From Component Reliability to Dynamic Predictive Reliability: A Multilayer Framework for Cyber-Physical Systems

/0 Comments/in /by

The reliability of cyber-physical systems (CPS) cannot be reduced to the failure probability of individual hardware components. Modern industrial, urban, educational and energy CPS combine physical processes, sensors, communication networks, software services, control algorithms, cyber-security mechanisms, human operators and environmental conditions. This paper proposes an integral-predictive view of CPS reliability based on two complementary models: the multilayer integral reliability index of a cyber-physical system (MINKFS) and the dynamic cognitive-predictive reliability model (DKPN). The framework takes different types of signals, converts them into standardized layer indices, combines them using a weighted structure, and updates the current reliability estimate by adding the predicted chance of future failure.  The empirical part uses a synthetic industrial CPS dataset of 336 hourly observations and demonstrates how layer statistics, scenario analysis, operational regimes and criticality rankings can support early warning and preventive decision-making. The model transforms diverse inputs to normalized layer indexes, integrates these using a weighted integrative model, and adds the forecast probability of future failure to the present measure of reliability. The empirical part studies a fabricated data set related to industrial CPS which consists of 336 hourly measurements and provides a demonstration of how layer, scenario, regime and criticality information is used for detection and prevention. It was observed that the cyber-resilience layer had the lowest average reliability as well as the highest critical contribution, whereas communications and environment-energy layers were characterized by high volatility. Cyber event scenario resulted in the lowest predictive reliability and highest likelihood of failure. These results confirm the thesis that reliability of a CPS is better analyzed as a time-dependent, interpretable, and scenario-dependent feature instead of being just another component coefficient.

Automation Efficiency in Cyber-Physical Systems: Interaction Between Technical Performance and Algorithmic Intelligence

/0 Comments/in /by

The performance of a cyber-physical system cannot be judged reliably from a single engineering variable. In the investigated micro-CPS, response time, communication latency and throughput describe the technical layer, whereas prediction accuracy captures the contribution of the learning component. The present paper examines how these two dimensions interact and how their combined effect is reflected in an overall efficiency index. A quasi-experimental comparison was carried out for a logistics and warehouse control configuration equipped with connected sensing, real-time data exchange and machine-learning support, and for a functionally similar conventional automated system. The empirical analysis was based on six variables, which were first standardized using z-scores and then assigned to four domains: technical efficiency, algorithmic intelligence, resource and energy efficiency, and economic efficiency. A clear difference emerged between the two system architectures. The CPS produced a mean composite index of 0.954, whereas the conventional automated system had a mean value of −0.954. The regression results further showed that technical efficiency and algorithmic intelligence contributed most strongly to the overall index. Of particular interest was their interaction. The interaction between technical efficiency and algorithmic intelligence was statistically significant (B = 0.154, β = 0.267, p = 0.004).  This interaction included to the model increased R² from 0.93 to 0.95. The finding indicates that the contribution of algorithmic intelligence to overall system performance depends, at least partly, on the level of technical efficiency achieved by the system. Rather, their contribution becomes more pronounced when the control and communication infrastructure already performs reliably. The sensitivity analysis led to the same overall ordering of the two architectures under alternative weighting schemes. Within the scope of the available dataset, this provides additional evidence that the observed difference is not simply an artefact of the selected weights.

Conventional Ion-Exchange and Chelating Resins for Metal Recovery: A Critical Comparison of Selectivity, Dynamic Performance, Regeneration, and Process Integration

