Abram Gracias

• University of Nevada, Reno

The rapid and accurate detection of infectious diseases remains a significant challenge, especially in low-resource settings where access to centralized laboratory facilities and trained personnel is limited. Paper-based microfluidic devices (µPADs) have emerged as a promising low-cost, portable, and user-friendly diagnostic platform capable of add...

This study explores the electrochemical characterization of voltage-gated sodium channels (VGSCs) using BioMEMS-integrated patch-clamp devices. The goal was to evaluate the advantages of this advanced technology in measuring VGSC activation, inactivation, and pharmacological modulation with high precision and throughput. The BioMEMS system enabled...

This study focuses on the development of a wearable microfluidic sweat sensor designed for real-time health monitoring. The sensor integrates a flexible microfluidic system, biosensors, and wireless data transmission to enable continuous, non-invasive tracking of key physiological biomarkers such as sodium, potassium, glucose, and lactate in human...

This study focuses on the design, fabrication, and in vivo testing of a flexible BioMEMS device developed for monitoring ion channel activity in neural tissues. Ion channels play a crucial role in neural communication, and real-time monitoring of their activity is essential for understanding brain functions and neurological disorders. Traditional r...

The development of microfabricated BioMEMS platforms for high-throughput automated patch-clamp analysis of ion channels has the potential to revolutionize electrophysiological research and drug discovery. This study introduces a novel platform that integrates microelectrode arrays and microfluidic channels to enable the parallel analysis of ion cha...

This study investigates the effects of sub-lethal doses of bio-nematicides on the reproductive and foraging behavior of cyst nematodes (Heterodera spp.), which are notorious plant parasites causing significant crop losses worldwide. The research was conducted under controlled laboratory and greenhouse conditions using selected bio-nematicides, incl...

Root-knot nematodes (RKNs) and soil-borne fungal pathogens are major threats to vegetable crops worldwide, often leading to significant yield losses. This study investigates the synergistic interaction between RKNs and fungal pathogens, focusing on their combined effects on plant health, root damage, disease severity, and crop yield. Greenhouse exp...

Large language models (LLMs) represent a significant milestone in the evolution of artificial intelligence, combining advanced neural architectures with vast datasets to achieve remarkable capabilities in natural language understanding and generation. This paper examines the sociotechnical evolution of LLMs, tracing their development from early arc...

Continual learning (CL) addresses a fundamental challenge in artificial intelligence: enabling neural networks to learn sequentially from non-stationary data distributions without catastrophically forgetting previously acquired knowledge. This capability mirrors human lifelong learning and is critical for real-world AI systems that must adapt to ev...

Graph Neural Networks (GNNs) have emerged as a powerful framework for machine learning on non-Euclidean data structures, enabling effective modeling of relational information that traditional deep learning architectures cannot process natively. By generalizing convolutional operations to irregular graph domains, GNNs provide a principled approach t...

AI alignment represents one of the most critical challenges in artificial intelligence research, focusing on developing systems whose objectives and behaviors remain robustly aligned with human values and intentions. As AI capabilities advance toward artificial general intelligence (AGI), the alignment problem grows increasingly urgent, addressing...

The rapid advancement of artificial intelligence (AI) and machine learning (ML) has driven organizations to adopt cloud-native approaches for developing, deploying, and scaling intelligent applications. Cloud-native AI development leverages the power of AWS, Azure, and Google Cloud Platform (GCP) to build robust, scalable, and cost-efficient ML mod...

Serverless AI architectures are transforming the deployment and scalability of machine learning (ML) pipelines by leveraging event-driven cloud computing platforms such as AWS Lambda and Azure Functions. These architectures eliminate the need for traditional server management, enabling cost-efficient, scalable, and fault-tolerant ML workflows. This...

