Search Results (18,438)

Recirculating cooling systems require reliable control of water concentration to limit scaling while avoiding unnecessary water losses associated with excessive blowdown and make-up water demand. This study developed and evaluated a paper-based colorimetric approach for rapid monitoring of concentration and scaling conditions in recirculating cooling water. The experimental validation was performed using make-up water from the cooling-water system of the Rivne Nuclear Power Plant and a laboratory-scale installation simulating progressive water concentration by controlled evaporation. Test A was used for chloride determination and subsequent calculation of the concentration factor (CF), whereas Test B was used for total hardness (TH) determination. The proposed measurements were validated against ISO 9297 and ISO 6059, respectively, and the concentration–hardness precipitation index (CPI) was calculated from the expected and measured TH values. In addition, the Langelier Saturation Index (LSI) and Ryznar Stability Index (RSI) were calculated to provide complementary scaling assessments. The colorimetric tests showed strong agreement with the reference methods, with Pearson correlation coefficients of 0.996 for Cl⁻, 0.997 for TH, and 0.9996 for CF (p < 0.0001). CPI, LSI, and RSI showed a consistent shift towards increasing scaling tendency with increasing CF. The proposed approach therefore provides a simple screening tool for operational control of recirculating cooling water and may support more efficient water use and circular water management. Full article

Artificial intelligence (AI) is increasingly used across the diagnostic pathway, from screening and disease detection to differential diagnosis, prognostic stratification, and treatment-response assessment. This narrative review examines predictive, generative, multi-modal, and agentic AI through a diagnostic-science lens and evaluates the evidence required for translation into clinical practice. Predictive models can identify complex patterns in images, physiological signals, laboratory data, and electronic health records, but high retrospective accuracy does not establish diagnostic utility. Clinical validity and utility depend on the intended use, target population, disease spectrum and prevalence, reference standard, operating threshold, calibration, and the consequences of false-positive and false-negative results. Generative AI may support problem representation, differential diagnosis, information synthesis, and documentation, yet fluent outputs can omit critical alternatives, amplify false premises, or convey unjustified certainty. Multimodal systems may better reflect clinical reasoning by integrating imaging, text, signals, pathology, and molecular data, but they must demonstrate incremental value over the best single-modality test and remain robust to missing data. Agentic systems can coordinate evidence retrieval, test selection, and sequential workflows, thereby increasing the need for bounded permissions, auditability, error recovery, and human escalation. Across all paradigms, a credible pathway to diagnostic implementation requires independent external and prospective validation, representative consecutive patients, subgroup analysis, workflow and human–AI evaluation, clinical impact studies, and post-deployment surveillance. The field should therefore be judged not by model capability alone, but by whether AI measurably improves diagnostic yield, timeliness, safety, equity, and patient-relevant outcomes in real care settings. Full article

Masonry walls constructed of brick or block units have tended to suffer more serious damage when subjected to loads of both static and cyclic (such as an earthquake) nature as compared to other concrete or steel structures. Hence, the static and cyclic loading behavior of these masonry building units should be carefully investigated prior to the design and construction of the structures. In this study, a damage plasticity (DP) model for the static and cyclic loading analysis of a concrete block masonry material is proposed by modifying the stress-strain regime of the widely used Concrete Damage Plasticity (CDP) model to be suitable for concrete block masonry units/materials. The various parameters of the DP model, which is based on the theory of continuum damage mechanics and plasticity, are calibrated using detailed experimental data obtained by testing a concrete block masonry material (CBMM) (including results from static or monotonic uniaxial compressive loading tests). The CBMM used is a laboratory lightweight concrete mixture aimed at approximating the behavior and properties of actual field concrete masonry units or materials. Results of model prediction/numerical simulation of static and cyclic loading on cylindrical samples of concrete masonry material performed using the DP model, implemented in the finite element software ABAQUS, are presented. The DP model is validated by comparing the model prediction output against experimental results of cyclic uniaxial compression tests performed on the selected concrete block masonry material with satisfactory findings. Full article