/0 Comments/in /by

Conventional ion-exchange and chelating resins are widely used for metal recovery, purification, and polishing, yet they are still frequently compared using equilibrium capacity rather than process function. This structured critical review examines how charge-driven exchange and ligand-controlled coordination translate into selectivity, kinetics, breakthrough behavior, regeneration, fouling, resin lifetime, and flowsheet integration. Topic-structured searches covering 2018–31 August 2026 yielded 313 candidate records, of which 110 peer-reviewed publications were retained for critical synthesis. Evidence was weighted from batch equilibrium and kinetic studies through fixed-bed or reactor operation, regeneration, real-matrix validation, pilot or multicolumn demonstrations, and selected industrial-scale applications. Across rare-earth elements, Cu–Ni–Co systems, battery-recycling liquors, Sc/V/Ga separations, Zr/Hf and radionuclide systems, precious metals, and industrial wastewaters, the evidence shows that equilibrium qmax and single-solute distribution coefficients are poor stand-alone indicators of process readiness. Representative dynamic studies report outcomes ranging from a Cu breakthrough capacity of 16.51 mg g−1 to an REE eluate concentration factor of 236×, while selected continuous and industrial-scale studies demonstrate high product purity, sustained metal recovery, and concentrated regenerates when feed chemistry and cycle design are controlled. Conventional and chelating resins are therefore best viewed as complementary rather than substitutive technologies. The review proposes a minimum reporting framework for scale-up-relevant studies based on representative feed chemistry, working capacity, breakthrough criteria, regenerant demand, product purity, multicycle durability, and flowsheet integration. Hybrid flowsheets emerge as a particularly defensible strategy for converting resin selectivity into process value.

Business Analytics Capability and Organizational Cyber Resilience: The Roles of Data-Driven Risk Detection and Cybersecurity Governance

/in /by

This study examines the relationships among business analytics capability, data driven risk detection, and organizational cyber resilience while evaluating the moderating influence of cybersecurity governance maturity. Drawing on Dynamic Capabilities Theory and Organizational Information Processing Theory, it conceptualizes organizational cyber resilience as the outcome of organizational capabilities that transform integrated analytical resources and cyber risk information into timely, coordinated, and adaptive responses. Cybersecurity governance maturity serves as a boundary condition that determines whether analytical insights can be translated into stronger resilience outcomes. A quantitative cross-sectional design gathered responses through a five point Likert scale questionnaire from 385 Vietnamese professionals knowledgeable about business analytics, cybersecurity, information technology, risk management, digital operations, organizational governance, or business continuity. IBM SPSS version 26 supported reliability assessment, exploratory factor analysis, and multiple linear regression, while Hayes’ Process Macro Model 1 examined the moderating effect. The findings show that business analytics capability (β = 0.573) and data driven risk detection (β = 0.638) significantly improve organizational cyber resilience. Data driven risk detection produces the stronger direct effect, emphasizing the importance of continuous monitoring, anomaly identification, and actionable cyber risk warnings. Cybersecurity governance maturity further strengthens the positive relationship between business analytics capability and organizational cyber resilience through a significant interaction coefficient of 0.407. The results demonstrate that analytical technologies alone cannot guarantee cyber resilience. Organizations must combine integrated analytical resources and timely risk detection with clear accountability, formal escalation procedures, structured oversight, continuous governance improvement, and sufficient authority to coordinate responses.

Enhancing E-Commerce Customer Satisfaction: A Multi-Method Approach Using Sentiment Analysis, IPA, and PGCV

/in /by

The advancement of information technology has enabled rapid expansion in e-commerce, establishing one of the platforms as a leading company in Indonesia. Subsequently, the authors name the e-commerce platform as XZ to preserve the anonymity of the research subject. This research intends to evaluate consumer sentiment regarding XZ’s e-service quality utilizing opinion mining based in the Support Vector Machine (SVM) method, while also determining enhancement priorities through Importance Performance Analysis (IPA) and Potential Gain in Customer Value (PGCV). The dataset comprises 50,000 data from the Google Play Store, categorized into positive and negative comments. The findings reveal that factors including the promptness in addressing customer complaints, the effectiveness of refund procedures, and XZ’s reliability in delivering promotional commitments, including shipping discount vouchers, are primary customer concerns. The IPA technique indicated that key service components should be emphasized to improve customer satisfaction, particularly attributes such as P4, P13, and P14. The PGCV methodology strengthened these results by determining the enhancement opportunities that would most significantly elevate service value, particularly attribute P4 associated with the promptness of addressing customer complaints. Also, P13 regarding the effectiveness of refund procedures for incorrectly shipped or defective products, and lastly P14 pertaining to XZ’s reliability in delivering on promotional commitments such as shipping discount vouchers. The results of this study have the potential to provide a benchmark for the businesses in formulating more efficient service enhancement plans and assist in advancing the field of quality management within the e-commerce industry.