Distributed machine learning (DML) in the cloud enables large-scale model training by leveraging GPU and TPU clusters, significantly reducing computation time and enhancing scalability. This paper explores the implementation of DML using cloud-based infrastructure and parallel computing frameworks such as Horovod, TensorFlow, and PyTorch Distribute...

MLOps (Machine Learning Operations) is a critical practice for automating and streamlining the deployment, monitoring, and management of machine learning models in production. This paper explores the implementation of end-to-end CI/CD (Continuous Integration and Continuous Deployment) pipelines for ML models on cloud platforms such as AWS, Azure, a...

AutoML (Automated Machine Learning) has emerged as a transformative approach to simplifying and accelerating the development of machine learning models. This paper explores cloud-based AutoML solutions, focusing on Google Vertex AI and Amazon SageMaker. We examine their capabilities in automating key ML processes such as data preprocessing, feature...

This study explores the relationship between entrepreneurial marketing strategies and firm performance within Oman's logistics sector, emphasizing the contingent role of market turbulence. As the logistics industry faces rapid changes driven by technological advancements and shifting consumer demands, firms must adopt innovative marketing strategie...

This study explores the relationship between entrepreneurial marketing strategies and firm performance within Oman's logistics sector, emphasizing the contingent role of market turbulence. As the logistics industry faces rapid changes driven by technological advancements and shifting consumer demands, firms must adopt innovative marketing strategie...

This research investigates the interplay between logistics integration, entrepreneurial marketing, and economic growth in the Sultanate of Oman, with a specific focus on the role of market turbulence. As Oman seeks to diversify its economy and enhance its logistics capabilities, understanding how these elements interact becomes crucial. The study e...

This study explores the relationship between entrepreneurial marketing strategies and firm performance within Oman's logistics sector, emphasizing the contingent role of market turbulence. As the logistics industry faces rapid changes driven by technological advancements and shifting consumer demands, firms must adopt innovative marketing strategie...

In the realm of distributed real-time operating systems (DRTOS), ensuring fault detection and recovery is critical for maintaining system reliability and performance. The integration of artificial intelligence (AI) techniques has emerged as a transformative approach to address these challenges. This paper explores the application of AI-driven metho...

The increasing complexity of real-time operating systems (RTOS) and the need for efficient resource utilization demand innovative solutions to meet stringent timing constraints. Artificial Intelligence (AI) and Machine Learning (ML) offer promising approaches to enhance predictive scheduling, enabling RTOS to handle dynamic workloads with improved...

Edge AI integrates artificial intelligence (AI) capabilities with edge computing to process data locally on devices, minimizing latency, improving efficiency, and ensuring data privacy. In the domain of image and video processing, Edge AI has revolutionized applications by enabling real-time analytics in resource-constrained environments. This abst...

The impact of legislative changes on construction law and commercial transactions has significant implications for the construction industry, business practices, and legal frameworks. Over time, evolving legislative reforms aimed at improving safety, environmental sustainability, labor conditions, and contractual clarity have reshaped the landscape...

The increasing environmental concerns associated with plastic waste, particularly in food packaging, have prompted the need for innovative solutions in recycling and upcycling processes. This paper explores the chemical engineering approaches to enhance the recyclability of food packaging materials, particularly polymers. Traditional recycling meth...

Real-time activity recognition is a pivotal component in applications ranging from healthcare monitoring to smart home automation. This study evaluates the performance of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models for real-time activity recognition using diverse sensor data. Each model was trained...

Enzyme-polymer interactions play a critical role in food processing by influencing reaction rates, product quality, and overall process efficiency. The interaction between enzymes and various polymers, such as starch, proteins, and cellulose, can significantly alter enzyme activity, stability, and specificity during food transformations like fermen...

The increasing demand for food safety, quality, and longevity has prompted significant advancements in food preservation techniques. Among these innovations, nano-polymers have emerged as a promising solution to enhance freshness and extend shelf life. Nano-polymers are materials engineered at the nanoscale, which exhibit unique properties that are...

