Probabilistic Solution Research Articles

The Bhagavad Gita – A Manual for Self-management

The Holy Indian Epic, the Bhagavad Gita is not merely a religious text of the Hindus; rather it is a comprehensive philosophy which has universal applicability. By applying the teachings of Bhagavad Gita in everyday regime, individuals can enlighten themselves, dispel their doubts and overcome their dilemmas. This philosophy acts as a guide that offers numerous self-management lessons. Self-management is a basic prerequisite for effective management of people, groups, organizations, and societies. It enables individuals to maximize their productivity, improve their performance and accomplish personal and professional goals. However, self-management has received scant attention in the western and modern management. On the contrary, the Indian philosophy has emphasized the relevance of self-management since time immemorial. This paper aims to explore our rich heritage to uncover probable solutions in the Bhagavad Gita that can be implemented to resolve problems that confront modern man in the discharge of his or her duties.

Quantum Computing Algorithms for Nonlinear Optimization Problems

The increasing complexity of real-world optimization problems highlights the importance of this research since classical algorithms are unable to provide efficient answers in these cases. Innovative methods for fast and scalable resolution of nonlinear optimization problems are required because these problems are prevalent in many fields. The potential for quantum computing to speed up optimization processes and overcome classical limitations is great, owing to its superposition principles and intrinsic parallelism. The integration of quantum algorithms (I-QA) into real-world applications, however, will not always be smooth sailing. There are significant challenges associated with preserving quantum coherence, correcting errors, and working within hardware limits. To enable the simultaneous exploration of solution spaces through quantum parallelism, this research proposes the Hybrid Quantum Gradient -Classical Approach (HQG-CA), which makes use of parameterized quantum circuits to represent probable solutions. Additionally, improves convergence rates through applying quantum gradient information to direct optimization in the quantum state space. Optimization of portfolios in finance, adjustment of model parameters in machine learning, and optimization of routes in logistics are a few examples of the many industries that find use for HQG-CA. These applications are explored in this abstract, which highlights the revolutionary potential of HQG-CA to solve optimization problems in the real world. The effectiveness of HQG-CA is assessed through a thorough simulation experiment. Performance measures such algorithmic speedup, solution accuracy, and scalability are discussed, which is based on extensive testing and comparison with classical alternatives. The present research provides a comprehensive evaluation of HQG-CA's potential for tackling nonlinear optimization problems.

Non-stationary probabilistic analysis of non-linear ship roll motion due to modulated periodic and random excitations

This paper is about the study on the non-stationary probability density function (PDF) solution of the non-linear ship rolling system under modulated periodic and random excitations. The quadratic and cubic damping terms are introduced to describe the non-linear damping moment and the power terms in state variables are adopted to describe the righting moment in the ship rolling system, respectively. The relevant Fokker–Planck–Kolmogorov (FPK) equation of the ship rolling system is constructed and the non-stationary probabilistic solutions are obtained by the presented solution procedure. In the solution process, the time variable t is introduced to the traditional exponential-polynomial-closure (EPC) method to obtain the non-stationary PDF solutions of the ship rolling system. The obtained non-stationary PDF solutions of the non-linear ship rolling systems demonstrate that the presented solution procedure can give high solution accuracy even in the PDF tails when they are compared with Monte Carlo simulation (MCS). Moreover, owing to the strong system nonlinearity and the interaction of the periodic excitation and random excitation, the achieved non-stationary PDF solutions of the system responses can deviate significantly from the Gaussian PDF. It is also found that the PDFs of responses are not even symmetrical about the means of responses due to the interaction of the periodic excitation and the random excitation even if the system only contains odd nonlinear terms. In addition, the computational efficiency is increased by over five hundred times by the presented solution procedure compared to MCS.

