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Techne: Journal of Engineering, Technology and Industrial Applications
Published by Kalam Practica Media
ISSN : -     EISSN : 31246559     DOI : -
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation
Techne: Journal of Engineering, Technology and Industrial Applications is a peer-reviewed open-access journal dedicated to advancing scholarly work in engineering, applied technology, and industrial innovation. Techne publishes high-quality empirical research, technical reports, experimental studies, design analyses, case studies, and emerging technology reviews that contribute to the development and application of engineering solutions. The journal aims to bridge academic research and real-world technological practices by providing a platform for researchers, engineers, practitioners, and industry experts to disseminate new insights. Techne encourages submissions that demonstrate originality, methodological rigor, and practical value in supporting technological growth and industrial problem-solving. All submitted manuscripts undergo an objective and constructive review process conducted by experts in relevant fields. Techne is published by the Kalam Practica Research Group and operates under an open-access policy to ensure global accessibility of scientific knowledge. Scopes include (but are not limited to): Mechanical, electrical, and civil engineering Industrial systems and manufacturing technology Information technology and applied computing Automation, robotics, and control systems Energy systems and sustainable engineering Material science and industrial design Geospatial engineering and technical instrumentation Innovation, prototyping, and applied industrial research Techne welcomes interdisciplinary works and studies that integrate engineering with technology-driven industrial applications.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 48 Documents
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Healthcare Emergency Triage: Quantifying Safety, Throughput, and Equity Risk Under Uncertainty, Crowding, and Decision Latency Sreyneang Keo Sreyneang Keo
Techne: Journal of Engineering, Technology and Industrial Applications Vol. 1 No. 4 (2025): Techne: Journal of Engineering, Technology and Industrial Applications
Publisher : Kalam Practica Media

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Abstract

This article proposes an engineering-oriented reliability framework for emergency triage that models end-to-end uncertainty propagation from initial symptoms and vital signs through decision rules, reassessment intervals, queueing dynamics, and escalation policies into distributional outcomes that matter for safety and service performance, including probability of under-triage, time-to-provider exceedance probability, adverse clinical event risk before definitive evaluation, over-triage burden, and an operational cost and harm index that integrates patient risk with resource strain. A scenario-based quantitative study is developed for a generic high-volume emergency department serving mixed-acuity adult patients, comparing four triage architectures: baseline scale-based triage with static rules and discretionary reassessment, expanded screening with more data but without governance, calibrated risk scoring with static thresholds, and a governance-optimized two-tier system that combines calibrated risk scoring, explicit uncertainty handling, capacity-aware dynamic thresholds, staged reassessment with trigger-based escalation, and safety-bounded operational controls. Results show that adding data or algorithms without governance can increase volatility and over-triage under crowding, that calibrated scoring improves stability but is fragile when queueing regimes shift, and that the two-tier governed architecture reduces under-triage and time-to-provider exceedances while stabilizing resource use and reducing inequitable failure patterns under documentation noise and surge conditions. Three copy-ready tables and complete prompts for data-driven figures are provided for Techne submission.
Semiconductor Wafer Fab Yield: Quantifying Defect Escape, Metrology Uncertainty, and Time-to-Containment Under Process Drift and Inspection Capacity Constraints Lê Thị Hồng Nhung Lê Thị Hồng Nhung
Techne: Journal of Engineering, Technology and Industrial Applications Vol. 1 No. 4 (2025): Techne: Journal of Engineering, Technology and Industrial Applications
Publisher : Kalam Practica Media

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Abstract

This article presents an engineering-oriented reliability framework for wafer fab yield management that models end-to-end uncertainty propagation from process drift and measurement uncertainty through sampling-based inspection, excursion detection, and containment decisions into distributional outcomes relevant to manufacturing performance, including probability of defect escape, expected affected wafers before containment, false containment probability, time-to-detection and time-to-containment distributions, and an economic yield-loss index. A scenario-based quantitative study is developed for a generic high-volume fab with multiple critical tools and a mix of in-line metrology and inspection, comparing four architectures: baseline control charts with fixed sampling, expanded inspection without governance, model-based excursion detection with limited capacity awareness, and a governance-optimized two-tier architecture that combines drift-aware metrology validation, dynamic sampling allocation based on risk and tool health, staged containment policies, and capacity-aware triage for engineering review. Results show that increasing inspection without governance can reduce defect escape but can increase false containment and cycle-time penalties, that model-based detection improves time-to-detection but can fail under miscalibration and review overload, and that a two-tier governed approach reduces expected yield loss by reducing tail propagation and stabilizing containment decisions under drift and capacity constraints. Three copy-ready tables and complete prompts for data-driven figures are provided for Techne submission.
Assessing Data Center Infrastructure Readiness Through Energy, Reliability, and Scalability Metrics Ahmed Al-Mansouri
Techne: Journal of Engineering, Technology and Industrial Applications Vol. 2 No. 1 (2026): Techne: Journal of Engineering, Technology and Industrial Applications
Publisher : Kalam Practica Media

