Search Results (9,061)

Background/Objectives: This study aimed to perform a comparative analysis of the original Viagra^® product and sildenafil-containing tablets obtained from illegal sources (the darknet). Specifically, the analyzed material consisted of samples seized by Polish law enforcement authorities from unverified vendors operating within the Central European darknet market. The study utilized thermal methods, specifically Thermogravimetry (TG), Derivative Thermogravimetry (DTG), and calculated Differential Thermal Analysis (c-DTA), as well as colorimetric analysis based on the International Commission on Illumination (CIE) L*a*b* system. Methods: Thermal analyses enabled the assessment of the thermal stability of the tested samples, identification of characteristic stages of thermal decomposition, and determination of differences in thermal behavior between the pure substance, the original preparation, and darknet samples. In turn, color measurements in the CIE L*a*b* space allowed for an objective comparison of tablet appearance and determination of the degree of color similarity to the original product. Results: The obtained results showed that only a few samples (V1, V3, V4, V6, V8) exhibited features similar to the original Viagra^®, both in terms of thermal profile and color. Most of the tested tablets were characterized by significant variability in physicochemical properties, indicating a lack of quality control and inconsistency in formulation. Samples V2 and V7 deviated particularly strongly—both thermally and visually—suggesting that they might not contain the original active substance or contained it in a different chemical form. Conclusions: The use of combined thermal and colorimetric methods proved to be an effective tool in the identification of counterfeit pharmaceutical products, enabling simultaneous evaluation of their composition and authenticity. The results confirm the validity of employing integrated physicochemical analyses for the detection of falsified medicines present on the illegal market. Full article

Rodents models of myocardial infarction (MI) continue to be frequently used in preclinical cardiovascular research, despite alternative approaches being on the rise. The commonly used coronary artery permanent ligation (PL) approach is often hampered by substantial perioperative mortality and variable success rates. We optimized the rat PL protocol by relying exclusively on isoflurane inhalation anesthesia by introducing a standardized intubation setup, maintaining strict control of body temperature throughout surgery, and surgical technique refinements. The latter included gentle mobilization of the Pectoralis major and thymus, a medial thoracotomy through the third intercostal space, and the use of a reference ligature to facilitate reliable identification and ligation of the left anterior descending coronary artery (LAD). Cardiac rhythm was continuously monitored, and extubation was carefully timed to reduce complications. With this protocol, perioperative mortality was reduced to zero and successful ligation was obtained in 94% of animals (n = 172). Echocardiography and histology confirmed consistent induction of infarcts. By lowering invasiveness and improving survival and reproducibility, the refined PL method enhances both the reliability of preclinical research and compliance with the 3Rs, representing a meaningful step forward for studies in cardiac regeneration. Full article

This study introduces an optimized and selective extraction methodology using dichloromethane/methanol (DCM/MeOH, 95:5, v/v) in combination with accelerated solvent extraction (ASE) for the targeted stilbenoid and phenanthrene derivatives from five orchid species: Cattleya nobilior (root), Cymbidium defoliatum (root and bulb), Dendrobium phalaenopsis (stem), Encyclia linearifolioides (leaf), and Phalaenopsis aphrodite (root). Sequential extraction was performed with hexane, followed by DCM/MeOH (95:5 and 1:1, v/v) under controlled temperatures (70 °C for hexane, 100 °C for DCM/MeOH), using three static cycles per stage. Chemical profiling by high-performance liquid chromatography with a diode-array-detector and tandem mass spectrometry (HPLC-DAD-MS/MS) enabled the identification of twenty specialized metabolites—seven stilbenoids and thirteen phenanthrenes—several reported here for the first time, including crepidatuol B, dendrosinen D, and coeloginanthridin. The analytical method showed excellent separation of structurally related phenolic compounds, demonstrating the efficiency of the extraction protocol and the selectivity of the solvent system. Many of the identification metabolites are known for cytotoxic, antioxidant, anti-inflammatory, and metabolic regulatory properties, while newly detected compounds remain unexplored and present promising candidates for future biological evaluation. The broad distribution of these metabolites across the studied orchids enhances the current understanding of their phytochemical diversity and suggests chemotaxonomic relevance within the Orchidaceae family. Importantly, the extraction strategy requires minimal plant material, offering ecological advantages when working with rare or endangered species. Overall, this environmentally conscious extraction approach provides a robust platform for metabolic discovery and supports future research in natural products chemistry, plant ecology, drug discovery, structure–activity relationships studies and biotechnological applications. Full article