Predicting steel column stability with uncertain initial defects using bayesian deep learning

The stability of steel columns is difficult to predict accurately due to uncertain initial defects such as geometric imperfections and residual stress. To address this issue, we propose a probabilistic model that uses variational autoencoder (VAE) and transfer learning to estimate the loading capacities of steel columns. Our model can predict the confidence intervals of buckling loads without knowing the exact distribution of initial defects, providing more comprehensive information for engineering applications than traditional deterministic strength index. We establish a dataset of 1500 load-displacement curves of steel columns using four data augmentation approaches, and analyze the data distribution to validate the model's assumptions. Various criterions, including the mean squared error (MSE), the prediction interval coverage probability (PICP), and the prediction interval normalized average width (PINAW), are adopted to comprehensively measure the performance of confidence interval prediction. The numerical experiment validates that the trained model accurately predicts the confidence intervals for load-displacement responses, which perfectly cover the true curves with reasonable PINAW. Finally, we conduct a case study with a practical experiment to illustrate the model's potential application in failure probability calculation and reliability design. Our proposed model provides a promising probabilistic solution for quantifying the impact of uncertain parameters on structural analysis and significantly simplifies probability-based reliability design and optimal design processes.

New class of solutions of the ultimate extinction probability from Galton-Watson processes

A new class of solutions of the ultimate extinction probability for a neutron fission chain have been derived from Galton-Watson processes, which allows for a significant simplification in their derivation with respect to the conventional probability balance approach. These solutions are not based on the one-group diffusion hypothesis, depend only on the effective multiplication value and allow to take into consideration a large variety of discrete fission neutron distributions. They have been compared to other solutions, derived from probability balance equations, and they are qualitatively in good agreement. Additionally, the polynomial form of a model based on the geometric distribution presented here, has allowed for the calculation of the alive neutrons probability distribution that has been compared, for different generation numbers, with criticality benchmarks’ distributions estimated via a neutron Monte Carlo code. The comparisons have shown good agreement for generation numbers larger than 1.

Practices of artificial intelligence to improve the business in Bangladesh

This research paper explores the practices of artificial intelligence (AI) and its impact on improving businesses in Bangladesh through the review of existing literature and primary data analysis. Additionally, a conceptual analysis has been conducted on key AI concepts and their potential applications in the Bangladeshi business context. The study's objectives include providing a detailed understanding of specific AI technologies, measuring the benefits of AI, assessing the challenges faced by the industry in implementing AI and finding probable solutions to overcome these challenges. Data were collected from 120 respondents from 10 types of businesses in Bangladesh and analyzed using MS Excel and SPSS (V. 25). According to the results, using AI in enterprises may have a significant positive impact on efficiency, decision-making, production, cost, fraud detection, and supply chain optimization. However, obstacles to AI deployment include a lack of qualified personnel, poor data quality, money, infrastructure, and legal frameworks. Businesses should raise employee understanding of AI, look for diversified financing and qualified personnel, work with the government on infrastructure support and legislation, address concerns about job displacement through training, and encourage employee acceptance of change in order to overcome these challenges. Businesses in Bangladesh may improve operations and competitiveness by using these techniques. Business executives, decision-makers, and academics interested in maximizing the potential of AI and enhancing business outcomes in Bangladesh might learn from this study.

ZnO2‐Based Carbon Composite Doped with Sulphur for the Removal of Highly Toxic Thiamethoxam from Wastewater