A Comparative Analysis of Advanced Nuclear Fuel and structural Design in Advanced Fission Reactor

/in /by

The rapid transition toward low-carbon energy systems and increasing global energy demand have renewed interest in advanced nuclear fission technologies, particularly Generation IV reactors and Small Modular Reactors. This study aims to comparatively evaluate advanced nuclear fuels and structural materials based on their thermal, neutronic, mechanical, chemical, irradiation, and safety performance. An analytical review of scientific and technical literature from academic databases and authoritative nuclear organizations was conducted. The literature was qualitatively synthesized according to fuel type, structural material, reactor concept, coolant, and neutron spectrum. The findings indicate that uranium nitride, uranium silicide, and tri-structural isotropic fuels provide important advantages over conventional uranium dioxide fuel, including higher thermal conductivity, greater fuel density, improved high-temperature performance, and enhanced fission-product retention. Among structural materials, iron-chromium-aluminum alloys, silicon carbide composites, and high-entropy alloys demonstrate promising oxidation resistance, thermal stability, mechanical strength, and irradiation tolerance. However, their performance is strongly dependent on reactor type, neutron spectrum, coolant chemistry, operating temperature, and irradiation conditions. The review further shows that integrating advanced fuels and structural materials with passive safety systems and modular reactor architectures can improve safety, thermal efficiency, sustainability, and economic potential. Major challenges remain in irradiation qualification, corrosion, manufacturing, regulatory approval, economic feasibility, and spent-fuel management. Overall, coordinated development of advanced fuels and structural materials represents a promising pathway toward safer, more efficient, sustainable, and economically viable nuclear energy systems.

From Magnesium Feedstocks to Reactive MgO: A Critical Review of Production Routes, Reactivity Control, Industrial Scalability, and Environmental Trade-Offs

/in /by

Reactive magnesium oxide (r-MgO) is often treated as a single material class, yet its performance is governed by a chain of coupled decisions extending from magnesium source and precursor chemistry to heat and mass transfer, calcination severity, atmosphere, particle size, purification, and post-calcination aging. This structured critical narrative review reframes reactive MgO production as a process–structure–reactivity engineering problem rather than a catalog of synthesis methods. A fixed core evidence corpus of 103 publications from 2020 through August 2026 was coded by production route, evidence scale, reactivity endpoint, and environmental/economic dimension; three pre-2020 foundational sources were used only for terminology and mechanistic context and were excluded from corpus counts. The review compares mineral-derived, brucite-derived, dolomitic, seawater/brine, waste-derived, sulfate-derived, and specialty precursors; conventional, flash, fluidized-bed, steam-assisted, solar/electrified, precipitation, carbonation, electrochemical, and hybrid routes; and the analytical methods used to describe reactivity. No single metric—calcination temperature, BET surface area, hydration rate, or CO₂ uptake—defines reactive MgO across applications. Instead, reactivity emerges from the preservation or destruction of accessible mesoporosity, surface defects, dissolution sites, crystallite-scale disorder, and pore connectivity, provided that sufficient precursor conversion and chemical purity are achieved. Fast thermal routes can reduce sintering exposure, whereas brine and residue routes can decouple purity from the original mineralogy but introduce reagent demand, washing, mother-liquor management, and scale-up penalties. Environmental advantage is likewise route-specific: avoiding magnesite decarbonation can reduce process CO₂, but upstream alkalis, electricity, solids handling, and unrealized carbonation can reverse apparent benefits. The industrially optimum product is therefore not the MgO with the highest nominal reactivity, but the product whose application-specific reactivity window is achieved at acceptable purity, energy demand, carbon footprint, cost, throughput, and consistency. The review concludes with a research agenda centered on standardized reporting, direct route-to-route experiments, impurity-tolerance maps, continuous pilot validation, and integrated mass–energy–carbon–cost assessment.

