Innovative Journal of Applied Science is a leading publication dedicated to advancing the field of applied science through the dissemination of new research and innovative solutions. The journal serves as a platform for scientists, engineers, and practitioners to share their findings, fostering interdisciplinary collaboration and driving progress across various applied science disciplines. Each issue features original research articles, reviews, and case studies that address real-world challenges and propose practical applications. With a commitment to excellence and a focus on innovation, the journal provides valuable insights into emerging technologies, novel methodologies, and impactful scientific discoveries. By bridging the gap between theory and practice, Innovative Journal of Applied Science plays an essential role in shaping the future of applied science.
DC-Guard: A Risk-Tiered Data-Centric Framework for Governing Dataset Shift in Credit-Risk Models — Empirical Validation on Fannie Mae Loans
Machine-learning credit-default models degrade when production data diverge from training distributions. We introduce DC-Guard, a governance framework that maps Population-Stability-Index (PSI) and model-level drift (AUC, ECE) to Green / Amber / Red risk tiers and prescribes auditable actions (monitor, diagnose, retrain, restrict). We first ground the policy on the canonical UCI credit-card data set; we then validate it end-to-end on 2.1 million Fannie Mae single-family loans originated 2020–2023. On the live portfolio DC-Guard triggered exactly one red alert (Sep-2022) when PSI ≥ 0.2 and AUC dropped 0.058, prompting human-in-the-loop review and selective automation freeze until performance recovered. No false-positive retraining occurred. All code and data are publicly available.
Copyright (c) 2025 Innovative Journal of Applied Science
Stratified Density Gravity: Gravitation Without Fundamental Time
We present Stratified Density Gravity (SDG), a reformulation of gravitation in which the fundamental manifold is described by (x,y,z,ρ) rather than (t,x,y,z), with ρ representing local gravitational depth / energy density. In this framework time is not a fundamental coordinate; instead, it emerges as a macroscopic ordering parameter associated with irreversible relaxation of density stratification (entropy production). We show that curvature is sourced by spatial stratification of ρ and we obtain a field equation in which local curvature depends on inhomogeneities in ρ, while large-scale curvature depends on the homogeneous background component of ρ. The local sector reproduces Newtonian gravity in the weak-field limit, which fixes the relevant coupling. The global sector yields a dynamical large-scale curvature term that provides an alternative to the cosmological constant Λ and explains cosmic acceleration as a background-density phase. Because curvature depends on second spatial derivatives of ρ, singularities do not form in collapsed regions: high density cores remain finite. The framework therefore preserves the known weak-field and observational successes of General Relativity (GR) while addressing the cosmological constant problem and the classical singularity problem.
Critical and Analytical Evaluation of the Relationship between Supply Chain Characteristics and Manufacturing Strategies: Case of Unilever
The research is based on carrying out a critical analysis for evaluating the relationship between Supply Chain (SC) characteristics and manufacturing strategies at Unilever. For this, the SC characteristics are identified as integrations, flexibility, visibility and geographical dispersion causing a considerable impact on the manufacturing strategies of a company. In order to analyze the relationship of these SC characteristics on Unilever manufacturing strategies overview of its non-financial indicators, its manufacturing approach and approach towards deployment of innovation techniques is carried out. For this the company’s annual reports and relevant data was selected, since the research conduction was impacted by COVID-19 epidemic, therefore only descriptive analysis by selecting secondary data was carried out. Findings of the research indicate the Unilever possess a strong stance of SC integration and geographical dispersion. As it has wide spread business, followed by vertical integration, it acquires more 60 mergers, acquisitions and partnership programs spanned for the duration of, 2010-19. Along with this, the findings indicated that attainment of innovation is a fundamental approach of SC integration and geographical dispersion at Unilever. Moreover, the company’s SC characteristics such as visibility and flexibility are also evidenced by its diverse products’ culture and approach for establishing stronger relations with its suppliers.