AbstractThe ongoing exponential increase in population intensifies the overexploitation of food, water, energy, and other resources. Overcoming the problem has emerged as a tedious task, and the most probable solution seems to be upgradation in agricultural practices. To achieve a superior quality harvest with maximum yield, synthetic herbicides, insecticides, weedicides, and pesticides have been extensively developed and employed. These pesticides generally wash down to the water bodies, polluting the water and causing serious health issues for human, animal, and aquatic lives. The suggested study uses a zinc peroxide (ZnO2) based carbon composite doped with sulphur adsorbent to remove the very hazardous pesticide thiamethoxam (TMX) from the contaminated water in a cost‐effective and efficient manner. In an acidic environment, a remarkable removal efficiency of 99.5 % is achieved at pH levels 1–3. Additionally, at pH level 11, an impressive removal rate of 96.4 % is obtained. Furthermore, this composite material exhibits a high adsorption capacity of 142.86 mg/g. The batch studies experiments revealed that the adsorption of TMX. complied with the Langmuir isotherm model and demonstrated Pseudo‐second order kinetics. The adsorbent eco‐friendly and effective, can be prepared easily, is ecologically friendly, and does not cause any sort of secondary pollution. The present work has high potential to take the science of water purification to the next step further.

Modeling elasticity, similarity, stochasticity, and congestion in a network equilibrium framework using a paired combinatorial weibit choice model

In the traffic assignment problem for predicting traffic flow patterns in a transportation network, it is important to account for route overlap and non-identical perception variance in route choice analysis. In this study, we establish a novel route choice model, named the paired combinatorial weibit (PCW) model, to capture the route overlap and route-specific perception variance. The PCW model retains a closed-form probability solution, which allows the development of an equivalent mathematical programming (MP) formulation for the PCW-based stochastic user equilibrium (PCW-SUE) model. Specifically, we propose two equivalent MP formulations for modeling the fixed demand (FD) and elastic demand (ED), named PCW-SUE-FD and PCW-SUE-ED, respectively. The PCW-SUE-ED model can address the abovementioned two issues in route choice for the FD scheme, but also can consider the effect level-of-service (LOS) in travel choice for the ED scheme. The equivalency and uniqueness of the PCW-SUE-FD and PCW-SUE-ED models are rigorously proved. In addition, a path-based partial linearization algorithm combined with a self-regulated averaging line search strategy is developed to solve the two SUE models. Numerical results are presented to illustrate the features of the PCW-SUE-FD and PCW-SUE-ED models and applicability of the solution algorithm to a real transportation network.

On the existence, uniqueness and calculation of Wiener path integral most probable paths for determining the stochastic response of nonlinear dynamical systems

Various techniques are developed for addressing the existence, uniqueness and numerical calculation of Wiener path integral (WPI) most probable path solutions. Specifically, the WPI technique for determining the stochastic response of diverse nonlinear dynamical systems treats the system response joint transition probability density function as a functional integral over the space of all possible paths connecting the initial and the final states of the response vector. This functional integral is evaluated, ordinarily, by resorting to an approximate approach that considers the contribution only of the most probable path. The most probable path corresponds to an extremum of the functional integrand and is determined by solving a functional minimization problem that takes the form of a deterministic boundary value problem (BVP).In this paper, first, it is shown that for the commonly considered case of the system nonlinearity being of polynomial form, there exist globally optimal solutions corresponding to the most probable path BVP. Further, relying on algebraic geometry concepts and tools, a condition is derived for determining if the BVP for the most probable path exhibits a unique solution over a specific region. Furthermore, a novel solution approach is developed for the BVP by relying on Sylvester’s dialytic method of elimination. Notably, the method reduces the complexity of the BVP system of coupled multivariate polynomial equations by eliminating one or more variables. Various numerical examples pertaining to diverse nonlinear oscillators are included for demonstrating the capabilities of the developed techniques.

Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system

The initial inception of the landslide susceptibility concept defined it as a static property of the landscape, explaining the proneness of certain locations to generate slope failures. Since the spread of data-driven probabilistic solutions though, the original susceptibility definition has been challenged to incorporate dynamic elements that would lead the occurrence probability to change both in space and in time. This is the starting point of this work, which combines the traditional strengths of the susceptibility framework together with the strengths typical of landslide early warning systems. Specifically, we model landslide occurrences in the norther sector of Vietnam, using a multi-temporal landslide inventory recently released by NASA. A set of static (terrain) and dynamic (cumulated rainfall) covariates are selected to explain the landslide presence/absence distribution via a Bayesian version of a binomial Generalized Additive Models (GAM). Thanks to the large spatiotemporal domain under consideration, we include a large suite of cross-validation routines, testing the landslide prediction through random sampling, as well as through stratified spatial and temporal sampling. We even extend the model test towards regions far away from the study site, to be used as external validation datasets. The overall performance appears to be quite high, with Area Under the Curve (AUC) values in the range of excellent model results, and very few localized exceptions.This model structure may serve as the basis for a new generation of early warning systems. However, the use of The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) for the rainfall component limits the model ability in terms of future prediction. Therefore, we envision subsequent development to take this direction and move towards a unified dynamic landslide forecast. Ultimately, as a proof-of-concept, we have also implemented a potential early warning system in Google Earth Engine.

Ideating a framework for sustainable livelihood of handicraft producers at the bottom of the pyramid: a mix-method study from India

PurposeThe handicraft sector is unorganized with huge potential for the economic upliftment of the bottom of the pyramid (BOP) craft producers. To ensure the sustainable livelihood of the artisans living in BOP, it is essential to identify the hurdles being faced by them so that they can be worked upon. This paper aims to look at the existing handicraft value chain in an integrated way to identify these hurdles and suggest probable solutions to mitigate them.Design/methodology/approachA two-stage study involving quantitative and qualitative approaches is used. Empirical observations from 218 artisans were analyzed using the relative to an identified distribution to rank the different variables affecting the functioning of the handicraft value chain. Next, 17 handicraft entrepreneurs were interviewed to identify solutions for the hurdles.FindingsA two-stage study examining the perspectives of artisans and handicraft entrepreneurs identified five significant challenges affecting the handicraft sector and viable solutions to address them. While presenting an operational model, a comprehensive picture of the handicraft business ecosystem was studied, from the procurement of raw materials to the marketing and sale of finished products. This can assist handicraft producers in sustaining their livelihoods and expanding the industry.Originality/valueStudies on the handicraft value chain at BOP are rare, and none have tried to look at the sector holistically to find solutions and propose an operational framework. The study has captured the voices of the artisans and handicraft entrepreneurs and, through a mixed-method approach, identified the main pain points of the BOP artisans to arrive at a workable solution for better business outcomes.

Gender Stereotyping: The Role of Traditional and social media: An Analysis of Audience Perception

Gender stereotyping has been rampant since the initiation of broadcast media in the late 50s and early 60s. However, as advertisements are a more persuasive form of communication, the perception and normalization of stereotypes and the degree of persuasion become more evident and probable. Television and social media advertisements differ in their portrayal. Hence, stereotypes and the level of influence through persuasion need to be studied separately. This research compares and analyses television and social media advertisements based on stereotypes. In addition, the audience’s perception and the level of persuasion are analyzed. For this purpose, the researcher has used content analysis and survey through questionnaire methods to identify the different socio-cultural factors that strengthened the process of stereotyping through media and to find out a probable solution to minimize, if not eliminate, this problem.

Mean attractors and invariant measures of locally monotone and generally coercive SPDEs driven by superlinear noise

We study the global solvability, mean attractors and invariant measures for an abstract locally monotone and generally coercive SPDEs driven by infinite-dimensional superlinear noise defined in a dual space of intersection of finitely many Banach spaces. The main feature of this abstract system is that it covers a larger class of fundamental models which are included or not included previously. Under an extended locally monotone variational setting, we establish the global well-posedness, Itô's energy equality and existence of mean random attractors in some high-order Bochner spaces. The existence, uniqueness, support, (high-order and exponential) moment estimates, ergodicity, (pointwise and Wasserstein-type) exponentially mixing and asymptotic stability of invariant measures and evolution systems of measures are discussed for autonomous and nonautonomous stochastic equations. A stopping time technique is used to prove the convergence of solutions in probability in order to overcome the difficulty caused by the local monotonicity and superlinear growth of the coefficients. Our abstract results and unified methods are expected to be applied to various types of SPDEs like 2D Navier-Stokes equations, 2D MHD equations, 2D magnetic Bénard problem, Burgers type equations, 3D Leray α-model, convective Brinkman-Forchheimer equations, fractional (s,p)-Laplacian equations with monotone nonlinearities of polynomial growth of arbitrary order, and others.

