Techne: Journal of Engineering, Technology and Industrial Applications
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 Media 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.
Green Manufacturing Practices and Sustainable Industrial Performance: A Mixed-Methods Study of Environmental Innovation in Indonesian Manufacturers
This study examined how green manufacturing practices influence sustainable industrial performance through environmental innovation implementation in Indonesian manufacturing firms. A sequential explanatory mixed-methods design was applied. Quantitative data were collected from 128 managers and supervisors in 24 manufacturing firms using a structured questionnaire and analyzed with partial least squares structural equation modeling. Qualitative evidence came from 16 semi-structured interviews with production, quality, maintenance, and environmental managers from high- and moderate-adopting firms. Green manufacturing practices were strongly associated with environmental innovation implementation (beta = 0.63, p < .001) and directly with sustainable industrial performance (beta = 0.29, p < .001). Environmental innovation implementation also predicted sustainable industrial performance (beta = 0.47, p < .001) and partially mediated the relationship between green manufacturing and performance. The model explained 40% of the variance in environmental innovation implementation and 69% of the variance in sustainable industrial performance. Interview data showed that energy monitoring, waste segregation, process digitization, and supplier coordination were the main levers of improvement, while capital intensity and skills gaps remained the main constraints. Green manufacturing contributed to higher sustainable industrial performance when firms translated environmental intentions into process-level innovations and measurement routines. The findings support a capability-based view of sustainability implementation and indicate that managerial attention should shift from symbolic environmental claims to operational redesign, data visibility, and cross-functional discipline.
Copyright (c) 2026 Sokha Sopheat (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Workload Analysis Using The FT (Full Time Equivalent) Method To Determine The Effective Employee Requirements At Pt. Bank Rakyat Indonesia
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.
Copyright (c) 2026 Nabil Habieby (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Industrial System Evaluation Model for Process Efficiency, Reliability, and Output Quality Measurement
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.
Copyright (c) 2026 Michael O. Adebayo (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Enabled Predictive Maintenance Framework for Industrial Sensor Data and Operational Analytics
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.
Copyright (c) 2026 Elena M. Petrova (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Integrated Digital Transformation System Model for Technology, Process, and Capability Alignment
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.
Copyright (c) 2026 Rakesh Pradhan (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Technical SEO as Web Systems Engineering for Crawlability, Indexability, and Platform Performance
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.
Copyright (c) 2026 Samuel N. Kato (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Assessing Data Center Infrastructure Readiness Through Energy, Reliability, and Scalability Metrics
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.
Copyright (c) 2026 Ahmed Al-Mansouri (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Semiconductor Wafer Fab Yield: Quantifying Defect Escape, Metrology Uncertainty, and Time-to-Containment Under Process Drift and Inspection Capacity Constraints
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.
Copyright (c) 2025 Techne: Journal of Engineering, Technology and Industrial Applications https://creativecommons.org/licenses/by/4.0
Healthcare Emergency Triage: Quantifying Safety, Throughput, and Equity Risk Under Uncertainty, Crowding, and Decision Latency
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.
Fintech Transaction Fraud Decision: Quantifying Loss, Friction, and Detection Risk Under Drift, Adversarial Adaptation, and Operational Latency
This article presents an engineering-oriented framework that models fraud prevention as an end-to-end reliability system and quantifies how model uncertainty, drift, and decision latency propagate into distributional outcomes relevant to operations and governance, including fraud capture rate, false decline rate, expected net loss, customer friction cost, review workload, and time-to-decision. A scenario-based quantitative study is developed for card-not-present and account-to-account style transactions across normal and disruption regimes, comparing four architectures: baseline rules with static thresholds, ML scoring without calibration or governance, calibrated ML with cost-sensitive thresholds, and a governance-optimized two-tier architecture that combines calibrated risk scoring, uncertainty-aware routing to manual review, step-up verification for medium-risk cases, and dynamic thresholding under drift detection. Results show that ungoverned model deployment can reduce fraud loss but increase friction and false declines during drift, that calibrated thresholds improve stability but remain vulnerable when review capacity saturates, and that a two-tier governed approach reduces net loss while stabilizing customer impact and operational workload, particularly during fraud surges. Three copy-ready tables and complete prompts for data-driven figures are provided for Techne submission.
