zernant, Jana; Lee, Winston; Podhajcer, Osvaldo L.; Fakin, Ana; Sajovic, Jana; AlTalbishi, Alaa; Valeina, Sandra; Taurina, Gita; Vincent, Andrea L.; Roberts, Lisa; Ramesar, Raj; Sartor, Giovanna; Luppi, Elena; Downes, Susan M.; van den Born, L. Ingeborgh; McLaren, Terri L.; De Roach, John N.; Lamey, Tina M.; Thompson, Jennifer A.; Chen, Fred K.; Tracewska, Anna M.; Kamakari, Smaragda; Sallum, Juliana Maria Ferraz; Bolz, Hanno J.; Kayserili, Hülya; Roosing, Susanne; Cremers, Frans P. M.
Inherited macular dystrophies (iMDs) are a group of genetic disorders, which affect the central region of the retina. To investigate the genetic basis of iMDs, we used single-molecule Molecular Inversion Probes to sequence 105 maculopathy-associated genes in 1352 patients diagnosed with iMDs. Within this cohort, 39.8% of patients were considered genetically explained by 460 different variants in 49 distinct genes of which 73 were novel variants, with some affecting splicing. The top five most frequent causative genes were ABCA4 (37.2%), PRPH2 (6.7%), CDHR1 (6.1%), PROM1 (4.3%) and RP1L1 (3.1%). Interestingly, variants with incomplete penetrance were revealed in almost one-third of patients considered solved (28.1%), and therefore, a proportion of patients may not be explained solely by the variants reported. This includes eight previously reported variants with incomplete penetrance in addition to CDHR1:c.783G>A and CNGB3:c.1208G>A. Notably, segregation analysis was not routinely performed for variant phasing—a limitation, which may also impact the overall diagnostic yield. The relatively high proportion of probands without any putative causal variant (60.2%) highlights the need to explore variants with incomplete penetrance, the potential modifiers of disease and the genetic overlap between iMDs and age-related macular degeneration. Our results provide valuable insights into the genetic landscape of iMDs and warrant future exploration to determine the involvement of other maculopathy genes.
An Extensive Investigation into the Use of Machine Learning Tools and Deep Neural Networks for the Recognition of Skin Cancer: Challenges, Future Directions, and a Comprehensive Review
(2024-03-18) Hussain, Syed Ibrar; Toscano, Elena
Skin cancer poses a serious risk to one’s health and can only be effectively treated with early detection. Early identification is critical since skin cancer has a higher fatality rate, and it expands gradually to different areas of the body. The rapid growth of automated diagnosis frameworks has led to the combination of diverse machine learning, deep learning, and computer vision algorithms for detecting clinical samples and atypical skin lesion specimens. Automated methods for recognizing skin cancer that use deep learning techniques are discussed in this article: convolutional neural networks, and, in general, artificial neural networks. The recognition of symmetries is a key point in dealing with the skin cancer image datasets; hence, in developing the appropriate architecture of neural networks, as it can improve the performance and release capacities of the network. The current study emphasizes the need for an automated method to identify skin lesions to reduce the amount of time and effort required for the diagnostic process, as well as the novel aspect of using algorithms based on deep learning for skin lesion detection. The analysis concludes with underlying research directions for the future, which will assist in better addressing the difficulties encountered in human skin cancer recognition. By highlighting the drawbacks and advantages of prior techniques, the authors hope to establish a standard for future analysis in the domain of human skin lesion diagnostics.