Background/Objectives: Obstructive ureterolithiasis complicated by infection may progress rapidly to systemic deterioration, while microbiological confirmation is generally unavailable during initial assessment. This study evaluated the association of admission procalcitonin (PCT), National Early Warning Score 2 (NEWS2), and routine urinary parameters with positive upper urinary tract culture in patients requiring urgent urinary decompression. Methods: This retrospective single-center observational study included 240 adults admitted with obstructive ureterolithiasis who underwent upper urinary tract decompression by ureteral stenting or percutaneous nephrostomy. Demographic, clinical, laboratory, and urinary parameters obtained at admission were compared according to upper urinary tract culture status. Factors associated with culture positivity were assessed using univariable and multivariable logistic regression. Four clinically defined models were compared: a reference model containing WBC, CRP, urinary nitrites, and proteinuria; models separately adding PCT or NEWS2; and a complete model containing both. Discrimination was compared using AUCs and the DeLong test, while model fit was assessed using likelihood-ratio tests. Internal validation was performed using 1000 bootstrap resamples. Results: Positive upper urinary tract cultures were identified in 86 patients (35.8%). In the complete model, PCT per doubling (aOR 1.820; 95% CI 1.502–2.205; p < 0.001) and NEWS2 (aOR 1.167; 95% CI 1.014–1.344; p = 0.031) remained independently associated with culture positivity. The reference model had an AUC of 0.691. Adding PCT increased the AUC to 0.831 (ΔAUC 0.140; p < 0.001), whereas adding NEWS2 alone resulted in an AUC of 0.720 (ΔAUC 0.029; p = 0.085). The complete model achieved an apparent AUC of 0.839 and an optimism-corrected AUC of 0.821. After PCT was included, NEWS2 improved overall model fit (p = 0.029) and produced an additional increase in AUC (ΔAUC 0.007; p = 0.448). Conclusions: Admission PCT and NEWS2 were independently associated with positive upper urinary tract culture The combined model showed good discrimination, although external validation is required before clinical application. Urinary nitrite positivity retained an independent association only in the exploratory cut-off-based analysis. Full article

The growing demand for safe autonomous landing of unmanned aerial vehicles (UAVs) in complex terrain requires reliable guidance throughout the final descent, even under degraded Global Navigation Satellite System (GNSS) conditions, where intentional interference, signal obstruction, and residual positioning errors reduce navigation continuity. This paper presents a complementary navigation system combining a ground-based and an onboard MIMO Frequency-Modulated Continuous-Wave (FMCW) radar, providing estimation of slant range, relative velocity, and angular deviation independently of GNSS signal quality. Navigation errors and trajectory corrections are modeled in simulation, and the radar signal processing and localization algorithms are supported by laboratory measurements in an anechoic chamber and by UAV flight tests. The results show that the range, velocity and angular parameters of the radar are sufficient for guidance throughout the descent and that the slant range and height to the landing site are obtained directly, without reliance on satellite geometry. The system is designed around commercially available automotive radar modules, enabling cost-effective implementation with technologically mature hardware. The proposed concept is designed to support autonomous guidance of multirotor UAVs, fixed-wing platforms, and helicopters to a predefined landing site from stand-off distances of several hundred meters and establishes a foundation for mobile take-off and landing stations and hybrid navigation systems. Full article

This study presents the design and preliminary bench evaluation of a sensor-integrated prosthetic-leg prototype. The platform combines a multi-material PAHT-CF/TPU 95A foot, articulated knee and ankle mechanisms, two surface electromyography channels, an MPU6050 inertial measurement unit, an Arduino Uno controller, and MG995 servomotors. EMG threshold crossings select discrete knee–servomotor commands, while an accelerometer-derived inclination estimate selects between two ankle–servomotor commands. The evaluation comprised exploratory finite-element analyses, separate static compression tests, and qualitative demonstrations of the sensor-to-actuator pathways. The foot model predicted a maximum displacement of 25.96 mm under 400 N, whereas a separate compression test recorded approximately 7 mm at 1000 N; these values do not constitute quantitative model validation because the loading, constraints, and displacement measures differed. The shank model produced a maximum equivalent stress of 23.89 MPa, and a separate shank–knee assembly test reached approximately 1.85 kN. The foot–ankle connection produced a stress estimate of 203.6 MPa against an assumed strength value of 60 MPa, identifying a critical design limitation. Functional observations demonstrated sensor-triggered command execution, but joint tracking accuracy, response time, actuator output under load, and control repeatability were not quantified. The prototype therefore provides an initial laboratory platform for investigating mechanical and sensing integration; structural redesign and quantitative bench testing are required before any load-bearing or clinical use can be considered. Full article

