Miracle-dz/UniFAR-FTUs huggingface dataset:
Preemptive Diagnosis of Alzheimer's Disease in the Eastern Province of Saudi Arabia Using Computational Intelligence Techniques

Alzheimer's Disease (AD) is a silent disease that causes the brain cells to die progressively, influencing consciousness, behavior, planning ability, and language to name a few. AD increases exponentially with aging, where it doubles every 5-6 years, causing profound implications, such as swallowing difficulties and losing the ability to speak before death. According to the Ministry of Health in Saudi Arabia, AD patients will triple by 2060 to reach 14 million patients worldwide. The rapid rise of patients is caused by the silent progress of the disease, leading to late diagnosis as the symptoms will not be distinguished from normal aging affect. Moreover, with the current medical capabilities, it is impossible to confirm AD with 100% certainty via specific medical examinations. The literature review revealed that most recent publications used images to diagnose AD, which is insufficient for local hospitals with limited imaging capabilities. Other studies that used clinical and demographical data failed to achieve adequate results. Consequently, this study aims to preemptively predict AD in Saudi Arabia by employing machine learning (ML) techniques. The dataset was acquired from King Fahad Specialist Hospital (KFSH) in Dammam, Saudi Arabia, containing standard clinical tests for 152 patients. Four ML algorithms, namely, support vector machine (SVM), k-nearest neighbors (k-NN), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost), were employed to preemptively diagnose the disease. The empirical results demonstrated the robustness of SVM in the pre-emptive diagnosis of AD with accuracy, precision, recall, and area under the receiver operating characteristics (AUROC) of 95.56%, 94.70%, 97.78%, and 0.97, respectively, with 13 features after applying the sequential forward feature selection technique. This model can assist the medical staff in controlling the progression of the disease at low costs.
Obstacle Avoidance and Path Planning of a Wheeled Mobile Robot Using Hybrid Algorithm

planning crucial. Robotic systems employ intelligence algorithms to plan the robot's path from one point to another. This paper proposes the fastest and optimal path planning of the wheeled mobile robot with collision avoidance to find the optimal route during wheeled mobile robot navigation from the start point to the target point. It is done using a modern meta-heuristic hybrid algorithm called IPSOGWO by combining Improved Particle Swarm Optimization (IPSO) with Grey Wolf Optimizer (GWO). The principal idea is based on boosting the ability to exploit in PSO with the exploration ability in GWO to the better-automated alignment between local and global search capabilities towards a targeted, optimized solution. The proposed hybrid algorithm tackles two objectives: the protection of the path and the length of the path. During, Simulation tests of the route planning by the hybrid algorithm are compared with individual results PSO, IPSO, and GWO concepts about the minimum length of the path, execution time, and the minimum number of iterations required to achieve the best route. This work's effective proposed navigation algorithm was evaluated in a MATLAB environment. The simulation results indicated that the developed algorithm reduced the average path length and the average computation time, less than PSO by (1%, 1.7%), less than GWO by (1%, 1.9%), and less than IPSO by (0.05%, 0.4%), respectively. Furthermore, the superiority of the proposed algorithm was proved through comparisons with other famous path planning algorithms with different static environments.
Beyond the Mentor-Mentee Model: A Case for Multi-Mentoring in Undergraduate Research