Risk-Aware Quality Assurance in Wastewater Treatment: Modeling Effluent Exceedance Probability Under Stochastic Influent and Sensor Decay
This article presents an engineering-oriented framework that treats wastewater treatment control as an end-to-end decision and reliability system, quantifying how uncertainty propagates from sensing and influent disturbances through control actions and process dynamics into distributional outcomes that matter operationally: probability of exceeding effluent thresholds for ammonia, total nitrogen, and phosphate; time-above-limit; nuisance alarm rate; energy and chemical cost index; and time-to-recovery under upset events. A scenario-based quantitative study is developed for a generic activated sludge facility with nitrification–denitrification and chemical phosphorus removal, comparing four control architectures: baseline PID with fixed setpoints, increased sensor deployment without drift governance, model-based soft-sensing and predictive control with limited alarm governance, and a governance-optimized two-tier architecture that combines drift-aware sensor validation, redundancy and plausibility checks, event-segmented control actions, and staged alarms aligned to compliance risk rather than raw sensor thresholds. Results demonstrate that compliance risk is dominated by tail behavior in influent and by sensing drift interacting with slow biological dynamics, that adding sensors without governance can increase nuisance interventions and destabilize operation, and that the two-tier governed architecture reduces exceedance probability while lowering unnecessary chemical dosing and stabilizing operator workload. Three copy-ready tables and complete prompts for data-driven figures are provided for Techne submission.
E-Commerce Fulfillment Reliability: Quantifying Order Promise Risk Under Inventory Inaccuracy, Pick-Pack Variability, and Carrier Uncertainty
This article presents an engineering-oriented framework that models end-to-end promise fulfillment as uncertainty propagation across (i) inventory record confidence and SKU-level availability risk, (ii) pick-pack execution time variability including congestion and exception loops, and (iii) lane-dependent carrier transit time distributions that exhibit heavy tails and disruption regimes. The framework evaluates operational architectures in terms of distributional service outcomes relevant to engineering management, including probability of promise violation, conditional lateness severity, stockout-at-pick cancellation probability, wrong-item defect probability, and cost index under normal and disrupted conditions. A scenario-based quantitative study is developed for a generic multi-node network with two fulfillment centers and one drop-ship node serving standard and expedited commitments, and four architectures are compared: baseline fixed-buffer promise logic, increased automation without governance redesign, distribution-aware (quantile) promise setting with limited inventory control, and a governance-optimized two-tier approach that integrates inventory confidence scoring, staged verification for low-confidence commitments, dynamic routing under congestion risk, and dynamic carrier selection based on lane variance. Results show that reliability gains are driven more by controlling promise tail risk and preventing low-confidence inventory commitments than by improving mean pick rates alone, that adding automation without revising promise governance can increase mispromises during disruptions, and that the two-tier governed architecture reduces promise violations and cancellations while lowering exception-driven defects and stabilizing labor escalation. The article provides three copy-ready tables and complete prompts for scientific, data-driven figures suitable for Techne submission.
Engineering for Cosmetic Product Quality and Supply Continuity: Modeling Defect Escape, Batch Release Latency, and Complaint Risk Under Measurement Uncertainty
This article presents an engineering-oriented framework that treats cosmetic quality assurance as an end-to-end decision system and quantifies how uncertainty propagates from incoming raw material variability through in-process controls, laboratory testing, and release decisions into distributional outcomes relevant to production and brand risk, including probability of defect escape, probability of false rejection, time-to-release distributions, complaint incidence risk, and expected cost per batch. A scenario-based quantitative study is developed for representative skin-care and color cosmetic products across three risk profiles, comparing four operational architectures: baseline end-of-line testing, increased testing density without governance, model-based risk scoring with limited drift handling, and a governance-optimized two-tier approach that constrains nuisance rejections while improving early detection through drift-aware controls, stratified sampling, and staged release decisions. Results indicate that defect escape is dominated by sampling and release latency tails rather than by mean lab accuracy, that expanding tests without governance can increase false rejection and slow release, and that a two-tier governed strategy reduces defect escape while stabilizing throughput and complaint risk. Up to three copy-ready tables and full prompts for data-driven figures are provided for Techne submission.
