Search Results (22,683)

In reliability engineering research, obtaining accurate information about the life expectancy of products or materials is essential. However, collecting such data under normal operating conditions is often challenging, particularly for highly reliable items. This paper addresses the problem of statistical inference for lifetime data following the power Rayleigh distribution. To reduce experimental cost and time, a partially step-stress-accelerated life test is employed under a Type-I generalized hybrid censoring scheme (GHCS). Point estimators of the model parameters, as well as the acceleration factor, are derived using both maximum likelihood and Bayesian approaches. Furthermore, interval estimation is developed based on the asymptotic normality of maximum likelihood estimators, in addition to a bootstrap method and Markov-chain Monte Carlo techniques. A real-life dataset is analyzed to demonstrate the applicability of the proposed model. Finally, a Monte Carlo simulation study is conducted to evaluate and compare the performance of the suggested model and estimation procedures. Full article

Air pollution from particulate matter (PM2.5 and PM10) poses a serious public health risk in urban environments, particularly in areas with heavy vehicular traffic. Against this backdrop, the present study proposes an Internet of Things (IoT)-based system designed to support air quality monitoring and evidence-based decision-making regarding PM2.5 and PM10 concentrations, integrating low-cost sensors with a machine learning prediction module. The study follows an experimental-applied design with a quantitative–comparative approach. Its scientific contribution is organized around an integrated IoT-ML framework addressing a concrete gap in the literature: the lack of local empirical evidence regarding which family of machine learning algorithms delivers the greatest accuracy, stability, and computational efficiency for particulate matter forecasting in mid-altitude urban environments using low-cost sensors. On one hand, the framework proposes and deploys a four-node IoT network for continuous PM2.5 and PM10 monitoring in high-traffic urban microenvironments—representing one of the first sustained deployments with low-cost, high-temporal-resolution sensors (10-minute intervals) in Arequipa, Peru. On the other hand, the study presents the most extensive benchmarking reported in the local literature: a systematic evaluation of 44 machine learning algorithms under homogeneous experimental conditions, covering classical statistical models, traditional machine learning techniques, deep learning architectures, and hybrid approaches, along with an analysis of ensemble learning strategies using Ridge stacking and K-Fold cross-validation. This unified comparative analysis—applying consistent metrics (MAE, RMSE, R², and MAPE), the same prediction horizon, and a shared dataset—provides replicable empirical evidence that had not previously been reported for the urban context of Arequipa. The results show that traditional statistical models perform poorly overall, while tree-based and boosting algorithms consistently achieve R² values above 0.90 for both pollutants. Ensemble models, particularly stacking with Ridge regression and cross-validation, yielded the strongest overall performance, demonstrating greater robustness and prediction stability. Explainability criteria were also incorporated, enabling an assessment of each base model’s individual contribution and identifying the variables most relevant to the prediction process. The methodological contribution provides future researchers with a rigorous reference framework for algorithm selection in environmental IoT systems. Taken together, the findings demonstrate that combining low-cost IoT networks with advanced machine learning and ensemble learning techniques constitutes an effective, scalable, and cost-efficient alternative for air quality monitoring, predictive analysis, and the support of informed mitigation strategies in urban environments. Full article

