Theory Techniques Research Articles

Estigma hacia habitantes de calle, consumo de drogas y género. Consecuencias en derechos humanos y salud pública

Objective: comprehend the stigmatization of the homeless population, its relationship with drug use, gender and its consequences from the perspective of the assistance personnel in Medellin. Methods: qualitative research with a descriptive-interpretative scope. Data were generated through focus groups. The information was analyzed with grounded theory techniques and contrasted with the scientific literature on the subject. Results: two conceptions of the homeless person emerged: disposable and citizen. About gender, the multiple stigmas towards women and the LGBTI population living on the streets were highlighted, and the consequences of stigmatization were described. Conclusion: Living on the street and having a lifestyle contrary to the dominant values, makes homeless people to be discriminated, dehumanized and corrected by society, which is worse when belong to non-hegemonic genders, this situation limits the guarantee of fundamental human rights, especially the right to health.

The least primary factor of the multiplicative group

Let [Formula: see text] denote the least primary factor in the primary decomposition of the multiplicative group [Formula: see text]. We give an asymptotic formula, with order of magnitude [Formula: see text], for the counting function of those integers [Formula: see text] for which [Formula: see text]. We also give an asymptotic formula, for any prime power [Formula: see text], for the counting function of those integers [Formula: see text] for which [Formula: see text]. This group-theoretic problem can be reduced to problems of counting integers with restrictions on their prime factors, allowing it to be addressed by classical techniques of analytic number theory.

Prioritization of control measures in leakage scenario using Hendershot theory and FBWM-TOPSIS.

Currently, there is increasing concern about the safety and leakage of process industries. Therefore, the present study aims to prioritize control measures before and after the leakage scenario by using the Hendershot theory and MCDM techniques. In this study, two proactive and reactive layers were selected before and after leakage of tanks, respectively. Then, criteria and alternatives were selected to perform fuzzy TOPSIS (FTOPSIS) and find the best alternative based on the literature review and Hendershot approach. The linear model of the fuzzy Best-Worst method (FBWM) was constructed and resolved using Lingo 17 software. Subsequently, criteria were assigned weights based on thorough calculations of the inconsistency rate. The weight of study experts was equal to 0.25. The results of FBWM showed that the reliability index with a weight of 0.3727 was ranked first and the inconsistency rate ([Formula: see text]) was calculated to be equal to 0.040. Inherent Safety Design (ISD) (0.899) and passive safety (0.767) also ranked first before and after tank leaks, respectively. Using the FBWM method leads to fewer pairwise comparisons and at the same time more stability. Although ISD and passive strategies are more valid and strict, elements of all strategies are necessary for a comprehensive process safety management program.

Investigation of Social Concerns Regarding a Sustainable Cement

This work investigates individual and social concerns regarding the integral sustainability of an alternative low-carbon cement. The framework of Integral Philosophy was used for the investigation. Social surveys were conducted in Australian, American, and Sri Lankan contexts. The data was collected through both closed-ended and open-ended questions in a questionnaire. Analysis of responses was carried out using fuzzy techniques and grounded theory techniques. Results indicate that individuals and societies in all three countries are more concerned about market price, quality standards, sustainability issues, and recommendations from professionals when selecting a new cement. In all three contexts, they also indicate that there is generally high support among individuals and societies for accepting low-carbon cement as an integrally sustainable cement. Combining the top-down Integral Theory approach with the bottom-up Grounded Theory is a valuable tool for social science research.

Asymptotic behavior for a semilinear Bresse system with variable coefficients and locally distributed nonlinear dissipation

In this paper, we consider a semilinear Bresse system in one dimensional bounded non homogeneous medium. We investigate stability issues for such a problem with a nonlinear damping acting in all three wave equations. We prove, firstly, the existence and uniqueness of the solutions by using the nonlinear semigroup method. Afterwords, we establish an uniform decay rates of the energy for these solutions without considering any restriction on the coefficients as well as the equality of the wave propagation speeds. Some techniques of the microlocal analysis theory are used in order to reach the desired asymptotics.

