Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Patient Length of Stay Using Artificial Intelligence to Assist Healthcare Professionals in Resource Planning and Scheduling Decisions

Artificial intelligence (AI) significantly revolutionizes and transforms the global healthcare industry by improving outcomes, increasing efficiency, and enhancing resource utilization. The applications of AI impact every aspect of healthcare operation, particularly resource allocation and capacity planning. This study proposes a multi-step AI-based framework and applies it to a real dataset to predict the length of stay (LOS) for hospitalized patients. The results show that the proposed framework can predict the LOS categories with an AUC of 0.85 and their actual LOS with a mean absolute error of 0.85 days. This framework can support decision-makers in healthcare facilities providing inpatient care to make better front-end operational decisions, such as resource capacity planning and scheduling decisions. Predicting LOS is pivotal in today's healthcare supply chain (HSC) systems where resources are scarce, and demand is abundant due to various global crises and pandemics. Thus, this research's findings have practical and theoretical implications in AI and HSC management.
Harnessing technology and innovation to address emerging issues: Covid-19: Opportunities and challenges

This study explores the opportunities and challenges presented through harnessing technology and innovation to address COVID-19 as an emerging issue in higher education institutions in Kenya. COVID-19 pandemic has presented unprecedented challenges to higher education institutions in sub-Saharan Africa, specifically in Kenya. The pandemic forced institutions to migrate to online learning, impacting the quality of education, enrollment rates, and mental health challenges students face. The study used a mixed-methods approach, incorporating secondary sources of information and primary data from experts such as Ministry of Education officials, university management, students' counsellors, and teaching staff from Kenyan universities. It focused on teaching and assessment delivery, enrollment rates, education quality, ICT infrastructure access, and mental health challenges. The study revealed that universities with established e-learning support departments were better equipped to transition to online learning, with the level of preparedness of teaching staff for online delivery is a significant factor in enhancing performance. However, enrollment rates did not increase significantly, and mental health challenges were common among students due to the digital divide. Higher education institutions in Kenya should invest in e-learning support departments and bridge the digital divide to prepare teaching staff for better online delivery. Universities should also consider a hybrid approach to teaching and learning that combines online and face-to-face delivery modes to ensure better learning outcomes.
Review of the modified finite particle method and application to incompressible solids

This paper focuses on the application of the Modified Finite Particle Method (MFPM) on incompressibile elasticity problems. MFPM belongs to the class of meshless methods, nowadays widely investigated due to their characteristics of being totally free of any kind of grid or mesh. This characteristic makes meshless methods potentially useful for the study of large deformations problems and fluid dynamics. In particular, the aim of the work is to compare the results obtained with a simple displacement-based formulation, in the limit of incompressibility, and some formulations proposed in the literature for full incompressibility, where the typical divergence-free constraint is replaced by a different equation, the so-called Pressure Poisson Equation. The obtained results show that the MFPM achieves the expected second-order accuracy on formulation where the equations imposed as constraint satisfies also the original incompressibility equation. Other formulations, differently, do not satisfy the incompressibility constraint, and thus, they are not successfully applicable with the Modified Finite Particle Method.
Toll-like Receptor 4 Is Upregulated in Parkinson’s Disease Patients and Co-Localizes with pSer129αSyn: A Possible Link with the Pathology

Growing evidence suggests a crucial role of neuroinflammation in the pathophysiology of Parkinson’s disease (PD). Neuroinflammation is linked to the accumulation and aggregation of a-synuclein (αSyn), the primary pathological hallmark of PD. Toll-like receptors 4 (TLR4) can have implications in the development and progression of the pathology. In this study, we analyzed the expression of TLR4 in the substantia nigra (SN) and medial temporal gyrus (GTM) of well-characterized PD patients and age-matched controls. We also assessed the co-localization of TLR4 with pSer129 αSyn. Using qPCR, we observed an upregulation of TLR4 expression in the SN and GTM in PD patients compared to controls, which was accompanied by a reduction in αSyn expression likely due to the depletion of dopaminergic (DA) cells. Additionally, using immunofluorescence and confocal microscopy, we observed TLR4-positive staining and co-localization with pSer129-αSyn in Lewy bodies of DA neurons in the SN, as well as in pyramidal neurons in the GTM of PD donors. Furthermore, we observed a co-localization of TLR4 and Iba-1 in glial cells of both SN and GTM. Our findings provide evidence for the increased expression of TLR4 in the PD brain and suggest that the interaction between TLR4 and pSer129-αSyn could play a role in mediating the neuroinflammatory response in PD.
Animation generation for object transportation with a rope using deep reinforcement learning