Accurate parameter estimation of photovoltaic (PV) models is fundamentally a challenging nonlinear optimization problem, characterized by strong nonlinearity, high dimensionality, and multiple local optima. These characteristics significantly hinder the convergence accuracy, stability, and efficiency of conventional metaheuristic algorithms when applied to PV parameter identification. Although the enterprise development (ED) optimization algorithm has shown promising performance in various optimization tasks, it still suffers from slow convergence, limited solution precision, and poor robustness in complex PV parameter estimation scenarios. To overcome these limitations, this paper proposes a multi-strategy enhanced enterprise development (MEED) optimization algorithm for high-precision PV model parameter estimation. In MEED, a hybrid initialization strategy combining chaotic mapping and adversarial learning is designed to enhance population diversity and improve the quality of initial solutions. Furthermore, a historical trend-guided position update mechanism is introduced to exploit accumulated search information and accelerate convergence toward the global optimum. In addition, a mirror-reflection boundary control strategy is employed to maintain population diversity and effectively prevent premature convergence. The proposed MEED algorithm is first evaluated on the IEEE CEC2017 benchmark suite, where it is compared with 11 state-of-the-art metaheuristic algorithms under 30-, 50-, and 100-dimensional settings. Quantitative experimental results demonstrate that MEED achieves superior solution accuracy, faster convergence speed, and stronger robustness, yielding lower mean fitness values and smaller standard deviations on the majority of test functions. Statistical analyses based on Wilcoxon rank-sum and Friedman tests further confirm the significant performance advantages of MEED. Moreover, MEED is applied to the parameter estimation of single-diode and double-diode PV models using real measurement data. The results show that MEED consistently attains lower root mean square error (RMSE) and integrated absolute error (IAE) than existing methods while exhibiting more stable convergence behavior. These findings demonstrate that MEED provides an efficient and reliable optimization framework for PV model parameter estimation and other complex engineering optimization problems. Full article

This study presents a frequency-domain analysis of a finite-element (FEM)-based rotor–nacelle model for wind turbines, validated against the open-source time-domain tool OpenFAST. The analysis was carried out using METHOD, an in-house computational framework implemented in Python. While time-domain models remain standard for nonlinear aeroelastic simulations, frequency-domain approaches offer advantages in early-stage design, control development, and system identification due to their efficiency, transparency, and suitability for parametric studies. The FEM model includes flexible blades, hub, and nacelle dynamics and includes tower and fixed or floating platform components with rotor–tower frequency interactions. In this work, a fixed tower is considered to isolate rotor behaviour. Beam-element formulation enables the computation of natural frequencies, mode shapes, and frequency response functions, and an equivalent rotor model is implemented in OpenFAST for consistent benchmarking. Validation results show close correspondence between the two modelling approaches. Key operational parameters agree within 3%, while structural responses, including flap-wise deflection, bending moments, and resultant quantities, typically fall within an overall accuracy range of 5–15%, consistent with expected differences arising from reference-frame conventions and modelling assumptions. Discrepancies are discussed in terms of numerical damping, model assumptions (differences in the axis system), and the influence of structural simplifications. Overall, the FEM model captures the dominant dynamic behaviour with satisfactory accuracy and a consistent orientation of global response. Computational efficiency results further highlight the advantages of the METHOD framework. Wind-field generation is completed roughly an order of magnitude faster, and long-duration aeroelastic simulations achieve substantial speed-ups, reaching more than one order of magnitude for multi-hour cases, demonstrating strong scalability relative to OpenFAST. Overall, the results confirm that a well-constructed yet still simplified frequency-domain FEM rotor model can provide a robust and computationally efficient alternative to conventional time-domain solvers. Moreover, the computational performance presented here represents a lower bound, as further improvements are readily achievable through parallelisation and solver-level optimisation. Future papers will present the full-system aero-hydro-elastic coupling for fixed and floating offshore wind turbine applications. Full article

