Baltic Journal of Multidisciplinary Research
Baltic Journal of Multidisciplinary Research (BJMR) is an international, peer-reviewed, open-access academic journal dedicated to publishing high-quality research across a broad spectrum of disciplines. As a platform for scholarly dialogue, BJMR seeks to foster interdisciplinary connections and promote innovative research that addresses complex issues from multiple perspectives. The journal welcomes original research articles, review papers, case studies, and theoretical discussions from diverse fields, including but not limited to: Natural and Applied Sciences Social Sciences and Humanities Engineering and Technology Economics, Business, and Management Education and Psychology Arts, Media, and Cultural Studies Health and Life Sciences Aims and Scope BJMR aims to: Encourage interdisciplinary and cross-disciplinary research that addresses contemporary global and regional challenges. Provide a platform for scholars, researchers, and practitioners to share insights, methodologies, and findings. Foster international academic collaboration, with a special emphasis on research relevant to the Baltic region while welcoming contributions from around the world. Key Features Open Access: All articles are freely available to readers worldwide, ensuring broad dissemination and impact. Rigorous Peer Review: Submissions undergo a double-blind peer review process to ensure high academic standards. Global Reach with Regional Insights: While international in scope, BJMR offers a special platform for issues pertinent to the Baltic countries and surrounding regions. Audience BJMR is intended for researchers, academics, practitioners, policymakers, and students seeking to engage with multidisciplinary research and broaden their understanding of complex issues through diverse disciplinary lenses. Publication Frequency BJMR is published quarterly and accepts submissions on a rolling basis.
Emergent Phenomenology of Artificial Agents Driven by Deep Neural Abstraction and Self-Evolving Learning Dynamics
Section: Articles Keywords: Emergent Phenomenology, Artificial Agents, Deep Neural Abstraction, Self-Evolving Learning, Adaptive Cognition, Meta-Learning, Reflexive Intelligence, Autonomous Systems Artificial intelligence has evolved beyond task-specific computation into systems capable of emergent cognition and adaptive behavior. This paper explores the phenomenology of artificial agents driven by deep neural abstraction and self-evolving learning dynamics. Neural abstraction allows agents to form multi-layered representations of their environment, encoding complex patterns of perception, inference, and decision-making. Self-evolving learning dynamics encompassing meta-learning, recursive adaptation, and autonomous model restructuring facilitate continuous refinement of both knowledge and operational strategies. The synergy of abstraction and self-evolution produces emergent agentic behavior characterized by reflexivity, contextual understanding, and adaptive intentionality. By analyzing these processes through a phenomenological lens, the paper investigates how artificial systems cultivate internal experiential frameworks that guide action, adaptation, and knowledge synthesis. This approach positions artificial intelligence as a dynamic, evolving ontology rather than a static computational tool, providing insights into the mechanisms that underlie emergent intelligence and self-directed agency.
Distributed Deep Learning Architectures for Cross-Domain Agentic Intelligence in LangChain–LangGraph Workload Ecosystems
Section: Articles Keywords: Distributed Deep Learning, Agentic Intelligence, LangChain, LangGraph, Cross-Domain Learning, Federated Optimization, Multi-Agent Systems, Knowledge Graphs, Contextual Reasoning, Neural Orchestration The convergence of LangChain and LangGraph ecosystems has enabled a new class of distributed deep learning architectures designed to support cross-domain agentic intelligence. These architectures integrate neural representation learning, graph-structured reasoning, and workflow-level orchestration to facilitate adaptive collaboration among intelligent agents across heterogeneous domains. By combining distributed deep learning with dynamic knowledge graphs, agents achieve contextual understanding, multi-hop reasoning, and self-evolving task coordination. The system leverages modular architectures, federated optimization, and inter-agent communication protocols to enable knowledge transfer and semantic alignment across tasks and domains. This paper investigates the principles, design patterns, and emergent behaviors of distributed deep learning architectures within LangChain–LangGraph workload ecosystems, highlighting their transformative potential in developing autonomous, scalable, and contextually aware AI frameworks for multi-agent intelligence.
Cooperative Deep Intelligence Networks for Multi-Agent Coordination and Autonomous Optimization of AI Workload Trajectories
Section: Articles Keywords: Cooperative deep intelligence, multi-agent coordination, autonomous workload optimization, deep representation learning, dynamic AI trajectories, distributed intelligence, cognitive routing, self-organizing AI systems Cooperative deep intelligence networks represent a new computational paradigm in which distributed neural agents collaborate through shared representations, synchronized reasoning loops, and adaptive task-exchange mechanisms to achieve autonomous optimization across large-scale AI workload environments. Traditional workload orchestration frameworks rely on static heuristics, centralized coordination, or rule-bound scheduling, all of which limit adaptability under dynamic, heterogeneous, and latency-sensitive execution paths. In contrast, cooperative deep intelligence networks embed deep learning architectures into multi-agent ecosystems, enabling nodes to jointly analyze context, redistribute responsibilities, anticipate congestion, and reconfigure processing trajectories in real time. These architectures unify representation learning, cooperative inference, and evolutionary optimization to yield intelligent workload trajectories that evolve based on system feedback, environmental changes, and agent-level decisions. The emergent coordination patterns produced by these networks allow multi-agent systems to maintain stability, resilience, and efficiency while executing multifaceted pipelines with fluctuating complexity. By integrating reflective communication protocols, predictive task flow estimation, and autonomous decision-making, cooperative deep intelligence networks enable AI infrastructures to self-organize, negotiate tasks, and optimize resource pathways without human supervision. This paper analyzes the theoretical foundations, core mechanisms, architectural layers, and emergent properties of cooperative deep intelligence networks, illustrating how they redefine multi-agent cognition, distributed intelligence, and autonomous workload optimization.