Big Data and Great Privacy Challenges in the Digital Era - A Comprehensive Study
In the era of globalization, industries are undergoing a rapid digital transformation, driven by big data ecosystems, conducting data intensive operations and interconnected technologies, generating huge data by means of IoT devices, AI systems, social media, Edge processing, smart systems and autonomous machines, all are designed to collect, process and transmit multi-modal data continuously and at a high rate. Even though these innovations enhance overall efficacy, productivity and competitiveness, they also present significant unavoidable data privacy challenges that threaten data breaches, operational continuity and regulatory compliance. Bearing in mind the criticality of the topic, this study targets to identify and evaluate the most critical big data privacy challenges, analyses their probability of occurrence, contextual relevance to the aviation and consequential impact on industrial operations. Although this research is industry-agnostic; however, illustrative examples were drawn from the aviation industry perspective to demonstrate applicability. A combined approach of systematic literature review and risk assessment frameworks, is used to examine mention challenges. The research highlights prominent challenges related to data privacy within the big data ecosystem, that can lead to multiple severe consequences like brand damage, loss of trust, business disruptions, financial losses and other undesirable outcomes. Finally, this study presents actionable workarounds to overcome existing challenges in the domain of big data privacy, followed by potential directions for further research.
Technical Procurement and Category Management: The Backbone of High-Performance Industries (Aviation, Construction, Oil and Gas)
Long lead times, volatile commodity prices and increased supply chain risk exposure have disrupted global procurement operations in the aviation, construction and oil and gas industries in a way never seen before. The study searches category management and cross-industry procurement patterns in an effort to find sector-specific differentiators and common strategic factors. This study used a desktop-based, qualitative research approach supported by the application of selected quantitative indicators and systematic content analysis. 62 publicly accessible documents from reputable international organizations (such as IATA, RIC, IEA, large OEM reports and business reports) were systematically tagged using NVivo and cross-sectionalized. The adoption of digital procurements, risk-management initiatives, supplier performance evaluation and the engineering-prescribed practice of specification control all exhibit significant thematic overlap. However, there were also certain pressure points: aviation was stressed by safety-related regulatory restrictions, construction was under pressure from geopolitical and technical standards and oil and gas sourcing were under pressure from both geopolitical pressure and commodity price volatility. This study will include a model that can be used to other industries to improve procurement maturity. It has created a chance for digital transformation and strategic alignment in the high-risk engineering sector.
Bridging Digital Currencies: A Technical Model for Multi-CBDC Ecosystems
The emergence of Central Bank Digital Currencies (CBDCs) has accelerated the digital transformation of monetary systems, yet cross-border interoperability remains fragmented across national and regional implementations. This paper presents a technical model for a multi-CBDC ecosystem designed to enable seamless value exchange between sovereign digital currencies while maintaining compliance, settlement finality and monetary sovereignty. Drawing from comparative analyses of CBDC initiatives in China, the European Union and emerging economies, the proposed model introduces a bridge-layer architecture that supports both wholesale and retail CBDCs through programmable smart contracts, distributed ledger interoperability and standardized messaging protocols. The framework integrates blockchain consensus mechanisms and token-based settlement pathways to establish a trusted exchange environment across heterogeneous digital ledgers. Building upon the policy foundations of cross-border harmonization and the design principle of interoperability, the model offers a scalable mechanism for liquidity routing, digital identity federation and regulatory auditability. A prototype simulation demonstrates how the architecture reduces latency, minimizes counterparty risk and enhances compliance through verifiable transaction layers. The findings contribute to the evolving discourse on multi-CBDC networks by emphasizing the necessity of interoperable standards and governance frameworks. Ultimately, this research argues that a harmonized multi-CBDC bridge can act as the foundation for an integrated, secure and inclusive global digital monetary ecosystem.
Rural and Urban Divide in Aspirations to Study Abroad: A Comparative Analysis
Many Indian High school students want to study abroad, but this aim is hampered by information, resource and support gaps between rural and urban areas. This study compares rural and urban students' awareness, financial resources, academic preparedness and social and cultural support using data from the Indian government and current research. We see that although the overall number of Indian students studying overseas has increased (to over 750,000 in 2023) there are still more hindrances for young people in rural areas. For example, just above 50% of Indian teenagers finish secondary school and children in rural areas do worse on English language tests than their urban counterparts. Villages now have more access to the internet (95.2% of Indian villages have mobile data), which can help close gaps. In fact, according to recent studies, over 55% of India's internet users now live in rural areas. This improved access to information, however, does not fully address the disparities in educational infrastructure and the quality of schooling, which remain significant challenges for rural students aspiring to pursue higher education abroad. Additionally, while mobile data penetration is high, the effective utilization of online resources for academic preparation and university applications still requires guidance and support is often less available in rural settings.