Undergraduate research is widely regarded as a high-impact educational practice (Kuh, 2008; Lopatto, 2010). Students who participate in undergraduate research show gains in cognitive development (e.g., Hunter, Laursen, & Seymour, 2007), academic success and persistence (e.g., Hathaway, Nagda, & Gregerman, 2002), and personal identity and affective growth (e.g., Hu, Kuh, & Li, 2008). Furthermore, benefits can be compensatory, where women, first-generation students, and those from traditionally underserved populations exhibit more pronounced gains compared to nonminority groups (e.g., Gregerman, 2009; Osborn & Karukstis, 2009; Owerbach & Oyekan, 2015). What qualifies as undergraduate research? The Council on Undergraduate Research defines undergraduate research as “an inquiry of investigation conducted by an undergraduate student that makes an original intellectual or creative contribution to the discipline. ” However, many educators prefer a broader definition that encompasses undergraduate research and scholarly and creative work, choosing to view the endeavor as “a continuum of knowledge production, from knowledge new to the learner to knowledge new to humankind, moving from the commonly known, to the commonly not known, to the totally unknown” (Willison & O’Regan, 2007, p. 394). Regardless of whether the research is original to the discipline or only to the student, a critical component that often determines whether the student actually recognizes the gains from the experience is the mentor (Aikens et al., 2016; Hartmann, Widner, & Carrick, 2013; Henne et al., 2008; Hunter et al., 2007, 2008; Kardash, Wallace, & Blockus, 2008; Laursen et al., 2010; Locks & Gregerman, 2008; Lopatto, 2004, 2010; Lundberg & Schreiner, 2004; Russell, 2008; Taraban & Logue, 2012; Thiry et al., 2010; Zydney et al., 2002).
How Legitimate Peripheral Participation in Engineering During College Affects Professional Identity

Identity is central to learning; in the professions, development of professional identity is a core aspect of the education and training process (Lave & Wenger, 1991). For this reason, educators in professional fields should consider the formation of professional identity as they make decisions about how to best prepare graduates to enter their chosen fields. This issue is especially salient in engineering education where a majority of students graduating from engineering programs remain unsure about their plans to enter engineering as a profession (Lichtenstein et al., 2009; Sheppard et al., 2010). If the field of engineering is to respond to national concerns about diversification of the engineering workforce and addressing the projected shortfall in graduates relative to job openings (National Academy of Sciences, National Academy of Engineering, & Institute of Medicine, 2011; President’s Council of Advisors on Science and Technology, 2012), a focus on engineering identity as a central aspect of the professional formation process is essential. Identification with engineering has been shown to assist in the recruitment of diverse students and improve student retention among all groups (Andriot, 2011; Carlone & Johnson, 2007; Matusovich, Streveler, & Miller, 2010).
Research economic impact of big data technology on sustainable production and consumption in live streaming e-commerce consumer behaviors

Sustainable production and consumption (SPC) is both a policy area and a multi-interdisciplinary research agenda that seeks to address the transformation of society toward economic and social sustainability. The SPC field encompasses diverse research topics and theoretical approaches, often characterized as falling into stronger and weaker versions, defined by a commitment to more or less systemic economic change. This study researched the economic impact of big data technology on sustainable production and consumption in live streaming E-commerce consumer behaviors. Based further on stimulus-organism-response (S-O-R) theory, the live streaming E-commerce’s anchor-controlled factors and viewers’ engagement provide the stimuli for the cognitive appraisals of the viewers (potential customers). Two cognitive appraisal results are trust and perceived value, known as an organism in the context of S-O-R theory, which, together with the stimuli, influence the flow experience behavior of viewers (potential customers). Flow experience is shown supportive of explaining impulsive purchasing. Towards this end, this study extends the S-O-R structure into S-O-B-C (stimulus-organism-behavior-consequence) paradigm. While organism and flow experience behavior (O and B) are domains of viewers and customers, stimuli (anchor’s reputation and professionalism, anchored interactivity, and the displays of offers) and consequences (impulsive purchasing and fan-becoming) are domains of the live streaming ecommerce. The findings resulted from a convenience-based sampling of customers and viewers who frequented popular live-streaming apps in China: Tao Bao, Jing Dong, Moquije, Little Red Book, and Jumeiyoupin. The data were subjected to neural network (NN) simulation and structural equation modeling (SEM), yielding similar results. SEM statistics and path configurations provide model fitting supports and mediating phenomena involving trust, perceived customer value, and flow experiences. Results confirm the significant values of cognitive appraisal theory, flow theory, S-O-R theory, and the extended S-O-B-C paradigm.
Modeling and Mathematical Analysis of the Dynamics of HPV in Cervical Epithelial Cells: Transient, Acute, Latency, and Chronic Infections