Cognitive Graph Intelligence through Deep Representation Learning and Self-Organizing Semantic Connectivity in LangGraph Systems
Section: Articles Keywords: Cognitive Graph Intelligence, Deep Representation Learning, LangGraph, Semantic Connectivity, Self-Organizing Systems, Graph Neural Networks, Distributed Cognition, Meta-Reasoning. The evolution of intelligent systems has increasingly converged on the integration of deep representation learning and graph-based reasoning as foundational elements of cognitive computation. LangGraph frameworks introduce a paradigm in which language-based agents interact through structured, self-organizing graph topologies that reflect both semantic relationships and cognitive dependencies. Within this ecosystem, deep representation learning serves as the neural substrate that enables agents to abstract, infer, and adapt across dynamically evolving data contexts. By encoding semantic entities as graph embeddings and enabling self-organizing link formation, LangGraph transcends traditional symbolic systems and static deep learning models. The result is a hybrid intelligence model where knowledge is not merely stored or retrieved but actively synthesized through emergent semantic connectivity. This paper explores the mechanisms, architectures, and emergent behaviors underlying cognitive graph intelligence—examining how distributed representation learning interacts with adaptive graph structures to yield scalable, explainable, and context-aware reasoning. It further investigates the cognitive dynamics that enable autonomous coordination, recursive abstraction, and self-optimization within LangGraph ecosystems, positioning such frameworks as foundational to the future of self-evolving artificial cognition.
Intellectual Property Governance in the Age of Algorithms: Balancing Creativity, Control, and Accountability
Section: Articles Keywords: Intellectual Property Governance, Artificial Intelligence, Algorithmic Creativity, Legal Accountability, Digital Innovation, Ethical Regulation, Autonomous Systems The proliferation of Artificial Intelligence (AI) and algorithmic systems has redefined the landscape of creativity, authorship, and innovation. As algorithms increasingly generate artistic works, inventions, and designs autonomously, traditional frameworks of intellectual property (IP) governance face unprecedented strain. The concept of ownership, once tied to human authorship and moral rights, now confronts challenges posed by autonomous systems capable of independent creative outputs. This paper examines the evolution of IP governance in the algorithmic age, analyzing tensions between innovation and regulation, autonomy and accountability, and openness and control. It evaluates current international legal responses, identifies critical governance gaps, and proposes a multidimensional framework that integrates ethical oversight, algorithmic transparency, and adaptive regulation. By reimagining IP governance as a dynamic ecosystem rather than a rigid legal structure, this paper underscores the need for balance between fostering creativity and ensuring accountability in an era dominated by algorithmic intelligence.
Zero Trust Architectures for Military and Government Cybersecurity
Section: Articles Keywords: Zero Trust Architecture, cybersecurity, military systems, government security, least-privilege access, advanced persistent threats, identity verification, policy enforcement, cyber resilience, national security The rising sophistication of cyberattacks against government institutions and military systems necessitates a shift from traditional perimeter-based security models to Zero Trust Architectures (ZTA). Unlike conventional models that assume trusted internal networks, Zero Trust enforces strict verification, continuous monitoring, and least-privilege access at every stage. This paper examines the critical role of Zero Trust in military and governmental cybersecurity, where national security, classified intelligence, and mission-critical operations are at stake. It explores the threat landscape targeting these domains, evaluates the principles and technological enablers of Zero Trust, and highlights its application in securing sensitive infrastructures. The discussion underscores how Zero Trust not only enhances defense against advanced persistent threats and insider risks but also aligns with policy, compliance, and interoperability demands across agencies. By adopting ZTA, military and government organizations can achieve greater resilience, adaptability, and assurance in safeguarding digital sovereignty.