Thermal in Cold Chain Logistics: Modeling Time-Above-Threshold Risk under Sensor Uncertainty, Door-Open Events, and Packaging Variability
This article presents an engineering-oriented framework that models cold chain thermal control as an end-to-end decision system and quantifies how uncertainty propagates from sensing and environment through exposure modeling and escalation logic into distributional outcomes that matter in operations, including probability of temperature threshold exceedance, expected time-above-threshold per shipment, probability of quality excursion beyond allowable exposure, and the effectiveness of mitigation actions such as pre-cooling, lane-specific packaging selection, and staged escalation during dwell. A scenario-based quantitative study is developed for generic refrigerated transport and cross-dock handling of high-risk perishables and temperature-sensitive products, comparing four operational architectures: baseline compliance logging, increased sensor density without governance, model-based exposure forecasting with limited drift handling, and a governance-optimized two-tier approach that constrains nuisance alarms while improving time-to-decision through drift-aware plausibility checks, door-event segmentation, and staged interventions. Results indicate that tail exposure behavior is dominated by door-open frequency and decision latency rather than by mean trailer temperature, that adding sensors without governance can increase workload and reduce response discipline, and that the two-tier governed approach reduces both exceedance probability and nuisance alarms while improving intervention timeliness, especially under high-variability loading conditions. Up to three copy-ready tables and full prompts for data-driven figures are provided for Techne submission.
Quantifying Defect Escape Risk in Additive Manufacturing: The Impact of Sensor Uncertainty and Control Latency
This article presents an engineering-oriented framework that models AM quality control as an end-to-end decision system, quantifying how uncertainty propagates from monitoring signals through feature extraction, thresholds, verification inspection, and corrective interventions into distributional outcomes that matter in production: probability of defect escape, probability of false alarm, scrap risk, rework burden, time-to-detection relative to layer deposition, and expected quality cost per part. A scenario-based quantitative study is developed for laser powder bed fusion (LPBF) manufacturing of safety-relevant components, comparing four quality control architectures: baseline post-build inspection, enhanced in-situ monitoring without governance, model-based anomaly scoring with limited drift handling, and a governance-optimized two-tier architecture that constrains nuisance alarms, uses drift-aware verification triggers, and applies staged corrective actions aligned with evidence strength and layer timing. Results show that (i) defect escape risk is dominated by decision latency and sampling limitations rather than by average signal quality, (ii) adding sensors without governance can increase scrap and operator fatigue due to nuisance triggers, and (iii) a governed two-tier approach reduces defect escape while stabilizing operational workload by using quantile-based alarm governance and early-stage corrective action windows. Up to three copy-ready tables and figure prompts are provided for Techne submission.
Dispatch Reliability in Commercial Aviation Maintenance Operations: Quantifying Delay Risk under Fault Uncertainty, MEL Governance, and Spare-Part Constraints
This article presents a reliability-centered engineering framework that treats maintenance dispatch as an end-to-end decision pipeline, explicitly modeling uncertainty propagation from fault detection and diagnostic confidence through MEL deferral decisions, spares availability, maintenance task duration variability, and crew scheduling constraints into distributional outcomes that operations manage, including probability of delay exceeding defined thresholds, expected delay minutes per departure, probability of cancellation, time-to-release distributions, and nuisance troubleshooting burden. A scenario-based quantitative study is developed for a representative narrow-body fleet operating a hub-and-spoke schedule, comparing four dispatch decision architectures: baseline reactive troubleshooting, enhanced diagnostics without governance, risk-based dispatch supported by confidence scoring, and a governance-optimized two-tier approach that constrains nuisance maintenance actions while preserving safety and compliance through standardized MEL decision rules, verification triggers, and spare-part risk pooling. Results show that (i) tail delay outcomes are dominated by diagnosis and spares-induced recovery latency rather than by mean task time, (ii) increased diagnostic messages without governance can increase nuisance actions and worsen punctuality, and (iii) the strongest reliability gains come from standardizing decision governance and aligning escalation with evidence confidence rather than from analytics alone. The paper provides up to three copy-ready tables and full prompts for data-driven figures suitable for Techne submission.