This article presents a reinforcement learning‐based approach to generate animation in which two agents use a rope to collaboratively transport a block. The challenge is that the agents need to master several skills, including approaching the block, using the rope to wrap around it, and then moving the block to a predefined goal position. We propose several reward terms to learn the transportation policy and the adjustment policy that govern the skills of the agents. Experiment results showed that the proposed approach was able to generate various animations in different settings, including rope lengths, block sizes, and block shapes. An ablation test revealed the effects of the reward terms. We also investigated factors that affected the performance of the two policies.
Targeted Fisetin-Encapsulated β-Cyclodextrin Nanosponges for Breast Cancer

Fisetin (FS) is considered a safer phytomedicine alternative to conventional chemotherapeutics for breast cancer treatment. Despite its surpassing therapeutic potential, its clinical utility is hampered by its low systemic bioavailability. Accordingly, as far as we are aware, this is the first study to develop lactoferrin-coated FS-loaded β-cyclodextrin nanosponges (LF-FS-NS) for targeted FS delivery to breast cancer. NS formation through cross-linking of β-cyclodextrin by diphenyl carbonate was confirmed by FTIR and XRD. The selected LF-FS-NS showed good colloidal properties (size 52.7 ± 7.2 nm, PDI < 0.3, and ζ-potential 24 mV), high loading efficiency (96 ± 0.3%), and sustained drug release of 26 % after 24 h. Morphological examination using SEM revealed the mesoporous spherical structure of the prepared nanosponges with a pore diameter of ~30 nm, which was further confirmed by surface area measurement. Additionally, LF-FS-NS enhanced FS oral and IP bioavailability (2.5- and 3.2-fold, respectively) compared to FS suspension in rats. Antitumor efficacy evaluation in vitro on MDA-MB-231 cells and in vivo on an Ehrlich ascites mouse model demonstrated significantly higher activity and targetability of LF-FS-NS (30 mg/kg) compared to the free drug and uncoated formulation. Consequently, LF-FS-NS could be addressed as a promising formulation for the effective management of breast cancer.
Approximate Solution of Linear Second Order Differential Equations

In a recent paper(1) Collar discussed some aeronautical applications of linear differential equations with variable coefficients. Part of the paper deals with the approximate solution: of the differential equation This asymptotic approximation, which dates back at least to Liouville, has an interesting history(2). It is widely known as the WKB approximation because of its use in quantum theory by Wentzel, Kramers and Brillouin. It has been applied to compressible flow by Imai(3). While very useful it breaks down at the zeros of n(t) and there are problems in joining solutions passing through such points. Recently(2,4,5) extensions of the approximation which circumvent this difficulty have been developed. This note deals with the extension due to Bailey. This approximation can be developed from the equivalence of equation (2) and the Riccati equation: —
Improved model of fused tapered single-mode-fiber coupler

The analysis of optical mode field in the coupler was carried out,is in the condition of weakly guiding,weakly coupling,an approximate shape of fused tapered coupler and the appropriate continuous functions description.Triangle function and Gauss function are mixed with variational coefficients to describe it.Base on local mode theory and variational tech- nique,propagation constant and coupling coefficient are analyzed in each part of fused tapered coupler.The relations between coupling coefficient,wavelength and geometric coefficients are revealed.Numerical simulation indicates that power coupling in the taper region must be taken into account.The coupling coefficients are affected by core and cladding,besides,the effects take place in the taper region and the crossing region in the opposite way.Thereby, a wavelength-flatten symmetry fiber coupler can be obtained through controlling the shape coefficients suitably.
Applying Deep Transfer Learning to Assess the Impact of Imaging Modalities on Colon Cancer Detection

The use of medical images for colon cancer detection is considered an important problem. As the performance of data-driven methods relies heavily on the images generated by a medical method, there is a need to inform research organizations about the effective imaging modalities, when coupled with deep learning (DL), for detecting colon cancer. Unlike previous studies, this study aims to comprehensively report the performance behavior for detecting colon cancer using various imaging modalities coupled with different DL models in the transfer learning (TL) setting to report the best overall imaging modality and DL model for detecting colon cancer. Therefore, we utilized three imaging modalities, namely computed tomography, colonoscopy, and histology, using five DL architectures, including VGG16, VGG19, ResNet152V2, MobileNetV2, and DenseNet201. Next, we assessed the DL models on the NVIDIA GeForce RTX 3080 Laptop GPU (16GB GDDR6 VRAM) using 5400 processed images divided equally between normal colons and colons with cancer for each of the imaging modalities used. Comparing the imaging modalities when applied to the five DL models presented in this study and twenty-six ensemble DL models, the experimental results show that the colonoscopy imaging modality, when coupled with the DenseNet201 model under the TL setting, outperforms all the other models by generating the highest average performance result of 99.1% (99.1%, 99.8%, and 99.1%) based on the accuracy results (AUC, precision, and F1, respectively).
Effects of tartaric acid on the structure and corrosion resistance for anodizing films of aerospace aluminium alloys