The Role of Generative AI in Offensive and Defensive Cyber Operations
Section: Articles Keywords: Generative AI, Cybersecurity, Offensive Operations, Defensive Operations, Artificial Intelligence, Deepfakes, GANs, Cyber Defense, LLMs Generative Artificial Intelligence (AI) has emerged as a transformative force in cybersecurity, reshaping both offensive and defensive operations. By leveraging models capable of creating realistic data, simulations, and adaptive attack or defense strategies, Generative AI introduces new paradigms in digital warfare and protection. Offensive applications include automated phishing, deepfake generation, and polymorphic malware creation, while defensive uses involve synthetic data generation for threat modeling, deception technologies, and automated vulnerability patching. This dual-use nature of Generative AI presents a paradox—enhancing system resilience while simultaneously introducing novel attack vectors. The integration of large language models (LLMs), generative adversarial networks (GANs), and diffusion models within cybersecurity infrastructures enables predictive, adaptive, and autonomous responses to complex threats. However, ethical, regulatory, and operational challenges persist, especially concerning attribution, accountability, and misuse prevention. This paper explores the evolving landscape of Generative AI in cyber operations, assessing its implications for future cyber defense frameworks and digital warfare ethics.
AI-Enhanced Cybersecurity for Protecting National Defense Systems
Section: Articles Keywords: AI-enhanced cybersecurity, national defense, artificial intelligence, advanced persistent threats, anomaly detection, predictive analytics, cyber resilience, military systems, cyber-enabled warfare, intelligent defense As cyber threats grow increasingly sophisticated, national defense systems face unprecedented risks from state-sponsored adversaries, advanced persistent threats (APTs), and cyber-enabled warfare tactics. Artificial Intelligence (AI) offers transformative potential in strengthening cybersecurity for defense environments through automation, predictive analytics, anomaly detection, and adaptive defense strategies. This paper explores the integration of AI-enhanced cybersecurity into military and government defense systems, examining its role in protecting critical infrastructures, mission-critical networks, and sensitive intelligence. The study discusses the evolving threat landscape, AI-driven technologies for cyber defense, and policy implications of deploying intelligent systems in national security contexts. While AI provides enhanced speed, accuracy, and scalability in combating threats, it also introduces challenges such as algorithmic bias, adversarial AI risks, and ethical dilemmas. By analyzing these opportunities and limitations, this paper underscores that AI-enhanced cybersecurity represents both a technological advancement and a strategic imperative for safeguarding national defense in the digital era.
Adversarial Attacks against Machine Learning-Based Security Systems: A Growing Threat
Section: Articles Keywords: Adversarial Attacks, Machine Learning Security, Cyber Threats, Model Robustness, Adversarial Defense, Deep Learning, AI Security, Intrusion Detection Machine learning (ML) has become a central component of modern cybersecurity, powering systems for intrusion detection, malware analysis, spam filtering, and anomaly detection. However, as these systems grow more intelligent, they have also become susceptible to a new and rapidly evolving class of threats known as adversarial attacks. These attacks exploit the vulnerabilities inherent in ML algorithms, manipulating input data to deceive models into making incorrect or harmful decisions. From evading malware classifiers to bypassing facial recognition and spam filters, adversarial attacks challenge the reliability and robustness of AI-driven security mechanisms. This paper examines the mechanisms behind adversarial attacks, their impact on machine learning-based security infrastructures, and emerging defense strategies such as adversarial training, model hardening, and explainable AI. By analyzing both the offensive and defensive dimensions, this study underscores the urgent need for resilient AI architectures capable of adapting to an adversarially intelligent threat landscape.
Using reinforcement learning in Dynamic Pricing Models
Section: Articles Keywords: Reinforcement Learning, Dynamic Pricing, Machine Learning, Q-Learning, Policy Gradient, Revenue Optimization, Pricing Strategy, Retail Analytics, Smart Pricing Models, Python Simulation Dynamic pricing methods have become more critical in sectors like e-commerce, airlines, and energy management, where real-time changes dictate adjustment of prices. Historically, rule-based and econometric systems have found difficulties in complex and volatile market dynamics. Reinforcement Learning (RL), a subfield in which machine learning algorithms deal with sequential decision-making problems, offers an attractive option because it enables an autonomous agent to learn optimal pricing policies while its interaction with the environment is ongoing. This study offers a comprehensive review and analysis of the reinforcement learning methods applied to dynamic pricing problems. We discuss theory underlying RL-based models with an emphasis on model-free methods including Q-learning and policy gradients, and analyze their performances within simulated and real-world settings. To this end, we created a simulated retail setting wherein prices would be dynamically adjusted by an RL agent on the basis of consumer behavior and competitor prices. The agent is constructed in a way that prioritizes high cumulative revenue with some reward consideration for maintaining a competitive stance within the market. Moreover, a fully modular architecture is proposed for the deployment of RL in dynamic pricing pipelines, which encapsulates state space modeling, environment simulation, and policy training using Python toolkits. Benchmarked against baseline pricing models, the RL demonstrated better adaptability and long-term revenue enhancement. We also discuss challenges presented by deployment, such as balancing exploration/exploitation, scaling, and interpretability in extremely high-dimensional action spaces. The paper concludes with a series of recommendations aimed at industrial practitioners and future academic researchers. The findings assert RL as transformative within modern pricing strategies: this empowers data-driven self-optimizing systems to respond intelligently to constantly evolving market conditions.
