Effect of Environmental Temperature on the Lower Bound of the Entropic Uncertainty Relation in the Presence of Quantum Memory

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Because realistic quantum systems inevitably interact with their surroundings, understanding how environmental factors affect the entropic uncertainty relation remains a major challenge. In this work, we analyze how ambient temperature impacts the lower bound of the memory-assisted entropic uncertainty relation. We model the coupling between the system and its thermal environment using a generalized amplitude damping channel, featuring temperature-dependent parameters. Notably, we find that the uncertainty lower bound drops when the damping parameter (r) decreases, and rises when it increases. By contrast, shifting the parameter) p (yields much smaller variations. This stark difference highlights our main novel finding: in a finite-temperature environment, the lower bound of the entropic uncertainty relation is significantly more sensitive to the damping parameter (r) than to (p). Ultimately, these results offer a clearer look at how thermal reservoirs influence open quantum systems and affect practical quantum information processing.

Tumor Detection with PET and CT: A Comparative Physical Review

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Positron emission tomography (PET) is an important molecular imaging modality in oncology because it provides functional and metabolic information that complements the anatomical information obtained from computed tomography (CT). However, the ability of PET to detect tumors is fundamentally determined by several physical characteristics of the imaging system, including spatial resolution, sensitivity, image contrast, noise, photon attenuation, scatter, random coincidences, and partial-volume effects. This review aims to examine the physical basis of PET for tumor detection and to compare its principal imaging characteristics with those of CT. A comparative narrative review was conducted using relevant literature on PET physics, detector instrumentation, image formation, image-quality determinants, tumor detectability, and the physical principles of CT. The reviewed evidence indicates that PET provides high sensitivity to radiotracer distribution and functional abnormalities, making it particularly valuable for detecting metabolically active tumors. However, its relatively limited spatial resolution and susceptibility to attenuation, scatter, noise, and partial-volume effects can restrict the detection and accurate characterization of small or low-contrast lesions. In contrast, CT provides superior anatomical detail and spatial resolution through X-ray attenuation measurements but offers substantially less direct functional information. The evidence therefore indicates that PET and CT possess complementary physical capabilities: PET is primarily advantageous for functional and metabolic tumor characterization, whereas CT is superior for anatomical localization and structural delineation. Understanding these physical differences is essential for interpreting the respective strengths and limitations of PET and CT in tumor detection and for optimizing their complementary use in hybrid PET/CT imaging.

Evaluation of the Role of Local Government in Strengthening Flash Flood Disaster Mitigation Capacity in Teluk Wondama Regency

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Teluk Wondama Regency remains highly exposed to flash-flood hazards, particularly along the Wasior-Wondiboy-Rasiei corridor, where settlements, public services, transport networks, and government functions are concentrated. The 2010 Wasior flash flood demonstrated the severity of potential consequences, while current evidence indicates that mitigation capacity still requires systematic strengthening. This study evaluates the role of local government in flash-flood mitigation, identifies enabling and constraining factors, and formulates strategies to strengthen mitigation capacity. A qualitatively oriented mixed-methods approach was applied within an evaluative case-study design. Data were obtained from disaster-management, development-planning, spatial-planning, environmental, program, and budget documents; semi-structured interviews with nine purposively selected informants; field observations in Wasior, Wondiboy, and Rasiei; and descriptive quantitative scoring of 30 indicators across six mitigation-capacity dimensions. The local government has performed roles as regulator and planner, coordinator, facilitator, service implementer, development controller, and community enabler; however, these roles have not yet formed a fully integrated mitigation system. The overall capacity score was 54 of 120, equivalent to 45.0 on a 0-100 scale and categorized as less adequate. Policy-planning and spatial planning-watershed management each scored 50, whereas technical systems-early warning-evacuation and community capacity each scored 40. The principal finding is a persistent gap between normative capacity and operational functionality. Priority strategies include updating risk and disaster-management plans, strengthening inter-agency coordination, operationalizing the emergency operations center and sub-watershed-based early warning, testing evacuation networks, controlling risk-prone land use, sustaining preventive financing, and institutionalizing community preparedness.