Testing Resilience of Envoy Service Proxy with Microservices: A Fault-Oriented, Evidence-Driven Methodology
Modern microservice platforms increasingly rely on sidecar or gateway proxies to provide reliability, security and observability at the network edge and between services. Envoy has emerged as a de facto data-plane for these plat-forms, exposing policy primitives such as circuit breaking, outlier detection, request hedging, retries with backoff, adaptive concurrency and fine-grained timeouts. Yet many organizations enable these controls without a systematic method to validate whether they actually improve resilience under realistic fault conditions. This paper presents a practical, fault-oriented methodology to evaluate and harden Envoy-mediated microservice systems. We define resilience as the capacity to maintain user-visible success, bounded latency and controlled error-budget burn in the presence of infrastructure instability, Partial dependencies, misconfigurations and traffic surges. Our approach constructs a reproducible testbed that couples a traffic generator, a programmable fault injector and a metrics pipeline with Envoy’s runtime and xDS configuration APIs. Faults are injected at multiple layers—network delay and loss, TCP reset, upstream 5xx and gRPC error codes, slow upstream handlers, dependency fan-out saturation, DNS anomalies and regional impairments—while policies are exercised along the axes of time-outs, retry budgets, circuit thresholds and concurrency limits. We emphasize the measurement of steady-state behavior and failure transients, comparing baselines with and without specific Envoy features. The method produces visual evidence through latency–throughput curves, success-rate timelines, failure-mode attribution charts and dependency heatmaps, enabling engineers and auditors to reason about trade-offs between availability and cost. We contribute an architecture blueprint for experiment orchestration, guidance on safe blast-radius control for production-like environments and a set of scenario templates that represent common failure archetypes such as brownouts, slow storms and partial partitions. A prototype implementation demonstrates that properly tuned outlier detection and time-bounded retries can reduce user-visible failures by more than half during brownouts, while misconfigured unbounded retries amplify tail latency and resource pressure. We also surface the overheads of Envoy features and show when they are negligible relative to the resilience benefit. The results are positioned as actionable evidence rather than universal prescriptions; different systems will require policy calibration aligned with their own SLOs and dependency graphs. By treating resilience as an empirically testable property of configurations rather than a checklist of enabled features, the methodology helps teams move from intuition to validated assurance and makes failures easier to predict, contain and recover from. The paper closes with open directions in automated policy synthesis, HTTP/3 and QUIC behaviors, WASM-based filters and continuous chaos pipelines integrated with service-level error budgets.
The Pluralistic Future of AI: A Comprehensive Analysis of Decentralized Large Language Models
The Centralized Status QuoThe current landscape of Artificial Intelligence (AI), particularly concerning Large Language Models (LLMs), is dominated by a centralized paradigm. Industry leaders such as OpenAI, Anthropic and Google DeepMind deploy and manage their models on massive computational infrastructure within expansive data centers. This architecture, where a single, colossal model serves millions of users and cloud-based APIs, has enabled unprecedented scalability, high performance and the capacity for continuous updates. However, this model is not without significant drawbacks. It requires substantial financial investment in infrastructure, is entirely reliant on constant internet connectivity and introduces considerable privacy and data control concerns, as sensitive information must be processed in the cloud. The user also frequently pays for compute capacity they do not fully utilize, leading to an inefficient cost model based on subscriptions or per-token usage.
Centralized LLM: Cloud-based large language model
Decentralized LLM: Locally deployed large language model
Gardner’s equation relates porous rock density to the P-wave velocity and is used to analyze seismic amplitude information. The exponential form of the equation is convenient for this use. However, the equation was found by fitting data from basins from all over the world. Here a simple basin compaction model is used to show that the form of Gardner’s equation is due primarily to basin compaction which happens to match with variation of rock density and P-wave velocity for sandstone but often fails for shale. An alternative equation is evaluated and shown to be a better choice for single basin use. It is also shown that when Gassmann’s equations are combined with Nur’s critical porosity model the resulting equations relating the shear wave velocity to the P-wave velocity and the density to the p-wave velocity can be written in a way that does not involve the pore fluid bulk modulus. This may explain why so many mono-mineral rocks display a hyperbolic relation between the shear wave velocity and the P-wave velocity with constants independent of porosity for a given pore fluid.