Advancing Point-of-Care Diagnosis: Digitalizing Combinatorial Biomarker Signals for Lupus Nephritis
(2024-03-18) Guo, Jiechang; Teymur, Aygun; Tang, Chenling; Saxena, Ramesh; Wu, Tianfu
To improve the efficiency and patient coverage of the current healthcare system, user-friendly novel homecare devices are urgently needed. In this work, we developed a smartphone-based analyzing and reporting system (SBARS) for biomarker detection in lupus nephritis (LN). This system offers a cost-effective alternative to traditional, expensive large equipment in signal detection and quantification. This innovative approach involves using a portable and affordable microscopic reader to capture biomarker signals. Through smartphone-based image processing techniques, the intensity of each biomarker signal is analyzed. This system exhibited comparable performance to a commercial Genepix scanner in the detection of two potential novel biomarkers of LN, VISG4 and TNFRSF1b. Importantly, this smartphone-based analyzing and reporting system allows for discriminating LN patients with active renal disease from healthy controls with the area-under-the-curve (AUC) value = 0.9 for TNFRSF1b and 1.0 for VSIG4, respectively, indicating high predictive accuracy.
The Variation in Atmospheric Turbidity over a Tropical Site in Nigeria and Its Relation to Climate Drivers
(2024-03-18) SoneyeArogundade, Olanrewaju Olukemi; Rappenglück, Bernhard
Atmospheric turbidity exhibits substantial spatial–temporal variability due to factors such as aerosol emissions, seasonal changes, meteorology, and air mass transport. Investigating atmospheric turbidity is crucial for climatology, meteorology, and atmospheric pollution. This study investigates the variation in atmospheric turbidity over a tropical location in Nigeria, utilizing the Ångström exponent (α), the turbidity coefficient (β), the Linke turbidity factor (T_L), the Ångström turbidity coefficient (β_EST), the Unsworth–Monteith turbidity coefficient (K_AUM), and the Schüepp turbidity coefficient (SCH). These parameters were estimated from a six-month uninterrupted aerosol optical depth dataset (January–June 2016) and a one-year dataset (January–December 2016) of solar radiation and meteorological data. An inverse correlation (R = −0.77) was obtained between α and β, which indicates different turbidity regimes based on particle size. T_L and β_EST exhibit pronounced seasonality, with higher turbidity during the dry season (T_L = 9.62 and β_EST = 0.60) compared to the rainy season (T_L = 0.48 and β_EST = 0.20) from May to October. Backward trajectories and wind patterns reveal that high-turbidity months align with north-easterly air flows from the Sahara Desert, transporting dust aerosols, while low-turbidity months coincide with humid maritime air masses originating from the Gulf of Guinea. Meteorological drivers like relative humidity and water vapor pressure are linked to turbidity levels, with an inverse exponential relationship observed between normalized turbidity coefficients and normalized water vapor pressure. This analysis provides insights into how air mass origin, wind patterns, and local climate factors impact atmospheric haze, particle characteristics, and solar attenuation variability in a tropical location across seasons. The findings can contribute to environmental studies and assist in modelling interactions between climate, weather, and atmospheric optical properties in the region.
Improved Bayesian Inferences for Right-Censored Birnbaum–Saunders Data
(2024-03-16) Jayalath, Kalanka P.
This work focuses on making Bayesian inferences for the two-parameter Birnbaum–Saunders (BS) distribution in the presence of right-censored data. A flexible Gibbs sampler is employed to handle the censored BS data in this Bayesian work that relies on Jeffrey’s and Achcar’s reference priors. A comprehensive simulation study is conducted to compare estimates under various parameter settings, sample sizes, and levels of censoring. Further comparisons are drawn with real-world examples involving Type-II, progressively Type-II, and randomly right-censored data. The study concludes that the suggested Gibbs sampler enhances the accuracy of Bayesian inferences, and both the amount of censoring and the sample size are identified as influential factors in such analyses.