Learning analytical chemistry requires not only mastery of analytical and calculation methods but also the ability to make consistent experimental decisions. This study tested a process-oriented assessment framework based on the experimental decision-making pathway (EDMP) construct and the analysis of student-specific decision-making trajectories. A quasi-experimental study involved 60 second-year students, 30 each in a project-based learning group and a control group with a traditional laboratory format. During an eight-week module on determining total iron in water, 2317 individual decisions were recorded; independence, justification, use of data, and revision patterns were coded for each event. Dynamics were analyzed using generalized linear mixed models and sequence analysis, and trajectory characteristics were compared with educational and analytical outcomes. In the experimental group, the proportion of independent decisions increased from 35.7 to 61.5%, and the proportion of data-driven decisions increased from 32.0 to 58.2%. In the control group, changes were less pronounced. The between-group difference was greater for analytical problem solving (d = 1.26) than for subject knowledge (d = 0.84). The proportion of data-driven solutions correlated positively with the final problem-solving score (ρ = 0.52) and negatively with the relative error in iron determination (ρ = −0.44). Overall, the results supported the hypotheses and demonstrated that process analysis complements the final indicators, allowing us to trace the transition from dependent procedure execution to more independent and adaptive experimental reasoning. Full article

Background and Objectives: In Turkey, access to adult rheumatology outpatient clinics requires referral from another specialty, mostly internal medicine, and no standardized referral algorithms are in use. We estimated the diagnostic yield of new inflammatory rheumatic disease (IRD) diagnoses among internal medicine referrals who completed rheumatology evaluation and identified referral-stage characteristics associated with a new IRD diagnosis. Materials and Methods: In this retrospective cohort study, 2233 patients referred from internal medicine clinics to rheumatology outpatient clinics between 1 September and 30 November 2022 were screened. Patients with a prior diagnosis of IRD (n = 539) and those who did not attend their scheduled appointment (n = 732) were excluded, leaving 962 patients who completed rheumatology evaluation for analysis. Findings documented at referral were compared between patients with and without a new IRD diagnosis, and associations were examined with a clinically selected multivariable logistic regression model without laboratory variables, because laboratory tests had been ordered selectively. Results: Among the 962 patients who completed rheumatology evaluation, 141 (14.6%) received a new IRD diagnosis, corresponding to 6.3% of all 2233 referrals; the diagnostic status of the 732 patients who did not attend is unknown. In the multivariable model, swollen and tender joints were associated with a new IRD diagnosis (odds ratio [OR] 5.24, 95% confidence interval [CI] 3.17–8.68), whereas sicca symptoms (OR 1.45, 95% CI 0.83–2.55) and constitutional symptoms (OR 1.66, 95% CI 0.76–3.64) were not independently associated. Conclusions: Among patients referred from internal medicine to a tertiary rheumatology clinic who completed evaluation, approximately one in seven received a new IRD diagnosis. Referral appropriateness was not assessed, and the high non-attendance rate limits generalization to all referrals. Swollen and tender joints were the only referral characteristic independently associated with a new diagnosis. Whether structured referral criteria, medical education, or triage strategies improve the yield of referrals is a hypothesis for prospective evaluation. Full article

Niobium can concentrate in TiCl₄ wet scrubber slurry during molten-salt chlorination of ilmenite-derived titanium slag. This study presents an engineering assessment using published Nb-routing data, characterization of an isolated slurry solid, HSC Chemistry 6 balances, process design, and techno-economic screening. The reference model combines a 58.2% slag-to-slurry Nb distribution with an independently published bulk grade of 11.6841 wt.% Nb. These inputs come from different datasets and define a scenario rather than a closed campaign balance. For slag containing 0.044 wt.% Nb₂O₅, the calculated slurry inventory is 0.179 kg Nb t⁻¹ slag and dry-intermediate yield 1.53 kg t⁻¹ slag. XRD and SEM–EDS characterize a heterogeneous isolated solid with local Nb-rich domains; laboratory preparation did not reproduce the proposed two-stage treatment. With complete assumed Nb retention, the upper-bound routing/recovery potential is 58.1%; 95% and 90% thermal-retention cases reduce it to 55.2% and 52.3%. The theoretical process-material duty is 8.02 MJ t⁻¹ slag and heating electricity 2.51 kWh t⁻¹ slag. At 30,000 t slag y⁻¹, screening CAPEX is USD 0.293 million and annualized thermal-section cost USD 2.34 t⁻¹ slag. Pilot testing is required to close the Nb balance and verify energy use and condensate composition before recovery or recycle performance can be demonstrated. Full article