Auditable AI Pipelines: Logging and Verifiability in ML Workflows
The increased reliance on AI in high-stake domains ranging from finance to healthcare to national security has given rise to mounting concerns about the lack of transparency and accountability in ML workflows. Traditional software audit techniques cannot confer sufficient traceability nor verifiability to complex, data-driven AI systems. This work presents a structured auditable AI pipeline framework that implies the embedding of thorough logging and verification units along all stages of the ML cycle. Thus, with the support of provenance in tracking changes and evidence, automated event logging, cryptographic checks by hashes and, optionally, immutability of records through blockchain, it assures operative transparency and forensic reproducibility. We have experimentally shown that through an MLOps implementation, an audit-ready infrastructure, model traceability and regulatory compliance may all be improved when compared to traditional ML environments. The results reassert the urgent need of designing AI pipelines while accounting for auditability as a first-class citizen and present avenues to remedy accountability for enterprise-scale machine learning systems
Nordics Prototype Data Hall Free Cooling Design
With the global rise in data center developments where Rack Densities keep rising and demanding more power and accordingly more cooling to reject the increased heat, optimizing energy efficiency and reducing power consumption are paramount. This study investigates an innovative cooling approach leveraging dry coolers and ice Thermal Storage tank systems to enhance data center performance, particularly in cold climates such as Nordics, specifically in this study the city of Pyhajoki in Finland. Traditional Air-Cooled Chillers (ACCH) dominate cooling infrastructure but consume significant power due to compressors and refrigerants circuits. Dry coolers, on the other hand consisting only of fans and heat exchangers, provide a more sustainable alternative, operating efficiently without refrigerants. However, their cooling capacity is limited by ambient temperature, restricting their standalone use. The proposed hybrid system integrates dry air coolers with ice tanks to capitalize on Finland’s low temperatures, where 96% of the year remains below 20°C. In cold conditions, dry air coolers handle the entire data center cooling load while simultaneously freezing ice tanks. During warmer periods (above 20°C), the ice tanks discharge, maintaining the required supply temperature for fan wall units to cool the Data Halls within ASHRAE Class A Racks (29°C Server inlet Temperature) without relying on chillers. This configuration omits chiller operation all over the year and allow for full free cooling during the 4% of the year, significantly reducing power consumption and supporting sustainability initiatives. The results demonstrate that this system achieves substantial energy savings, enhanced mechanical efficiency and lower environmental impact.
Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework
In applied machine learning, concept drift, which is either gradual or abrupt changes in data distribution, can significantly reduce model performance. Typical detection methods, such as statistical tests or reconstruction-based models, are generally reactive and not very sensitive to early detection. Our study pro- poses a hybrid framework consisting of Transformers and Autoencoders to model complex temporal dynamics and provide online drift detection. We create a distinct Trust Score methodology, which includes signals on (1) statistical and reconstruction-based drift metrics, more specifically, PSI, JSD, Transformer-AE error, (2) prediction uncertainty, (3) rules violations and (4) trend of classifier error aligned with the combined metrics defined by the Trust Score. Using a time- sequenced airline passenger data set with synthetic drift, our proposed model allows for a better detection of drift using as a whole and at different detection thresholds for both sensitivity and interpretability compared to baseline methods and provides a strong pipeline for drift detection in real time for applied machine learning. We evaluated performance using a time-sequenced airline passenger dataset having the gradually injected stimulus of drift in expectations, e.g. permuted ticket prices in later batches, broken into 10-time segments. In the data, our results support that the Transformation-Autoencoder detected drift earlier and with more sensitivity than the autoencoders commonly used in the literature and provided improved modeling over more error rates and logical violations. Therefore, a robust framework was developed to reliably monitor concept drift.
Comprehensive Evaluation of Advanced Water Filtration Techniques: Assessing Efficacy in Removing Hazardous Contaminants and Inhibiting Bacterial Proliferation
Access to clean drinking water is a fundamental necessity, yet water contamination remains a pressing global concern. This study investigates the effectiveness of various filtration methods in removing harmful contaminants—primarily nitrates, nitrites and sulfates—from water sources while assessing bacterial presence post-filtration. We designed an innovative, eco-friendly and cost-efficient filtration system and compared its performance against conventional filtration methods, including reverse osmosis, activated carbon and LifeStraw filters. Our results demonstrated that the newly designed filtration system effectively reduced nitrate, nitrite and sulfate concentrations while minimizing bacterial presence. Contrary to prior assumptions, our study did not evaluate E. coli survival and reproduction but instead focused on culturing and quantifying bacteria already present in the water samples. The findings indicate that the eco-friendly filtration system outperforms traditional methods in improving water safety, making it a viable solution for communities with limited access to clean drinking water.