Mahesh Foundation – care for the uncared

Learning outcomes The learning outcomes of this case study are to identify the role of non-governmental organizations (NGOs) in social upliftment of developing countries, understand the functioning of NGOs, understand the challenges faced by the NGOs in day-to-day operations and discuss the probable solutions for the same, appreciate the role of leader and leadership in an NGO, study the working style of NGO leaders, appreciate the importance of having a formal organizational structure for these informal organizations (NGOs) to ensure the sustainability of their ventures and design a sustainable organization structure having a proper succession plan for the NGOs. Case overview/synopsis The case study is about one NGO – Mahesh Foundation – located in a small town named Belagavi, nestled in the state of Karnataka, India. Mahesh Foundation worked towards the upliftment of poor children infected with HIV. Today, fighting against all odds, Mahesh Foundation is a safe shelter home for 45 HIV-infected children in the age group of 6–18 years and has reached more than 2,000 beneficiaries from the time of its inception (2008). In addition, Mahesh Foundation also provides skill-based education to the HIV-infected, slum and underprivileged children. The foundation also supported the livelihood of underprivileged women and till date has supported more than 1,500 needy women. Mr Mahesh Jadhav, the founder member of Mahesh Foundation, has been successful in overcoming different challenges faced by the NGO, may it be the requirement for funds or shelters or social agitation. However, Jadhav was worried about the succession planning of his organisation. Mahesh Foundation, being run as a one-man show, Jadhav was bothered about its sustainability after him. The case study therefore highlights and discusses the importance of having a formal organization structure for such informal organizations, thereby having a proper succession plan to ensure their perpetual existence. Complexity academic level This case study is best taught as part of a graduate and postgraduate Business Administration (BBA/MBA) programme, Management Development Programme or Executive MBA Programme. Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.

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.

Environmental impact and thermodynamic comparative optimization of CO2-based multi-energy systems powered with geothermal energy

The continuous increase in global population and energy demands has enhanced the use of fossil fuels. The evident climate change effect in recent years has brought about the necessity to reduce fossil fuels. Hence, there exists an aggregate problem of meeting energy demands and reducing carbon emissions. Renewable energy sources have been proffered as the probable solution; however, multi-energy systems are effective options/alternatives to solving this problem. In recent literature, geothermal energy has been proposed as a renewable energy source that can continuously meet energy demands. However, there exists a significant gap in literature about the most viable temperature range for geothermal energy applications in multi-energy systems. In this study, two innovative CO2-based systems namely, high-temperature geothermal multi-energy system (HTGMES) and low-temperature geothermal multi-energy system (LTGMES) are designed, modelled, analyzed, and compared using a thermodynamic approach. While the HTGMES is modelled to predominantly use CO2 as a working fluid, a novel modified absorption system is integrated with the LTGMES. The two systems are modelled to produce electricity, cooling/refrigeration effect, space heating, hydrogen, and hot water. The energy, exergy environmental, exergy destruction, and exergoeconomic methodology is used to evaluate the performance of the innovative GMESs. Also, multi-objective optimization of the HTGMES and the LTGMES is carried out in a bid to minimize the total product unit cost and maximize overall exergy efficiency. The environmental impact analysis of the proposed system is presented considering CO2 mitigation. The results from analyses showed that the overall energetic and exergetic efficiencies in steady state are 44. 22 % and 33.5 % for the HTGMES and 45.40% and 32.9 % for the LTGMES. The optimized LCOE, LCOC, and LCOH based on the total unit cost are 0.0573 $/kWh, 0.1833 $/kWh, and 11.59 $/kg for the HTGMES; 0.0323 $/kWh, 0.0032 $/kWh, and 11.9$/kg for the LTGMES. Furthermore, the optimized exergetic performance showed that the LTGMES can achieve as high as 64.54 % and 40.96 % energy and exergy efficiency.