A clinical decision support system using rough set theory and machine learning for disease prediction

ObjectiveTechnological advances have led to drastic changes in daily life, and particularly healthcare, while traditional diagnosis methods are being replaced by technology-oriented models and paper-based patient healthcare records with digital files. Using the latest technology and data mining techniques, we aimed to develop an automated clinical decision support system (CDSS), to improve patient prognoses and healthcare delivery. Our proposed approach placed a strong emphasis on improvements that meet patient, parent, and physician expectations. We developed a flexible framework to identify hepatitis, dermatological conditions, hepatic disease, and autism in adults and provide results to patients as recommendations. The novelty of this CDSS lies in its integration of rough set theory (RST) and machine learning (ML) techniques to improve clinical decision-making accuracy and effectiveness. MethodsData were collected through various web-based resources. Standard preprocessing techniques were applied to encode categorical features, conduct min-max scaling, and remove null and duplicate entries. The most prevalent feature in the class and standard deviation were used to fill missing categorical and continuous feature values, respectively. A rough set approach was applied as feature selection, to remove highly redundant and irrelevant elements. Then, various ML techniques, including K nearest neighbors (KNN), linear support vector machine (LSVM), radial basis function support vector machine (RBF SVM), decision tree (DT), random forest (RF), and Naive Bayes (NB), were employed to analyze four publicly available benchmark medical datasets of different types from the UCI repository and Kaggle. The model was implemented in Python, and various validity metrics, including precision, recall, F1-score, and root mean square error (RMSE), applied to measure its performance. ResultsFeatures were selected using an RST approach and examined by RF analysis and important features of hepatitis, dermatology conditions, hepatic disease, and autism determined by RST and RF exhibited 92.85 %, 90.90 %, 100 %, and 80 % similarity, respectively. Selected features were stored as electronic health records and various ML classifiers, such as KNN, LSVM, RBF SVM, DT, RF, and NB, applied to classify patients with hepatitis, dermatology conditions, hepatic disease, and autism. In the last phase, the performance of proposed classifiers was compared with that of existing state-of-the-art methods, using various validity measures. RF was found to be the best approach for adult screening of: hepatitis with accuracy 88.66 %, precision 74.46 %, recall 75.17 %, F1-score 74.81 %, and RMSE value 0.244; dermatology conditions with accuracy 97.29 %, precision 96.96 %, recall 96.96 %, F1-score 96.96 %, and RMSE value, 0.173; hepatic disease, with accuracy 91.58 %, precision 81.76 %, recall 81.82 %, F1-Score 81.79 %, and RMSE value 0.193; and autism, with accuracy 100 %, precision 100 %, recall 100 %, F1-score 100 %, and RMSE value 0.064. ConclusionThe overall performance of our proposed framework may suggest that it could assist medical experts in more accurately identifying and diagnosing patients with hepatitis, dermatology conditions, hepatic disease, and autism.

DEFM: Delay-embedding-based forecast machine for time series forecasting by spatiotemporal information transformation.

Making accurate forecasts for a complex system is a challenge in various practical applications. The major difficulty in solving such a problem concerns nonlinear spatiotemporal dynamics with time-varying characteristics. Takens' delay embedding theory provides a way to transform high-dimensional spatial information into temporal information. In this work, by combining delay embedding theory and deep learning techniques, we propose a novel framework, delay-embedding-based forecast Machine (DEFM), to predict the future values of a target variable in a self-supervised and multistep-ahead manner based on high-dimensional observations. With a three-module spatiotemporal architecture, the DEFM leverages deep neural networks to effectively extract both the spatially and temporally associated information from the observed time series even with time-varying parameters or additive noise. The DEFM can accurately predict future information by transforming spatiotemporal information to the delay embeddings of a target variable. The efficacy and precision of the DEFM are substantiated through applications in three spatiotemporally chaotic systems: a 90-dimensional (90D) coupled Lorenz system, the Lorenz 96 system, and the Kuramoto-Sivashinsky equation with inhomogeneity. Additionally, the performance of the DEFM is evaluated on six real-world datasets spanning various fields. Comparative experiments with five prediction methods illustrate the superiority and robustness of the DEFM and show the great potential of the DEFM in temporal information mining and forecasting.