ABSTRACT Anodising films are obtained by anodising 2099 aluminium-lithium alloys in tartaric acid-sulphuric acid solutions with different concentrations. The microscopic surface morphology of the anodic-oxide film is observed by field emission scanning electron microscope (FG-SEM), and the corrosion resistance of the film was investigated by open-circuit potential, electrochemical experiments and neutral salt spray experiments. The results show that the aluminium-lithium alloy can form a uniform and dense anodic oxide film in different concentrations of tartaric acid-sulphuric acid solution. Tartaric acid does not change the film formation mechanism of the anodic oxide film, but reduced the dissolution rate of the oxide film, so that the growth rate and thickness of the oxide film increased first and then decreased with the increase of the tartaric acid concentration in the experimental study. This result generally indicates that the addition of tartaric acid can also significantly improve the corrosion resistance of the anodic-oxide film, and the more detailed features are discussed.
Digital line and plane recognition using combinatorial optimization by multiresolutional representation

Line and plane recognition is one of the essential tasks in the field of computer vision. While methods for digital plane recognition based on digital geometry can detect the global optimal solution, they require enormous computation time. In this paper, we propose a digital line/plane recognition method using multiresolutional representation as mixed integer linear programming problems which require much less computation time than the conventional methods. Our method reduces both the outliers of points and the search space of the line/plane parameters at low resolutions. Experimental results show that the computation time was reduced significantly by our method.
A Parallel Implementation of the Galerkin Method for Solving Partial Differential Equations on a Triangular Mesh

A Parallel Implementation of the Galerkin Method for Solving Partial Differential Equations on a Triangular Mesh Rachel N. Hess Director: Robert C. Kirby, Ph.D. Finite Element Methods are techniques for estimating solutions to boundary value problems for partial differential equations from an approximating subspace. These methods are based on weak or variational forms of the BVP that require less of the problem functions than what the original PDE would suggest in terms of order of differentiability and continuity. In the scope of this project, we focused on implementing the Galerkin Finite Element Method, which provides a best approximation to the true solution from a finite-dimensional subspace of piecewise polynomial functions defined on a triangular mesh. For this thesis, we developed a shared memory parallel implementation of the Galerkin Method that can be executed on a GPU to minimize runtime by means of multiple processors working simultaneously in unison on each calculation. For this purpose, we used the open-source libraries PyOpenCL and Loo.py. Thus we are able to explore how essential tasks in the solution process map onto shared memory platforms, such as the construction of the stiffness matrix from the connectivity data of the triangular mesh that may then be used to approximate the true solution with numerical methods. APPROVED BY DIRECTOR OF HONORS THESIS: Dr. Robert C. Kirby, Department of Mathematics APPROVED BY THE HONORS PROGRAM: Dr. Andrew Wisely, Director
Nash equilibria in normal form games

We show that there exists a Nash equilibrium in mixed strategies in all normal form games such that pure strategy sets are nonempty compact metric spaces and utility functions are continuous. The player set can be arbitrary nonempty set. The utility functions of the players are defined on the set of pure strategy profiles, the product space of individul players� pure strategy sets. This product space is equipped with the product topology and the product sigma-algebra, so the product space is a compact Hausdorff space, and the continuous utility functions are also measurable. Continuity and compactness ensure that each utility function depends only on countably many coordinates. This suggests that the existence of a Nash equilibrium in the general case follows actually from the existence of equilibrium in games with countably many players. This indeed is the case. In the proof of the main result the player set is equipped with a well-ordering, so the Axiom of Choice is assumed. However, both the Tychonoff theorem and the product measure existence theorem (or the Kolmogorov�s existence theorem) already depend on the Axiom of Choice. The former ensures that the set of strategy profiles is a nonempty compact space, and the latter ensures that players can �calculate� expected utilities associated with mixed strategy profiles. Therefore we need the Axiom of Choice (at least implicitly) already if we want to talk about non-trivial normal form games with arbitrary player sets.
Adaptive fuzzy particle swarm optimization algorithm

Standard particle swarm algorithm is easy to fall into local optimum.An Adaptive Fuzzy Particle Swarm Optimization(AFPSO) based on the uncertainty of the PSO is proposed.In the improved algorithm,the inertia weight and the particle position update are controlled by fuzzy membership function,and the global optimal value is replaced by the weighted average of all particles optimal value to enhance the learning ability among particles.The experimental results show that,compared with the correlation algorithm,the proposed method is simple and more flexible to adjust global and local search,which also avoids the premature convergence problem,and improves the search accuracy.
Educational intervention performed by nurses for blood pressure control: a systematic review with meta-analysis

