Probabilistic Solution Research Articles

Theories, determinants, and intervention models and approaches on inequalities of undernutrition amongst under fives: A literature review.

One of the greatest public health problems of the 21st century is undernutrition in children under the age of 5 years (CAUFY). Globally, over 232 million CUAFY are undernourished and approximately 45% of mortality in this population are undernutrition-induced. This paper reviewed and critically explained the factors perpetuating undernutrition in CUAFY in the global space. It further explained the multi-level determinants that influence health inequalities and consequently exacerbate undernutrition amongst CUAFY globally. It also went further to explain the intervention models and approaches that can be used to tackle undernutrition in CUAFY. Demiris et al.'s approach to narrative review was utilized for this paper. Relevant articles on child nutrition were retrieved from multiple credible databases and websites of foremost health organizations. Using an iterative process, multiple combinations of search terms were done by stringing relevant key terms and their synonyms with Boolean Operators. This process was constantly refined to align search results with the study aim. Database search produced relevant and resourceful publications which were utilized to develop this review. The global burden of undernutrition remains high, especially in Oceania with the highest prevalence of stunting and wasting (41.4% and 12.5%), with Africa and Asia following closely. Malnutrition eradication is a global health issue of high priority as demonstrated by the "Goal 2" of the Sustainable Development Goals (SDGs), and the United Nations (UN) Decade of Action on Nutrition 2016-2025. The review identified no significant positive outcome from previous interventions due to the endemic health inequalities. Determinants of the multi-level health inequalities associated with undernutrition in CUAFY, and probable solutions are explained with theoretical models of health inequalities. A diagonal intervention approach was proposed as a viable solution to ending undernutrition in CUAFY. The application of relevant theoretical models and context-specific intervention approaches can be utilized by stakeholders to close the existing inequality gaps, thereby reducing undernutrition amongst CUAFY globally.

Non-Gaussian normative modelling with hierarchical Bayesian regression

Abstract Normative modelling is an emerging technique for parsing heterogeneity in clinical cohorts. This can be implemented in practice using hierarchical Bayesian regression, which provides an elegant probabilistic solution to handle site variation in a federated learning framework. However, applications of this method to date have employed a Gaussian assumption, which may be restrictive in some applications. We have extended the hierarchical Bayesian regression framework to flexibly model non-Gaussian data with heteroskdastic skewness and kurtosis. To this end, we employ a flexible distribution from the sinh-arcsinh (SHASH) family, and introduce a novel reparameterisation and a Markov chain Monte Carlo sampling approach to perform inference in this model. Using a large neuroimaging dataset collected at 82 different sites, we show that the results achieved with this extension are equivalent or better than a warped Bayesian linear regression baseline model on most datasets, while providing better control over the parameters governing the shape of distributions that the approach is able to model. We also demonstrate that the attained flexibility is useful for accurately modelling highly nonlinear relationships between aging and imaging derived phenotypes, which shows that the extension is important for pushing the field of normative modelling forward. All methods described here are available in the open-source pcntoolkit.

EFFECTS OF ANTI-PREDATOR BEHAVIOR ON A STOCHASTIC PREDATOR-PREY SYSTEM

An ecosystem with anti-predator behavior is established in both deterministic and stochastic environments. This means that adult prey could attack weak predators. Bifurcation diagrams are used to analyze the deterministic case, while a tool called the most probable trajectory, defined by the spatial extreme point of the probability density function (PDF), is employed to explore the stochastic case. The Fokker-Planck equation is solved using the stochastic averaging method of energy envelope, which provides an analytical expression for the PDF. The results show that in the deterministic case, effective anti-predator behavior can dampen predator-prey oscillations and mitigate negative effects caused by the time delay. Additionally, it can accelerate the transient solution to reach a steady state and reduce the ratio of predator-to-prey densities in coexistence. In the stochastic case, effective anti-predator behavior can raise the noise threshold that leads to population extinction. Furthermore, it can also reduce the randomness of solutions. It’s worth noting that appropriate anti-predator behavior can ensure that the most probable solution in the stochastic system approximates the solution in the deterministic system. Monte Carlo simulations verify the accuracy of these analytical results.

