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Hepatitis B is a global health burden and can persist for years, with nearly two billion infections worldwide, where its spread is influenced by environmental heterogeneity, host-pathogen interactions, and vaccination-induced immune variability. Proper understanding and developing models with a suitable framework is essential to accurately capture the complexity of the hepatitis B virus (HBV) and its transmission. In this work, we present a novel framework of a stochastic model and a forward neural network that combines neural networks and stochastic differential equations to analyze the dynamics of hepatitis B virus transmission, as it is important to capture the inherent uncertainty of disease spread in heterogeneous environments. We formulate the stochastic model with a saturated incidence rate, incorporating the long-term persistence of the disease following key characteristics of the disease transmission. The theoretical analysis of the model is proven to ensure the well-posedness and to determine the conditions for extinction and persistence of the disease. Further, a set of real data of hepatitis B reported cases will be used to produce stochastic simulations, and to train a feed-forward neural network (FFNN), while approximating the model dynamics more effectively. To evaluate the efficacy of the hybrid framework, we demonstrate its performance by the presenting mean squared error (MSE), absolute error (AE), and regression analysis showing strong agreement between the stochastic simulations and neural network predictions.

Maxillofacial trauma can be limited to superficial lacerations, abrasions, and facial bone fractures. The objective of this study was to determine the etiology, pattern, and predictors of soft tissue and bony injuries. This study was conducted in the department of maxillofacial surgery Lady Reading hospital Pakistan from Jan 2019 to June 2021. The nonprobability consecutive sampling technique was used for the selection of patients. All patients were assessed clinically and radiologically. The neurosensory examination was done for any altered sensation, anesthesia, or paresthesia. Motor nerve function was also assessed clinically. Data were analyzed using SPSS version 26. The etiology and pattern of maxillofacial trauma were stratified among age and genders using the chi-square test to see effect modifiers. Tests for regression analysis were also applied. P[less than or equal to]0.05 was considered significant. A total of 253 patients meeting inclusion criteria were included in this study. The majority of these patients were males, 223 (88.1%), while only 30 (11.9%) were females. The mean age for the group was 25.4 ± 12.6 years. RTAs were the most common causes of trauma (63.6%) followed by assault (15.0%), falls (11.5%), FAIs (5.9%), and sports (0.4%). The most vulnerable skeletal part was the mandible (22.9%) followed by Zygoma (7.1%), significantly predicted by RTAs. Soft tissue laceration analysis showed a high frequency of multiple lacerations (38%) significantly predicted by FAIs. The frequency of trigeminal nerve injury was 5.5% (14 patients) and that of the facial nerve was 1.6% (4 patients). The strongest association of nerve injury was with firearm injury (47%), followed by road traffic accidents and sports injuries. Road traffic accident was the most common etiological factor and mandible fracture was commonly predicted by RTA. Trigeminal nerve injuries were common, frequency of nerve injuries was highly associated with mandible fracture and was predicted by FAI.

In today’s digitally interconnected world, social media emerges as a powerful tool, offering different opportunities for modern businesses. Not only do organizations use social media for marketing purposes, but they also endeavor to influence consumer psychology and behavior. Although prior studies indicate social media’s efficacy in disseminating corporate social responsibility (CSR) communications, there remains a dearth of research addressing the impact of CSR-related messaging from banks on consumers’ brand advocacy behavior (CBAB). Our study seeks to bridge this gap, exploring the CSR-CBAB relationship within the banking sector of an emerging economy. Additionally, we investigate the roles of consumers’ emotions and values in mediating and moderating their CBAB, introducing two mediating factors, consumer happiness (HP) and admiration (BRAD), and moderating variable altruistic values (ATVL). Data collection involved an adapted questionnaire targeting banking consumers. The structural analysis revealed a positive correlation between a bank’s CSR-related social media communications and CBAB. HP and BRAD were identified as mediators in this relationship, while ATVL emerged as a moderator. These findings hold significant theoretical and practical implications. For instance, our research highlights the indispensable role of social media in effectively conveying CSR-related information to banking consumers, subsequently enhancing their advocacy intentions.