Controlled-release systems (CRS) are advanced delivery systems designed to regulate the release of active compounds over time. In the context of nutrient and preservative delivery, polymer-based controlled-release systems offer an effective approach to enhance the stability, bioavailability, and therapeutic or preservative effects of the compounds....

The numerical approximation of eigenvalues in large matrices is a critical area of research with applications spanning quantum mechanics, structural analysis, and machine learning. This study develops and evaluates efficient computational methods for eigenvalue approximation in large-scale systems, addressing challenges posed by the size and comple...

Wavelet theory has emerged as a powerful mathematical tool for analyzing and processing signals across various domains, providing significant advantages over traditional methods. This study explores wavelet-based techniques in signal processing, focusing on three critical applications: compression, noise reduction, and feature extraction. The wavel...

Control theory plays a pivotal role in ensuring the stable and efficient operation of robots and autonomous systems. This study focuses on the application of fundamental control algorithms, including Proportional-Integral-Derivative (PID) control and optimal control techniques, to enhance the performance of robots and autonomous vehicles. PID contr...

Mathematical modeling plays a crucial role in understanding the dynamics of disease transmission and evaluating control strategies in epidemiology. This study focuses on the development of models for disease spread, incorporating critical factors such as vaccination, mutation, and treatment effects. We present a set of differential equations that d...

The increasing integration of connected vehicles into the Internet of Things (IoT) ecosystem has raised concerns about the security of automotive software, with malware posing significant risks to the safety and functionality of these systems. This paper explores the application of deep learning models to identify and neutralize malware targeting s...

The integration of Artificial Intelligence (AI) in autonomous vehicles has brought significant advancements in safety and decision-making capabilities. However, the increasing complexity of AI-driven systems, particularly in cybersecurity, raises concerns regarding their interpretability and transparency, which are crucial for real-time critical re...

As the integration of sensor fusion systems, including LiDAR and camera inputs, becomes more prevalent in critical applications such as autonomous vehicles and industrial monitoring, the vulnerability to cyber-attacks targeting these systems increases. This paper explores the application of deep learning techniques to detect and respond to cyber-at...

In recent years, the rise of cyber-attacks has posed significant threats to the resilience of critical infrastructures. To mitigate these risks, there is a growing need for advanced methods to simulate and predict the impact of cyber threats in real-time. This study presents the development of a digital twin model designed to simulate cyber-attacks...

The dual role of auditors providing both assurance and advisory services raises significant ethical concerns and potential conflicts of interest that can undermine the credibility and independence of the auditing profession. This paper examines the ethical considerations associated with auditors offering advisory services to their clients, focusing...

The increasing involvement of auditors in advisory roles has raised concerns regarding its potential impact on audit quality and auditor independence. This study explores the dynamic relationship between auditors' advisory services and their primary auditing responsibilities, focusing on the dual pressures of maintaining high audit quality and ensu...

Fast Healthcare Interoperability Resources (FHIR) is emerging as a transformative standard for health data exchange, enabling seamless communication and integration across diverse healthcare systems. Developed by HL7, FHIR combines the best features of previous standards with modern web technologies, such as RESTful APIs, to facilitate real-time ac...

Fast Healthcare Interoperability Resources (FHIR) is emerging as a transformative standard for health data exchange, enabling seamless communication and integration across diverse healthcare systems. Developed by HL7, FHIR combines the best features of previous standards with modern web technologies, such as RESTful APIs, to facilitate real-time ac...

Assisted reproductive technologies (ART) offer hope to individuals facing infertility, yet the psychological impact of these treatments on women is a growing concern. This study explores the mental health outcomes in women undergoing ART, including in-vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI), and other related procedures....

Cervical cancer remains a significant public health concern, particularly in low-income populations, where access to preventive measures like screening is often limited. This study explores the barriers to cervical cancer screening in these communities, focusing on socioeconomic, cultural, and healthcare system challenges. Key barriers identified i...