Integration of Digital Dentistry in Deep Margin Elevation Procedures: Enhancing Precision and Predictability
Section: Articles Keywords: Deep Margin Elevation, Digital Dentistry, CAD/CAM, Marginal Adaptation,, Intraoral Scanning, Precision, Predictability, Minimally Invasive Dentistry The integration of digital technologies into restorative dentistry has revolutionized treatment precision and workflow efficiency, particularly in managing subgingival restorations through Deep Margin Elevation (DME). This study evaluates the impact of digital dentistry on the accuracy, predictability, and clinical outcomes of DME procedures. Conventional manual DME techniques often present limitations in margin adaptation and reproducibility, leading to potential microleakage and restoration failure. With advancements in intraoral scanning, CAD/CAM fabrication, and digital design systems, clinicians can now achieve superior marginal precision, improved visualization, and reduced operator variability. Comparative analysis between conventional and digital-assisted DME approaches demonstrated that digital workflows enhance marginal fit, minimize procedural errors, and improve time efficiency. The findings highlight that digital integration not only refines the technical execution of DME but also aligns with the principles of minimally invasive and evidence-based dentistry, promoting long-term restoration success and patient satisfaction.
Effect of Deep Margin Elevation on Periodontal Health and Gingival Microleakage: A Clinical and Microbiological Assessment
Section: Articles Keywords: Deep Margin Elevation, Periodontal Health, Gingival Microleakage, Microbiological Assessment, Subgingival Restorations, Biological Width, Adhesive Dentistry, Minimally Invasive Technique. Subgingival restorations pose significant clinical challenges due to their proximity to the gingival tissues and potential to disrupt periodontal health. Deep Margin Elevation (DME) has emerged as a minimally invasive technique aimed at relocating deep restorative margins coronally, thereby improving restorative access while preserving biological width. This study aimed to assess the effect of DME on periodontal health and gingival microleakage through combined clinical and microbiological evaluation. Patients requiring posterior restorations with subgingival margins were divided into two groups: restorations performed with DME and conventional restorations without margin elevation. Clinical parameters, including Plaque Index, Gingival Index, Probing Depth, and Clinical Attachment Level, were recorded at baseline, 3, 6, and 12 months. Microbiological analysis of gingival crevicular samples was performed to detect bacterial infiltration and assess marginal leakage. The results revealed that restorations performed with DME maintained stable periodontal parameters and demonstrated reduced gingival microleakage compared to conventional restorations, provided that isolation and adhesive protocols were properly executed. These findings indicate that DME, when performed under controlled conditions, supports periodontal health and minimizes bacterial penetration, confirming its clinical and biological compatibility in subgingival restorative procedures.
Comparative Study of EDDY, EndoVac, and Passive Ultrasonic Irrigation Systems in Apical Third Debridement
Section: Articles Keywords: Apical third debridement, EDDY system, EndoVac, Passive Ultrasonic Irrigation, root canal irrigation, debris removal, apical extrusion, endodontic treatment Effective debridement of the apical third is a critical factor in successful endodontic treatment. Various irrigation systems, including EDDY, EndoVac, and Passive Ultrasonic Irrigation (PUI), have been developed to enhance cleaning efficacy and minimize procedural complications. This comparative study evaluates the performance of these systems in apical third debridement using extracted human teeth. Standardized root canal instrumentation was performed, followed by irrigation protocols specific to each system. Debris removal and apical extrusion were assessed using established scoring methods and quantitative measurements. Results demonstrated that EDDY and PUI exhibited superior debris removal compared to EndoVac, with EDDY showing the highest cleaning efficacy. Conversely, EndoVac significantly minimized apical extrusion, while EDDY showed a higher risk of extrusion. PUI offered an effective balance between cleaning efficiency and safety. These findings highlight the importance of selecting an irrigation system based on clinical priorities, balancing apical cleanliness with the risk of extrusion. The study provides guidance for clinicians aiming to optimize endodontic outcomes in the apical third.
Emerging Trends in Biocompatible Irrigation Solutions: Balancing Antimicrobial Efficacy and Dentin Integrity
Section: Articles Keywords: Biocompatible irrigants, antimicrobial efficacy, dentin integrity, root canal irrigation, nano-modified solutions, herbal irrigants, cytotoxicity, endodontic treatment Effective root canal irrigation is essential for eliminating microbial pathogens while preserving dentin integrity, a balance that remains a significant challenge in endodontics. Conventional irrigants, such as sodium hypochlorite, demonstrate high antimicrobial efficacy but can compromise dentin structure and biocompatibility. Emerging biocompatible irrigation solutions, including herbal extracts, nano-modified formulations, buffered agents, and combination approaches, aim to optimize microbial control while minimizing adverse effects on dentin. Recent studies highlight their potential to disrupt biofilms, reduce cytotoxicity, and maintain dentin microhardness. Evaluation of these solutions involves assessing antimicrobial efficacy, dentin preservation, biocompatibility, and clinical feasibility. While trade-offs between cleaning efficiency and tissue preservation exist, integrating biocompatible irrigants into endodontic protocols offers a promising avenue to enhance treatment outcomes. Continued research is required to validate long-term clinical performance and ensure safety in diverse clinical scenarios.