Genuinely nonabelian partial difference sets

AbstractStrongly regular graphs (SRGs) provide a fertile area of exploration in algebraic combinatorics, integrating techniques in graph theory, linear algebra, group theory, finite fields, finite geometry, and number theory. Of particular interest are those SRGs with a large automorphism group. If an automorphism group acts regularly (sharply transitively) on the vertices of the graph, then we may identify the graph with a subset of the group, a partial difference set (PDS), which allows us to apply techniques from group theory to examine the graph. Much of the work over the past four decades has concentrated on abelian PDSs using the powerful techniques of character theory. However, little work has been done on nonabelian PDSs. In this paper we point out the existence of genuinely nonabelian PDSs, that is, PDSs for parameter sets where a nonabelian group is the only possible regular automorphism group. We include methods for demonstrating that abelian PDSs are not possible for a particular set of parameters or for a particular SRG. Four infinite families of genuinely nonabelian PDSs are described, two of which—one arising from triangular graphs and one arising from Krein covers of complete graphs constructed by Godsil—are new. We also include a new nonabelian PDS found by computer search and present some possible future directions of research.

Unveiling Entrepreneurial Learning Dynamics: Insights from the Women Entrepreneurs of Creative Industries

Purpose –This research aims to explore social learning aspects of entrepreneurial learning within the creative industries. The creative industries produce customised products, where creative entrepreneurs co-create goods according to the customer’s preferences. The entrepreneurial learning literature includes empirical and theoretical studies from different industry sectors. However, little attention is evident in the entrepreneurship research from the creative industries. The limited literature on entrepreneurial learning in the creative industries indicates that creative industries entrepreneurs rarely pursue formal education to enhance their skills and knowledge. Instead, their business learning is deeply rooted in the social, cultural, and economic contexts of the real world in which they are situated. These brief findings demand an in-depth exploration of entrepreneurial learning processes and sources within the creative industries context. Design/methodology/approach – This empirical study is based on qualitative research, employing in-depth case studies of creative industries entrepreneurs. The data was collected through semi-structured interviews with women entrepreneurs who work as designers in the creative industries. To analyze the data, a cross-case comparison was conducted, along with the use of grounded theory techniques. Findings – The findings highlight that entrepreneurial learning is a dynamic process where the interaction of personal and social experiences of entrepreneurs during their entrepreneurial journey is of utmost importance. This learning process involves activities such as observing, practices, doing, trial and error, and constant interaction with the entrepreneurial community. Furthermore, understanding the learning process relies largely on the contextual environment. Creative industries’ entrepreneurs employ the variety of sources for learning, out of which personal networks (family, friends, etc.) and technological resources (internet, software) are vital. Social media also plays a significant role in their learning process, as it serves as a valuable source of information and learning. Implications – The study improves our understanding of how women entrepreneurs engage in entrepreneurial learning within a specific industry and social context. Furthermore, the research identifies the challenges that are critical to address through entrepreneurship education and support organizations – that cater to community entrepreneurs. Originality/value – The literature has acknowledged the strong influence of cultural and social context on women entrepreneurs, particularly in developing countries. This research contributes to the limited body of knowledge on entrepreneurial learning among women creative entrepreneurs by exploring the interplay between personal and social learning experiences along with multiple contextual forces.

