Southeast Missouri State University

Optical character recognition (OCR) is vital in digitizing printed data into a digital format, which can be conveniently used for various purposes. A significant amount of work has been done in OCR for well-resourced languages like English. However, languages like Urdu, spoken by a large community, face limitations in OCR due to a lack of resources and the complexity and diversity of handwritten scripts. One of the major hindrances in the development of OCR for low-resource languages like Urdu is the lack of extensive datasets. However, such datasets can be obtained from old handwritten books with reference text available online. This study presents a method to leverage this resource and automatically process Urdu handwritten poetry books with corresponding scripts available online. The images are segmented at the sentence level using automated neighborhood-connected component analysis, followed by manual adjustment. Corresponding Unicode text for each image are obtained by web scraping followed by text similarity analysis. A sample dataset collected comprises purely handwritten Urdu text images for Urdu poetry by Mirza Ghalib and Allama Iqbal, arguably the two most influential poets in Urdu. The dataset comprises 2888 images with Unicode transcriptions from poetry by Mirza Ghalib and Allama Iqbal. • The method automates OCR dataset creation by segmenting handwritten text images and scraping corresponding text from the web for alignment. • Handwritten images are segmented into sentences using a resource-efficient Neighborhood Component Analysis approach. • Possible text samples are scraped from the web, and the corresponding labels are aligned with images based on the minimum edit distance between the scraped text and the predictions by an OCR engine.

Modern modular service ecosystems rely on microservices for scalability and flexibility, yet performance constraints such as bottlenecks and resource contention remain critical challenges. This research presents a novel insight-driven framework for detecting and mitigating performance anomalies in microservices-based applications. By leveraging graph-based learning models and multi-source telemetry analysis, our approach effectively identifies independent, dependent, and cascading bottlenecks, facilitating timely intervention. We introduce and evaluate GAMMA, an advanced anomaly detection and localization model that integrates attention-based graph convolution networks with a multi-expert learning mechanism. Empirical evaluations on large-scale datasets demonstrate that GAMMA significantly outperforms existing methods in detecting and diagnosing multiple bottlenecks, achieving an F1 score of 0.92 for anomaly detection and 0.89 for bottleneck localization. The proposed framework enhances performance management in modular service architectures, ensuring resilient and adaptive cloud-native applications.

Vehicular edge computing enables vehicles to offload compute-intensive tasks to nearby edge servers integrated with roadside units to enhance performance and conserve battery power. In practical environments, vehicles often experience intrinsic task dependencies, stringent deadlines, and speeds and locations within highly dynamic networks, which is still a significant challenge in vehicular communications. Numerical optimization, game theory, and heuristic schemes struggle to meet these diverse and dynamic requirements in optimizing the computation offloading decisions. To this end, we propose a Proximal Policy Optimization (PPO)-based algorithm that optimally manages offloading decisions by employing a policy gradient approach with surrogate clipping to ensure stable and reliable updates. This is crucial in dynamic vehicular networks, where policy updates can significantly impact system performance. Furthermore, we use Generalized Advantage Estimation to further enhance the stability and efficiency by accurately estimating advantages over multiple steps. Our proposed PPO algorithm effectively balances exploration and exploitation and yields optimal offloading decisions while minimizing delays and dropped task ratio. Extensive experiments validate our approach’s efficacy, demonstrating substantial improvements in task completion rates and minimizing delays while meeting the requirements of intrinsic task dependencies and stringent deadlines in dynamic vehicular setups. For example, results show that the proposed method surpasses DQN by $13.85\%$, DDQN by $11.24\%$, DRQN by $19.48\%$, and DA-TODDPG by $8.81\%$ in terms of total delay, as well as achieving improvements of approximately $30\%$ over DQN, $17.65\%$ over DDQN, $9.68\%$ over DA-TODDPG, and $44\%$ over DRQN in terms of dropped task ratios at an RSU capability of $140 \, \text {GHz}$.

The performing arts industry places unique pressures on individuals, often leading to higher rates of mental health issues. Minimal information exists about how to create on-site intervention strategies for undergraduate performing art students. Athletic trainers at a dedicated university performing arts campus searched their electronic medical record (EMR) for reports of mental health-related issues in the dance, musical theater, and theater undergraduate students. The data analysis revealed anxiety and overstress conditions were primarily reported among dance and musical theater students. After communicating with stakeholders, the athletic trainers implemented a multi-faceted mental health intervention strategy for academic majors across the performing arts campus. The athletic trainers worked with the stakeholders and university counseling offices to destigmatize mental health conditions, reduce barriers, and implement mental health referrals and counseling across the campus. Reviewing internal data and listening to patient concerns enhanced mental health services in this undergraduate performing arts student population.