Performance Metrics for Leak Detection and Pressure Control in Urban Water Networks: Assessing Latency, Drift, and Response Efficacy
This article presents an engineering-oriented framework that treats leak detection and pressure management as an end-to-end decision system, modeling uncertainty propagation from instrumentation through inference and threshold governance into outcomes that utilities actually manage, including probability of missed leaks, false alarm rate, time-to-detection and time-to-intervention distributions, expected volume loss before containment, and probability of customer pressure noncompliance under mitigation actions. A scenario-based quantitative study is developed for a generic but realistic distribution network with district metered areas and pressure zones, using flow and pressure telemetry augmented by periodic acoustic surveys, and four operational architectures are compared: baseline fixed-threshold minimum night flow, increased sensing without governance, model-based residual detection with limited drift management, and a governance-optimized two-tier architecture that combines nuisance-constrained thresholds, drift-aware plausibility checks, adaptive confirmation sampling, and staged pressure interventions aligned with evidence strength. Results show that (i) leakage loss and customer impact are dominated by tail behavior in detection and verification latency rather than by mean leak rate, (ii) sensor drift and baseline instability drive false stability that delays detection when fixed thresholds are used, and (iii) reliability improves most when governance reduces long-tail verification times and when pressure management is staged to reduce loss without causing pressure violations. The paper provides copy-ready tables and complete prompts for data-driven figures suitable for Techne submission and for adaptation to utility telemetry datasets.
Integrated Decision Framework for Railway Signaling and Train Protection: Modeling Latency, Detection Uncertainty, and Capacity-Safety Trade-Offs Under Faults
This article presents an engineering-oriented framework that treats railway signaling as an end-to-end decision pipeline and quantifies how uncertainty propagates through occupancy detection, train localization, interlocking logic, radio block center messaging, onboard supervision, and operational recovery procedures to determine risk-relevant metrics such as probability of separation violation, probability of spurious braking, time-to-restrict and time-to-restore distributions, and expected capacity degradation during degraded modes. A scenario-based quantitative study is developed around three representative architectures that span fixed-block signaling with track circuits, fixed-block signaling with axle counters, and a moving-block style supervision concept consistent with modern CBTC or ETCS-level architectures, and for each architecture the study compares baseline thresholds and governance against a reliability-governed strategy that uses nuisance-constrained decision limits, drift-aware plausibility checks, redundant evidence fusion, and staged interventions. Results show that the tail of recovery time and the tail of nuisance restriction duration dominate operational cost, while dangerous exposure is dominated by rare false-clear and localization integrity failures that become most consequential when decision latency is long and when degraded-mode rules are ambiguous or inconsistently applied. The paper provides copy-ready tables and full prompts for data-driven figures suitable for Techne submission, emphasizing applied engineering interpretation rather than purely theoretical safety discussions.