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Abstract

Data centers have become critical infrastructure for digital services, yet their expansion increases pressure on electricity systems, cooling capacity, system resilience, and service scalability. Infrastructure readiness cannot be assessed only through facility availability or energy efficiency because digital service continuity depends on the interaction between power use, thermal stability, redundancy, and workload growth capacity. This study developed and tested a quantitative experimental readiness assessment model for data center infrastructure based on energy efficiency, system reliability, and digital service scalability. A simulated experimental dataset representing 30 operational scenarios was generated from a medium-scale data center configuration with five workload levels, three cooling setpoint strategies, and two redundancy configurations. Energy efficiency was measured using Power Usage Effectiveness, cooling load ratio, and server utilization. Reliability was measured using modeled availability, mean time between failure, mean time to repair, and thermal incident frequency. Scalability was measured using workload absorption capacity, latency stability, and resource headroom. A composite Data Center Infrastructure Readiness Index was calculated using normalized weighted scores. The optimized configuration produced the highest readiness score of 84.72 out of 100, with mean Power Usage Effectiveness of 1.42, modeled availability of 99.982%, and workload absorption capacity of 81.30%. The baseline configuration scored 68.45, while the constrained configuration scored 57.18. One-way analysis of variance indicated statistically significant differences among configurations for readiness score, energy efficiency, and scalability indicators. The proposed model offers a replicable engineering approach for assessing data center readiness before facility expansion, workload migration, or digital service scaling.
Technical SEO as Web Systems Engineering for Crawlability, Indexability, and Platform Performance Samuel N. Kato
Techne: Journal of Engineering, Technology and Industrial Applications Vol. 2 No. 1 (2026): Techne: Journal of Engineering, Technology and Industrial Applications
Publisher : Kalam Practica Media

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Abstract

Technical search engine optimization has increasingly shifted from keyword-oriented optimization toward web systems engineering, where crawlability, indexability, rendering efficiency, structured data, and performance architecture determine whether digital platforms can be discovered, processed, and served effectively by search systems. This study examined the effect of technical SEO engineering indicators on digital platform performance using a simulated panel dataset of institutional and commercial websites. The study used a balanced panel design covering 12 websites observed monthly across 12 periods, producing 144 website-month observations. The dependent variable was organic visibility performance, measured as a composite score of impressions, indexed pages, organic clicks, and average ranking stability. Independent variables included crawl efficiency, index coverage, Core Web Vitals pass rate, structured data validity, mobile usability, internal link depth, sitemap health, canonical consistency, and server response time. Fixed-effects and random-effects panel regressions were estimated, followed by Hausman testing, variance inflation diagnostics, and robustness checks using lagged technical indicators. Fixed-effects estimation showed that index coverage, Core Web Vitals pass rate, structured data validity, and crawl efficiency had positive and statistically significant effects on organic visibility performance. Server response time had a negative significant effect. The optimized technical SEO configuration increased the mean visibility score from 61.84 to 78.26 over the observation window. Technical SEO can be conceptualized as a web systems engineering approach because search visibility depends on measurable infrastructure, architecture, and performance conditions. The findings support integrating technical SEO audits into engineering governance for digital platforms.
Integrated Digital Transformation System Model for Technology, Process, and Capability Alignment Rakesh Pradhan
Techne: Journal of Engineering, Technology and Industrial Applications Vol. 2 No. 1 (2026): Techne: Journal of Engineering, Technology and Industrial Applications
Publisher : Kalam Practica Media

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Abstract

Digital transformation has become a strategic engineering and organizational challenge because technology adoption alone rarely produces sustainable performance improvement. Organizations need an integrated system model that aligns digital technologies, business processes, and organizational capabilities. This study designed a Digital Transformation System Model that explains how technology architecture, process redesign, and organizational capability development can be integrated into a coherent transformation system. A structured literature review was conducted using 42 peer-reviewed and high-relevance academic sources published between 2013 and 2025. The review followed identification, screening, eligibility, coding, synthesis, and model-building stages. Thematic coding was applied to classify transformation components into technology, process, capability, governance, value creation, and feedback mechanisms. The review identified three dominant transformation layers: digital technology infrastructure, business process reconfiguration, and organizational capability orchestration. Technology-related constructs appeared in 83.33% of reviewed studies, process-related constructs in 66.67%, and capability-related constructs in 78.57%. The proposed model links digital infrastructure, data integration, process modularity, agile governance, workforce capability, analytics capability, and performance feedback into a closed-loop transformation system. Digital transformation should be designed as a socio-technical system rather than as isolated technology implementation. The proposed model offers a structured framework for organizations seeking to align digital investments with process performance, capability maturity, and measurable transformation outcomes.
AI-Enabled Predictive Maintenance Framework for Industrial Sensor Data and Operational Analytics Elena M. Petrova
Techne: Journal of Engineering, Technology and Industrial Applications Vol. 2 No. 1 (2026): Techne: Journal of Engineering, Technology and Industrial Applications
Publisher : Kalam Practica Media