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and a leading cause of dementia worldwide. Understanding the interplay between genetic predispositions and environmental factors is critical for unraveling its complex etiology. This study explores the multifactorial nature of AD, focusing on key genetic risk factors such as mutat...

Stroke remains a leading cause of mortality and disability worldwide, with distinct gender differences influencing risk factors, incidence, outcomes, and recovery trajectories. This study examines the biological, behavioral, and societal factors contributing to these disparities. Women face unique risks due to hormonal fluctuations, pregnancy, and...

Pharmacogenomics, the study of how genes influence an individual's response to drugs, is a crucial aspect of personalized medicine, offering the potential to tailor drug treatments to maximize efficacy and minimize adverse effects. This paper evaluates the integration of pharmacogenomics in clinical settings, examining its benefits in improving dru...

Polypharmacy, defined as the use of multiple medications by a patient, is a common issue among elderly individuals due to the increased prevalence of chronic diseases in this population. While medications play a crucial role in managing health conditions, polypharmacy can lead to numerous risks, including adverse drug reactions, drug interactions,...

Antibiotic resistance (ABR) represents a significant and growing global health threat, undermining the effectiveness of essential antimicrobial therapies. This phenomenon occurs when bacteria evolve mechanisms to resist the effects of drugs that once killed or inhibited their growth. Key mechanisms of resistance include the alteration of drug targe...

The opioid crisis has become a significant public health concern, with an increasing number of individuals suffering from opioid addiction, overdose, and death linked to the misuse of prescription opioids. A primary contributor to the crisis is the widespread use of opioids for the management of chronic pain. This paper explores alternative pain ma...

The development of novel anticancer agents has revolutionized cancer treatment, yet the complexities of their pharmacokinetics (PK) and pharmacodynamics (PD) remain a critical challenge in clinical trials. PK studies focus on the absorption, distribution, metabolism, and excretion (ADME) of these agents, while PD assessments examine their biochemic...

The rise of social media platforms has revolutionized the way health information is disseminated, but it has also led to the widespread distribution of health misinformation. This phenomenon poses significant challenges for public health policy, as misinformation can influence individual behaviors, shape public perceptions, and undermine trust in h...

Access to quality healthcare remains a significant challenge for rural populations worldwide, often due to geographic isolation, limited medical infrastructure, and shortages of healthcare professionals. Telemedicine, leveraging advancements in communication and digital technology, offers a promising solution to bridge these gaps. This study explor...

This study investigates the relationship between cybersecurity practices and trust in cryptocurrency platforms, an area of growing importance due to the rapid adoption of cryptocurrencies and the increasing number of cyber threats targeting digital assets. As trust is a crucial factor in the success and sustainability of cryptocurrency platforms, u...

This study investigates the influence of financial literacy on consumers' intentions to purchase life insurance, employing a structural equation modeling (SEM) approach to reveal complex relationships among financial knowledge, attitudes, and purchasing behaviors. With life insurance playing a critical role in financial security and risk management...

Climate change poses significant risks to organizations across various sectors, impacting their operational, financial, and reputational stability. The growing frequency and intensity of extreme weather events, shifting regulatory landscapes, and evolving consumer expectations compel organizations to reassess traditional risk management frameworks....

The rapid acceleration of digital transformation has made cybersecurity, AI implementation, and data privacy central concerns for organizations across industries. As businesses adopt advanced digital technologies, they face a complex array of risks that require proactive and strategic management to ensure both security and regulatory compliance. Cy...

The 2008 financial crisis exposed significant vulnerabilities in the global financial system, prompting a reevaluation of risk management practices within financial institutions. This paper examines the evolving approaches to managing credit, market, and operational risks in the post-crisis era. It discusses how institutions have implemented more s...