Data Center Thermal and Energy Efficiency: Modeling Sensor Degradation, Control Latency, and Hotspot Probability During Workload Transitions
This article develops an engineering-oriented framework that treats data center cooling as a reliability decision system, quantifying how uncertainty propagates through measurement, state estimation, threshold governance, and control execution to determine hotspot exceedance probability, excursion duration distributions, time-to-mitigation, nuisance interventions, and energy overhead. A scenario-based comparative quantitative study is presented for a representative row-based data hall with variable-speed CRAC/CRAH control and workload-driven power variability, comparing four operational architectures: baseline threshold control, increased sensing without governance, model-predictive control with limited drift handling, and a governance-optimized two-tier architecture combining nuisance-constrained alarms, drift-aware verification, workload-aware preemptive control, and staged mitigation actions. Results indicate that (i) tail risk of hotspot duration is dominated by control latency and sensor bias drift rather than by average temperature, (ii) dense sensing reduces random uncertainty but can increase nuisance intervention if alarms are not governed, and (iii) a governed two-tier strategy reduces hotspot tail risk while maintaining energy efficiency by shifting effort from disruptive interventions to bounded verification and preemptive setpoint shaping. The paper provides copy-ready tables and full prompts for data-driven figures suitable for Techne submission and adaptation to site-specific telemetry.
Decision Reliability for Real-Time Release in Pharmaceutical Manufacturing: Modeling Sensor Drift, Sampling Error, and Time-to-Release Under Quality Constraints
This article develops an engineering-oriented framework that treats real-time release as an end-to-end decision pipeline, quantifying how uncertainty propagates through measurement, inference, threshold governance, verification logic, and disposition actions to determine probability of false release, probability of false rejection, time-to-release, nuisance hold rate, and expected cost of quality under batch and continuous production contexts. A scenario-based quantitative study is presented for a representative solid oral dose process where near-infrared spectroscopy and inline weight/density sensing are used to infer critical quality attributes, and four operational architectures are compared: baseline fixed-threshold PAT, increased sampling without governance, model-based inference with limited drift management, and a governance-optimized two-tier architecture that combines nuisance-constrained thresholds, drift-aware verification, adaptive sampling, and staged disposition. Results show that quality risk is dominated by tail events in which drift or sampling bias produces false stability and delays intervention, while operational sustainability is dominated by the rate and duration of nuisance holds, and that a governed two-tier approach provides the best cost–risk balance by shrinking the tail of time-to-decision while controlling nuisance actions through explicit false-alarm constraints and verification pathways. The paper provides copy-ready results tables and full prompts for data-driven figures suitable for Techne submission and adaptation to site-specific datasets.
Decision in High-Variability Manufacturing: Integrating SPC, APC, and Metrology through Risk-Aligned Governance
This paper proposes a reliability-oriented framework that treats monitoring and control as an engineered decision pipeline rather than as a collection of independent charts and sensors. The framework integrates sampling design, uncertainty quantification, chart governance, verification logic, corrective action mechanisms, and escalation rules into a unified architecture evaluated by decision-relevant reliability metrics. We define quantitative measures for time-to-detection, expected lots-at-risk prior to intervention, false-hold burden, and time-to-disposition under constrained engineering resources. A representative case-based analysis compares alternative governance designs, including conservative versus risk-aligned thresholds, staged verification strategies, and coupling of SPC alerts with APC adjustments and metrology confirmation. Results indicate that reliability improvements are driven less by increased sensing density than by disciplined governance that constrains nuisance alarms while preserving early detection of sustained drift. Risk-aligned thresholds combined with verification tiers reduce lots-at-risk and maintain manageable hold rates, improving release decision traceability without degrading throughput. The study provides practical guidance for designing maintainable monitoring policies that minimize both escape risk and operational overload, with implications for high-mix, high-precision production environments where uncertainty and drift are unavoidable.