Objective: This study examined whether distinct mindfulness profiles explain physical health complaints common among college students. Participants: Participants were 535 college students. Methods: Participants completed the Five Facet Mindfulness and Physical Health Questionnaires. Latent profile analysis and the Bolck-Croon-Hagenaars method examined whether mindfulness profiles uniquely explained sleep disturbances, headaches, gastrointestinal problems, and respiratory illness symptoms. Results: Three mindfulness profiles were identified: Judgmentally Observing, Average Mindfulness, and High Mindfulness/Nonjudgmentally Aware. The High Mindfulness/Nonjudgmentally Aware profile students tended to report having the best physical health (i.e., fewer sleep disturbances, headaches, gastrointestinal problems, and respiratory illness symptoms). Conversely, students with the Judgmentally Observing profile reported the worst physical health outcomes (i.e., more sleep disturbances, headaches, gastrointestinal problems, and respiratory illness symptoms). Conclusions: By exploring the associations between mindfulness profiles and physical health outcomes, this study offers a deeper understanding of the impact targeting specific mindfulness skills can have for promoting college student health.

This letter proposes a novel filtering antenna with an alternated form of radiative and non‐radiative resonators. The coupling effects between radiative and non‐radiative resonators are thoroughly analyzed with proposed equivalent circuit models. Within its passband, the continuous phase changes between two radiators are discussed in detail. To obtain an in‐phase superposition between two radiators at central frequency, the original half‐wavelength non‐radiative resonator is prolonged to one wavelength. Meanwhile, the phase shifting between two radiators yields a continuous beam steering performance in the passband. With the help of the proposed circuit model, the conditions for obtaining the maximum radiation gain, the key factors affecting the antenna bandwidth, and the prediction of the scanning range are discussed. The proposed method can indeed benefit the entire design procedure quantitatively rather than qualitatively. Finally, a prototype is demonstrated and fabricated. The results show that a third‐order filtering antenna is successfully designed with a relatively high gain of 9.84 dBi and a wide frequency scanning range of −29° to 21°. The proposed antenna enjoys a rather simple structure without extra feeding networks.

This study aims to analyze and assess the human-induced pressures on the Marchica lagoon ecosystem in north-eastern Morocco, a designated Site of Biological and Ecological Interest (SBEI) known for its rich biodiversity. Employing a multifaceted approach, the research will: examine existing scientific literature on the lagoon's biodiversity and documented human activities, utilize spatial data (e.g., land use maps, pollution levels) to assess the spatial distribution of human activities and potential impacts, and potentially conduct field observations to gather data on the current state of the lagoon. However, human activities have progressively exerted ever-increasing pressure on this fragile ecosystem more than 70% of the pressure comes from urban and agricultural activities. This comprehensive analysis aims to identify and quantify human-induced pressures on the lagoon's health and biodiversity, ultimately highlighting the importance of SBEI preservation and informing future management strategies for the lagoon's long-term sustainability.

The Substitution box (S-box) is pivotal in block cipher cryptosystems, as it provides critical properties of non-linearity and confusion essential for robust security. Despite the development of various S-boxes categorized by their robustness-high, medium, or low-evaluating their effectiveness manually remains labor-intensive and inefficient. This paper addresses this issue by introducing a machine learning model that leverages parameters such as bit independence criterion, nonlinearity, strict avalanche criterion, linear approximation probability, and differential uniformity to assess S-box strength automatically. Additionally, a novel lightweight image encryption scheme is proposed tailored for IoT applications that integrate these robust S-boxes alongside four advanced cryptographic techniques: chaotic maps, discrete wavelet transform, substitution box, and dynamic random phase encoding. The proposed approach significantly enhances encryption security. The proposed scheme is evaluated using both statistical and visual analyses, evaluating parameters such as entropy, correlation, chi-square analysis, energy, computational complexity analysis, and resilience to various attacks, including noise and occlusion. The scheme demonstrates exceptional security metrics, with an entropy value of 7.9991, a correlation of 0.0001, and a chi-square value of 268. Additionally, the computational complexity of the scheme is 0.08 s, indicating efficient performance. Furthermore, a detailed comparison between the proposed encryption scheme and existing methods is performed to show that the proposed approach surpasses existing schemes in terms of security and computational efficiency.

In modern communication systems, the demand for efficient and secure data transmission has become increasingly important due to the rapid growth of wireless technologies and connected devices. This paper addresses the power minimization problem in secure multiple unmanned aerial vehicle (UAV)-aided multiuser multiple-input multiple-output (MU-MIMO) networks, where balancing secrecy and minimizing energy consumption poses a significant challenge. We formulate a non-convex problem with the secrecy target signal-to-interference-plus-noise ratio (SINR) constraints and UAV movement. We establish the optimality conditions, and the problem is then decoupled into the following sub-problems: power allocation, combiner optimization, precoder optimization, and UAV trajectory optimization, which are jointly optimized. We obtain the necessary and sufficient conditions for the first three sub-problems to show their equivalence to the established conditions. The power allocation, combiner, and precoder optimization are solved via the Jacobi recursion-based algorithm, and closed-form solutions are obtained for each. UAV trajectory is solved via successive convex approximation (SCA)-based algorithm. Aiming at providing real-time solution, we design a learning-based real-time power minimization (LRPM). LRPM consists of two parts; the first one is a recurrent convolutional neural network (RCNN) to optimize the UAV trajectory. The second part is designed using deep unfolding with the help of the closed-form solutions of the first three sub-problems. Simulation results show the efficiency of the proposed framework in minimizing power consumption and improving secrecy in real-time scenarios.