Organizations face a dynamic environment where balancing risk and opportunity is crucial for achieving strategic objectives. The establishment and maintenance of a risk appetite framework play a pivotal role in aligning risk-taking with an organization's goals. This framework helps in identifying, assessing, and managing risks that are acceptable w...

In the contemporary business landscape, the integration of sustainability into corporate strategies has become imperative, with green leadership emerging as a pivotal driver of Corporate Social Responsibility (CSR). This paper explores the symbiotic relationship between green leadership, effective communication, and the cultivation of a sustainabil...

In the context of increasing environmental concerns and the urgent need for sustainable management practices, green communication has emerged as a critical tool for promoting eco-friendly initiatives and engaging stakeholders in sustainability efforts. This paper explores the challenges and barriers that hinder effective green communication in orga...

In an era marked by unprecedented environmental challenges, leadership in policy advocacy is crucial to driving sustainable change. Green leadership refers to the influence exerted by leaders committed to environmental sustainability in shaping public policy, rallying support, and driving action toward ecological preservation. This paper explores t...

In the era of digital transformation, network security has become a critical concern for organizations worldwide. Traditional intrusion detection systems (IDS) often struggle to keep pace with evolving threats, leading to an increased focus on machine learning (ML) techniques to enhance detection capabilities. This paper explores the development an...

Generative Adversarial Networks (GANs) have emerged as a powerful tool for data augmentation in various machine learning tasks, particularly in domains with limited labeled data. GANs consist of two neural networks, the generator and the discriminator, that compete against each other to improve the quality of generated data. This paper explores the...

In an era of increasing cyber threats and data breaches, the demand for secure authentication mechanisms has never been more critical. This paper explores the integration of biometric data and artificial intelligence (AI) in the development of robust authentication protocols. Biometric identifiers, such as fingerprints, facial recognition, and iris...

As artificial intelligence (AI) increasingly permeates business analytics, it raises significant ethical considerations that merit urgent attention. This paper explores the multifaceted ethical implications of employing AI in business decision-making processes, particularly concerning data privacy, bias, accountability, and transparency. The integr...

Sentiment analysis has emerged as a vital application of Natural Language Processing (NLP), enabling the extraction of subjective information from textual data. This study conducts a comparative analysis of various machine learning algorithms employed in sentiment analysis, including traditional models such as Naïve Bayes, Support Vector Machines (...

Precision medicine aims to tailor medical treatment to individual characteristics, such as genetic makeup, environmental factors, and lifestyle. Achieving this goal necessitates the integration and analysis of diverse data sources, including genomic, proteomic, clinical, and environmental data. This abstract explores the methodologies and technolog...

The integration of Machine Learning (ML) and Artificial Intelligence (AI) into precision medicine represents a transformative shift in healthcare, promising tailored and more effective treatments for individual patients. This paper explores the synergistic role of ML and AI in advancing precision medicine, focusing on their applications in diagnost...

Genomic sequencing technologies have revolutionized the field of precision medicine by enabling a deeper understanding of genetic variations and their implications for individual health. Recent advancements in high-throughput sequencing, such as Next-Generation Sequencing (NGS) and Single-Molecule Real-Time (SMRT) sequencing, have significantly inc...

Pharmacogenomics, the study of how genetic variations influence individual responses to medications, is a crucial component of precision medicine. This field aims to optimize drug efficacy and minimize adverse effects by tailoring treatments based on a person's genetic profile. Advances in genomic technologies and bioinformatics have significantly...

Precision medicine and population health represent two complementary approaches to improving health outcomes. Precision medicine tailors medical treatment to individual characteristics, such as genetic, environmental, and lifestyle factors, to optimize therapeutic efficacy and minimize adverse effects. In contrast, population health focuses on impr...

Precision medicine represents a transformative approach in the management and treatment of cardiovascular disease (CVD), tailored to the individual characteristics of each patient. This paper investigates the applications of precision medicine within the realm of CVD, focusing on its integration into diagnosis, treatment, and prevention strategies....