Aspirin prevents preterm birth in some women with preeclampsia but not others, yet no longitudinal multi-indicator study has examined subtype differences in aspirin response. We aimed to identify subtypes most likely to benefit. In this retrospective cohort of 53,362 deliveries (4505 preeclamptic women), we applied a two-stage machine learning framework using 15 indicators (14 laboratory tests plus systolic blood pressure) from two-time windows: before 16 weeks and within 2 weeks before delivery. A long short-term memory autoencoder with K-means clustering identified subtypes, and causal forest estimated the average treatment effect of aspirin on preterm birth (<37 weeks) for each subtype, adjusted for confounders. Five stable subtypes were identified (silhouette coefficient 0.542; mean adjusted Rand index 0.903). One subtype (n = 345), characterised by mild liver enzyme elevation and coagulation abnormalities in late pregnancy, had the highest preterm birth rate (57.7%) and ICU admission rate (9.6%), and showed the largest aspirin-associated reduction in preterm birth (ATE −0.026, 95% CI: −0.032 to −0.021), consistent across 70/30 splits. Sensitivity analyses using 34 indicators confirmed similar effects (ATE −0.027, 95% CI: −0.031 to −0.023), while no benefit was observed in non-preeclamptic women. These findings suggest that aspirin prophylaxis may be associated with a greater reduction in preterm birth in this preeclampsia subtype, but further validation is required. Full article

Cyclone separators are widely used for gas–solid separation; however, improving separation efficiency generally increases pressure drop, making cyclone design a challenging multi-objective optimization problem. This study aims to optimize a high-efficiency Stairmand cyclone separator by simultaneously maximizing separation efficiency and minimizing pressure drop through a CFD-based optimization framework integrating Computational Fluid Dynamics (CFD), Design of Experiments (DOE), Response Surface Methodology (RSM), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The optimization was performed using two geometric design variables: the vortex finder length (Lv) and the spigot diameter (Du). CFD simulations generated the data required to construct surrogate models, enabling rapid evaluation of candidate geometries during the optimization process. The Pareto-optimal solutions obtained with NSGA-II were subsequently verified using independent CFD simulations before manufacturing the selected configuration as a physical prototype. The optimized cyclone achieved a separation efficiency of approximately 93% while maintaining a low pressure drop of about 60 Pa. Experimental testing showed excellent agreement with the numerical predictions, with a deviation of only 1.51% in separation efficiency, confirming the reliability of the proposed optimization framework. The complete validation chain, from CFD simulation and surrogate-assisted optimization to prototype fabrication and laboratory testing, demonstrates that the proposed methodology provides a practical and reliable approach for the multi-objective design and optimization of industrial cyclone separators. Full article

Background and Objectives: This study aims to compare, for the first time, the impact of different substances detected by immunoassay, either alone or in combination, in the values of neutrophil-to-lymphocyte ratio (NLR) as a marker of systemic inflammation in a large series of cases. Materials and Methods: Patients with immunoassay urine tests positive for at least one of the following substances of abuse: cannabis, cocaine, amphetamine or opioids, from January 2021 to December 2025 at the Doctor Negrín University Hospital Laboratory were included in this study. A complete blood count, including leukocyte differential count, performed at the time of the substance detection urine analysis, was mandatory. Results: A total of 5008 samples were included in the present study. Cannabis alone was detected in 1831 cases, showing lower NLR (median 2.39) than cocaine (1055 cases, median: 2.44), opioids (227 cases, median: 3.01) and amphetamine/methamphetamine (487 cases, median: 3.28) (p < 0.0001). The detection of combined substances increased the NLR (p < 0.0001) mainly in combinations including amphetamines. In a lineal regression analysis, age (p < 0.0001), sex (p = 0.012) and substance consumption pattern (p < 0.0001) were independent predictors of NLR values. Conclusions: Our study demonstrated for the first time that the inflammatory effects of cannabis, cocaine, opioids and amphetamine/methamphetamine differ significantly, with amphetamine/methamphetamine and opioids being related to the highest NLR values. We also analyzed, for the first time, in the largest patient series studied, how the concomitant detection of different patterns of substances of abuse affects systemic inflammation patterns. The first finding is that an increase in the number of substances detected increases the NLR values. The second finding is that combinations including amphetamine/methamphetamine showed the highest inflammation marker (NLR) values. Full article