Background and Objectives: Early detection of chronic diseases is essential for improving health outcomes and reducing long-term complications. In Poland, the POZ PLUS pilot programme introduced the Health Check-up (Bilans Zdrowia, BZ) tool, a structured preventive assessment designed to support early identification of chronic conditions in primary care. This study aimed to assess the association between participation in the Health Check-up and the detection (diagnostic yield) of hypertension, type 2 diabetes, lipid metabolism disorders, elevated fasting blood glucose, hypothyroidism, and non-toxic goiter by comparing outcomes in an intervention group and a control group. Materials and Methods: A non-randomised comparative study was conducted using routine clinical data from Health Check-ups performed within the POZ PLUS pilot. Detection rates were compared with those obtained in standard primary care practice during the same period. The study group consisted of 865 adults who met the inclusion criteria and underwent the BZ procedure, while the control group comprised 3199 patients with comparable eligibility who received usual care. Data analysis was performed using R and RStudio. Results: Hypertension detection was similar in both groups: 4.6% (95% CI: 3.3–6.3%) in the intervention group versus 4.5% (95% CI: 3.8–5.3%) in the control group (p = 0.9505). No significant difference was observed in type 2 diabetes detection: 0.7% (95% CI: 0.3–1.5%) versus 0.4% (95% CI: 0.2–0.7%) (p = 0.4134). In contrast, detection rates were significantly higher in the Health Check-up group for lipid metabolism disorders (10.3% vs. 2.6%; p < 0.001), abnormal fasting glucose (2.9% vs. 1.8%; p = 0.0465), and thyroid diseases, including hypothyroidism and non-toxic goiter (4.3% vs. 2.3%; p = 0.0016). Conclusions: The Health Check-up tool was associated with higher detection rates of lipid disorders, impaired fasting glucose, and thyroid diseases compared with usual care, suggesting increased diagnostic yield under a structured preventive assessment pathway. Further research should evaluate downstream clinical outcomes and cost-effectiveness. Given the non-randomised design and differential diagnostic intensity between groups, these findings should be interpreted as associations with diagnostic yield rather than causal effects on disease incidence or clinical outcomes. Full article

Background/Objectives: Behavioral inhibition (BI) has been extensively studied as an early-appearing risk factor for adverse developmental outcomes. One pathway through which BI may confer risk is via reduced competence to interact effectively with peers. Research demonstrating concurrent relations between BI and social competence supports this pathway, yet not all inhibited children experience social difficulties. This study adopted a person-centered approach to examine heterogeneity of temperament traits within a highly inhibited preschool sample and to identify how broader temperament traits contribute to variability in social functioning. Methods: Parents of preschoolers (N = 254) who met criteria for BI (≥85th percentile on the Behavioral Inhibition Questionnaire) completed measures of their child’s temperament (Children’s Behavior Questionnaire) and social competence (Social Skills Improvement System). Latent Profile Analysis was conducted using six temperament traits reflecting regulation and reactivity (anger, attentional focusing, inhibitory control, high-intensity pleasure, perceptual sensitivity, and approach). Profile differences in social competence were examined using multivariate analyses controlling for age and gender. Results: A three-profile solution emerged: Regulated, Unregulated and Angry, and Typical BI. Profile membership accounted for almost 37% of the variance in social skills scores. The Regulated group, marked by high attentional and inhibitory control and low anger, demonstrated the strongest social skills and lowest internalizing and externalizing problems. The Unregulated and Angry group, characterized by high anger and poor regulation, exhibited the greatest social difficulties. BI level itself did not significantly differentiate profiles or predict social competence. Conclusions: Findings underscore that BI is not a uniform risk factor but joins with other temperamental traits to shape social outcomes. Level of BI did not differentiate profiles or relate to social functioning, highlighting the importance of considering co-occurring regulatory and reactive traits to explain variability in outcomes among inhibited children. Identifying specific temperamental constellations may enhance early identification and inform targeted interventions for socially at-risk inhibited children. Full article

Background: This paper presents an analysis and a structured framework for improving inventory accuracy in an automotive factory, considering the current context of global disruptions. In 2023, the company recorded 20,340 inventory adjustments (1695 per month) and a 0.24% monthly net value discrepancy (EUR 256,594 YTD), with a baseline absolute discrepancy of 2.21% of sales. The project aimed to reduce adjustments to below 700 per month and the net value discrepancy to 0.1%. Methods: The research followed the Six Sigma methodology’s Define, Measure, Analyze, Improve and Control (DMAIC) phases, integrating Root Cause Analysis (RCA) and Failure Mode and Effects Analysis (FMEA) to enhance inventory accuracy in manufacturing operations. Results: Implementation significantly improved inventory accuracy: monthly adjustments decreased from 1695 to 971, the highest RPN was reduced from 576 to 144, and the absolute discrepancy-to-sales ratio stabilized at 0.98% (a 56% improvement). Financial variance was reduced to EUR 1948.10 in Q4 2024, while organizational discipline, role clarity and process control also increased. Conclusions: The integrated DMAIC–RCA–FMEA framework proved effective and replicable, enabling systematic identification of root causes, targeted corrective actions and sustainable KPI-driven improvements. The results demonstrate a scalable approach to inventory optimization that supports operational resilience and supply chain performance. Full article