Unlocking Electrode Performance of Disordered Rocksalt Oxides Through Structural Defect Engineering and Surface Stabilization with Concentrated Electrolyte

Abstract Li‐excess cation‐disordered rocksalt oxides can boost the energy density of rechargeable batteries by using anionic redox, but the inferior reversibility of anionic redox hinders its use for practical applications. Herein, a binary system of Li3NbO4–LiMnO2 is targeted and the systematic study on factors affecting electrode reversibility, i.e., percolation probability, electronic conductivity, defect concentrations, and electrolyte solutions, is conducted. A Mn‐rich sample, Li1.1Nb0.1Mn0.8O2, delivers a smaller reversible capacity compared with a Li‐rich sample, Li1.3Nb0.3Mn0.4O2, because of the limitation of ionic migration associated with insufficient percolation probability for disordered oxides. Nevertheless, a larger reversible capacity of Li1.3Nb0.3Mn0.4O2 originates from excessive activation of anionic redox, leading to the degradation of electrode reversibility. The superior performance of Li1.1Nb0.1Mn0.8O2, including Li ion migration kinetics and electronic transport properties, is unlocked by the enrichment of structural defects for nanosized oxides. Moreover, electrode reversibility is further improved by using a highly concentrated electrolyte solution with LiN(SO2F)2 through the surface stabilization on high‐voltage exposure. Superior capacity retention, >100 cycles, is achieved for nanosized Li1.1Nb0.1Mn0.8O2. The electrolyte decomposition and surface stabilization mechanisms are also carefully examined, and it is revealed that the use of highly concentrated electrolyte solution can effectively prevent lattice oxygen being further oxidized and transition metal ion dissolution.

Towards explainable artificial intelligence through expert-augmented supervised feature selection

This paper presents a comprehensive framework for expert-augmented supervised feature selection, addressing pre-processing, in-processing, and post-processing aspects of Explainable Artificial Intelligence (XAI). As part of pre-processing XAI, we introduce the Probabilistic Solution Generator through the Information Fusion (PSGIF) algorithm, leveraging ensemble techniques to enhance the exploration and exploitation capabilities of a Genetic Algorithm (GA). Balancing explainability and prediction accuracy, we formulate two multi-objective optimization models that empower expert(s) to specify a maximum acceptable sacrifice percentage. This approach enhances explainability by reducing the number of selected features and prioritizing those considered more relevant from the domain expert's perspective. This contribution aligns with in-processing XAI, incorporating expert opinions into the feature selection process as a multi-objective problem. Traditional feature selection techniques lack the capability to efficiently search the solution space considering our explainability-focused objective function. To overcome this, we leverage the Genetic Algorithm (GA), a powerful metaheuristic algorithm, optimizing its parameters through Bayesian optimization. For post-processing XAI, we present the Posterior Ensemble Algorithm (PEA), estimating the predictive power of features. PEA enables a nuanced comparison between objective and subjective importance, identifying features as underrated, overrated, or appropriately rated. We evaluate the performance of our proposed GAs on 16 publicly available datasets, focusing on prediction accuracy in a single objective setting. Moreover, we test our multi-objective model on a classification dataset to show the applicability and effectiveness of our framework. Overall, this paper provides a holistic and nuanced approach to explainable feature selection, offering decision-makers a comprehensive understanding of feature importance.

Weak Mean Random Attractor of Reversible Selkov Lattice Systems Driven By Locally Lipschitz Lévy Noises

This paper is concerned with weak pullback mean random attractor of reversible Selkov lattice systems defined on the entire integer set \(\mathbb{Z}\) driven by locally Lipschitz Lévy noises. Firstly, we formulate the stochastic lattice equations to an abstract system defined in the non-concrete space \(\ell^2\times\ell^2\) of square-summable sequences. Secondly, we establish the global well-posedness of the systems with locally Lipschitz diffusion terms. Under certain conditions, we show that the long-time dynamics can be captured by a weakly compact and weakly attracting mean random attractor in the Bochner space \(L^2(\Omega,\ell^2\times\ell^2)\). To overcome the difficulty caused by the drift and diffusion terms, we adopt a stopping time technique to prove the convergence of solutions in probability. The mean random dynamical systems theory proposed by Wang (J. Differ. Equ., 31:2177-2204, 2019) is used to deal with the difficulty caused by the nonlinear noise.

Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study

Background: Breast cancer (BC) is considered the most diagnosed cancer among women globally. This is because of its high possibility of metastasis and high resistance to chemotherapy. Cisplatin is a platinum-based antitumor agent that is used to treat various types of cancer. However, the main obstacle to using this drug is drug resistance. Drug resistance is a cause of most relapses of cancer which eventually lead to death. Nowadays, combining natural products is a trend to overcome drug resistance. Thymoquinone (TQ) is a natural phytochemical that exists mainly in blackseed. It has been used in medicine for decades, especially as an anticancer agent. Silymarin is a milk thistle compound that exhibits anticancer, hepatoprotective, and neuroprotective activity. Hence, the combination of TQ and silymarin could be a probable solution to treat cancer and reduce chemoresistance. Methods: This study tested this combination on cisplatin-sensitive (EMT6/P) and cisplatin-resistant (EMT6/CPR) mouse mammary cell lines. Apoptotic and antiproliferative activity was assessed for TQ and silymarin in vitro using caspase-3 and [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide] (MTT) assays, respectively. An in vivo study was performed to evaluate the effect of TQ and silymarin combination in mice inoculated with EMT6/P and EMT6/CPR cells. The safety profile was also examined using creatinine and liver enzyme assays. Results: In vitro, the TQ and silymarin combination synergized in both cell lines. Also, this combination caused apoptosis induction at a higher rate than the single treatment in both cell lines. In vivo, TQ and silymarin combination resulted in a remarkable reduction in tumor size and enhanced the cure rate in mice implanted with EMT6/P and EMT6/CPR cell lines. According to the safety profile results, TQ and silymarin combination was safe. Conclusion: In conclusion, the combination of TQ and silymarin provides a promising solution in treating BC resistant to cisplatin by inducing apoptosis. Further studies are needed to define the exact anticancer mechanisms of this combination.

Drone Detection and Identification Using Artificial Intelligence

Autonomous drone detection systems offer a probable solution to overcoming the issue of potential drone misuse, such as drug smuggling, violating people’s privacy, etc. Detecting drones can be difficult, due to similar objects in the sky, such as airplanes and birds. The objective of the project is to evaluate state-of-the-art models and training strategies for drone detection

A probabilistic solution to geophysical inverse problems in complex variables and its application to complex resistivity imaging

SUMMARY We introduce a novel probabilistic framework for the solution of non-linear geophysical inverse problems in complex variables. By using complex probability distributions, this approach can simultaneously account for individual errors of real and imaginary data parts, independently regularize real and imaginary parts of the complex model, and still take into account cross-sensitivities resulting from a complex forward calculation. The inverse problem is solved by means of optimization. An application of the framework to complex resistivity (CR) imaging demonstrates its advantages over the established inversion approach for CR measurements. We show that CR data, with real and imaginary parts being subject to different errors, can be fitted adequately, accounting for the individual errors and applying independent regularization to the real and imaginary part of the subsurface conductivity. The probabilistic framework itself serves as a basis for the future application of global sampling approaches, such as Markov chain Monte Carlo methods.

Addressing Recycling Inefficiencies in Shanghai Using Circular Economy

The recycling rate in Shanghai, despite being a role model among Chinas cities, still has significant room for improvement. If the recycling rate in Shanghai is improved, peoples lives will be better. Being aware of it, and since the lack of value recapture in Shanghai is not encouraging, we are interested in offering a possible solution for the current situation. The difficulty for private sectors to enter leads us to the main discussion. By case studying German and other developed countries incentives toward this issue and researching SRRTAs, Biohms and other websites reports, this paper will respond to the challenges demonstrated above by offering a probable solution: By providing governmental help toward the private sectors, the informal sectors efficiency of recycling can be elevated.

Difficult to treat superficial fungal infections: Which factors one should consider in clinical practice – An Indian perspective

Dermatophytosis is a superficial fungal infection caused by filamentous fungi- dermatophytes affecting the skin, hair and/or nails. They have also been termed tinea infections. Dermatophytes, the most common contributing agents, are pretending high importance in developing countries like India. Emerging Pathogen Causing Recalcitrant Dermatophytosis like Trichophyton indotineae, which is terbinafine resistant. Development of Anti-fungal resistance due to irrational usage of antifungals, steroid misuse, availability of topical creams with steroid combination on OTC, improper and inadequate treatment, lack of patient counselling and education are the most important factors which made dermatophyte difficult to treat. Proper diagnosis, correct selection of anti-fungal, with adequate duration of therapy, avoidance of steroid, patient education on lifestyle changes will be probable solutions in resolving current challenges.

Problems of Young Minds in India: NCC Training May be the Solution

Our society as well as our country is passing by an era which is showing us some peculiar problems which never haunted us previously in a major way. The name of the recent threat is ‘depression’. It affects our youths so badly that if we do not take this problem very seriously with utmost importance then a large number of young lives will be lost. Recent reports showing us that the students of premier Institutes of our country committing suicides and in the major cases the cause behind this is depression. In this paper I will try to excavate the reasons behind this and discuss on some probable solutions and explore how National Cadet Corps(NCC) training can help us to get rid of this problem.