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Abstract

Industrial systems increasingly rely on sensor networks, cyber-physical infrastructure, and operational analytics to maintain equipment reliability under complex production conditions. Predictive maintenance has become a central Industry 4.0 strategy because artificial intelligence can detect degradation patterns, forecast failures, and support maintenance decisions before breakdowns occur. This study developed an integrated literature-based framework for applying artificial intelligence to predictive maintenance in industrial systems using sensor data and operational analytics. A structured literature review was conducted on 45 academic sources published between 2015 and 2025. The review coded studies according to data source, algorithmic approach, industrial application, prediction task, evaluation metric, deployment barrier, and decision-support function. The synthesis produced a multi-layer framework linking sensor acquisition, data preprocessing, feature engineering, model development, remaining useful life estimation, anomaly detection, maintenance decision analytics, and feedback learning. Machine learning appeared in 73.33% of reviewed studies, deep learning in 57.78%, remaining useful life prediction in 62.22%, anomaly detection in 48.89%, and hybrid or physics-informed approaches in 28.89%. The proposed framework identifies five critical implementation layers: sensing infrastructure, data engineering, artificial intelligence modeling, operational decision support, and continuous reliability feedback. Artificial intelligence strengthens predictive maintenance when it is embedded into an industrial analytics pipeline rather than treated as an isolated prediction model. The proposed framework can support industrial managers, engineers, and maintenance planners in designing scalable and trustworthy predictive maintenance systems.
Industrial System Evaluation Model for Process Efficiency, Reliability, and Output Quality Measurement Michael O. Adebayo
Techne: Journal of Engineering, Technology and Industrial Applications Vol. 2 No. 1 (2026): Techne: Journal of Engineering, Technology and Industrial Applications
Publisher : Kalam Practica Media

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Abstract

Industrial systems require integrated performance evaluation because production outcomes are shaped by the interaction between process efficiency, equipment reliability, and output quality. Conventional performance measurement often isolates productivity, downtime, or defect indicators, which limits the ability to diagnose system-level operational readiness. This study designed and tested an Industrial System Evaluation Model to measure process efficiency, production reliability, and output quality using a panel data approach. A balanced simulated panel dataset was developed from 10 production lines observed over 12 monthly periods, producing 120 line-month observations. The model integrated three dimensions: Process Efficiency Score, Production Reliability Score, and Output Quality Score. Fixed-effects and random-effects panel regressions were estimated to evaluate the relationship between operational indicators and the composite Industrial System Evaluation Index. The Hausman test, variance inflation diagnostics, robustness checks, and sensitivity analysis were used for validation. Results: The optimized production-line group achieved the highest mean evaluation score of 82.46, compared with 69.38 for the baseline group and 58.72 for the constrained group. Fixed-effects estimation showed that throughput achievement, cycle-time adherence, machine availability, mean time between failure, first-pass yield, and defect reduction significantly improved the composite index. Mean time to repair and unplanned downtime had significant negative effects. The proposed model provides a replicable panel-data-based framework for evaluating industrial systems as integrated operational systems rather than as separate efficiency, reliability, or quality domains.
Workload Analysis Using The FT (Full Time Equivalent) Method To Determine The Effective Employee Requirements At Pt. Bank Rakyat Indonesia Nabil Habieby
Techne: Journal of Engineering, Technology and Industrial Applications Vol. 2 No. 2 (2026): Techne: Journal of Engineering, Technology and Industrial Applications
Publisher : Kalam Practica Media

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Abstract

Human resources (HR) are a strategic factor that determines the success of an organization in achieving its goals and maintaining performance sustainability, especially in service sectors such as banking. This study aims to analyze employee workload using the Full Time Equivalent (FTE) method to determine the ideal number of employees at PT Bank Rakyat Indonesia. This research uses a descriptive quantitative approach through observation, interviews, and documentation. Workload analysis is conducted by calculating effective working time and work activities in each employee position. The results show that several employee positions experience excessive workload conditions (overload) with FTE values >1.28. Meanwhile, some other employee positions are categorized as normal. This study recommends adding staff to positions with excessive workload to improve work effectiveness and service quality. The implementation of the FTE method has been proven effective in supporting workforce planning decisions objectively and systematically in corporate institutions.

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