The aim of this paper is to model the dynamics of the human papillomavirus (HPV) in cervical epithelial cells. We developed a mathematical model of the epithelial cellular dynamics of the stratified epithelium of three (basale, intermedium, and corneum) stratums that is based on three ordinary differential equations. We determine the biological condition for the existence of the epithelial cell homeostasis equilibrium, and we obtain the necessary and sufficient conditions for its global stability using the method of Lyapunov functions and a theorem on limiting systems. We have also developed a mathematical model based on seven ordinary differential equations that describes the dynamics of HPV infection. We calculated the basic reproductive number (R0) of the infection using the next-generation operator method. We determine the existence and the local stability of the equilibrium point of the cellular homeostasis of the epithelium. We then give a sufficient condition for the global asymptotic stability of the epithelial cell homeostasis equilibrium using the Lyapunov function method. We proved that this equilibrium point is nonhyperbolic when R0 = 1 and that in this case, the system presents a forward bifurcation, which shows the existence of an infected equilibrium point when R0 > 1. We also study the solutions numerically (i.e., viral kinetic in silico) when R0 > 1. Finally, local sensitivity index was calculated to assess the influence of different parameters on basic reproductive number. Our model reproduces the transient, acute, latent, and chronic infections that have been reported in studies of the natural history of HPV.
On the symmetry of the electronic density in the three-electron parabolic quantum dot

The structure of the lowest states of a three-electron axially symmetric parabolic quantum dot in a zero magnetic field is investigated. It is shown that the electronic density of the triplet states possesses certain approximate symmetry which is best seen when using Dalitz plots as the visualization tool. It is demonstrated that the origin of that symmetry is caused by the symmetry of the potential energy in the vicinity of its minimum. The discovered symmetry could provide an insight into the problem of the separation of slow and fast variables in the Schro\"dinger equation for the axially or spherically symmetric quantum dots.
Modern Solutions for Ancient Pathogens: Direct Pathogen Sequencing for Diagnosis of Lepromatous Leprosy and Cerebral Coenurosis

Abstract Microbes unculturable in vitro remain diagnostically challenging, dependent historically on clinical findings, histology, or targeted molecular detection. We applied whole-genome sequencing directly from tissue to diagnose infections with mycobacteria (leprosy) and parasites (coenurosis). Direct pathogen DNA sequencing provides flexible solutions to diagnosis of difficult pathogens in diverse contexts.
Hydroxyl-Functionalized Covalent Organic Frameworks as High-Performance Supercapacitors

Covalent organic frameworks (COFs) have attracted significant interest because of their heteroatom-containing architectures, high porous networks, large surface areas, and capacity to include redox-active units, which can provide good electrochemical efficiency in energy applications. In this research, we synthesized two novel hydroxy-functionalized COFs—TAPT-2,3-NA(OH)2, TAPT-2,6-NA(OH)2 COFs—through Schiff-base [3 + 2] polycondensations of 1,3,5-tris-(4-aminophenyl)triazine (TAPT-3NH2) with 2,3-dihydroxynaphthalene-1,4-dicarbaldehyde (2,3-NADC) and 2,6-dihydroxynaphthalene-1,5-dicarbaldehyde (2,6-NADC), respectively. The resultant hydroxy-functionalized COFs featured high BET-specific surface areas up to 1089 m2 g–1, excellent crystallinity, and superior thermal stability up to 60.44% char yield. When used as supercapacitor electrodes, the hydroxy-functionalized COFs exhibited electrochemical redox activity due to the presence of redox-active 2,3-dihydroxynaphthalene and 2,6-dihydroxynaphthalene in their COF skeletons. The hydroxy-functionalized COFs showed specific capacitance of 271 F g−1 at a current density of 0.5 A g−1 with excellent stability after 2000 cycles of 86.5% capacitance retention. Well-known pore features and high surface areas of such COFs, together with their superior supercapacitor performance, make them suitable electrode materials for use in practical applications.
MiR-18a-5p Targets Connective Tissue Growth Factor Expression and Inhibits Transforming Growth Factor β2-Induced Trabecular Meshwork Cell Contractility