Enriched string-net models and their excitations

Boundaries of Walker-Wang models have been used to construct commuting projector models which realize chiral unitary modular tensor categories (UMTCs) as boundary excitations. Given a UMTC A representing the Witt class of an anomaly, the article \cite{MR4640433} gave a commuting projector model associated to an A-enriched unitary fusion category X on a 2D boundary of the 3D Walker-Wang model associated to A. That article claimed that the boundary excitations were given by the enriched center/Müger centralizer ZA(X) of A in Z(X).In this article, we give a rigorous treatment of this 2D boundary model, and we verify this assertion using topological quantum field theory (TQFT) techniques, including skein modules and a certain semisimple algebra whose representation category describes boundary excitations. We also use TQFT techniques to show the 3D bulk point excitations of the Walker-Wang bulk are given by the Müger center Z2(A), and we construct bulk-to-boundary hopping operators Z2(A)→ZA(X) reflecting how the UMTC of boundary excitations ZA(X) is symmetric-braided enriched in Z2(A).This article also includes a self-contained comprehensive review of the Levin-Wen string net model from a unitary tensor category viewpoint, as opposed to the skeletal 6j symbol viewpoint.

Climate finance across sub-Saharan Africa: Decision trees and network flows

The structure of climate finance flows from donors (multilateral sources) to recipients (sub-Saharan African countries) was studied. This is the first study to provide a comprehensive network structure of the climate finance flows into Africa, based on the global public climate finance governance system. Network theory and decision tree techniques were employed. The results obtained generally fit the multilateral funding units (MFUs) into two categories: central funding units (CFUs), which simultaneously attend to the climate-related projects of many African countries, and the boundary funding units (BFUs), which cater to only a few countries at once. An isolated region with no BFUs was identified. African countries within this group could be more exposed to climate financial risk as they rely on only the CFUs. In general, with the exception of mitigation REDD (reducing emissions from deforestation and forest degradation) climate finance, a disproportionate distribution of climate themes, with particular reference to adaptation finance, was observed across sub-Saharan Africa. This has real implications for equitable resource allocation of climate funds. The need for African-bred region-wide MFUs is recommended.

Collaboration of Vague Theory and Mathematical Techniques for Optimizing Healthcare by Recommending Optimal Blockchain Supplier

The Internet of Medical Things (IoMTs) has the potential to revolutionize healthcare delivery by connecting medical devices and applications to healthcare IT systems via the internet. This connectivity enables the collection, transmission, and analysis of patient data, facilitating remote healthcare delivery and enhancing patient outcomes. However, the security and privacy of the data transmitted and stored within IoMT systems remain critical concerns. Blockchain technology offers a promising solution to address these challenges in IoMTs applications. Blockchain provides a decentralized and immutable ledger where transactions, in this case, patient data, are securely recorded and cannot be altered retroactively. This ensures the integrity and confidentiality of sensitive medical information while eliminating the need for a central authority to manage data exchange. This study proposes a comprehensive fuzzy decision-making framework for selecting the best blockchain provider in the IoMT field. The framework comprises two main steps: Determining the relative importance weights of the criteria utilizing the Stepwise Weight Assessment Ratio Analysis (SWARA) method. Determining the most suitable supplier for blockchain services providers employing the Ranking Alternatives with the Fuzzy ROOT Assessment Method (RAM) technique. A comparison between Fuzzy Multi-Criteria Decision-Making (MCDM) methods is employed to ensure the robustness and validity of the proposed framework.

Object Detection and Tracking with YOLO and the Sliding Innovation Filter.

Object detection and tracking are pivotal tasks in machine learning, particularly within the domain of computer vision technologies. Despite significant advancements in object detection frameworks, challenges persist in real-world tracking scenarios, including object interactions, occlusions, and background interference. Many algorithms have been proposed to carry out such tasks; however, most struggle to perform well in the face of disturbances and uncertain environments. This research proposes a novel approach by integrating the You Only Look Once (YOLO) architecture for object detection with a robust filter for target tracking, addressing issues of disturbances and uncertainties. The YOLO architecture, known for its real-time object detection capabilities, is employed for initial object detection and centroid location. In combination with the detection framework, the sliding innovation filter, a novel robust filter, is implemented and postulated to improve tracking reliability in the face of disturbances. Specifically, the sliding innovation filter is implemented to enhance tracking performance by estimating the optimal centroid location in each frame and updating the object's trajectory. Target tracking traditionally relies on estimation theory techniques like the Kalman filter, and the sliding innovation filter is introduced as a robust alternative particularly suitable for scenarios where a priori information about system dynamics and noise is limited. Experimental simulations in a surveillance scenario demonstrate that the sliding innovation filter-based tracking approach outperforms existing Kalman-based methods, especially in the presence of disturbances. In all, this research contributes a practical and effective approach to object detection and tracking, addressing challenges in real-world, dynamic environments. The comparative analysis with traditional filters provides practical insights, laying the groundwork for future work aimed at advancing multi-object detection and tracking capabilities in diverse applications.