Condition-Based Maintenance in Offshore Wind Turbines: Modeling Fault Progression, Detection Latency, and Time-to-Repair Under Environmental and Sensor Uncertainty
This article presents a reliability-centered condition-based maintenance framework for offshore wind turbines that explicitly models how uncertainty propagates through monitoring, diagnostics, and maintenance scheduling to determine time-to-decision, probability of missed detection before functional failure, probability of opportunistic repair within access windows, and expected energy production loss. A quantitative scenario-based study is developed for drivetrain and power conversion subsystems, comparing four maintenance architectures that span threshold-based monitoring, model-based diagnostics with redundancy, risk-based maintenance scheduling with spares governance, and a two-tier verification architecture that constrains nuisance alarms while enabling staged intervention. Results show that (i) fleet availability is dominated by the upper tail of time-to-repair rather than by mean time between failures, because weather and logistics amplify delays once a fault progresses beyond a controllable stage, (ii) moderate sensor bias and baseline drift can substantially increase false stability events and shift detection later into the fault progression curve, producing outsized downtime penalties even when average alarm rates appear acceptable, and (iii) governance that couples quantile-based alarm thresholds with verification and repair staging provides a superior cost–risk balance by reducing the probability of late detection without driving unsustainable nuisance maintenance. The study provides copy-ready tables and full prompts for data-driven figures suitable for Techne submission and adaptation to site-specific fleet data.
Protection and Safety in Battery Energy Storage Systems: Modeling Fault Detectability, Isolation Latency, and Thermal Runaway Escalation Under Sensor and Control Uncertainty
This article presents a reliability-centered framework for BESS protection and safety that treats detection, decision, and isolation as an end-to-end process, quantifying how uncertainty propagates through sensing and control logic to determine the probability of undetected faults, time-to-isolation, likelihood of thermal runaway propagation beyond a module, nuisance trip rate, and expected downtime cost under operational constraints. A scenario-based quantitative study is developed using Monte Carlo simulation of representative fault classes, including internal cell short development, connection resistance growth, and coolant loss, with explicit modeling of sensor noise and drift, estimation uncertainty, and actuation delays. Four architectures are compared, spanning baseline BMS thresholding, redundant sensing with model-based diagnostics, fast hardware interlocks with conservative trip logic, and a governance-optimized two-tier decision architecture that couples early warning with verification and staged isolation. Results show that (i) escalation risk is governed more by detection latency distributions than by mean detectability, (ii) moderate sensor bias can materially increase false stability and delay isolation during incipient faults even when average alarms look stable, and (iii) hybrid governance that controls nuisance alarms while enabling early staged intervention provides the best cost–risk balance for grid-deployed BESS, reducing propagation probability without driving excessive operational trips. The study provides copy-ready tables and figure prompts suitable for Techne submission and for adaptation to site-specific datasets.
Reliability-Centered Process Control in Wastewater Treatment: Quantifying Effluent Compliance Risk Under Sensor Drift, Process Upsets, and Control-Loop Uncertainty
This article presents a reliability-centered process control framework that treats wastewater treatment as an end-to-end decision system in which sensor uncertainty, model mismatch, and actuator constraints propagate into effluent quality risk. A quantitative scenario-based study is developed for a conventional activated sludge process with nitrification and denitrification, comparing four operational architectures that span baseline PID control, feedforward-enhanced aeration, hybrid risk-based control with supervisory logic, and governance-optimized operation with drift-aware sensing, quantile-based alarm thresholds, and two-tier intervention pathways. The analysis uses engineering metrics that translate to plant management and compliance planning, including probability of exceeding effluent limits for biochemical oxygen demand and ammonia, expected duration and severity of violations, time-to-detection of process upsets, and cost–risk trade-offs that incorporate energy usage and chemical dosing. Results indicate that (i) compliance risk is dominated by tail events driven by combined hydraulic shocks and influent ammonia spikes rather than by steady-state control quality, (ii) sensor drift in dissolved oxygen and ammonia probes can create false stability that delays corrective action and increases violation duration even when average readings appear acceptable, and (iii) reliability improvements are achieved more consistently through governance of sensing and alarms and through structured escalation logic than through controller sophistication alone. The paper provides copy-ready tables and publication-ready figure prompts to support Techne submission and practical implementation.