Monitoring the dynamic response of structures subjected to operational loads is a key component of structural health assessment, providing valuable information for safety evaluation and maintenance planning. In the last decade, video-based measurements have received growing attention for modal identification and damage detection applications, offering a promising alternative to traditional sensor-based approaches. Unlike conventional monitoring systems, which provide discrete measurements and often require extensive instrumentation, computer vision techniques enable dense, non-contact measurements while reducing installation costs and accessibility constraints. Moreover, Motion Magnification algorithms can be combined with computer vision-based identification techniques to amplify displacements within selected frequency ranges, facilitating the detection of low-amplitude structural vibrations. In this work, a semi-automated methodology for structural identification is presented and validated through two experimental applications involving vibrating systems monitored with commercial cameras. The proposed framework combines computer vision algorithms, Motion Magnification (MM), correlation analysis, and Principal Component Analysis (PCA), the latter being adopted as a noise-reduction and dimensionality-reduction tool to extract the most informative features from large sets of time-histories. In contrast to previous studies primarily focused on damage detection and frequency evolution tracking, the present work specifically investigates the influence of key user-defined parameters on the reliability of the identified frequencies and provides practical calibration guidelines for future applications. The methodology was validated against reference measurements obtained from an optical monitoring system and it successfully identified the natural frequencies of the analysed structures with errors ranging from 0.84% to 1.75%. Sensitivity analyses performed on the region of interest size and position, as well as on the correlation threshold, demonstrated the robustness of the proposed workflow. The results confirm that the proposed approach represents a reliable, low-cost, and minimally invasive alternative to conventional dynamic monitoring techniques, while providing practical recommendations for its implementation in real-world structural health monitoring applications. Full article

Medicinal plants are being investigated as a source of compounds with biological activities related to diabetes. The antidiabetic properties of the plant Carica papaya have been reported in experimental models. This study aimed to evaluate the phytochemical composition, antioxidant activity, and inhibitory activity of extracts from C. papaya leaves against α-glucosidase and pancreatic lipase. Plant material was collected in Tabasco, Mexico, and extracted by sequential maceration with solvents of increasing polarity: hexane, dichloromethane, methanol, and methanol:water. The extracts were fractionated by column chromatography, and the most active fractions were selected for further purification. The phytochemical identification of the active compounds was performed, and their structures were elucidated using spectroscopic and spectrometric techniques. The methanolic extract, rich in phenols and flavonoids, showed the highest antioxidant capacity (DPPH: 8.99 mmol TE/g; ABTS: 35.94 mmol RE/g; FRAP: 48.62 mmol Fe²⁺/g). The hydroalcoholic extract exhibited α-glucosidase inhibitory activity (38.44%), and bioassay-guided fractionation led to the identification of clitorin. The dichloromethane extract showed pancreatic lipase inhibition (52.2%), and the most active fraction contained loliolide. These findings demonstrate that C. papaya leaves contain bioactive compounds with antioxidants and digestive enzyme inhibitory activities, suggesting they could be candidates for further research in the management of diabetes. Full article

Gammaherpesviruses (GHVs) represent a global clinical burden as the causative agents of Kaposi’s sarcoma and mononucleosis, among other diseases. Kaposi’s sarcoma-associated herpesvirus (KSHV) and Epstein–Barr virus (EBV) are the most studied human GHVs, and murine gammaherpesvirus 68 (MHV-68) is a recognized experimental model. GHVs are defined by their modulation of the host cell to establish lifelong latent infections and increase host dysregulation during periodic reactivation. Due to their ubiquitous changes in host cells, systems-level techniques are well-suited to study GHV infections at all stages of the central dogma: genomics, transcriptomics, and proteomics. Furthermore, metabolomics can reveal the final metabolic changes across numerous host cellular pathways. This review assesses the current knowledge on GHV infections gained through omics techniques. We also identify gaps and propose future directions, including the development of new therapeutic strategies. Early omics techniques have characterized large swaths of infection for EBV, KSHV, and MHV-68, revealing conserved genes, homologous transcripts, and proteins. Modern omics techniques have enabled higher-resolution studies, yielding insights into heterogeneity in viral-host gene, transcript, and protein modulation strategies across geographical populations, viral subtypes, inter- and intra-patient infections, and latent and lytic states. The metabolome during GHV infections remains the least understood, but current studies have identified essential modulations of nucleotide, amino acid, and lipid synthesis by EBV, KSHV, and MHV-68. Importantly, the application of integrative omics methods to GHV infections remains a promising direction of study as the increased resolution of modern techniques meets the need for greater understanding of differences in each GHV infection. Full article