Spectroscopic characterizations, RDG and docking study of 2-[3-(4-chlorophenyl)-5-(4-(propane-2-yl) phenyl)-4,5-dihydro-1H pyrozol-1-yl]-4-(4-fluorophenyl)-1,3-thiazole

Abstract The theoretical calculations for 2-[3-(4-chlorophenyl)-5-(4-(propane-2-yl) phenyl)-4,5-dihydro-1H-pyrazol-1-yl]-4-(4-fluorophenyl)-1,3-thiazole (CPDFT) are performed using the Density Functional Theory (DFT) technique employing the B3LYP/cc-pVDZ and LanL2MB basis sets. Theoretical infrared (IR) and Raman frequencies as well as structural investigation were performed. The molecular structure demonstrating the presence of charge transfer and determining the bond length, bond angle of the header molecule. FMO deals about the both occupied and unoccupied orbitals of the molecule are computed. A molecular electrostatic potential map was created and analysed to identify the sites of electrophilic and nucleophilic areas of CPDFT. The ligand-protein interaction of the title compound was assessed by docking studies, indicating a strong affinity between the title compound and the target macromolecules. A reduced density gradient graph, electron localization electron and Localized orbital locator was employed to discern the non-covalent interactions of CPDFT.

Two-step inertial Tseng’s extragradient method for solving quasimonotone variational inequalities

We focus on quasimonotone variational inequality problems with non-Lipschitz operator and combine some operator theory techniques to establish a weak convergent fixed point iterative method to approximate the solution of the presented problems. The convergence analysis assumes very mild conditions and strictly weaker assumption on the cost operator associated with the problems and thus generalizes and extend recent related results in the literature. Finally, several numerical examples illustrate the potential and advantages of the proposed algorithm over some existing methods are presented.

Abstract 4825: Addressing financial toxicity in cancer clinical trial participation: Barriers and facilitators of enrolling in a reimbursement program for out-of-pocket travel costs