Fracture Resistance Behavior of Endodontically Treated Teeth Obturated with Bioceramic versus Gutta-Percha Systems
Section: Articles Keywords: Endodontically treated teeth, Fracture resistance, Bioceramic sealer, Gutta-percha, Root canal obturation, Mechanical strength This study investigates the fracture resistance behavior of endodontically treated teeth obturated with bioceramic and gutta-percha systems. The research aims to evaluate the mechanical performance and durability of these two obturation materials, which are critical to the long-term success of root canal therapy. Thirty extracted human teeth with standardized root canal preparation were divided into two groups: one obturated using a bioceramic sealer and the other with a conventional gutta-percha system. The specimens were subjected to compressive loading using a universal testing machine until fracture occurred. The results demonstrated that teeth filled with bioceramic-based materials exhibited higher mean fracture resistance compared to those treated with gutta-percha, suggesting superior bonding and reinforcement properties. These findings indicate that bioceramic obturation systems enhance the structural integrity of endodontically treated teeth, potentially improving their clinical performance and longevity.
Artificial Intelligence as a Clinical Decision-Support System in Endodontic Retreatment: Challenges and Clinical Implications
Section: Articles Keywords: Artificial Intelligence, Clinical Decision-Support System, Endodontic Retreatment, Machine Learning, Deep Learning, Diagnostic Accuracy, Treatment Planning, Clinical Implications Artificial Intelligence (AI) is increasingly transforming dental practice, particularly in endodontics, by serving as a clinical decision-support system (CDSS). In endodontic retreatment, treatment planning and prognosis are often complex due to anatomical variations, prior interventions, and the risk of treatment failure. AI technologies, including machine learning and deep learning algorithms, have shown promise in assisting clinicians with accurate diagnosis, risk assessment, and evidence-based treatment planning. However, the integration of AI in clinical practice faces several challenges, including limited high-quality data, ethical and legal concerns, workflow compatibility, and clinician acceptance. Despite these hurdles, the potential clinical implications are substantial, with AI offering enhanced diagnostic accuracy, improved treatment outcomes, and more efficient clinical decision-making. This review explores the current applications, challenges, and future directions of AI as a CDSS in endodontic retreatment, emphasizing the need for validated models and standardized protocols to ensure safe and effective clinical adoption.
Predictive Analytics and Neural Networks in Assessing Endodontic Treatment Prognosis: A New Frontier in Digital Dentistry
Section: Articles Keywords: Predictive analytics, neural networks, endodontic prognosis, digital dentistry, artificial intelligence, , treatment outcome prediction The advent of digital dentistry has transformed endodontic practice, offering innovative tools to enhance treatment planning and prognosis assessment. Predictive analytics and neural networks have emerged as powerful technologies capable of analyzing complex patient and clinical data to forecast the outcomes of endodontic treatments. By leveraging machine learning algorithms, neural networks can interpret radiographic images, detect subtle pathological changes, and provide data-driven predictions of treatment success. Integrating predictive analytics with neural network models enables clinicians to make more informed decisions, reduce treatment failures, and personalize patient care. Despite the promising potential, challenges such as data quality, algorithm transparency, and clinical adoption remain. Continued research and development in this domain are essential to fully realize the transformative impact of AI-driven predictive tools in endodontics, paving the way for more precise, efficient, and patient-centered care.
Assessment of Reinforcement Potential of Novel Root Canal Filling Systems in Preserving Tooth Strength
Section: Articles Keywords: Root canal filling systems, bioceramic sealers, tooth strength, fracture resistance, endodontic reinforcement, bioactive materials. The structural integrity of endodontically treated teeth largely depends on the reinforcement potential of the root canal filling materials used after instrumentation. Conventional systems, such as Gutta-Percha combined with resin-based sealers, have demonstrated limitations in enhancing fracture resistance, leading to a greater risk of tooth fragility. This study aims to assess the reinforcement potential of novel root canal filling systems in preserving tooth strength. Extracted human teeth were prepared and obturated using both traditional and newly developed bioactive and nanocomposite-based sealers. Fracture resistance testing was conducted using a universal testing machine, and results were statistically analyzed to determine comparative performance. The findings revealed that novel bioceramic and bioactive systems significantly improved the mechanical strength of treated teeth, providing better adaptation and bonding to dentinal walls. These results suggest that the use of advanced root canal filling systems can enhance the long-term durability and fracture resistance of endodontically treated teeth. The study underscores the clinical relevance of selecting materials with superior reinforcement properties to ensure better treatment outcomes.