Named Data Networking (NDN) is one of the capable applicants for the future Internet architecture, where communications focus on content rather than providing content. NDN implements Information-Centric Networking (ICN) with its unique node structure and significant characteristics such as built-in mobility support, multicast support, and efficient content distribution to end-users. It has several key features, including inherent security, that protect the content rather than the communication channel. Despite the good features that NDN provides, it is nonetheless vulnerable to a variety of attacks, the most critical of them is the Content Poisoning Attack (CPA). In this survey, the existing solutions presented for the prevention of CPA in the NDN paradigm have been critically analyzed. Furthermore, we also compared the suggested schemes based on latency, communication overhead, and security. In addition, we have also shown the possibility of other possible NDN attacks on the suggested schemes. Finally, we adds some open research challanges.

Employees are the main driving force in organizations that undergo recruitment, promotion, and transfer. There are many employee management systems, but most of them are centralized. Centralization leads to problems such as lack of transparency, auditability issues, vulnerability, and a single point of failure. Blockchain technology addresses these concerns. Despite some blockchain-based HRM and employee management systems, none of the existing blockchain-based systems have addressed the problems of employee transfers. We propose a secure and transparent blockchain-based employee transfer (E-transfer) system to address this. The proposed system uses smart contracts for transfer management, recording outcomes on an unchangeable ledger. We conducted tests on Hyperledger Fabric for our proposed system. Our study aims to introduce an automated, auditable, and efficient transfer process. The findings of the study validate the effectiveness of the blockchain-based E-transfer system.

Utilizing context-based clustering offers a robust method for analyzing and categorizing unlabeled textual data. Textual descriptions of mobile applications often contain latent semantic meanings. Many of current studies still rely on, traditional embedding techniques, however, traditional embedding methods struggle to extract the contextual meanings from textual descriptions of mobile applications. As a result, these methods frequently fail to capture the full range of application functionalities. Moreover, mobile app marketplaces contain millions of applications and comparing a single application with all the available apps (pair wise similarity matching) on such a large scale is practically infeasible. Our study addresses these limitations by leveraging advanced transformer-based models to enhance context. These models produce contextual embeddings that accurately represent semantic meaning, enabling precise clustering of mobile applications offering similar features and services. We apply hierarchical clustering to these sophisticated contextual embeddings. Hierarchical clustering presents multiple perspectives of the app store at different levels of detail. Our evaluation on dataset shows the effectiveness of our approach, demonstrated by silhouette coefficient and Davies-Bouldin score results.

Optimal egg size theory predicts females must balance investment per offspring to maximize fitness based on environmental quality. In wetlands, environmental quality can be duration of water and predator presence. Ectotherms using habitats that dry or contain predators are likely under selection to optimize offspring production. We measured reproductive output of wood frogs (Rana sylvatica) in 30 wetlands in Subarctic Canada, where rapid climate changes are accelerating wetland drying. We predicted wetlands with short hydroperiods would have larger ova, smaller clutch sizes, and larger ovum‐to‐clutch‐sizes than wetlands with long hydroperiods or with fish predators. We found partial support for predictions with larger ova in habitats with short hydroperiods and no fish but no evidence of larger clutch sizes in wetlands with fish. Our study implicates changes to wetland hydroperiod as a source of plasticity affecting one aspect of reproductive effort (ovum size) in an ectotherm but not another (clutch size).

Purpose The purpose of the study was to understand diabetes self-management practices among African American individuals living with type 2 diabetes (T2D) in rural communities. Methods This qualitative descriptive study, undergirded by the theory of integration, purposively sampled African Americans (N = 34) diagnosed with T2D living in rural communities. Thematic analysis employed both a priori and inductive coding to identify salient themes. Results Participants’ mean age was 65.9 (SD 12.3) years, with an average T2D diagnosis duration of 14 (SE 11.2) years. Two major themes emerged: deciphering the cues and body sensing, which the participants used to monitor their glucose level using a personalized feedback loop. Those with longer diabetes duration demonstrated an ability to recognize hypoglycemic or hyperglycemic symptoms (deciphering the cues), informing their decision-making and self-management strategies (body sensing). Conclusions The decision-making involved in glycemic level management emerges as a complex developmental process influenced by disease trajectory and cultural and environmental factors. These findings may inform a conceptual framework to guide future inquiries and provide insights for primary care clinicians and diabetes educators to better understand the complexities of T2D management among African American individuals in rural settings.