Exponential Growth Research Articles

Aspect-Level Sentiment Analysis of Customer Reviews Based on Neural Multi-task Learning

In the era of big data, major e-commerce platforms are facing the challenge of an exponential growth in the number of user comments. Effectively utilizing these comments has become an urgent issue. Traditional manual statistical methods are no longer able to meet the demands for accuracy and real-time analysis. The rise of artificial intelligence-based text mining techniques provides a new approach to address this problem. By building deep learning analysis models, it is possible to uncover user preferences and product characteristics, helping businesses proactively adjust sales strategies, improve product and service quality, and achieve precise marketing. Text sentiment analysis is one of the important research directions in the field of text mining. Its basic idea is to transform subjective texts with emotional color into structured data, and then use machine learning, deep learning, and other artificial intelligence technologies to extract emotional features and discover knowledge patterns. This article proposes a deep learning-based sentiment analysis model, which includes a shared sentiment prediction layer used to transfer emotional knowledge between different aspect categories and alleviate the problem of insufficient data. The model consists of two parts: the Aspect Category Detection (ACD) classifier based on attention mechanism and the Aspect Category Sentiment Analysis (ACSA) classifier. The ACD part generates word weights using attention mechanism, while the ACSA part predicts the sentiment of words and combines weights to determine the sentiment of aspect categories within sentences.

Solutions for a nonlinear equation with competing operators and supercritical exponential growth

The existence of a generalized solution is established for a Dirichlet problem whose equation is driven by possibly competing operators and whose reaction term exhibits supercritical exponential growth. It is also shown that if the operators are not competing, the solution is in the weak sense and is positive.

Magnesium Oxide Nanoparticles: Biogenic Synthesis and Biomedical Applications

AbstractCurrently, nanotechnology is developing at an exponential rate because of its widespread applications in various fields of life sciences. Traditionally, nanoparticles are fabricated through a variety of physical and chemical approaches but because of their limited efficiency in biomedical applications, biogenic synthesis of nanoparticles has gained much attention. Biogenically fabricated magnesium oxide nanoparticles (MgONPs) possess unique features that enabled their use in therapeutics and environmental biotechnology. Biogenically MgONPs can be fabricated using plant extracts (Manihot esculenta, Rhizophora lamarckii, Ocimum sanctum, rosemary, Bauhinia purpurea, and others), bacteria (S. coelicolor, A. johnsonii RTN), fungi (Aspergillus flavus TFR‐12, Aspergillus brasiliensis TFR 2, white‐button mushroom's) and algae (S. whigti). These biogenically synthesized MgONPs are being reported as antifungal, antioxidant, antibacterial, antipyretic, anti‐inflammatory, and anticancer agents. Moreover, their photocatalytic activity is far being discovered against certain organic dyes. This review focuses on the biogenic synthesis of MgONPs, their applications in therapeutics, and as antimicrobial agents as well as future prospects associated with their applications in the biomedical field.

Mobile edge computing based cognitive network security analysis using multi agent machine learning techniques in B5G

The proliferation of wireless applications at an exponential rate has made spectrum problems worse. Saturation in the unlicensed frequency spectrum is rapidly increasing as a result of the increasing data rates required by new wireless devices. A proposed solution to this problem is cognitive radio, which allows for the opportunistic use of licenced spectrum in less crowded areas. Cognitive network-based security evaluations using mobile edge computing and a Beyond 5G' (B5G) machine learning (ML) model are the focus of this research. In this case, the security study was carried out using cognitive network data transfer and multi-agent reinforcement encoder neural network and mobile edge computing (MRENN-MEC), a multi-agent reinforcement encoder neural network with mobile edge computing. Scalability, quality of service, throughput, and forecast accuracy are some of the network properties that undergo experimental analysis.

Heterotrophy Compared to Photoautotrophy for Growth Characteristics and Pigment Compositions in Batch Cultures of Four Green Microalgae.