Performance analysis of hybrid deep learning framework using a vision transformer and convolutional neural network for handwritten digit recognition

Digitization created a demand for highly efficient handwritten document recognition systems. A handwritten document consists of digits, text, symbols, diagrams, etc. Digits are an essential element of handwritten documents. Accurate recognition of handwritten digits is vital for effective communication and data analysis. Various researchers have attempted to address this issue with modern convolutional neural network (CNN) techniques. Even after training, CNN filter weights remain unchanged despite the high identification accuracy. As a result, the process cannot flexibly adapt to input changes. Hence computer vision researchers have recently become interested in Vision Transformers (ViTs) and Multilayer Perceptrons (MLPs). The shortcomings of CNNs gave rise to a hybrid model revolution that combines the best elements of the two fields. This paper analyzes how the hybrid convolutional ViT model affects the ability to recognize handwritten digits. Also, the real-time data contains noise, distortions, and varying writing styles. Hence, cleaned and uncleaned handwritten digit images are used for evaluation in this paper. The accuracy of the proposed method is compared with the state-of-the-art techniques, and the result shows that the proposed model achieves the highest recognition accuracy. Also, the probable solutions for recognizing other aspects of handwritten documents are discussed in this paper.•Analyzed the effect of convolutional vision transformer on cleaned and real-time handwritten digit images.•The model's performance improved with the implication of cross-validation and hyper-parameter tuning.•The results show that the proposed model is robust, feasible, and effective on cleaned and uncleaned handwritten digits.

Unveiling the presence of fluoride in commercial tea infusion: Factors influencing its release and its adsorptive elimination by indigenously developed nanoadsorbents

Drinking tea, which is globally one of the most widely consumed beverages, is a potential source of fluoride toxicity. This research aimed at fluoride estimation in the infusions of commercial black tea and green tea samples, purchased from the local Indian market, systematic evaluation of the factors influencing the fluoride release and the adsorptive removal of fluoride using the indigenously developed nanoadsorbents. All the prepared infusions had fluoride content above the permissible WHO limits. Various factors, which affect the release of fluoride from tea leaves into the infusion include type of water, tea dosage and brewing time. The investigations revealed that, the fluoride content in the black tea infusions was much higher than the green tea infusions. Further, it also depended on the amount of tea leaves used for brewing. The fluoride, present in tea leaves, is released gradually and within just 1 min of brewing, the concentration of fluoride in the infusions was well above the permissible WHO limits. It was also observed that, the dried unprocessed tea leaves, when brewed in water, had high fluoride content. This further confirmed the presence of fluoride in tea infusion, which demands an efficient remediation technique. The developed nanoadsorbents exhibited efficient defluoridation of groundwater, and were therefore tested for their efficiency in defluoridating tea infusions as well. These nanoadsorbents could efficiently eliminate fluoride from both green and black tea infusions, bringing down the fluoride content below the WHO limits, thereby rendering the infusions safe. They exhibited rapid kinetics with high efficiency in adsorbing fluoride from tea infusions. These properties make them potential adsorbents for defluoridating tea infusion, which provides a probable solution to the problem of fluoride toxicity from drinking tea. This is one of the first reports on a technique for eliminating fluoride from tea infusions.

Well-posedness of a nonlinear stochastic model for a chemical reaction in porous media and applications

<p>In this paper, we considered a stochastic model of chemical reactive flows acting through porous media under the influence of nonlinear external random fluctuations, where the interchanges of chemical flow across the skeleton's surface are represented by a nonlinear function. We studied the existence and uniqueness of strong probabilistic solutions for the model under consideration. We also show the positivity for the concentration of the solute in the fluid face as well as the concentration of reactants on the surface of the skeleton under extra reasonable assumptions on the data. Initially, we approximated the solution of the nonlinear stochastic diffusion equation using Galerkin's approximation, and obtained important bound estimates along with probabilistic compactness results. Thereafter, we passed the limit and obtained a weak probabilistic solution. This was followed by the path-wise uniqueness of the solution, which leads to the existence and uniqueness of strong probabilistic solutions as a result of Yamada-Watanabe's theorem. Finally, we discuss some important numerical applications such as Langmuir and Freundlich kinetics using the extended stochastic non-conforming finite element method to illustrate the efficiency of this approach and compare it to the deterministic approach in both cases. Let us mention that well-posedness, positivity, and numerical simulations have not been considered so far for such a nonlinear stochastic model.</p>