Quantum-Enhanced Simulations for High-Dimensional Stress Testing in Diversified Banking Risk Portfolios
Section: Articles Keywords: Quantum computing, High-dimensional stress testing, Banking risk portfolios, Quantum Monte Carlo, Tail-risk estimation, Hybrid quantum-classical algorithms, Systemic risk modeling, Financial simulations. The complications of diversified banking portfolios have compounded the difficulties of properly rating systemic risk in extreme market regimes. Conventional stress testing techniques are not always able to achieve a high degree of predictive and computational efficiency because of the combinatorial explosion of high-dimensional risk factors. This paper introduces a quantum-enhanced stress testing theory in high-dimensional representations with the help of quantum algorithms like Quantum Amplitude Estimation (QAE) and quantum-classical quantum-classical systems, to effectively simulate tail-risk conditions. The framework brings together credit, market, liquidity, and operational risk dimensions to show faster convergence in Monte Carlo-type simulations without compromising on the accuracy of extreme-event forecasts. Comparative experiments to classical ones show large computational speedup, and better scalability to the complexly correlated portfolios. The results indicate that quantum computing could provide a revolutionary instrument in banking risk management, allowing regulators and institutions to more effectively predict systemic weaknesses and allocate capital efficiently in the face of extreme stress events. Further efforts will focus on quantum-native machine learning models to implement adaptive scenario generation and monitor portfolio resilience in real-time.
Section: Articles Keywords: Computing, Information Technology, Artificial Intelligence, Data Processing, Digital Transformation Computing has become the backbone of modern civilization, influencing industries, education, healthcare, and communication. The integration of computing technologies has enhanced problem-solving, data management, and decision-making processes across various fields. This paper explores the evolution of computing, its applications, and its impact on daily life, while also considering the challenges that arise from rapid technological advancements.
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Resilient Cloud Applications with Apex Design Patterns: A Salesforce Perspective
Section: Articles Keywords: Salesforce, Apex, Cloud Applications, Resilience, Design Patterns, Governor Limits, Performance, Maintainability, Fault Tolerance Salesforce has emerged as a dominant platform for building cloud-native enterprise applications, enabling organizations to streamline processes, personalize customer engagement, and scale operations. Yet, as applications grow in scope and complexity, ensuring resilience—defined as the ability to withstand, recover from, and adapt to failures—becomes a central architectural challenge. Apex, Salesforce’s proprietary programming language, offers the flexibility to design custom solutions, but without structured approaches, systems can become brittle, inefficient, and vulnerable to platform constraints. This paper explores how Apex design patterns strengthen the resilience of Salesforce applications. It highlights patterns such as Singleton, Factory, Strategy, Repository, Unit of Work, Bulkification, and Cache-aside, and examines their role in improving fault tolerance, maintainability, performance, and compliance with Salesforce’s governor limits. Through a Salesforce-focused lens, this paper argues that leveraging Apex design patterns is essential for architecting robust, adaptable, and future-proof cloud applications.
Architecting Robust Salesforce Solutions through Apex Design Patterns
Section: Articles Salesforce has evolved into one of the most widely adopted cloud platforms, enabling enterprises to build customized, scalable solutions that power customer engagement, automation, and digital transformation. However, as applications on the Salesforce platform grow in complexity, developers face challenges related to scalability, maintainability, and performance. Apex, Salesforce’s proprietary programming language, provides the flexibility to address these challenges, but without disciplined architectural practices, applications risk becoming inefficient and brittle. This paper examines how leveraging Apex design patterns enables the development of robust Salesforce solutions. It explores key design patterns—including Singleton, Factory, Strategy, Repository, and Unit of Work—demonstrating how they can enhance modularity, optimize performance, enforce separation of concerns, and ensure compliance with Salesforce’s governor limits. By aligning architectural principles with Salesforce’s multi-tenant constraints, these patterns help organizations build sustainable, high-performing systems. The study concludes that design patterns are not merely best practices but critical architectural enablers for delivering robust Salesforce solutions in modern enterprises.
Harnessing Sales force for End-to-End Digital Transformation in SMEs
Section: Articles Keywords: Salesforce, SMEs, Digital Transformation, CRM, Workflow Automation, Low-Code Development, Einstein AI, Business Growth, Cloud Platforms, Customer Experience Small and medium-sized enterprises (SMEs) are increasingly turning to digital transformation as a means of improving competitiveness, customer engagement, and operational efficiency. However, limited resources, budget constraints, and the complexity of managing multiple technologies often hinder their transformation journeys. Salesforce, as a leading cloud-based platform, provides SMEs with an integrated ecosystem of customer relationship management (CRM), automation, analytics, and artificial intelligence tools that enable end-to-end digital transformation. This paper examines how Salesforce empowers SMEs to digitize operations, enhance customer experiences, and scale effectively. It discusses Salesforce’s role in unifying sales, marketing, service, and commerce within a single platform, while also exploring the strategic advantages of its low-code development tools, AI-driven insights, and ecosystem integrations. Furthermore, it evaluates challenges SMEs may face in adopting Salesforce and offers perspectives on how Salesforce can serve as a catalyst for sustainable growth in the digital economy.