Thermal Reliability Engineering in Cold Chain Logistics: Modeling Temperature Excursion Risk and Product Quality Loss Under Sensor and Process Uncertainty
This article presents a reliability-centered framework for cold chain engineering that integrates stochastic excursion modeling, sensor uncertainty characterization, and product degradation kinetics into a unified decision pipeline that supports shipment-level risk scoring, lane qualification, packaging selection, and escalation rules for intervention. A scenario-based quantitative study is developed using representative distributions of ambient conditions, dwell times at transfer nodes, refrigeration performance, and door-open events, while incorporating realistic sensor noise and bias drift to evaluate how monitoring quality influences the probability of detecting excursions and the confidence of acceptance decisions. Comparative analysis is performed across four operational strategies that span passive packaging, active temperature control, hybrid risk-based controls, and governance-optimized monitoring with intervention triggers. Results demonstrate that (i) time-to-excursion is dominated by interface dwell and door-open variability rather than by steady-state transport, (ii) small sensor bias and sampling-rate limitations can materially distort excursion severity and time-above-threshold estimates, leading to both false compliance and unnecessary rejection, and (iii) a governed two-tier decision architecture, where monitoring triggers verification or operational intervention rather than serving as the sole acceptance authority, yields the best cost–risk balance for typical pharmaceutical and perishable profiles. The study provides implementable tables, figure prompts, and decision rules suitable for applied engineering practice and journal submission.
Reliability-Centered Quality Control in Additive Manufacturing: Quantifying Defect Detectability, Dimensional Uncertainty, and Qualification Risk Across the Digital Thread
This article presents a reliability-centered quality control framework for metal powder bed fusion that treats quality assurance as a decision system spanning in-situ monitoring, post-build inspection, uncertainty propagation, and acceptance governance across the digital thread, and it quantifies decision performance using probability of defect non-detection, probability of tolerance exceedance, time-to-disposition, and expected cost of quality under controlled nuisance-alarm constraints. A scenario-based quantitative study is developed using Monte Carlo simulation of a multi-build production campaign with realistic drift events, defect-size distributions, and measurement-system uncertainties, comparing four architectures that range from inspection-heavy qualification to monitoring-forward production with risk-based sampling and governed baseline updating. Results show that monitoring value is maximized when indicators are engineered around defect mechanisms and tolerance-critical features, because generic anomaly scoring can either overload operations with unstable alarms or inflate thresholds to the point of insensitivity when baseline variance is large, while hybrid strategies that allocate high-resolution inspection to high-uncertainty builds based on calibrated risk scores can reduce total inspection burden and disposition time without increasing residual acceptance risk.
End-to-End Quality Control in Additive Manufacturing: Evaluating Defect Detectability, Dimensional Uncertainty, and Qualification Risk Across the Digital Thread
This article presents a reliability-centered quality control framework for AM that treats quality as a decision system spanning in-process monitoring, post-process metrology, and acceptance logic, and that explicitly quantifies uncertainty propagation from process signals to defect detectability and dimensional compliance. A scenario-based quantitative study is developed for powder bed fusion production, comparing quality control strategies that combine melt pool monitoring, layerwise imaging, computed tomography sampling, and coordinate measurement verification. The study uses engineering metrics that translate directly to production decisions, including probability of defect non-detection, probability of tolerance exceedance, time-to-disposition, and cost of quality under false-reject and false-accept trade-offs. Results show that (i) monitoring value is maximized when it is calibrated to the defect and geometry mechanisms that dominate part performance rather than treated as a generic anomaly detector, (ii) decision reliability is governed by how thresholds are engineered under controlled false alarm rates and how measurement systems are validated, and (iii) hybrid inspection policies that allocate high-resolution metrology to the highest-risk builds based on monitored uncertainty can reduce total cost while increasing acceptance confidence. The paper concludes with implementable guidance for designing AM quality control architectures as reliability systems rather than as isolated sensing upgrades.