Integration of Multi-Omics, Histological, and Biochemical Analysis Reveals the Toxic Responses of Nile Tilapia Liver to Chronic Microcystin-LR Exposure
(2024-03-14) Li, Yichao; Yang, Huici; Fu, Bing; Kaneko, Gen; Li, Hongyan; Tian, Jingjing; Wang, Guangjun; Wei, Mingken; Xie, Jun; Yu, Ermeng
Microcystin-LR (MC-LR) is a cyanobacterial metabolite produced during cyanobacterial blooms and is toxic to aquatic animals, and the liver is the main targeted organ of MC-LR. To comprehensively understand the toxicity mechanism of chronic exposure to environmental levels of MC-LR on the liver of fish, juvenile Nile tilapia were exposed to 0 μg/L (control), 1 μg/L (M1), 3 μg/L (M3), 10 μg/L (M10), and 30 μg/L (M30) MC-LR for 60 days. Then, the liver hepatotoxicity induced by MC-LR exposure was systematically evaluated via histological and biochemical determinations, and the underlying mechanisms were explored through combining analysis of biochemical parameters, multi-omics (transcriptome and metabolome), and gene expression. The results exhibited that chronic MC-LR exposure caused slight liver minor structural damage and lipid accumulation in the M10 group, while resulting in serious histological damage and lipid accumulation in the M30 group, indicating obvious hepatotoxicity, which was confirmed by increased toxicity indexes (i.e., AST, ALT, and AKP). Transcriptomic and metabolomic analysis revealed that chronic MC-LR exposure induced extensive changes in gene expression and metabolites in six typical pathways, including oxidative stress, apoptosis, autophagy, amino acid metabolism, primary bile acid biosynthesis, and lipid metabolism. Taken together, chronic MC-LR exposure induced oxidative stress, apoptosis, and autophagy, inhibited primary bile acid biosynthesis, and caused fatty deposition in the liver of Nile tilapia.
Application of Natural-Resource-Based View to Nature-Based Tourism Destinations
(2024-03-13) Wang, Xi; Kim, Jewoo; Kim, Jaewook; Koh, Yoon
The present study investigates the impact of natural environments on tourism destinations in a holistic approach. Specifically, the impact of accessibility to beaches and environmental quality aspects (temperature, visibility, air quality, and water quality) on tourism businesses can be accessed based on a natural-resource-based view. Dynamic panel estimation is employed to analyze the financial performance of U.S. coastal hotels between 2008 and 2017. By employing the Generalized Method of Moments (GMM) analysis, this aims to estimate coefficients consistently and impartially, thereby addressing endogeneity issues. According to findings of the present study, as hotels are close to beaches, they earn higher revenues and higher revenue per-available-room. Also, all four environmental factors are significant on coastal hotels’ financial performance. These findings underscore the importance of beach and environmental factors as location-specific tourism resources that provide a competitive advantage and demonstrate the application of natural-resource-based view to tourism destinations.
Symmetric-Type Multi-Step Difference Methods for Solving Nonlinear Equations
(2024-03-08) Argyros, Ioannis K.; Shakhno, Stepan; Regmi, Samundra; Yarmola, Halyna; Argyros, Michael I.
Symmetric-type methods (STM) without derivatives have been used extensively to solve nonlinear equations in various spaces. In particular, multi-step STMs of a higher order of convergence are very useful. By freezing the divided differences in the methods and using a weight operator a method is generated using m steps (m a natural number) of convergence order 2 m. This method avoids a large increase in the number of operator evaluations. However, there are several problems with the conditions used to show the convergence: the existence of high order derivatives is assumed, which are not in the method; there are no a priori results for the error distances or information on the uniqueness of the solutions. Therefore, the earlier studies cannot guarantee the convergence of the method to solve nondifferentiable equations. However, the method may converge to the solution. Thus, the convergence conditions can be weakened. These problems arise since the convergence order is determined using the Taylor series which requires the existence of high-order derivatives which are not present in the method, and they may not even exist. These concerns are our motivation for authoring this article. Moreover, the novelty of this article is that all the aforementioned problems are addressed positively, and by using conditions only related to the divided differences in the method. Furthermore, a more challenging and important semi-local analysis of convergence is presented utilizing majorizing sequences in combination with the concept of the generalized continuity of the divided difference involved. The convergence is also extended from the Euclidean to the Banach space. We have chosen to demonstrate our technique in the present method. But it can be used in other studies using the Taylor series to show the convergence of the method. The applicability of other single- or multi-step methods using the inverses of linear operators with or without derivatives can also be extended with the same methodology along the same lines. Several examples are provided to test the theoretical results and validate the performance of the method.