European Classification and Labelling of Hazardous Products

/in /by

By adopting the CLP Regulation, the European Union has aligned its classification and labelling system for hazardous substances with a globally accepted standard based on the United Nations’ Globally Harmonised System of Classification and Labelling of Chemicals (GHS). This article summarizes the key aspects of the current status of the CLP Regulation. It introduces the available labelling elements, such as hazard pictograms, signal words, hazard statements, and precautionary statements. This is followed by an overview of physical hazard classes, health hazards, environmental hazards, and an additional hazard class. The communication elements for each hazard class are presented in a tabular overview. Subsequently, general and specific criteria for the classification of mixtures are listed. The discussion section examines current legislative trends.

IoT-Based 100 WP Solar Power Plant for Ultraviolet Lighting in Dragon Fruit Cultivation: Design and Performance Comparison of Tracking and Non-Tracking Systems

/in /by

The use of solar energy for agricultural applications can reduce dependence on conventional electricity while enabling operation in locations where grid access is limited. This study aimed to design and evaluate a 100 Wp photovoltaic system integrated with an Internet of Things (IoT) monitoring and control system for lighting in a dragon fruit plantation. The system employed a 100 Wp monocrystalline solar panel, a solar charge controller, a battery storage unit, an inverter, an ESP32 microcontroller, voltage and current sensing, and Telegram as the remote monitoring interface. Electrical parameters were observed hourly from 09:00 to 17:00 WIB under two panel configurations: solar tracking and fixed non-tracking. Panel voltage, panel current, battery voltage, and calculated panel power were analyzed. For the tracking configuration, the average panel voltage and current were 19.64 V and 4.12 A, respectively, corresponding to an average calculated power of 81.47 W. The maximum calculated power was 100.94 W at 12:00. Under the non-tracking configuration, the average voltage and current were 18.69 V and 3.61 A, with an average calculated power of 68.08 W and a maximum of 91.65 W at 12:00. Thus, the tracking configuration produced approximately 19.7% higher average calculated panel power than the non-tracking configuration. The ESP32–Telegram system successfully transmitted operating information in real time when a stable Internet connection was available. The results demonstrate that combining solar tracking with IoT monitoring can improve the observed electrical performance and facilitate remote management of a small off-grid photovoltaic lighting system.

From Upgraded Titanium Slag to Modern Metallurgical Slag Engineering: Technologies, Industrial Applications, and Readiness- A Critical Review

/in /by

Metallurgical slags are increasingly treated as dynamic secondary resources rather than inert residues, yet the literature remains fragmented across metal recovery, mineral-phase engineering, material valorization, carbon management, and industrial implementation. This structured critical narrative review evaluates a recent core corpus of 111 publications from 2020–2026 and supplements it with selected pre-2020 foundational sources required to establish the historical Upgraded Slag (UGS) lineage in titanium metallurgy. The recent corpus was descriptively coded by primary slag family, evidence function, and implementation maturity; steelmaking/ferrous systems account for 60 studies, copper/fayalitic slags for 16, cross-cutting or mixed systems for 15, ferroalloy/Cr–Mn slags for 7, Ti-bearing slags for 4, and Ni/ferronickel slags for 3. Eighty publications primarily provide mechanistic or product-performance evidence, 13 provide system-assessment evidence, and 12 are reviews or contextual sources. The synthesis distinguishes historical titanium-slag upgrading from the broader family of modern thermal, chemical, redox, physical, hydrometallurgical, carbonation, and electrochemical interventions. It shows that credible upgrading requires more than high recovery: a controllable phase transformation must be coupled with effective separation, qualified metal or material products, a safe and useful residual matrix, and realistic integration with plant infrastructure. Representative industrial evidence is strongest in copper-slag flotation and settling, selected hot-stage steel-slag operations, and heat-recovery applications, whereas many high-value functional-material routes remain laboratory-led. A five-gate framework—value inventory, engineerability, separation and safety, integration, and system performance—is applied to representative routes to connect mechanisms with product specifications, TEA/LCA, and industrial readiness. The resulting perspective positions slag upgrading as site-specific product and process design within circular metallurgy rather than as generic residue reuse.