The Role of Salesforce in Cloud-Native Enterprise Architectures
Section: Articles Keywords: Salesforce, Cloud-Native Architecture, Microservices, Serverless, CRM, Low-Code Development, Enterprise Integration, Digital Transformation, Salesforce Flow, Einstein AI Cloud-native enterprise architectures have emerged as the backbone of modern digital transformation strategies, enabling organizations to build scalable, resilient, and flexible systems. Salesforce, as a leading cloud-based platform, plays a pivotal role in this paradigm by offering a comprehensive suite of services for customer relationship management, application development, data analytics, and workflow automation. This paper explores Salesforce’s contribution to cloud-native architectures, emphasizing its ability to integrate seamlessly with microservices, serverless computing, and containerized environments. It highlights how Salesforce enhances scalability, agility, and operational efficiency while fostering innovation through its low-code development tools and AI-driven insights. Furthermore, the paper discusses the architectural advantages, challenges, and future directions of leveraging Salesforce in cloud-native enterprises.
Assessment of Microleakage in Endodontically Treated Teeth Restored with Different Core Build-Up Materials
Section: Articles Keywords: Endodontically treated teeth, microleakage, coronal seal, resin composite, glass ionomer, resin-modified glass ionomer, bioactive restorative materials In endodontically treated teeth, there are special treatment and restoration problems: coronal tooth structure loss, biomechanical changes, and a coronal seal. The selection of the core build-up material is critical in the restoration of tooth shape and the restoration of tooth functionality and the prevention of coronal micro leakages, which may jeopardize endodontic success. A synthesis of laboratory and translational findings involving commonly used core build-up materials such as resin composites, glass ionomer cements (GICs) and resin-modified GICs (RMGICs), amalgam, bulk-fill resins, and newer bioactive/core materials is proposed, with particular emphasis placed on the sealing ability, mechanisms that influence microleakage, and the methodological heterogeneity that make the comparison of these materials difficult. A summary of the most commonly used microleakage testing modalities (dye penetration, fluid filtration, bacterial leakage models, and micro-computed tomography), is provided including how aging conditions (thermocycling, mechanical loading) affect the results, and the interplay between adhesive strategy, preparation configuration (presence/absence of posts, ferrule, remaining tooth structure), and material properties (polymerization shrinkage, modulus, chemical adhesion) in predicting the integrity of coronal seals. Even though most in vitro findings predict that well bonded resin-based build-ups and current bioactive materials have the tendency to report positive immediate sealing, long-term microleakage resistance has been highly contingent on adhesive protocol, incremental method, cyclic fatigue and hydrolytic degradation resistance which differ widely across studies. RMGICs and traditional GICs have continuous fluoride release and chemical adhesion which can be useful in certain clinical applications, whereas their reduced mechanical properties can restrict usage in high drive cases only as core materials. Finally practical suggestions are made about the choice of material and standardization of laboratory tests with defined areas of priorities in the future to establish in vivo and standardized in vitro studies to define the clinical applicability of laboratory measures of microleakage.
Influence of Obturation Technique on Postoperative Pain and Healing Outcomes
Section: Articles Keywords: Obturation techniques, postoperative pain, periapical healing, root canal therapy, bioceramic sealers. Obturation is an important part of root canal treatment, which is concerned with securing a three-dimensional seal to avoid reinfection and facilitate periapical healing. The effect of obturation technique on the postoperative pain and the long-term healing effects have been a long-standing issue because depending on the technique used may influence not only the comfort of the patients in the short-term but also the outcome of the treatment in the long-term. Although cold lateral condensation is dependable and commonly applied, it can possibly relate with more voids and greater discomfort in the postoperative stage because of its mechanical forces. In vertical compaction, warm vertical compaction offers the best adaptation to canal walls, and has risks of overextension and possible periapical irritation. Obturation systems that are carried out with carriers are efficient and homogeneously filled, but prone to sealer extrusion. The single-cone method in which bioceramic sealers are used has been more recently considered due to its simplicity, bioactivity and good prognosis. It has been argued that apical extrusion of materials, preoperative status, and postoperative pain are significant predictors of postoperative pain, and obturation quality and biological compatibility of sealers are related to long-term healing. The balance between the technical efficiency, patient comfort, and biological properties of materials should, therefore, be maintained in clinical decision-making. The further developments of bioceramic systems and outcome assessment tools can optimize obturation strategies to give better patient-centered outcomes.