Increased trabecular meshwork (TM) cell and tissue contractility is a driver of the reduced outflow facility and elevation of intraocular pressure (IOP) associated with primary open-angle glaucoma (POAG). Connective tissue growth factor (CTGF) is an established mediator of TM cell contractility, and its expression is increased in POAG due to transforming growth factor β 2 (TGFβ2) signalling. Inhibiting CTGF upregulation using microRNA (miRNA) mimetics could represent a new treatment option for POAG. A combination of in silico predictive tools and a literature review identified a panel of putative CTGF-targeting miRNAs. Treatment of primary human TM cells with 5 ng/mL TGFβ2 for 24 h identified miR-18a-5p as a consistent responder, being upregulated in cells from five different human donors. Transfection of primary donor TM cells with 20 nM synthetic miR-18a-5p mimic reduced TGFβ2-induced CTGF protein expression, and stable lentiviral-mediated overexpression of this miRNA reduced TGFβ2-induced contraction of collagen gels. Together, these findings identify miR-18a-5p as a mediator of the TGFβ2 response and a candidate therapeutic agent for glaucoma via its ability to inhibit CTGF-associated increased TM contractility.
A review of differentiability in relation to robustness with application to seismic data analysis

The introduction of the Frechet derivative into statistics from the mathematical literature has spomed a wealth of study of the relationships between robust statistics and mathematical• derivatives. The current view is that differentiability of the Frechet type with respect to the supremum norm is a valid approach and necessarily some well known statistics such as the arithmetic mean and the median do not have the attribute of differentiability in this sense. As an illustration of a Frechet differentiable estimating functional an L2 minimum distance estimator is applied to some data collected from seismic analysis and this provides estimates in an area where estimation previously was ad hoc and estimation was not always possible. The theoretical estimation techniques known from the study of mixtures of normal distributions are applied to this data and illustrate some of the features of using robust techniques as opposed to always resorting to the maximum likelihood estimation methods.
The free-electron laser - Maxwell's equations driven by single-particle currents

It is shown that if single particle currents are coupled to Maxwell's equations, the resulting set of self-consistent nonlinear equations describes the evolution of the electron beam and the amplitude and phase of the free-electron-laser field. The formulation is based on the slowly varying amplitude and phase approximation, and the distinction between microscopic and macroscopic scales, which distinguishes the microscopic bunching from the macroscopic pulse propagation. The capabilities of this new theoretical approach become apparent when its predictions for the ultrashort pulse free-electron laser are compared to experimental data; the optical pulse evolution, determined simply and accurately, agrees well with observations.
Garbage-Man-In-The-Middle Attack on the Luc 4 Cryptosystem

This paper reports an investigation into an attack on the LUC 4 cryptosystem. LUC 4 cryptosystem is derived from a fourth order linear recurrence relation and is based on the Lucas function. This cryptosystem is analogous to the RSA, LUC and LUC 3 cryptosystems. Therefore, the security for this cryptosystem is similar to the RSA cryptosystem because they are depend on the intractability of factorization. There are numerous mathematical attacks on RSA-type cryptosystem, one of them is polynomial attacks. The Garbage-man-in-the-middle attack is one of the polynomial attacks on LUC 4 cryptosystem. This type of attacks is exploiting the polynomial structure of RSA. Based on the analysis and implementations, the security aspects will be looked into and appear to depend on the intractability of factorization.
WE‐G‐BRA‐06: Application of Systems and Control Theory‐Based Hazard Analysis to Radiotherapy