Four strains of green microalgae (Scenedesmus acutus, Scenedesmus vacuolatus, Chlorella sorokiniana, and Chlamydomonas reinhardtii) were compared to determine growth and pigment composition under photoautotrophic or heterotrophic conditions. Batch growth experiments were performed in multicultivators with online monitoring of optical density. For photoautotrophic growth, light-limited (CO2-sufficient) growth was analyzed under different light intensities during the exponential and deceleration growth phases. The specific growth rate, measured during the exponential phase, and the maximal biomass productivity, measured during the deceleration phase, were not related to each other when different light intensities and different species were considered. This indicates species-dependent photoacclimation effects during cultivation time, which was confirmed by light-dependent changes in pigment content and composition when exponential and deceleration phases were compared. Except for C. reinhardtii, which does not grow on glucose, heterotrophic growth was promoted to similar extents by acetate and by glucose; however, these two substrates led to different pigment compositions. Weak light increased the pigment content during heterotrophy in the four species but was efficient in promoting growth only in S. acutus. C. sorokiniana, and S. vacuolatus exhibited the best potential for heterotrophic biomass productivities, both on glucose and acetate, with carotenoid (lutein) content being the highest in the former.

Quantification of biochemical PSA dynamics after radioligand therapy with [177Lu]Lu-PSMA-I&T using a population pharmacokinetic/pharmacodynamic model

BackgroundThere is an unmet need for prediction of treatment outcome or patient selection for [177Lu]Lu-PSMA therapy in patients with metastatic castration-resistant prostate cancer (mCRPC). Quantification of the tumor exposure–response relationship is pivotal for further treatment optimization. Therefore, a population pharmacokinetic (PK) model was developed for [177Lu]Lu-PSMA-I&T using SPECT/CT data and, subsequently, related to prostate-specific antigen (PSA) dynamics after therapy in patients with mCRPC using a pharmacokinetic/pharmacodynamic (PKPD) modelling approach.MethodsA population PK model was developed using quantitative SPECT/CT data (406 scans) of 76 patients who received multiple cycles [177Lu]Lu-PSMA-I&T (± 7.4 GBq with either two- or six-week interval). The PK model consisted of five compartments; central, salivary glands, kidneys, tumors and combined remaining tissues. Covariates (tumor volume, renal function and cycle number) were tested to explain inter-individual variability on uptake into organs and tumors. The final PK model was expanded with a PD compartment (sequential fitting approach) representing PSA dynamics during and after treatment. To explore the presence of a exposure–response relationship, individually estimated [177Lu]Lu-PSMA-I&T tumor concentrations were related to PSA changes over time.ResultsThe population PK model adequately described observed data in all compartments (based on visual inspection of goodness-of-fit plots) with adequate precision of parameters estimates (< 36.1% relative standard error (RSE)). A significant declining uptake in tumors (k14) during later cycles was identified (uptake decreased to 73%, 50% and 44% in cycle 2, 3 and 4–7, respectively, compared to cycle 1). Tumor growth (defined by PSA increase) was described with an exponential growth rate (0.000408 h−1 (14.2% RSE)). Therapy-induced PSA decrease was related to estimated tumor concentrations (MBq/L) using both a direct and delayed drug effect. The final model adequately captured individual PSA concentrations after treatment (based on goodness-of-fit plots). Simulation based on the final PKPD model showed no evident differences in response for the two different dosing regimens currently used.ConclusionsOur population PK model accurately described observed [177Lu]Lu-PSMA-I&T uptake in salivary glands, kidneys and tumors and revealed a clear declining tumor uptake over treatment cycles. The PKPD model adequately captured individual PSA observations and identified population response rates for the two dosing regimens. Hence, a PKPD modelling approach can guide prediction of treatment response and thus identify patients in whom radioligand therapy is likely to fail.