In modern industrial environments, camera-based monitoring is essential for workflow optimization, safety, and process control, yet it raises significant privacy concerns when people are recorded. Realistic full-body anonymization offers a potential solution by obscuring visual identity while preserving information needed for automated analysis. Whether such methods also conceal biometric traits from human pose and gait remains uncertain, although these biomarkers enable person identification without appearance cues. This study investigates the impact of full-body anonymization on gait-related identity recognition using DeepPrivacy2 and a custom CCTV-like industrial dataset comprising original and anonymized sequences. This study provides the first systematic evaluation of whether pose-preserving anonymization disrupts identity-relevant gait characteristics. The analysis quantifies keypoint shifts introduced by anonymization, examines their influence on downstream gait-based person identification, and tests cross-domain linkability between original and anonymized recordings. Identification accuracy, domain transfer between data types, and distortions in derived pose keypoints are measured to assess anonymization effects while retaining operational utility. Findings show that anonymization removes appearance but leaves gait identity largely intact, indicating that pose-driven anonymization is insufficient for privacy protection. Effective privacy requires anonymization strategies that explicitly target gait characteristics or incorporate domain-adaptation mechanisms. Full article

This paper evaluates the efficacy of the Black-Winged Kite Algorithm (BKA) for parameter estimation in single-, double-, and triple-diode photovoltaic (PV) models. This study targets key electrical parameters, including photocurrent, reverse saturation current, series, and shunt resistances, and diode ideality factor(s) using experimental I-V data from an RTC France silicon cell. Performance is assessed using the root mean square error (RMSE) and convergence behavior and benchmarked against established metaheuristics including the Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), and Ant Lion Optimizer (ALO). The results show that BKA achieves competitive RMSE values with stable convergence for the investigated dataset. BKA employs coupled exploration and exploitation updates inspired by hunting and migration behaviors, and its limited number of control parameters supports straightforward deployment in nonlinear PV identification tasks. The results support BKA as a viable optimization option for PV model fitting in this setting, while also reflecting the typical trade-offs between search diversity and computational effort inherent to population-based methods. Full article

Background/Objectives: The increasing rate of complex patients with multiple chronic somatic and/or mental disorders in modern medicine is challenging, necessitating special management programs. The aim of the present study was to identify clinical variables and the use of health services associated with the primary-physician-driven inclusion of complex patients in the “Team Management for Complex Patients” (TMCP) special program. Methods: Using validated electronic medical records of a nationwide health maintenance organization, a case–control study was performed. The study compared parameters before enrollment of complex patients included in the TMCP program with those of complex patients during the same time period who were not included, and were matched using a propensity score for age, sex, socioeconomic status, place of residence, ethnicity, smoking status, physical activity, and the balance before the day of enrollment for the major body measurements and laboratory results. Results: The control group was well-balanced, except for the South region and no physical activity. Several respiratory, cardiac, gastrointestinal, neurological, inflammatory and autoimmune diseases were significantly more common among patients included in the TMCP program than among those not included. Complex patients included in the program presented significantly higher previous rates of attending outpatient urgent care centers, visiting hospital emergency departments, hospitalization, and medication use. Conclusions: Although limited by subjective inclusion criteria and potential confounding, the present comparative study identified clinical variables associated with the identification of complex patients for enrollment into a special managed program. These associations may inform future work to develop and validate criteria to support physician decision-making in selecting complex patients for managed programs and designing healthcare resources for patients who need them most. We currently meticulously follow the outcomes of the patients included in the special managed program. Full article