Abstract Financial toxicity has been associated with adverse outcomes for patients with cancer. Out-of-pocket (OOP) costs contribute to the high financial toxicity (FT) experienced by patients with cancer. Cancer clinical trial (CCT) participants face significant extra OOP expenses presenting a barrier to participation and retention. To address FT in CCTs, the Lazarex Foundation created the Improving Patient Access to Cancer Clinical Trials (IMPACT) Program, a financial reimbursement program (FRP) for OOP travel and lodging costs associated with therapeutic CCT participation. Our study aimed to quantify the FT experienced by patients in CCTs and understand their experience to determine the barriers and facilitators of enrolling in the Lazarex IMPACT Program. Patients enrolled in a CCT were referred to the IMPACT team to determine eligibility. Patients were eligible for IMPACT if they were active on a therapeutic CCT and their household income was less than 700% of the most recent HHS federal poverty guidelines. Patients who enrolled in the program were invited to participate in a brief semi-structured interview via telephone call. We used grounded theory techniques of analysis to identify themes of barriers and facilitators to enrollment in the Lazarex IMPACT program at our cancer center. Table 1 shows the emerging themes from the interviews as barriers and facilitators of enrolling in IMPACT. As a part of the interview process, patients also completed the COST-FACIT questionnaire, a patent reported outcomes measure to evaluate the degree of FT associated with cancer. COST scores in our cohort (n=39) ranged from 14-39.6, mean=27.12, median=27.5; SD=7.96. Following enrollment in the program, patients on average had COST scores indicating low degrees of financial distress. Taken altogether, our results have the potential to inform the development of FRPs for patients with cancer to facilitate their participation in CCTs. Table 1. Emerging themes from grounded analysis of semi-structured interviews and their frequency Barrier to enrolling in FRP program. No. of Participants Facilitator of enrolling in FRP program No. of Participants Patients wanted to be made aware of program earlier in their CCT timeline 4 Enrollment process was easy and straightforward 17 Patients made aware of program after their CCT started 3 IMPACT team contacted patients to offer program 13 CCT Team unaware of program availability 3 Patients learned about the program through their CCT team 8 Patients reported difficulty with technology access and literacy 3 Patients reported sufficient technology access and literacy 4 Patients reported hesitancy sharing financial information 2 Cancer foundation was timely and helpful in enrollment process 2 Patients reported difficulty navigating vast resources at cancer center 1 Cancer Center social workers provided connection to program 1 Citation Format: Elizabeth Ramos, Vivian Nguyen, Ashley Santaniello, Dana Dornsife, Robert G. Johnson, Paul E. Wileyto, Robert H. Vonderheide, Carmen E. Guerra. Addressing financial toxicity in cancer clinical trial participation: Barriers and facilitators of enrolling in a reimbursement program for out-of-pocket travel costs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4825.

Heat transfer with interfacial barrier in a fine scale mixture of two highly different conductive materials

The paper deals with the asymptotic behavior of the heat transfer in a bounded domain formed by two $\eps$-periodically interwoven components, of highly different conductivities. Both components might be connected. At the interface, the heat flux is continuous and the temperature subjects to a first-order jump condition. The homogeneous Dirichlet condition is imposed on the exterior boundary. We determine the macroscopic law when the order of magnitude of the jump transmission coefficient is $\eps^r, -1 r \leq 1$, using the two-scale convergence technique of the homogenization theory.

Triazole-bearing sulfonamide linkage: Synthesis, characterization, and investigation as a versatile corrosion inhibitor

Novel focused three 1,4-disubstituted-1,2,3-triazoles 5a-c labeled ISMs (ISM-H, ISM-F & ISM-N) and tethering sulfonamide moieties were designed and synthesized by the 1,3-dipolar cycloaddition of some appropriate aromatic O-alkyne derivatives 3a-c and 4-azido-N-carbamimidoylbenzenesulfonamide 4 as an efficient and common approach in click chemistry. The structures of the resulted click products were elucidated based on their spectral data. The newer synthesized triazoles were well investigated as corrosion inhibitors for carbon steel and Al alloy in HCl solution utilizing electrochemical, weight loss, surface research, and density functional theory (DFT) techniques. The electrochemical impedance spectroscopy (EIS) results revealed that a low inhibitor dosage of compounds ISM-H and ISM-F exhibited potential corrosion inhibition. The ISM-N and ISM-H derivatives exhibited a chemisorption nature and showed the potential inhibition at 60 °C. The surface studies: scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX) and Fourier Transform Infrared Spectroscopy (FTIR) reveals the formation inhibitor film above the metal surface.

Scattering of spinning compact objects from a worldline EFT

We study the EFT of a spinning compact object and show that with appropriate gauge fixing, computations become amenable to worldline quantum field theory techniques. We use the resulting action to compute Compton and one-loop scattering amplitudes at fourth order in spin. By matching these amplitdes to solutions of the Teukolsky equations, we fix the values of Wilson coefficients appearing in the EFT such that it reproduces Kerr black hole scattering. We keep track of the spin supplementary condition throughout our computations and discuss alternative ways to ensure its preservation.

Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data.

Aging is the basis of neurodegeneration and dementia that affects each endemic in the body.Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15years, 25 to 35years, and 45 to 75years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal-Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.