Criminal Profiling as a Method of Detecting Lies in Nonverbal Communication

The exponential growth of crime, violence, and wrongdoing is linked to dysfunction in restraining aggressive impulses, leading to neglect and disrespect of others’ feelings, rights, and needs. Two basic forms of communication, verbal and nonverbal, serve individuals to interact and exchange ideas and attitudes with other people in everyday life situations, including within the framework of criminal investigations and criminal justice. The purpose of this study was to draw an analogy between verbal communication (oral speech, written speech) and nonverbal communication (body language, gestures, and silence), with the tendency to explore different components of nonverbal communication and place them in the context of criminal profiling as a method for detecting lies in nonverbal communication. The aim of this study was to emphasize the importance of nonverbal communication as an equal part of the communication process, and often a predominant one in this domain, which helps us demystify suspects, perpetrators, and criminal acts.

Active Learning and Bayesian Optimization: A Unified Perspective to Learn with a Goal

AbstractScience and Engineering applications are typically associated with expensive optimization problem to identify optimal design solutions and states of the system of interest. Bayesian optimization and active learning compute surrogate models through efficient adaptive sampling schemes to assist and accelerate this search task toward a given optimization goal. Both those methodologies are driven by specific infill/learning criteria which quantify the utility with respect to the set goal of evaluating the objective function for unknown combinations of optimization variables. While the two fields have seen an exponential growth in popularity in the past decades, their dualism and synergy have received relatively little attention to date. This paper discusses and formalizes the synergy between Bayesian optimization and active learning as symbiotic adaptive sampling methodologies driven by common principles. In particular, we demonstrate this unified perspective through the formalization of the analogy between the Bayesian infill criteria and active learning criteria as driving principles of both the goal-driven procedures. To support our original perspective, we propose a general classification of adaptive sampling techniques to highlight similarities and differences between the vast families of adaptive sampling, active learning, and Bayesian optimization. Accordingly, the synergy is demonstrated mapping the Bayesian infill criteria with the active learning criteria, and is formalized for searches informed by both a single information source and multiple levels of fidelity. In addition, we provide guidelines to apply those learning criteria investigating the performance of different Bayesian schemes for a variety of benchmark problems to highlight benefits and limitations over mathematical properties that characterize real-world applications.

Longitudinal vibrations of a bar in memoriam to Professor Luiz Adauto Medeiros

This paper is concerned with the deduction of a mathematical model for the small longitudinal vibrations of a bar where one end is clamped and in the other end is glued a concentrated mass. The existence of global solutions of this model perturbed with a general nonlinear internal dissipation and with a general nonlinear boundary dissipation is derived. For a particular case, the exponential decay rate of the energy is obtained.

Research Paper Summarization and Recommendation

The exponential growth of academic literature has made it increasingly challenging for researchers to keep up with the latest developments in their respective fields. This paper presents an approach for summarizing and recommending research papers to help researchers efficiently manage and navigate the vast amount of academic literature. We propose using a transformer-based model for generating abstractive summaries of research papers and a cosine similarity-based recommendation system for suggesting similar papers to a given paper. The proposed approach is evaluated on a dataset of research papers, and the results show that it is effective in generating coherent and concise summaries and recommending relevant research papers. The findings of this study have important implications for researchers and practitioners in the field of natural language processing and information retrieval.

A Multi-Objective Optimization Method for Single Intersection Signals Considering Low Emissions