Although non-structural walls do not determine the structural safety of a building, they are responsible for its functionality by serving as acoustic, thermal, and fire-resistant partitions. They may be freely located and relocated and are typically constructed during the finishing stage of building works. Reliable performance of non-structural walls depends on appropriate connections to floors and adjacent walls. Connections to walls are most commonly achieved using traditional masonry bonding or sufficiently durable wall connectors, usually made of steel. An alternative to steel connectors may be connectors made of polymer-based materials or meshes. This paper proposes an innovative method for connecting non-structural masonry walls using a spatially arranged mesh, which serves not only as reinforcement of the wall connection but also as reinforcement of the bed joints. The aim of the study was to evaluate the effectiveness of this method in comparison with other connection techniques, including traditional solutions. Experimental investigations were carried out using an original test setup on 12 specimens made of AAC masonry units, divided into three series: series P—traditional connection (reference series), series H—connection with mesh placed in bed joints, and series SHP—connection with spatially arranged mesh. Silica Glass Mesh (SGM), intended for reinforcement of bed joints in AAC masonry, was used in the study. The experiments focused on the analysis of connection behavior and load-bearing capacity, with particular emphasis on maximum load values and failure mechanisms. Individual stages of the behavior of mesh-reinforced connections were identified, and empirical relationships enabling estimation of maximum loads were developed. The results confirmed that the traditional connection achieved the highest load-bearing capacity. However, as expected, the mesh-reinforced connections—particularly those with the spatial mesh arrangement—exhibited a more stable response and a greater ability for progressive load transfer. The SHP series connections with spatially arranged meshes exhibited significantly lower load-bearing capacity compared to the reference unreinforced connections, while at the same time demonstrating substantially greater deformability. The stiffness degradation in the mesh-reinforced connections did not occur abruptly, as observed in the reference models, which makes them an effective alternative for practical applications. Technical models for predicting forces and displacements of connections reinforced with spatially arranged meshes and meshes placed in bed joints were also developed. Full article

Traditional asphalt mix design approaches are trial-and-error processes and are limited in their ability to find high-performance asphalt mixtures to ensure long-term highway durability. The Materials Genome Initiative (MGI) launched in 2011 is designed to accelerate the development of advanced materials by integrating experiments, computational modeling, and big data analytics and has achieved remarkable progress in other materials design, which demonstrates strong potential for applying the MGI framework to optimize asphalt mix design. This study systematically reviews the application prospects of MGI methodologies in asphalt mixture design. Specifically, it first reviews recent progress in MGI for materials design and summarizes current research efforts to optimize asphalt mix design using traditional laboratory testing, numerical simulations (e.g., finite element method and discrete element method), and artificial intelligence while identifying their respective limitations. To overcome the limitations of each pillar, it is necessary to integrate these individual modules into a comprehensive MGI-based asphalt mix design framework. Furthermore, this study reviews the research on advanced experimental techniques for the multiscale characterization of asphalt binders and aggregates and defines the asphalt mixture genome, including binder-related genes, aggregate-related genes, and compaction-related genes. Finally, the specific steps for developing an MGI-based asphalt mix design framework are elaborated. Full article