Systematic Literature Synthesis of Indonesia’s IT Industry: Ecosystem Capability, Talent Dynamics, and Governance for Digital Competitiveness
This article presents a systematic literature synthesis (SL) of Indonesia’s IT industry with an applied technology orientation, focusing on three interconnected determinants of competitiveness: (i) ecosystem capability and production capacity in software and digital services, (ii) talent pipelines and skills formation under rapid technology change, and (iii) governance and regulation shaping trust, interoperability, and innovation scaling. The SL protocol defines database-specific search strings, eligibility rules, a quality appraisal rubric, and a transparent extraction matrix structured to capture mechanisms rather than merely catalog topics, enabling translation into an operational framework for IT firms, policymakers, and project owners. The synthesis consolidates the evidence into six thematic clusters: industry structure and value-chain positioning, firm-level capability building and maturity, digital transformation demand and procurement patterns, human capital and skills gaps, cybersecurity and trust infrastructure, and policy and institutional enablers. Across themes, the literature converges on a central tension: Indonesia’s IT growth is simultaneously demand-driven and constraint-limited, meaning that expanding digital demand does not automatically translate into sustained competitiveness unless capability formation, talent governance, and standards-based interoperability are engineered as coordinated system components. The article contributes a coherent conceptual framework, copy-ready SL tables for reporting, and figure prompts suitable for Techne submissions, while explicitly distinguishing protocol-based claims from elements that require empirical population through database execution by the authors.
Thermal Reliability Engineering in Cold Chain Logistics: Quantifying Time–Temperature Exposure, Packaging Performance, and Spoilage Risk in Distribution Networks
This article presents an applied thermal reliability framework that links distribution operations to product risk using engineering metrics that can be measured and governed in practice. The framework combines (i) a time–temperature exposure model using exceedance time and mean kinetic temperature as reliability indicators, (ii) a packaging thermal performance model that represents insulation and refrigerant buffering as a transient heat transfer system, and (iii) a product degradation and spoilage risk model based on temperature-accelerated kinetics. A scenario-based quantitative study is developed for representative distribution networks including cross-docking, refrigerated line-haul, and last-mile delivery, with comparisons across packaging tiers and monitoring-control strategies. Results show that the dominant drivers of spoilage risk are not average temperature or nominal setpoints, but the upper-tail of exposure created by dwell-time uncertainty, door openings, and staging delays, and that packaging upgrades and sensor-triggered interventions reduce risk most effectively when applied to the highest-variance legs rather than uniformly across the network. The paper concludes with implementable guidance for risk-based monitoring, packaging selection, and operational governance that improves thermal reliability while controlling cost.
Topography-Aware Geospatial Digital Marketing: A Systematic Literature Synthesis of Local SEO, Spatial Targeting, and Accessibility-Driven Consumer Intent
This article presents a systematic literature synthesis (SL) that consolidates research across digital marketing, information systems, spatial analytics, and urban studies to build an engineering-oriented understanding of how topography and geospatial constraints influence local SEO performance, location-based advertising effectiveness, and omnichannel conversion outcomes. Using a transparent SL protocol, the review organizes findings into thematic clusters: (1) geospatial signals in local search and discovery, (2) geo-targeting and geo-fencing for paid media, (3) accessibility-based segmentation and service areas, (4) place-based content strategy and location credibility, (5) measurement, attribution, and privacy constraints, and (6) operational models and decision support for topography-aware marketing. The synthesis identifies a persistent gap: limited integration of topographic friction and network travel-time into mainstream digital marketing workflows, despite strong evidence that accessibility mediates consumer intent and moderates campaign outcomes. The article proposes a conceptual framework, offers copy-ready tables for SL reporting and practice translation, and provides figure prompts for scientific visualizations suitable for Techne submissions. The resulting contribution is an applied roadmap for designing digital marketing as a location-intent system grounded in physical accessibility rather than simplistic distance, with implications for local SEO governance, media efficiency, conversion reliability, and equitable service delivery.
Maria Lourdes Santos Maria Lourdes Santos
Jose Miguel Dela Cruz Jose Miguel Dela Cruz
Angela Mae Villanueva Angela Mae Villanueva