The exponential growth of urban centers has exacerbated the prevalence of traffic-related issues. This surge has amplified the conflict between the escalating need for travel among individuals and the constricted availability of road infrastructure. Consequently, the escalation of traffic accidents and the exacerbation of environmental pollution have emerged as increasingly pressing concerns. Urban road intersections, serving as pivotal junctures for vehicle convergence and dispersal, have remained a focal point for scholarly inquiry regarding enhanced operational efficacy and safety. Concurrently, vehicles navigating intersections are subject to external influences, such as pedestrian crossings and signal controls, causing frequent fluctuations in their operational dynamics. These fluctuations contribute to heightened exhaust emissions, exacerbating air pollution and posing health risks to pedestrians frequenting these intersections. A reasonable signal timing scheme can enable more vehicles to pass through the intersection safely and smoothly and reduce the pollutants generated by transportation. Therefore, optimizing signal timing schemes at intersections to alleviate traffic problems is a topic that needs to be studied urgently. In this paper, the emission model based on specific power is analyzed. Through an analysis of the correlation between specific power distribution intervals and the emission rates of individual pollutants, it has been observed that vehicle emission rates are at their lowest during idle speed, progressively increasing with rising vehicle speeds. Investigation into specific power distribution based on variables, such as vehicle type, frequency of stops, and varying delays, has led to the deduction that the peak specific power of vehicles at intersections consistently occurs within the (0, 1) interval. Furthermore, it has been established that high-saturation intersections exhibit higher peak specific power compared to low-saturation intersections.

The propagation behavior of reaction wave for Ni/Al clad particle composites under shock loading.

Prior studies indicate that the reaction wave can propagate from the impact surface, but the possibility and the influencing factors of the reaction wave formation are still unclear. This work investigates the propagation behavior of the shock-induced reaction wave for Ni/Al clad particle composites with varying stoichiometry (from 0.5 to 0.75 of the Ni mole fraction) through molecular dynamics simulations. It is found that the solid-state reaction processes with or without wave propagation strongly depend on the conjunction of stoichiometry and shock intensity. Within the cases of wave propagation, the calculated propagation velocity (in the range of 135-170m/s) increases linearly or exponentially with the Ni mole fraction. Furthermore, the thermodynamic criteria for the reaction wave formation, including Al melting at the collision surface and higher temperature gradient, are established by analysis of the shock-induced high-entropy layer. In addition, microstructural characterization reveals the intrinsic mechanisms of the propagation of the reaction wave and the formation of additional reaction wave, namely, the dissolution of Ni into Al and the coalescence of reaction zones. Apart from the propagation behavior, the initial stoichiometry influences the crystallization-dissolution of B2-NiAl during reaction processes, notably through an exponential growth relationship between maximum crystallinity and the Ni mole fraction. These findings may provide a physical basis for improving traditional reaction rate models to break through phenomenological understanding.

A systematic search, heuristic evaluation and analysis of dental trauma mobile applications.

Despite the exponential growth of mobile device applications in promoting awareness on traumatic dental injuries (TDIs), the overall validity of existing applications in content, engagement, usability, functionality, esthetics, and user perception remains largely uncertain. The primary aim was to systematically search and assess the quality, functionality, perceived impact and usability of available applications related to TDIs using the User Version of the Mobile Application Rating Scale (uMARS) and the Coventry, Aberdeen, and London-Revised (CA-LO-RE) scale. A heuristic evaluation was additionally performed. The secondary aim was to categorize existing mobile applications according to their target users, including general population, dentists, dental students, medical healthcare professionals, and researchers. A systematic search for dental trauma applications (free and paid) was conducted using the AppStore and Google Play store. Two reviewers independently performed data extraction and quality assessment for all the included applications using uMARS and CA-LO-RE. A user experience architect conducted a heuristic evaluation. Only 6 of the 3061 initially screened applications were eligible for qualitative and quantitative assessments. These applications' average star uMARS rating was 12.5 (standard deviation [SD] 2.6). The mean and SD for the uMARS objective items were as follows: A-engagement (mean 12.1 [SD 3.3]), B-functionality (mean 15.5 [SD 1.4]), C-esthetics (mean 9.5 [SD 2.5]), and D-information (mean 13 [SD 4.5]). The severity rating for the heuristic evaluation criteria was low to moderate for all the included applications, excluding tooth emergencies with a high severity score. Despite moderate quality, functionality, user perception, behavioural impact and usability scores, the inclusion of advanced features to "engagement" and "usability" in existing trauma based apps will enhance their purpose. A design-thinking approach coupled with interactive features is recommended for future mobile applications on TDIs.