Evaluating Log-Likelihood for Confidence Estimation in LLM-Based Multiple-Choice Question Answering
Reliable deployment of Large Language Models (LLMs) in question-answering tasks requires well-calibrated confidence estimates. This work investigates whether token-level log-likelihoods—sums of log-probabilities over answer tokens—can serve as effective confidence signals in Multiple-Choice Question Answering (MCQA). We compare three methods: (1) Raw log-likelihood, (2) length-normalized log- likelihood and (3) conventional softmax-based choice probability. Across four diverse MCQA benchmarks, we find that no single scoring method is universally best. Length normalization can significantly improve calibration but may reduce accuracy, while softmax and raw log-likelihood yield identical predictions. These results highlight important trade-offs between calibration and accuracy and offer insights into selecting or adapting confidence measures for different tasks. Our findings inform the design of more trustworthy LLM-based QA systems and lay groundwork for broader uncertainty quantification efforts.
Multiple-Choice Question Answering (MCQA)
AccessABX Implementation and Impact Plan
Antimicrobial Resistance (AMR) poses a growing threat in Low-and Middle-Income Countries (LMICs), driven by the misuse of antibiotics and the widespread circulation of Substandard and Falsified (SF) medicines. Addressing this complex challenge requires integrated, scalable and context-sensitive solutions. AccessABX is a digital, One Health platform designed to improve antibiotic access, quality and stewardship in LMICs, with an initial focus on India. The platform combines mobile technology, real-time data analytics and community engagement to enhance antibiotic stock management, detect counterfeit drugs and support evidence-based prescribing across human and animal health sectors. This pilot study adopted a mixed-methods approach involving technology deployment, stakeholder training and field-based monitoring. Key features of AccessABX include mobile and SMS-based inventory tracking, drug verification through QR codes and AI-powered packaging analysis and blockchain-enabled traceability for high-risk medicines. Implementation focused on rural districts with high AMR burden, limited infrastructure and strong local partnerships. Initial results show improved stock visibility, increased medicine verification rates and a shift toward more appropriate antibiotic use. AccessABX demonstrates the potential of digital health tools to combat AMR in resource-constrained settings while promoting cross-sector collaboration. These findings lay the groundwork for broader regional and global scaling of the solution.
Real-world vs simulated world for AI vehicles switching from 2 lanes to 1 lane
The transition of Autonomous Vehicles (AVs) from two lanes to one lane presents significant challenges and opportunities for enhancing track management and safety. This complex maneuver is crucial in urban environments where lane merges often lead to congestion and bottlenecks, thereby necessitating precise navigation and decision-making by AVs. As the development of AV technology accelerates, understanding the nuances of both real-world and simulated environments becomes essential in ensuring these vehicles operate electively and safely under diverse driving conditions. Realism in simulations plays a pivotal role in the training of AI systems for AVs. Discrepancies between simulated and actual driving conditions can result in substantial errors, potentially compromising the performance of AV algorithms in real-life scenarios. Highlights simulations are designed to replicate the unpredictable nature of human driving, enabling AI systems to learn from a wide array of driving dynamics, including collisions and interactions with other vehicles. However, while simulations allow for extensive testing without the constraints of real-world data limitations, they also face challenges, particularly in accurately modeling rare edge cases and environmental factors that may act decision making. The integration of AI in track management systems further complicates the landscape, as these technologies analyze and optimize lane transitions to enhance overall track row and reduce emissions. Nevertheless, ethical considerations also arise in the design and deployment of AVs, particularly concerning decision-making in critical scenarios where harm is unavoidable. The need to reconcile these ethical dilemmas with technological advancements is crucial for the responsible development of autonomous driving systems. Overall, the comparison between real-world and simulated environments for AVs underscores the importance of utilizing both data sources electively. By integrating insights from real- world track scenarios with controlled simulations, researchers can enhance the reliability of AI systems, ultimately leading to safer and more ancient autonomous vehicle operations on our roads.