Precision medicine holds transformative potential for the diagnosis and management of rare diseases, offering a tailored approach that moves beyond the limitations of conventional diagnostic methods. Unlike traditional models that rely on generalized treatment protocols, precision medicine leverages genetic, environmental, and lifestyle information...

Precision medicine, an innovative approach that tailors medical treatments to individual genetic, environmental, and lifestyle factors, holds great promise for revolutionizing healthcare. By enabling more accurate diagnoses and targeted therapies, it has the potential to improve patient outcomes and reduce unnecessary treatments. However, alongside...

Precision oncology represents a transformative approach in cancer treatment, focusing on tailoring therapeutic strategies to the individual characteristics of each patient's cancer. Recent advancements in genomics, bioinformatics, and molecular profiling have significantly enhanced our ability to identify specific genetic mutations and biomarkers a...

Precision medicine, with its promise of personalized healthcare solutions based on genetic, environmental, and lifestyle factors, has the potential to revolutionize medical treatment. By targeting interventions more precisely to individuals, it holds the potential to improve patient outcomes and reduce the trial-and-error approach traditionally see...

The transition to renewable energy sources is critical for sustainable development, particularly in rural regions with limited access to electricity. Solar-powered microgrids offer a promising solution for rural electrification by providing reliable, clean energy that can enhance economic opportunities and improve quality of life. This paper presen...

In the evolving landscape of digital marketing, cross-channel attribution modeling plays a crucial role in understanding and optimizing the customer journey across various touchpoints. As consumers interact with brands through multiple channels—such as social media, email, search engines, and display ads—accurately attributing conversions to the ri...

Perovskite-silicon tandem solar cells represent a significant advancement in photovoltaic technology, aiming to overcome the efficiency limitations of traditional silicon-based solar cells. This paper explores recent developments in the fabrication and optimization of these tandem cells, focusing on improving both efficiency and long-term stability...

The integration of solar energy systems with agricultural practices, known as agrivoltaic systems, presents a promising solution for advancing sustainable food and energy production. This approach involves installing photovoltaic panels on agricultural lands, which allows for simultaneous crop cultivation and energy generation. Agrivoltaic systems...

Edge Artificial Intelligence (Edge AI) is transforming the deployment of machine learning (ML) models by enabling real-time processing on resource-constrained devices such as Internet of Things (IoT) sensors, mobile devices, and embedded systems. Unlike traditional cloud-based AI, Edge AI reduces latency, enhances data privacy, and minimizes bandwi...

Generative Diffusion Models have emerged as a powerful alternative to Generative Adversarial Networks (GANs) for high-quality data synthesis, offering improved stability, diversity, and realism. Unlike GANs, which rely on adversarial training, diffusion models generate data through an iterative denoising process, progressively refining noisy inputs...

Neuro-symbolic AI represents a transformative paradigm that integrates the pattern recognition strengths of neural networks with the interpretability and reasoning capabilities of symbolic systems. This hybrid approach addresses fundamental limitations of pure connectionist or symbolic methods by combining subsymbolic learning with structured knowl...

Federated Learning (FL) has emerged as a privacy-preserving machine learning paradigm that enables collaborative model training across decentralized devices or institutions without direct data sharing. By keeping raw data localized and only exchanging model updates, FL addresses critical challenges of data privacy, security, and regulatory complian...

Artificial intelligence is revolutionizing scientific discovery by augmenting human researchers with powerful tools for hypothesis generation, experimental design, and data analysis across STEM disciplines. Modern AI systems are now capable of identifying hidden patterns in complex scientific data, predicting material properties, and even autonomou...

The integration of Artificial Intelligence (AI) into the workplace is reshaping the dynamics of the Information Technology (IT) workforce. This paper explores the role of AI in fostering a resilient IT workforce capable of adapting to rapidly changing technological demands. It examines various strategies that leverage AI to promote sustainable work...