Advances in lung transplantation: 60 years on.

Lung transplantation is a well-established treatment for advanced lung disease, improving survival and quality of life. Over the last 60 years all aspects of lung transplantation have evolved significantly and exponential growth in transplant volume. This has been particularly evident over the last decade with a substantial increase in lung transplant numbers as a result of innovations in donor utilization procurement, including the use donation after circulatory death and ex-vivo lung perfusion organs. Donor lungs have proved to be surprisingly robust, and therefore the donor pool is actually larger than previously thought. Parallel to this, lung transplant outcomes have continued to improve with improved acute management as well as microbiological and immunological insights and innovations. The management of lung transplant recipients continues to be complex and heavily dependent on a tertiary care multidisciplinary paradigm. Whilst long term outcomes continue to be limited by chronic lung allograft dysfunction improvements in diagnostics, mechanistic understanding and evolutions in treatment paradigms have all contributed to a median survival that in some centres approaches 10 years. As ongoing studies build on developing novel approaches to diagnosis and treatment of transplant complications and improvements in donor utilization more individuals will have the opportunity to benefit from lung transplantation. As has always been the case, early referral for transplant consideration is important to achieve best results.

Deferral of Treatment for Small Choroidal Melanoma and the Risk of Metastasis: An Investigation Using the Liverpool Uveal Melanoma Prognosticator Online (LUMPO).

We estimated metastatic-death risk when the treatment of small choroidal melanomas is deferred until growth is observed. In 24 patients with choroidal melanoma (median diameter 5.85 mm), the exponential growth rate estimated by a mixed-effects model was 4.3% per year. Using the Liverpool Uveal Melanoma Prognosticator Online v.3 (LUMPO3), we measured changes in 15-year metastatic and non-metastatic death risks according to whether the tumor is treated immediately or after observing growth 4 or 12 months later, considering age, sex, and metastasis predictors. In 40-year-old females with 10 mm, disomy 3 and monosomy 3 choroidal melanomas (prevalence 16%), the 15-year absolute risks of metastatic death are 4.2% and 76.6%, respectively, increasing after a 4-month delay by 0.0% and 0.2% and by 3.0% and 2.3% with tumor growth rates of 5.0% and 20.0%, respectively. With 12-month delays, these risks increase by 0.0% and 0.5% and by 1.0% and 7.1%, respectively. Increases in metastatic-death risk are less with smaller tumors and with a higher risk of non-metastatic death. Deferring treatment of choroidal melanomas until documentation of growth may delay iatrogenic visual loss by months or years and is associated with minimal increase in metastatic mortality, at least with small tumors with usual growth rates of up to 40% per year.

Exploring Machine Learning Techniques

Machine learning, emerging as a subfield of artificial intelligence in the 1950s, experienced limited progress initially. However, its resurgence in the 1990s led to significant advancements, propelled by the growing challenge of managing and analysing vast datasets. Machine learning's core premise lies in deriving optimal models from existing data to predict outcomes for new data, a process crucially tied to the expanding data landscape. Consequently, research in machine learning continues to evolve alongside the exponential growth of data. This study delves into the historical trajectory, methodologies, application domains, and ongoing research in machine learning, aiming to disseminate its knowledge and applications to contemporary researchers. Key themes include machine learning algorithms, artificial intelligence, and the implications of big data.

Machine learning approaches for predicting shielding effectiveness of carbon fiber-reinforced mortars