The Illusion of AI Authority: How ChatGPT Repeatedly Misinterprets the Bosnian Pyramids
The emergence of large language models like ChatGPT has transformed how the public and scholars interact with scientific knowledge. This study evaluates the consistency, accuracy and epistemological behavior of two generations of ChatGPT—version 3.5 (May 2023) and GPT-4o (May 2025) in response to a scientifically grounded, yet publicly controversial topic: the Bosnian Pyramid of the Sun. Despite improvements in language generation, both AI versions repeated unsupported claims, provided contradictory geological analogies and failed to acknowledge peer-reviewed evidence regarding the pyramid’s geometric precision and cardinal alignment. The comparison reveals limitations in machine learning's ability to meaningfully “learn” from expert input or correct misinformation over time. While GPT-4o showed a more refined tone and a greater willingness to concede error, it ultimately echoed the same core biases as its predecessor. These findings raise concerns about the use of AI as an authority in controversial or emerging scientific domains.
Advancement In Mechanical Properties of Bioplastics Using Brown Algae and Eggshells— A Sustainable Method
Plastic pollution poses a major threat to ecosystems and human health due to the slow decomposition of conventional plastics. This study aims to develop a sustainable bio-plastic with enhanced mechanical properties using sodium alginate extracted from brown algae and calcium carbonate from organic eggshells. The bioplastic was fabricated and tested for tensile strength, water absorbability and biodegradability. The results revealed that the incorporation of sodium alginate and eggshell powder increased hanging strength by 1800% compared to control samples, while biodegradability improved significantly, with complete degradation achieved within seven days. However, an increase in water absorbability was observed, attributed to the hydrophilic nature of sodium alginate. Future studies will focus on enhancing the hydrophobic properties and thermal strength of the bioplastic. These findings contribute to the growing efforts to develop affordable, high-performance biodegradable plastics, supporting sustainability goals in the United Arab Emirates and beyond.
AI Models for Software Defect Prediction: Comparative Study
Predicting software defects is crucial in modern software engineering, enabling organizations to boost reliability while minimizing maintenance costs. Traditional methods like static code analysis and rule-based techniques often yield high false positive rates and limited applicability across projects. Recent machine learning advances have led to automated defect prediction techniques using supervised and unsupervised models, achieving higher accuracy. This study compares the prediction capabilities of Random Forest, Support Vector Machines, Neural Networks, K-means Clustering and Autoencoders using NASA MDP and PROMISE datasets. Performance metrics included accuracy, precision, recall, F1-score and ROC-AUC. Results show supervised models, particularly Neural Networks (94.2% accuracy, 92.5% F-score), outperform unsupervised models in defect prediction. However, unsupervised approaches like Autoencoders show promise for anomaly detection in large codebases. The findings suggest that combining supervised and unsupervised learning into hybrid models could further enhance defect detection. The study also explores addressing class imbalance using SMOTE oversampling, ensuring reliable predictions. The results offer insights for industry practitioners and researchers on applying AI-driven defect prediction models for software quality assurance, potentially improving real-world defect detection capabilities.
SIRRIPA: The Stock-Tailored Yield to Maturity (YTM) and the Emergence of a Cross-Asset Valuation Metric
This paper introduces the total Stock Internal Rate of Return Including Price Appreciation (SIRRIPA), a new, comprehensive metric for equity valuation derived from the Potential Payback Period (PPP) methodology. SIRRIPA consolidates the present value of future earnings and capital appreciation into a single, annualized return figure. Conceptually and mathematically, SIRRIPA plays for equities the same role that Yield to Maturity (YTM) plays for bonds. While YTM discounts future coupon payments and face value to equate them with the bond's current price, SIRRIPA applies the same principle to Earnings Per Share (EPS) and an expected exit price. This unified return metric represents a groundbreaking cross-asset tool, enabling direct and consistent comparisons between stocks and bonds and supporting more coherent portfolio strategies and efficient capital allocation.