Automated foreign-fiber sorting in textile processing requires efficient robotic path planning, yet conventional sampling-based planners often struggle in narrow passages because of limited entrance accessibility and constrained tree expansion. To address this problem, this study proposes HA-Bi-RRT*, an improved Bi-RRT* planner incorporating a cooperative breakthrough mechanism, hybrid adaptive sampling, and hierarchical path refinement. When tree expansion is blocked, the cooperative mechanism guides the search along obstacle boundaries to facilitate passage-entrance localization. During passage traversal, the adaptive sampler enlarges the local sampling radius after consecutive failures to improve exploration around blocked regions. The method is evaluated in a two-dimensional benchmark, a three-dimensional narrow-passage environment, a ROS-based 5-DOF manipulator simulation, and a physical manipulator experiment. The observed results show favorable finite-budget trade-offs in planning success, runtime, and path cost relative to the selected baselines. The ROS simulation and physical experiments demonstrate the feasibility of converting the generated workspace paths into executable manipulator motions under the tested laboratory conditions. Full article

The global avocado industry generates seed waste typically directed to sanitary landfills despite rich starch, phenolic, and fiber content. This study evaluates the environmental sustainability of a multi-product cascade biorefinery through the first consequential life cycle assessment (cLCA) of a system converting Hass avocado seeds into native starch, a biodegradable starch-PLA-fiber biofilm, and a microencapsulated phenolic bioinsecticide, using laboratory-validated inventory data scaled through Aspen Plus V15 simulation. Following ISO 14040/44, the gate-to-gate cLCA applies ReCiPe 2016 Midpoint (H) with ecoinvent 3.11 in openLCA 2.6, resolves multi-functionality through system expansion, and uses 1000 kg seed waste as the functional unit (FU). Under deterministic conditions, the biorefinery exceeds the landfill baseline in all five evaluated midpoint categories: climate change (5215.95 vs. 674.03 kg CO₂-eq), land use (275.71 vs. 2.94 m²a crop-eq), fossil energy (2514.56 vs. 6.17 kg oil-eq), water use (8.67 vs. 0.27 m³), and freshwater ecotoxicity (341.35 vs. 279.09 kg 1,4-DCB-eq per FU). Monte Carlo uncertainty propagation shows that this deterministic ranking is robust only for land use. For climate change, fossil energy, water use, and freshwater ecotoxicity, high uncertainty in avoided-product credits prevents a robust burden-increase claim against the landfill baseline. A deterministic credit-dependency test further shows that pesticide substitution is the dominant avoided-product credit for climate change, fossil energy resources, and water use, whereas starch substitution is most relevant for land use. Hotspot analysis identifies energy supply and acetone as dominant climate change drivers, and solid waste management as the principal freshwater ecotoxicity contributor. A combined improvement scenario achieves 63.8% climate change reduction and 43.2% freshwater ecotoxicity reduction, establishing renewable energy integration and solvent substitution as priority interventions for cascade Hass avocado seed biorefineries. The results identify the conditions required to improve the environmental sustainability of Hass avocado seed valorization. Full article

Accurate state-of-health (SOH) estimation is essential for the safe, reliable, and cost-effective operation of lithium-ion battery systems. However, practical SOH modeling remains challenging because battery degradation data are often limited, degradation trajectories vary substantially across temperatures, and high-capacity deep sequence models typically require large, labeled datasets and considerable computational resources. To address these limitations, this study proposes a data-efficient GAN-Informer framework for lithium-ion battery SOH estimation. In the proposed method, a generative adversarial network is employed to augment the feature-space degradation distribution and improve the effective coverage of limited training samples, while an optimized Informer network with sparse multi-head attention learns long-range dependencies between aging-sensitive features and SOH trajectories. Laboratory cycling experiments were conducted under three representative temperature conditions: 20 °C, −20 °C, and 40 °C. The results show that the proposed framework achieves accurate capacity-tracking performance under both within-temperature and cross-temperature prediction settings. Compared with the Box–Cox-LSTM baseline, GAN-Informer consistently reduces MAE and RMSE across all tested datasets; for example, the RMSE decreases from 0.2235 to 0.0847 on CX1 with 50% training data and from 0.2385 to 0.1187 in the CX1-to-CX3 transfer scenario. These findings demonstrate that adversarial data augmentation combined with sparse-attention sequence modeling provides a promising route for robust, small-sample, and cross-temperature battery health estimation. Full article