Objetivo: to assess the effect of an educational intervention performed by nurses for blood pressure control in people with arterial hypertension, when compared to usual care. Método: a systematic review with meta-analysis of randomized clinical trials, conducted in six databases. The studies included were those in which an educational intervention was performed by nurses on people with arterial hypertension. The risk of bias was assessed by means of the Risk of Bias Tool, the meta-analysis was performed in the Review Manager software and certainty of the evidence was calculated in the Grading of Recommendations Assessment, Development and Evaluation system. Resultados: a total of 1,692 studies were found, which were peer-reviewed, including eight of them in the meta-analysis. The meta-analysis was calculated for the “systolic blood pressure” and diastolic blood pressure” outcomes, in subgroups by time and by intervention performance type. For the in-person educational intervention, performed individually combined with a group activity, the effect estimate was -12.41 mmHg (95% Confidence Interval: from -16.91 to -7.91, p<0,00001) for systolic pressure and -5.40 mmHg (95% Confidence Interval: from -7.98 to -2.82, p<0,00001) for diastolic pressure, with high certainty of evidence. Conclusión: the educational intervention performed by nurses, individually and combined with a group activity, presents a statistically significant clinical effect. PROSPERO registration No.: CRD42021282707.
Analysis of the Short-Term Dynamics of Morphing Aircraft Caused by Shape Change Based on the Open-Loop Response and the Reachable Set Theory

This work investigates the short-term dynamics caused by shape changes of morphing aircraft. We select a symmetric variable sweep morphing aircraft as the object of study and establish a six-degree-of-freedom multi-loop cascade model, and the coupling between derivative terms is eliminated by matrix transformation. Considering that the change in aerodynamic shape significantly affects the aerodynamic forces of the aircraft in a short period of time, and the variation in mass distribution generates additional aerodynamic forces and moments, we analyze the effects of these factors on the dynamic characteristics of the aircraft based on the open-loop response starting from the steady-state flight conditions. In addition, we analyze the improvement in maneuvering performance brought by morphing as an additional control input. We apply reachable set theory to multi-loop equations of motion and use the size of the reachable set to measure the maneuverability of aircraft. The results confirm that morphing can effectively improve the maneuverability of the aircraft.
Core–Shell Structured Silica-Coated Iron Nanowires Composites for Enhanced Electromagnetic Wave Absorption Properties

In this study, we successfully prepared core–shell heterostructured nanocomposites (Fe NWs@SiO2), with ferromagnetic nanowires (Fe NWs) as the core and silica (SiO2) as the shell. The composites exhibited enhanced electromagnetic wave absorption and oxidation resistance and were synthesized using a simple liquid-phase hydrolysis reaction. We tested and analyzed the microwave absorption properties of Fe NWs@SiO2 composites with varied filling rates (mass fractions of 10 wt%, 30 wt%, and 50 wt% after mixing with paraffin). The results showed that the sample filled with 50 wt% had the best comprehensive performance. At the matching thickness of 7.25 mm, the minimum reflection loss (RLmin) could reach −54.88 dB at 13.52 GHz and the effective absorption bandwidth (EAB, RL < −10 dB) could reach 2.88 GHz in the range of 8.96–17.12 GHz. Enhanced microwave absorption performance of the core–shell structured Fe NWs@SiO2 composites could be attributed to the magnetic loss of the composite, the core–shell heterogeneous interface polarization effect, and the small-scale effect induced by the one-dimensional structure. Theoretically, this research provided Fe NWs@SiO2 composites with highly absorbent and antioxidant core–shell structures for future practical applications.
Characterization of Weekly Cumulative Rainfall Forecasts over Meteorological Subdivisions of India Using a GCM

Abstract Weekly cumulative rainfall forecasts were made for the meteorologically homogeneous areas of the Indian subcontinent, divided into meteorological subdivisions, by performing 7-day integrations of the operational Indian T80 Global Spectral Model every Wednesday during the six southwest monsoon seasons of 1994–99. Objective evaluations of the bias and accuracy of these forecasts during that 6-yr period are made through various forecast verification methods and are presented here. The skill or relative accuracy of the forecasts and some verification measures are quantified by computing the Heidke skill score (HSS), Hanssen–Kuipers discriminant (HKS), threat score (TS), hit rate (HR), probability of detection (POD), bias score, and false-alarm rate (FAR). The study revealed that the T80 model has a tendency to underpredict rainfall over most of the subdivisions falling on the windward side of the Western Ghats and sub-Himalayan areas. The model exhibited negative bias in rainfall simulations over the.