Challenging Conventional Wisdom: How a Stock with a P/E Ratio of 200 Can Outperform One with a P/E Ratio of 20 and How an Entire Stock Market Can Defy Expectations
This article challenges the conventional wisdom that a high P/E ratio necessarily signals poor investment potential and that elevated market valuations always precede correction. By introducing the Potential Payback Period (PPP) and the Stock Internal Rate of Return (SIRR), it offers a forward-looking framework that accounts for earnings growth, interest rates and risk — elements neglected by traditional metrics like the P/E ratio, PEG ratio and Earnings Yield. Using data as of January 24, 2025, the article demonstrates how Broadcom (P/E 188) outperformed Applied Materials (P/E 22) thanks to superior fundamentals and how the S&P 500 was not overvalued, despite appearances. The retrospective value of this analysis is heightened by the political and market volatility that followed, showing how entire markets — as well as individual stocks — can defy expectations when properly understood through dynamic valuation metrics. The findings are further contextualized in a companion piece on the bear market triggered by the Trump administration’s early economic agenda.
Cross-Border Marriages and Divorces: Navigating Legal and Social Challenges
This paper explores the complex legal and social issues involved in cross-border divorces and marriages, contextualized by globalization, migration and intersecting cultural, religious and socioeconomic factors. With the increase in transnational marriages owing to mobility and economic inequalities, they face jurisdictional conflicts, competing legal systems and financial settlements following divorce. Legal challenges forum shopping, foreign divorce recognition and religion-based personal laws-cross with social issues such as cultural adaptation, gender inequities and domestic violence fuelled by the stress of migration. The critique emphasizes how economic marginalization, especially for women, determines patterns in these marriages, with family networks and business brokers serving as intermediaries influencing marriage migration in many cases. Ethnographic and case law studies uncover renegotiation of transnational gender roles, citizenship and belonging within social spaces, while economic settlements highlight differences in spousal rights and duties between jurisdictions. Through the blending of bio-politics, human rights and gender justice paradigms, the research emphasizes the importance of policies that remediate systemic discrimination, safeguard marginalized groups and harmonize global legal standards.
Fashion Fusion: AI-Based Clothing Pattern Creator
The clothing industry demands unending novelty in attire designs to meet developing client advantages. However, established forms of founding attire patterns are labor-exhaustive, high-priced and frequently impassable to tiny designers and arising fashion brands. Fashion Fusion presents an AI-compelled answer that automates the production of singular and superior attire patterns, leveraging the capacity of deep education and fruitful models. By streamlining the design process, this method allows designers to speedily original creative patterns, lowering two together opportunity and cost. The manifesto democratizes approach to progressive design finishes, enabling consumers to forge embodied designs outside the need for thorough mechanics knowledge. With this resolution, the impediments to fashion artistry are considerably reduced, promoting a more all-embracing and vital design environment.
Redefining Data Management with BDaaS-Big Data As-a-Service A Deep Dive into BDaaS Governance, Compliance and Security
Big Data as a Service (BDaaS) is reshaping data management by providing scalable, cost-effective solutions that enable organizations to leverage the full potential of big data. This paper investigates the integration of BDaaS with critical aspects of data governance, compliance and protection. It explores the strategic frameworks and methodologies for effectively implementing BDaaS, highlighting its role in improving data accessibility, analytics and decision-making processes. The paper further examines real-world applications across industries and best practices. Additionally, it also identifies challenges and risks associated with BDaaS, such as data security, privacy concerns, regulatory compliance and operational complexities. Via an in-depth analysis, this study aims to offer an overall perspective on how BDaaS can be integrated into modern data ecosystems while ensuring compliance with data protection laws, minimizing risks and optimizing data governance strategies.
Subatomic Particle Simulations using Monte Carlo and Molecular Dynamics Algorithms to Simulate Stable Atom and Model Electronic Structures
A novel approach was presented in this study where molecular dynamics and Monte Carlo methods were applied to subatomic particles to simulate an atom using pseudo potentials. Pseudo potentials were developed for subatomic particles by conceptualizing them as conventional particles, exhibiting attractive and repulsive forces between them, ensuring the stability of an atom. A stable nucleus was formed at the center with electrons distributed around, resulting in the formation of an atom. Subatomic particle simulations impart a comprehensive perspective and a profound understanding of electron trajectories that correlates with atomic properties such as electron energies and atomic radius. These approaches intricately capture the impact of protons and neutrons motion in the nucleus on electron trajectories. Hydrogen and carbon atoms were considered and their analyses were reported in this study. Time step for carbon atom simulation was calculated from dimensionless variables and found to be 1.67 attoseconds. The Pilot-wave theory was implemented to simulate the wave nature of subatomic particles in an atom. Electrons motion was guided by the interference pattern produced by the electron and proton aether medium waves. Molecular dynamics simulations on subatomic particles were implemented on an oxygen molecule, giving insights into electronic structures with electron trajectories shared by two atoms.