Sentiment analysis has emerged as one of the most popular natural language processing (NLP) tasks in recent years. A classic setting of the task mainly involves classifying the overall sentiment polarity of the inputs. However, it is based on the assumption that the sentiment expressed in a sentence is unified and consistent, which does not hold in the reality. As a fine-grained alternative of the task, analyzing the sentiment towards a specific target and aspect has drawn much attention from the community for its more practical assumption that sentiment is dependent on a particular set of aspects and entities. Recently, deep neural models have achieved great successes on sentiment analysis. As a functional simulation of the behavior of human brains and one of the most successful deep neural models for sequential data, long short-term memory (LSTM) networks are excellent in learning implicit knowledge from data. However, it is impossible for LSTM to acquire explicit knowledge such as commonsense facts from the training data for accomplishing their specific tasks. On the other hand, emerging knowledge bases have brought a variety of knowledge resources to our attention, and it has been acknowledged that incorporating the background knowledge is an important add-on for many NLP tasks. In this paper, we propose a knowledge-rich solution to targeted aspect-based sentiment analysis with a specific focus on leveraging commonsense knowledge in the deep neural sequential model. To explicitly model the inference of the dependent sentiment, we augment the LSTM with a stacked attention mechanism consisting of attention models for the target level and sentence level, respectively. In order to explicitly integrate the explicit knowledge with implicit knowledge, we propose an extension of LSTM, termed Sentic LSTM. The extended LSTM cell includes a separate output gate that interpolates the token-level memory and the concept-level input. In addition, we propose an extension of Sentic LSTM by creating a hybrid of the LSTM and a recurrent additive network that simulates sentic patterns. In this paper, we are mainly concerned with a joint task combining the target-dependent aspect detection and targeted aspect-based polarity classification. The performance of proposed methods on this joint task is evaluated on two benchmark datasets. The experiment shows that the combination of proposed attention architecture and knowledge-embedded LSTM could outperform state-of-the-art methods in two targeted aspect sentiment tasks. We present a knowledge-rich solution for the task of targeted aspect-based sentiment analysis. Our model can effectively incorporate the commonsense knowledge into the deep neural network and be trained in an end-to-end manner. We show that the two-step attentive neural architecture as well as the proposed Sentic LSTM and H-Sentic-LSTM can achieve an improved performance on resolving the aspect categories and sentiment polarity for a targeted entity in its context over state-of-the-art systems.

This article examines hepatitis B dynamics under distinct infection phases and multiple transmissions. We formulate the epidemic problem based on the characteristics of the disease. It is shown that the epidemiological model is mathematically and biologically meaningful of its well-posedness (positivity, boundedness, and biologically feasible region). The reproductive number is then calculated to find the equilibria and the stability analysis of the epidemic model is performed. A backward bifurcation is also investigated in the proposed epidemic problem. With the help of two control measures (treatment and vaccination), we develop control strategies to minimize the infected population (acute and chronic). To solve the proposed control problem, we utilize Pontryagin’s Maximum Principle. Some simulations are conducted to illustrate the investigation of the analytical work and the effect of control analysis.

In this paper, we present several new and generalized Hermite-Hadamard type inequalities for s-convex as well as s-concave functions via classical and Riemann-Liouville fractional integrals. As applications, we provide new error estimations for the trapezoidal formula.

The purpose of this study was to assess the frequency and characteristics of discharge medication discrepancies as identified by pharmacists during discharge medication reconciliation. We also attempted to identify the factors that influence the occurrence of drug discrepancies during medication reconciliation. From June to December 2019, a prospective study was performed at the cardiac center of King Fahad Medical City (KFMC), a tertiary care hospital in Riyadh. The information from discharge prescriptions as compared to the medication administration record (MAR), medication history in the cortex system, and the patient home medication list collected from the medication reconciliation form on admission. The study included all adult patients discharged from KFMC’s cardiac center. These participants comprised 776 patients, 64.6 percent of whom were men and 35.4 percent of whom were women. Medication discrepancies were encountered in 180 patients (23.2%) out of 776 patients. In regards to the number of discharged medications, 651(83.9%) patients had ≥ 5 medications. Around, 174 (73.4%) discrepancies were intentional, and 63 (26.6%) were unintentional discrepancies. The risk of unintentional medication discrepancy was increased with an increasing number of medications (P-value = 0.008). One out of every four cardiac patients discharged from our hospital had at least one medication discrepancy. The number of drugs taken and the number of discrepancies was found to be related. Necessary steps should be taken to reduce these discrepancies and improve the standard of care.

[This corrects the article DOI: 10.1371/journal.pone.0265042.].

Excessive use of agrochemicals for weed controlling infestation has serious agronomic and environmental repercussions associated. An appropriate amount of pesticide/ chemicals is essential for achieving the desired smart farming and precision agriculture (PA). In this regard, targeted weed control will be a critical component significantly helping in achieving the goal. A prerequisite for such control is a robust classification system that could accurately identify weed crops in a field. In this regard, Unmanned Aerial Vehicles (UAVs) can acquire high-resolution images providing detailed information for the distribution of weeds and offers a cost-efficient solution. Most of the established classification systems deploying UAV imagery are supervised, relying on image labels. However, this is a time-consuming and tedious task. In this study, the development of an optimized semi-supervised learning approach is proposed, offering a semi-supervised generative adversarial network for crops and weeds classification at early growth stage. The proposed algorithm consists of a generator that provides extra training data for the discriminator, which distinguishes weeds and crops using a small number of image labels. The proposed system was evaluated extensively on the Red Green Blue (RGB) images obtained by a quadcopter in two different croplands (pea and strawberry). The method achieved an average accuracy of 90% when 80% of training data was unlabeled. The proposed system was compared with several standards supervised learning classifiers and the results demonstrated that this technique could be applied for challenging tasks of crops and weeds classification, mainly when the labeled samples are small at less training time.