Bayesian Inversion, Uncertainty Analysis and Interrogation Using Boosting Variational Inference

AbstractGeoscientists use observed data to estimate properties of the Earth's interior. This often requires non‐linear inverse problems to be solved and uncertainties to be estimated. Bayesian inference solves inverse problems under a probabilistic framework, in which uncertainty is represented by a so‐called posterior probability distribution. Recently, variational inference has emerged as an efficient method to estimate Bayesian solutions. By seeking the closest approximation to the posterior distribution within any chosen family of distributions, variational inference yields a fully probabilistic solution. It is important to define expressive variational families so that the posterior distribution can be represented accurately. We introduce boosting variational inference (BVI) as a computationally efficient means to construct a flexible approximating family comprising all possible finite mixtures of simpler component distributions. We use Gaussian mixture components due to their fully parametric nature and the ease with which they can be optimized. We apply BVI to seismic travel time tomography and full waveform inversion, comparing its performance with other methods of solution. The results demonstrate that BVI achieves reasonable efficiency and accuracy while enabling the construction of a fully analytic expression for the posterior distribution. Samples that represent major components of uncertainty in the solution can be obtained analytically from each mixture component. We demonstrate that these samples can be used to solve an interrogation problem: to assess the size of a subsurface target structure. To the best of our knowledge, this is the first method in geophysics that provides both analytic and reasonably accurate probabilistic solutions to fully non‐linear, high‐dimensional Bayesian full waveform inversion problems.

Faster than FFT: Conformal accelerations method

Fourier-Laplace transform technique allows one to represent several classes of important probability distributions and solutions of basic boundary problems for wide classes of fractional differential equations as integrals of functions enjoying two key properties: analytic continuation to a cone or the union of a cone and tube domain, and regular decay at infinity. Integral representations for Wiener-Hopf factors, fractional moments and special functions enjoy these properties as well. In the paper, we present the general methodology which allows one to evaluate the integrals enjoying these properties very fast and accurately. Among applications, we derive new efficient realizations of the Fourier, Laplace and Z-transforms, representations for probability distributions in Levy models, stable ones including, and algorithms for pricing contingent claims, Monte-Carlo simulations and evaluation of special functions.

A note on optimal liquidation with linear price impact

In this note the maximization of the expected terminal wealth for the setup of quadratic transaction costs is considered. First, a very simple probabilistic solution to the problem is provided. Although the problem was largely studied, as far as authors know up to date this simple and probabilistic form of the solution has not appeared in the literature. Next, the general result is applied for the numerical study of the case where the risky asset is given by a fractional Brownian motion and the information flow of the investor can be diversified.

Recent technological advancements, V2G systems, and its implications for increased adoption and scalability using Artificial Intelligence

The slow and steady transition in the direction of cleaner and green energy sources warrant the application of electric vehicles (EVs) as the mainstream platform for transportation. This investigation highlighted a few significant challenges, which include extreme burden on EV batteries at the time of V2G, the absence of suitable bidirectional charging infrastructure, technological complexities and the likelihood of V2G practices that may prove to be uneconomical and unscheduled. Contemporary research and reports from international agencies are reviewed in order to present probable solutions to the emerging bottlenecks and identify the regions that may require additional research. V2G could hold immense promises when it comes to clean mobility especially with the emergence of novel V2G technologies, however, significant collaboration, funding, and technology maturation are the basic requirements for the success of any relevant schemes.

APPLICATION OF DIESEL-RK SIMULATION TOOL FOR REDUCING NOX EMISSION FROM BIODIESEL BLEND FUELS AT RETARDED INJECTION CONDITION

In renewable energy resources, although biodiesels have occupied the top place as an alternative for compression ignition engines, their high NO<sub>x</sub> discharge causes concern. Retarded fuel injection is a probable solution to reduce NO<sub>x</sub> emission. In the present work, tests are conducted with corn seed methyl ester (CSME) blend at standard and retarded fuel injection timings. Improvement in brake thermal efficiency and reduction in NO<sub>x</sub> emissions are observed due to the retardation of fuel injection timing. DIESEL-RK engine simulation software is used as a potential tool to simulate retardation conditions, and simulation results are compared with experimental values. It is seen that along with the performance and combustion parameters, the simulated NO<sub>x</sub> emission results are in good agreement with experimental values. Hence, the modeling used in the DIESEL-RK software captures the experimental observations well.