Water Demand Management in the Island of Crete, Greece

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The supply and demand management of water resources in Crete is necessary to avoid water shortages in the island predicted in the future due to climate change. Water demand management in Crete should aim not simply to reduce water consumption, but to ensure that available water is used efficiently, economically and sustainably. Several studies related to water resources in Crete have been reviewed and the water consumption in several sectors has been estimated. The island is sufficient in water resources under average meteorological conditions while climate change is foreseen to reduce water precipitation in the coming decades resulting in water shortages. Agriculture is by far the greatest water consumer in the island using around 80% of the total water use. Crete’s tourism industry is growing fast while its share in total water demand is around 3%. Several technical and non-technical measures can be used to reduce the water demand in Crete. The efficient use of water in agriculture and tourism can reduce significantly the total water consumption which was estimated at 610.94 hm3 in 2016. A 10% reduction in water consumption in these two sectors can reduce the total annual water consumption in the island by 8.15 % or by 49.78 hm3/year. Appropriate water pricing, use of financial tools, awareness raising and education of several water-related stakeholders would contribute in the decrease of water demand in Crete. The results of the current study could be used from policy makers, local and regional authorities as well as from water-related organizations in the island.

Data-Driven Robust MPC for an Industrial Evaporator under Model Uncertainty and Sensor Degradation

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Model predictive control is attractive for industrial processes because constraints and multivariable interactions can be handled explicitly, but its performance depends on the credibility of the prediction model and the measurements supplied to the controller. This article develops a data-driven robust MPC framework for an industrial evaporator in which model uncertainty and sensor degradation are treated jointly. The open DaISy industrial evaporator benchmark (code 96-010) supplies the data basis: 6305 samples, three manipulated inputs and three measured outputs. Publicly reproducible system-identification results for this benchmark report a first experiment of 3300 samples, an order-eight prediction-error model, and five-step output mean-square errors of 0.0576, 0.1564 and 0.0193. These error scales are used to define relative residual uncertainty for a closed-loop benchmark-constrained surrogate. The controller combines a trust-weighted state estimate with scenario-based robust prediction across a finite uncertainty ensemble. Bias, drift, precision loss and dropout are injected into the sensor channels, and at the same time plant matrices are perturbed independently across Monte Carlo trials. In 200 composite-degradation scenarios with 12% plant-model uncertainty, the proposed robust MPC reduces mean normalized tracking RMSE from 0.2750 for nominal MPC to 0.2421, and reduces normalized performance-envelope exceedance from 14.12% to 6.02%. The improvement is obtained at a 6.75% increase in average control effort. The results show that a practical data-driven robust MPC architecture should not treat identification error and sensor integrity as separate problems; both must enter the online state estimate and the predictive optimization layer.

Prioritizing Indian E-Wallets Through the Analytic Hierarchy Process

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This study develops an Analytic Hierarchy Process framework for prioritizing online e-wallets in India. The framework evaluates competing criteria, including security, ease of use, transaction costs, service reliability, and merchant acceptance. E-wallet selection is organized hierarchically around the overall goal, evaluation criteria, and alternative platforms. Pairwise comparisons are used to determine criterion weights and rank the selected e-wallets, while consistency analysis assesses the reliability of the judgments. The proposed framework offers a systematic approach for comparing digital payment platforms, supporting informed consumer choices and helping fintech providers improve security, service quality, and user satisfaction. criteria weights and the final ranking of alternatives is PhonePe > Google Pay > Paytm > Amazon Pay > MobiKwik.

Comparative Study of the Liquid Drop and Collective Models for Nuclear Binding and Low-Energy Structural Properties of Selected Nuclei

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This study comparatively evaluates the applicability of the liquid drop and collective nuclear models in describing the energy and structural properties of four selected even–even nuclei, ¹⁵²Sm, ¹⁶⁸Er, ¹³²Sn, and ²⁰⁸Pb. The nuclei were deliberately selected to represent two contrasting structural regimes: deformed, collective systems and nearly spherical, closed-shell systems. The study is a review-based comparative analysis supported by numerical evaluation and validation against published nuclear data. For the liquid-drop description, nuclear binding is considered through the semi-empirical mass-formula framework, including volume, surface, Coulomb, asymmetry, and pairing contributions. The collective description is assessed through low-lying excitation energies, rotational-band systematics, the ratio R₄/₂ = E(4₁⁺)/E(2₁⁺), and the quadrupole deformation parameter β₂. Binding-energy information was taken from the AME2020 evaluation, while level-structure information was drawn from ENSDF/NNDC and IAEA nuclear-data resources. The analyzed values give B/A of 8.24, 8.13, 8.36, and 7.87 MeV for ¹⁵²Sm, ¹⁶⁸Er, ¹³²Sn, and ²⁰⁸Pb, respectively. The corresponding E(2₁⁺) values are 121.8, 79.8, 4041, and 4085 keV, while β₂ is 0.30, 0.34, approximately zero, and approximately zero. The results show that the liquid-drop framework is effective for global binding-energy trends, whereas the collective model provides a more appropriate description of low-energy rotational behavior and deformation in ¹⁵²Sm and ¹⁶⁸Er. Neither framework alone describes all observed features; closed-shell effects require explicit microscopic treatment.

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

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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

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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

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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.