Purpose: The process of delivering radiation occurs in a complex socio-technical system heavily reliant on human operators. Furthermore, both humans and software are notoriously challenging to account for in traditional hazard analysis models. High reliability industries such as aviation have approached this problem through using hazard analysis techniques grounded in systems and control theory. The purpose of this work is to apply the Systems Theoretic Accident Model Processes (STAMP) hazard model to radiotherapy. In particular, the System-Theoretic Process Analysis (STPA) approach is used to perform a hazard analysis of a proposed on-line adaptive cranial radiosurgery procedure that omits the CT Simulation step and uses only CBCT for planning, localization, and treatment. Methods: The STPA procedure first requires the definition of high-level accidents and hazards leading to those accidents. From there, hierarchical control structures were created followed by the identification and description of control actions for each control structure. Utilizing these control structures, unsafe states of each control action were created. Scenarios contributing to unsafe control action states were then identified and translated into system requirements to constrain process behavior within safe boundaries. Results: Ten control structures were created for this new CBCT-only process which covered the areas of hospital and department management, treatment design and delivery, and vendor service. Twenty three control actions were identified that contributed to over 80 unsafe states of those control actions resulting in over 220 failure scenarios. Conclusion: The interaction of people, hardware, and software are highlighted through the STPA approach. STPA provides a hierarchical model for understanding the role of management decisions in impacting system safety so that a process design requirement can be traced back to the hazard and accident that it is intended to mitigate. Varian Medical Systems, Inc.
Linking Terrorist Network Structure to Lethality: Algorithms and Analysis of Al Qaeda and ISIS

Without measures of the lethality of terrorist networks, it is very difficult to assess if capturing or killing a terrorist is effective. We present the predictive lethality analysis of terrorist organization (PLATO) algorithm, which merges machine learning with techniques from graph theory and social network analysis to predict the number of attacks that a terrorist network will carry out based on a network structure alone. We show that PLATO is highly accurate on two novel datasets, which cover Al Qaeda (AQ) and the Islamic State (ISIS). Using both machine learning and statistical methods, we show that the most significant macrofeatures for predicting AQ’s lethality are related to their public communications (PCs) and logistical subnetworks, while the leadership and operational subnetworks are most impactful for predicting ISISs lethality. Across both groups, the average degree and the diameters of the strongly connected components (SCCs) within these networks are strongly linked with lethality.
Recent Developments in 3D Printing of Rare-Earth-Free Permanent Magnets

This article reviews the advances in additive manufacturing of magnetic ceramics and alloys without rare-earth elements. Near-net-shaped permanent magnets with varying shapes and dimensions overcome traditional limitations of the cast, sintered, and bonded magnets. The published articles are categorized based on material types and 3D printing techniques. Selective laser melting and electron beam melting were predominantly used to produce alnico magnets. In addition to the electron beam melting, manganese aluminium-based alloys were successfully printed by fuse filament fabrication. By incorporating magnetic powders in polymers and then printing via extrusion, the fuse filament fabrication was also used to produce strontium ferrite magnets. Moreover, hard ferrites were printed by stereolithography and extrusion free-forming, without drawing composites into filaments. Magnetic properties in some cases are comparable to those of conventional magnets with the same compositions. Currently, available software packages can simulate magnetic fields for designing magnets and optimize the integration in electrical machines. These developments open up opportunities for next-generation permanent magnet applications.
Scattering Representations for Recognition

This thesis addresses the problem of pattern and texture recognition from a mathematical perspective. These high level tasks require signal representations enjoying specific invariance, stability and consistency properties, which are not satisfied by linear representations. Scattering operators cascade wavelet decompositions and complex modulus, followed by a lowpass filtering. They define a non-linear representation which is locally translation invariant and Lipschitz continuous to the action of diffeomorphisms. They also define a texture representation capturing high order moments and which can be consistently estimated from few realizations. The thesis derives new mathematical properties of scattering representations and demonstrates its efficiency on pattern and texture recognition tasks. Thanks to its Lipschitz continuity to the action of diffeomorphisms, small deformations of the signal are linearized, which can be exploited in applications with a generative affine classifier yielding state-of-the-art results on handwritten digit classification. Expected scattering representations are applied on image and auditory texture datasets, showing their capacity to capture high order moments information with consistent estimators. Scattering representations are particularly efficient for the estimation and characterization of fractal parameters. A renormalization of scattering coefficients is introduced, giving a new insight on fractal description, with the ability in particular to characterize multifractal intermittency using consistent estimators.