Kinetic Analysis of Reduction Reaction of MnO from High Carbon Ferromanganese Slag using Fractional Differential Equations (FDE)
In this paper, a kinetic model for the reduction process by solid carbon of MnO in high-carbon ferromanganese slags using Fractional Differential Equations (FDE) was developed, the relationship between fractional order, fractional rate constant and temperature was determined and the accuracy of the fractional order model was verified. The fractional order q is 0.892, 0.808 and 0.522, at 1,450 ℃, 1,500 ℃ and 1,600 ℃ respectively and the fractional rate constant kq 1.176E-03, 2.856E-03 and 3.477E-02. A linear relationship exists between the fractional order and the temperature and an exponential relationship exists with the fractional rate constant. Comparing the conversion factors calculated from the FDE and previous model with the experimental values, the RMSE were 0.005 and 0.029, respectively and the r2 0.999 and 0.980. This means the FDE model is more accurate. The apparent activation energy of the MnO reduction process calculated using the model was 181.1 kJ/mol.
A Comparative Analysis of SSL/TLS and Blockchain-Based Approaches for Web-based Transactions
This study examines how well SSL/TLS and blockchain work to secure online transactions, especially purchase orders. Reviewing literature from 2013 to 2024 shows each method's key themes, benefits, and weaknesses. SSL/TLS is known for its strong encryption and solid framework but often has issues like centralization and threats from different attacks, leading to transaction delays and bottlenecks. On the other hand, blockchain technology uses decentralized protocols and features such as zero-knowledge proofs, promising better scalability and security, allowing for smooth and safe transactions without traditional middlemen. This comparison clarifies how effective each method is. It highlights the rise of blockchain as a viable option to the limits of SSL/TLS, deserving more study in e-commerce security.
Enhanced Mechanical Properties of Single Carbon Fibres via Electromagnetic Treatment: A Comparative Analysis
This study investigates the effect of Electromagnetic Field Treatment (EMFT) on the mechanical properties of polyacrylonitrile (PAN)-based single carbon fibres, which are critical materials in high-performance composites widely utilised in the aerospace and automotive sectors due to their superior strength and stiffness. Carbon fibres were subjected to controlled electromagnetic field exposure, with both treated and untreated fibres rigorously evaluated through tensile testing. The treated fibres exhibited a notable 6.12% increase in yield strength, along with a substantial improvement in tensile modulus compared to the control samples. Scanning Electron Microscopy (SEM) analysis revealed a smoother surface morphology in the treated fibres, potentially contributing to enhanced flexibility and a reduction in microscale defects. These findings suggest that EMFT may serve as an effective technique for optimising the mechanical performance of carbon fibres, thereby enhancing their suitability for advanced applications.
A Real-Time Computer Vision-Based System for Detecting Defects in Injection-Molded Products
This study proposes an automated system for detecting and classifying defects in products manufactured through the injection molding process. Existing deep learning-based detection technologies provide high accuracy, but have limitations in that they require massive data and complex learning processes, and require re-learning when new products are introduced. On the other hand, simple image processing techniques can be applied quickly without a learning process, but there are limits to accuracy when defect types are complex or diverse. To solve this problem, this study introduced advanced computer vision technology to overcome the limitations of existing technology and designed a system that excludes the complexity of deep learning. In particular, this system combines various pre-processing and post-processing techniques such as grayscale conversion, binarization, Gaussian filtering, and histogram smoothing to simultaneously improve the accuracy and efficiency of defect detection. In addition, it was designed to quickly detect defects occurring in the manufacturing process through a system architecture that optimizes real-time processing. Using ultrasonic sensors and automated conveyor belts, the product movement and detection process is precisely controlled, enabling real-time data processing and product classification. As a result of the experiment, this system significantly reduced detection delays and classification errors that commonly occurred in existing research, and demonstrated its potential to contribute to quality control and productivity improvement in a manufacturing environment.
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