A Study of Convergence of Sixth-Order Contraharmonic-Mean Newton’s Method (CHN) with Applications and Dynamics
(2024-01-10) Singh, Manoj K.; Argyros, Ioannis K.; Regmi, Samundra
We develop the local convergence of the six order Contraharmonic-mean Newton’s method (CHN) to solve Banach space valued equations. Our analysis approach is two fold: The first way uses Taylor’s series and derivatives of higher orders. The second one uses only the first derivatives. We examine the theoretical results by solving a boundary value problem also using the examples relating the proposed method with other’s methods such as Newton’s, Kou’s and Jarratt’s to show that the proposed method performs better. The conjugate maps for second-degree polynomial are verified. We also calculate the fixed points (extraneous). The article is completed with the study of basins of attraction, which support and further validate the theoretical and numerical results.
Emotion-Aware Scene Adaptation: A Bandwidth-Efficient Approach for Generating Animated Shorts
(2024-03-04) Yang, Yi; Feng, Hao; Cheng, Yiming; Han, Zhu
Semantic communication technology in the 6G wireless system focuses on semantic extraction in communication, that is, only the inherent meaning of the intention in the information. Existing technologies still have challenges in extracting emotional perception in the information, high compression rates, and privacy leakage due to knowledge sharing in communication. Large-scale generative-model technology could rapidly generate multimodal information according to user requirements. This paper proposes an approach that leverages large-scale generative models to create animated short films that are semantically and emotionally similar to real scenes and characters. The visual content of the data source is converted into text expression through semantic understanding technology; emotional clues from the data source media are added to the text form through reinforcement learning technology; and finally, a large-scale generative model is used to generate visual media, which is consistent with the semantics of the data source. This paper develops a semantic communication process with distinct modules and assesses the enhancements garnered from incorporating an emotion enhancement module. This approach facilitates the expedited generation of broad media forms and volumes according to the user’s intention, thereby enabling the creation of generated multimodal media within applications in the metaverse and in intelligent driving systems.
A Multiuser, Multisite, and Platform-Independent On-the-Cloud Framework for Interactive Immersion in Holographic XR
(2024-03-01) Neeli, Hosein; Tran, Khang Q.; Velazco-Garcia, Jose Daniel; Tsekos, Nikolaos V.
Background: The ever-growing extended reality (XR) technologies offer unique tools for the interactive visualization of images with a direct impact on many fields, from bioinformatics to medicine, as well as education and training. However, the accelerated integration of artificial intelligence (AI) into XR applications poses substantial computational processing demands. Additionally, the intricate technical challenges associated with multilocation and multiuser interactions limit the usability and expansion of XR applications. Methods: A cloud deployable framework (Holo-Cloud) as a virtual server on a public cloud platform was designed and tested. The Holo-Cloud hosts FI3D, an augmented reality (AR) platform that renders and visualizes medical 3D imaging data, e.g., MRI images, on AR head-mounted displays and handheld devices. Holo-Cloud aims to overcome challenges by providing on-demand computational resources for location-independent, synergetic, and interactive human-to-image data immersion. Results: We demonstrated that Holo-Cloud is easy to implement, platform-independent, reliable, and secure. Owing to its scalability, Holo-Cloud can immediately adapt to computational needs, delivering adequate processing power for the hosted AR platforms. Conclusion: Holo-Cloud shows the potential to become a standard platform to facilitate the application of interactive XR in medical diagnosis, bioinformatics, and training by providing a robust platform for XR applications.

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