Major challenges and probable scientific solutions toward the large-scale production of plant-based metallic nanoparticles: a systematic review

Major challenges and probable scientific solutions toward the large-scale production of plant-based metallic nanoparticles: a systematic review

Health Literacy among the Adolescent Girls: A Socio-Economic Analysis of the Adolescent Girls of Darrang District, Assam

In the present century the most needed quality that every society should have is the “Health Literacy”. In this century where we are facing pandemics, where people are witnessing or to be specific suffering from various savage troubles people are really required to be conscious about their health. We have chosen to study this problem more importantly from the perspective of adolescent girls as adolescence is the most critical age of development full of multifaceted problems and challenges. They not only experience rapid developmental changes but also face various psycho-social trauma and anxiety. We have chosen Darrang district as our subject area to study. We have sufficiently made investigation on the issue to grasp the real scenario. This paper will surely throw light on the rate of health consciousness or literacy among the adolescent girls of this specific area. If there is any inefficiency then through this paper, we will make effort to provide probable solution to it. We have properly studied the adolescent girls between the age of 12 to 18 through primary and secondary sources. We have used survey as well as personal interview among the subject to find out the outcome.

An interactive feature selection method based on multi-step state transition algorithm for high-dimensional data

Feature selection (FS) has been extensively employed in classification tasks to reduce data dimensionality and enhance prediction performance effectively. Recently, hybrid filter-wrapper methods have exhibited promising results in FS problems by leveraging both advantages. However, the inadequate integration of the filter method into the wrapper method leads to the hybrid algorithms exhibiting poor efficiency in classification as datasets grow in complexity. In this paper, an interactive feature selection framework based on state transition algorithm (STA) is proposed to address high-dimensional FS problems. In this framework, prior knowledge of the features is formed via the mutual information-based filter method. The STA is a powerful search engine that traverses the feature space in the wrapper stage. Moreover, an external trainer with prior knowledge will guide the exploration direction of STA in a simple but efficient way to accelerate the search process. And a self-adaptive mechanism is proposed to adjust the prior knowledge during the search process. Specifically, the external trainer establishes the interactive loop, while the self-adaptive mechanism aims to feed feature information back to the loop. In addition, the search process is prevented from being trapped in local optima by employing a multi-step STA, which allows for continuous transformations of the solution in probability. Finally, the proposed FS method is applied to various public classification datasets. The experimental results demonstrate that the proposed method is a highly competitive FS method, outperforming several state-of-the-art algorithms in generating an optimal subset of features.

Fast reconstruction algorithm based on HMC sampling

In Noisy Intermediate-Scale Quantum (NISQ) era, the scarcity of qubit resources has prevented many quantum algorithms from being implemented on quantum devices. Circuit cutting technology has greatly alleviated this problem, which allows us to run larger quantum circuits on real quantum machines with currently limited qubit resources at the cost of additional classical overhead. However, the classical overhead of circuit cutting grows exponentially with the number of cuts and qubits, and the excessive postprocessing overhead makes it difficult to apply circuit cutting to large scale circuits. In this paper, we propose a fast reconstruction algorithm based on Hamiltonian Monte Carlo (HMC) sampling, which samples the high probability solutions by Hamiltonian dynamics from state space with dimension growing exponentially with qubit. Our algorithm avoids excessive computation when reconstructing the original circuit probability distribution, and greatly reduces the circuit cutting post-processing overhead. The improvement is crucial for expanding of circuit cutting to a larger scale on NISQ devices.