Zero Trust-Enabled Software-Defined Networking: Policy Enforcement Through Multi-Layer Defense Orchestration
Section: Articles Keywords: Zero Trust, Software-Defined Networking, policy enforcement, multi-layer defense, microsegmentation, network security, adaptive orchestration The convergence of Zero Trust security principles with Software-Defined Networking (SDN) offers a transformative framework for enforcing dynamic, fine-grained, and context-aware security policies in modern distributed infrastructures. Traditional perimeter-based defenses are no longer sufficient in the face of advanced persistent threats, insider risks, and cloud-native attack surfaces. Zero Trust shifts the security paradigm toward continuous verification, least privilege, and microsegmentation, while SDN provides the programmability and centralized control required for adaptive enforcement. This paper examines the integration of Zero Trust with SDN through multi-layer defense orchestration, where security policies are enforced across the data plane, control plane, and application plane. It discusses mechanisms such as identity-based flow management, real-time traffic monitoring, and adaptive policy updates driven by contextual risk assessments. Furthermore, it explores the orchestration of complementary security layers—such as intrusion detection, endpoint verification, and encryption—to create a cohesive defense-in-depth strategy. Challenges in scalability, interoperability, and policy consistency are highlighted alongside open research opportunities. The study argues that Zero Trust-enabled SDN represents a critical architectural shift toward resilient, adaptive, and proactive network defense in heterogeneous digital ecosystems.
Self-Supervised Robustness Enhancement for Multimodal Neural Networks Under Cross-Domain Adversarial Perturbations
Section: Articles Keywords: Self-supervised learning, multimodal neural networks, adversarial robustness, cross-domain perturbations, contrastive learning, representation learning, defense mechanisms Multimodal neural networks have become foundational in artificial intelligence, enabling systems to learn and reason from diverse modalities such as vision, language, and audio. However, these models remain highly vulnerable to cross-domain adversarial perturbations, where subtle but carefully crafted manipulations across one or more modalities lead to significant performance degradation. Traditional supervised defense mechanisms struggle to address these threats, primarily due to the lack of labeled adversarial data and the complexity of multimodal interactions. This paper proposes self-supervised robustness enhancement as a promising defense paradigm. By leveraging self-supervised pretext tasks and representation learning, multimodal models can learn modality-invariant, semantically consistent features that improve resilience against adversarial inputs. We explore contrastive learning, masked prediction, and redundancy-driven objectives as self-supervised strategies to fortify robustness. The analysis highlights how these methods mitigate cross-domain perturbations without explicit adversarial labels, while also improving generalization and interpretability. The discussion further outlines challenges in scalability, stability, and real-world deployment, positioning self-supervised learning as a crucial pathway toward robust and trustworthy multimodal AI.
Game-Theoretic Defense Mechanisms Against Cross-Domain Adversarial Attacks in Multimodal Learning Systems
Section: Articles Keywords: Game theory, multimodal learning, cross-domain adversarial attacks, defense mechanisms, Stackelberg games, security in AI, robust machine learning The rapid integration of multimodal learning systems into critical infrastructures has raised new concerns regarding their vulnerability to cross-domain adversarial attacks. Unlike traditional adversarial threats that target unimodal data streams, cross-domain attacks exploit the complex interactions between modalities such as text, vision, and audio, amplifying the difficulty of detection and mitigation. This paper explores the application of game-theoretic defense mechanisms as a systematic framework to counteract these threats. By modeling adversarial interactions as strategic games between attackers and defenders, game theory provides predictive insights into adaptive attack strategies and offers robust defensive equilibria. We examine static and dynamic game-theoretic approaches, the role of Stackelberg games in anticipating adversarial moves, and the potential of cooperative game formulations for multimodal defense optimization. The analysis highlights both the strengths and limitations of game-theoretic frameworks while identifying open challenges in scalability, interpretability, and real-time adaptability. The findings emphasize the importance of integrating strategic reasoning into adversarial resilience research, laying the groundwork for resilient and trustworthy multimodal AI systems.
Cross-Domain Adversarial Attack Taxonomy for Multimodal Neural Networks: Threat Landscape and Open Challenges
Section: Articles Keywords: Adversarial attacks, multimodal neural networks, cross-domain vulnerabilities, threat taxonomy, robustness, transferability, deep learning security, interpretability, data fusion, trustworthy AI Multimodal neural networks have emerged as powerful frameworks for integrating diverse data modalities such as text, images, audio, and video, enabling advancements in fields ranging from autonomous systems to healthcare and security. However, their increasing adoption has exposed them to adversarial attacks that exploit vulnerabilities across multiple domains simultaneously. Unlike unimodal systems, multimodal networks present expanded attack surfaces due to cross-modal dependencies, complex fusion strategies, and heterogeneous data representations. This paper introduces a taxonomy for cross-domain adversarial attacks targeting multimodal neural networks, categorizing them by modality manipulation, attack vectors, and fusion-stage vulnerabilities. We highlight how these attacks undermine robustness, reliability, and interpretability, while also complicating detection and defense mechanisms. Through a detailed exploration of the threat landscape, we emphasize challenges such as cross-domain transferability of adversarial perturbations, coordinated attacks exploiting multimodal fusion, and real-time adversarial adaptation. Finally, we identify open challenges and research directions for developing resilient, interpretable, and trustworthy multimodal systems capable of withstanding evolving adversarial threats.