The exponential growth of communication technologies and the ubiquitous use of electronic devices raise concerns about unintended electromagnetic interference (EMI). Existing research exploring mortars incorporated with carbon fibers for EMI shielding primarily relies on empirical experiments investigating the interplay between shielding effectiveness (SE) and mixed design parameters. This research aims to establish predictive models for SE, focusing on the frequency of radiation and mortar constituents (water-to-cement ratio (W/C), fiber content, sand-to-cement ratio (S/C), and fiber aspect ratio). Employing a diverse array of machine learning algorithms, including stochastic gradient descent (SGD), gradient boosting, random forest, gene expression programming (GEP), AdaBoost, decision tree, K-nearest neighbors, and stepwise linear regression, this study aims to forecast the shielding efficacy of the mortar. Results demonstrate robust model performance, with R2 values and correlation coefficients surpassing 0.85 and 0.95, respectively, across training, testing, and validation datasets. Modeling errors, such as MSE, RMSE, MAE, and MAPE, remain within acceptable bounds for all models. The accuracy of predictions is evidenced by experimental-to-predicted SE ratios falling within the 0.5–1.5 range. Based on available data, parametric investigations conducted using the GEP-derived equation reveal a positive correlation between SE and S/C ratio, aspect ratio, fiber content, and radiation frequency, while indicating an inverse relationship with the W/C ratio. Moreover, employing Shapley additive explanation (SHAP)—a technique grounded in cooperative game theory—enhances the interpretability of SE. In summary, this study provides valuable insights into SE prediction for carbon fiber reinforced cement mortar, underscoring the efficacy of machine learning models in enhancing SE.

One-degree-of-freedom galloping instability of a 3D bluff body pendulum at high Reynolds number

The cross-flow swinging dynamics of a cube pendulum is studied experimentally in a flow at high Reynolds numbers (Re∼105) with a low free-stream turbulence intensity. A galloping instability is observed and results in the exponential growth of the swinging motion. The onset of galloping is found to be very sensitive to the static yaw angle of the cube. Despite the 3D geometry of the cube, flow mechanisms similar to the case of a square cylinder appear to govern the onset of the instability. A quasi-steady linear model of the motion is assessed to predict the stability of the pendulum.For the lowest reduced velocity investigated (U∗=18.5), unsteady phenomena arise during the saturation phase of the pendulum oscillations. From the analysis of the unsteady loads and the pressure distribution on the faces of the cube, an unsteady phase delay between the wake and the pendulum dynamics is identified. It produces an energy loss in the pendulum motion which favors its saturation.

Mapping pathway towards future HR transformation: prospects &amp; possibilities

The outbreak of pandemic caused disruptive changes for the people, planet and profit made sustainability the paradigm theme for policy making. The sustainability of planet and business depends upon the survivability of the mankind. The central theme of the paper focuses on the avenues to survive and weather such changes required for innovative HR transformations through HR functions, HR practices and HR professionals which will foster growth and development of the organization as well as the economy at large. The HR transformations will make the workforce skilled, adaptive, tech-friendly and competitive to remain buoyant in the VUCA world. The study has made a realistic attempt to showcase innovative HR transformational practices which acts as a panacea for exponential growth for twenty-first century organizations in the days to come.

A Kustaanheimo–Stiefel regularization of the elliptic restricted three-body problem and the detection of close encounters with fast Lyapunov indicators

We present the Kustaanheimo–Stiefel (KS) regularization of the elliptic restricted three-body problem (ER3BP) at the secondary body P2, and discuss its use to study a category of transits through its Hill’s sphere (fast close encounters). Starting from the Hamiltonian representation of the problem using the synodic rotating–pulsating reference frame and the true anomaly of P2 as independent variable, we perform the regularization at the secondary body analogous to the circular case by applying the classical KS transformation and the iso-energetic reduction in an extended 10-dimensional phase-space; this translates into an efficient algorithm that can be readily implemented to numerically integrate the equations of motion. Using such a regularized Hamiltonian we recover a definition of fast close encounters in the ER3BP for small values of the mass parameter μ (while we do not require a smallness condition on the eccentricity of the primaries), and we show that for these encounters the solutions of the variational equations are characterized by an exponential growth during the fast transits through the Hill’s sphere. Thus, for small μ, we justify the effectiveness of the regularized fast Lyapunov indicators (RFLIs) to detect orbits with multiple fast close encounters. Finally, we provide numerical demonstrations and show the benefits of the regularization in terms of the computational cost.