The control of wastewater treatment plants (WWTPs) is an ecologically, economically, and socially important objective. In the case of plants using activated sludge (ASP) processes, their control presents a significant challenge due to the complexity of the dynamics of these processes (a consequence of their biological nature). To objectively evaluate control strategies, standardized benchmark simulation models (BSMs) are used. This article tests the feasibility and evaluates the performance, in a simulation environment, of a specific fuzzy model-based predictive control strategy, called FMBPC/CLP, applied to the BSM1 reference model. In each iteration, this strategy first uses an FMBPC-type algorithm, which determines the basic control action (based on a fuzzy model and applying functional predictive control) that guarantees the local stability of the closed-loop system. Then, a second predictive control algorithm, called closed-loop predictive control (CLP-MPC), calculates a compensating term that is added to the basic control law and ensures compliance with constraints in the control action. In the simulation experiments carried out, the plant structure described in the BSM1 benchmark (reactor divided into five tanks, followed by a settling tank) was maintained, but the default control configuration was modified. The alternative control configuration designed for the BSM1 test bench includes two control loops: one to regulate the oxygen concentration in compartment 5 of the reactor (maintaining the PI algorithm of the default control configuration) and another loop to regulate the nitrate concentration (nitrate and nitrite) in tank 2 and, simultaneously, the ammonia concentration in tank 5, using the alternative FMBPC/CLP strategy. This control hybrid configuration was tested and evaluated considering values of the influent (dry, rainy, and stormy weather), and performance measurement criteria, both standardized in the BSM1 platform. The base model of the plant to be controlled, necessary for the FMBPC strategy, is obtained by prior fuzzy identification, from open-loop input and output data. The identification is achieved with the help of a software tool that uses mathematical clustering methods (based on the Gustafson–Kessel algorithm) that allow for the extraction of fuzzy models of the Takagi–Sugeno type from the numerical input–output data of a given plant. The FMBPC strategy is potentially appropriate for the control of complex, changing or unknown systems and this article demonstrates that this strategy is viable, with satisfactory performance, and that it can even be competitive when compared with more traditional control strategies. Full article

This study evaluated the influence of soil preparation method and initial moisture content on the compressive strength of cement-stabilized rammed earth (CSRE). Cube samples stabilized with 7–12% cement were compacted using a manual rammer, cured for up to 28 days, and tested according to adapted EN 12390-3 procedures. These results indicated that eliminating the powdering step improved laboratory efficiency and produced specimens more representative of field practice. These findings demonstrate that labor-intensive powdering of natural soils is unnecessary, provided that moisture is accurately determined, thereby improving both laboratory efficiency and consistency with field practice. The outcomes contribute to optimizing laboratory methodologies for earthen construction materials. Full article

Accurate authentication of herbal medicine Descurainiae Semen, the tiny seeds of Descurainia sophia, is challenging due to their morphological similarity to various adulterants. To develop a precise and reliable molecular identification method, we conducted comparative analyses of rDNA-ITS sequences using D. sophia and five adulterant species and subsequently developed species-specific sequence-characterized amplified region (SCAR) markers. The discriminatory power and detection limits of these markers were evaluated using serially diluted genomic DNA from each species and commercially available Descurainiae Semen, respectively. The SCAR markers developed in this study enabled the detection of adulterant contamination at levels as low as 0.01–1%. Among several potential adulterants tested using 17 herbal medicines, Erysimum macilentum was found to be the most common adulterant in commercial products, with a ratio of 88%. The SCAR-PCR assay established in this study provides a rapid and accurate tool for identifying D. sophia and illegal adulterants at the species level and at very low contamination levels, thereby supporting improved quality control and enhancing consumer confidence in the herbal medicine industry. Full article

Globally, Spodoptera frugiperda is a major threat to many important crops, including maize, rice, and cotton, causing significant economic damage. To control this invasive pest, environmentally friendly pest control techniques, including pheromone detection and identification of potential molecular targets to disrupt S. frugiperda mating communication, are needed. Female moths biosynthesize pheromones and emit them from the pheromone gland, which significantly depends on the intrinsic factors of the moth. Male S. frugiperda have a sophisticated olfactory circuit on their antennae that recognizes pheromone blends via olfactory receptor neurons (ORNs). With its potential to significantly modify the insect genome, CRISPR/Cas9 offers a revolutionary strategy to control this insect pest. The impairing physiological behaviors and disrupting the S. frugiperda volatile-sensing mechanism are the main potential applications of CRISPR/Ca9 explored in this review. Furthermore, the release of mutant S. frugiperda for their long-term persistence must be integral to the adoption of this technology. Looking forward, CRISPR/Cas9-based gene drive systems have the potential to synergistically target pheromone signaling pathways in S. frugiperda by disrupting pheromone receptors and key biosynthesis genes, thereby effectively blocking intraspecific communication and reproductive success. In conclusion, CRISPR/Cas9 provides an environmentally friendly and revolutionary platform for precise, targeted pest management in S. frugiperda. Full article