Reinforced concrete frame structures (RCFSs) subjected to strong seismic excitation may enter a metastable semi-ruin state before global collapse, characterized by severe local damage, degraded stability, and high secondary collapse risk. However, systematic experimental investigations and quantitative identification techniques for this critical transitional state are still lacking in existing seismic engineering literature, forming a notable research gap for post-earthquake safety evaluation. To investigate this critical transition, a Digital Image Correlation (DIC)-assisted shaking table test was conducted on a 1/25-scale RCFS specimen derived from an earthquake-damaged exterior-corridor teaching building, using the Wolong ground motion recorded during the 2008 Wenchuan earthquake as input. DIC was employed to track the full-field evolution of cracking, through-crack development, and concrete cover spalling under incremental seismic loading. Four local damage indices—crack line density (CLD), crack propagation rate (CPR), through-crack ratio (TCR), and concrete spalling ratio (CSR)—were extracted and evaluated with the inter-story drift ratio (IDR) to quantify local-to-global degradation. The results show that visible cracks initiated at PGA = 0.3 g, while accelerated crack propagation occurred at 0.7–0.8 g, with CPR peaks of 1187.5 and 1140 mm/g, respectively. At 0.5–1.0 g, the crack number increased from 13 to 26, total crack length reached 0.443 m, CLD increased to 3.9 × 10⁻⁴, and TCR reached 37.04%. At 1.1–1.5 g, crack development approached saturation, with total crack length of 0.552 m, maximum TCR of 63.6%, and CLD of 4.8 × 10⁻⁴. Under ultimate excitation of 1.6–1.8 g, the crack number stabilized at 33–34, TCR remained around 63%, cumulative spalling area reached 1026 mm², CSR reached 0.015, and the third-floor IDR approached the 1/50 elastoplastic limit. Severe through-cracking, reinforcement exposure, concrete spalling, and residual inclination indicated the onset of the semi-ruin state. The proposed multi-index framework provides quantitative support for semi-ruin-state identification and post-earthquake secondary collapse risk assessment of RCFSs. Full article

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation method with neuromodulatory capacity in neurodegenerative diseases such as multiple sclerosis (MS). Its therapeutic value is linked to its activity against oxidative stress by activation of antioxidant defenses. The sex hormones, estrogens (E), progesterone (P) and testosterone (T), have demonstrated their power as adjuvants to TMS, improving cortical excitability. The aim of this study was to evaluate the effect of these hormones as adjuvants to extremely low-frequency electromagnetic fields (ELFEFs) in the treatment of experimental autoimmune encephalomyelitis (EAE), the experimental model of MS. The effect of these hormones as replacement therapy was also evaluated in ovariectomized rats treated with ELFEFs. Sixty-five female Dark Agouti rats were divided into 13 groups (5 rats/group), in which biomarkers of oxidative stress and the glutathione redox cycle in non-nervous organs (kidney, liver, heart, intestines and blood) were analyzed. The results show that ELFEFs alone are more effective against oxidative stress. However, P and E were more effective than ELFEFs, both as adjuvants and in hormone replacement therapy, in activating the glutathione system. Therefore, it could be concluded that sex hormones play an important role against MS, enhancing the antioxidant effect of ELFEFs. Full article

The efficient resolution of complete NP problems, such as the Traveling Salesman Problem (TSP), particularly for large instances, remains a major challenge in operations research and combinatorial optimization, especially for many businesses, particularly in sectors such as logistics, urban planning, and networks, where efforts are made daily to optimize routes and delivery times. Optimization methods inspired by collective behavior, such as Ant Colony Optimization (ACO), offer competitive results for solving these types of problems. The main problem is the size of the instances because, when it becomes large, many existing algorithms fail to converge to a good solution within a reasonable timeframe: the execution time is generally very long, and the solution obtained is generally far from being the optimal solution to the problem. In this article, we propose a new way of approaching the resolution of the TSP through new metaheuristics inspired by artificial intelligence techniques and ant colony theory. To evaluate the effectiveness of our methodology, particularly the Multi-colony Ant Colony Optimization version 2-SK (MACOV2SK) method, simulations were performed on several instances of the TSP, focusing on large-scale instances. The experimental results clearly demonstrate that the proposed approach significantly improves upon several other approaches in the literature in terms of execution time and solution quality, especially for large-scale problems. Full article

Large Language Models (LLMs) have rapidly become central components of cognitive computing systems and AI-assisted knowledge work. However, the effectiveness of LLM-generated outputs depends not only on the model’s capabilities but also on the structure of the prompts used to guide them. This study investigates how structured prompting techniques influence perceived output usefulness in business-oriented tasks. First, we conduct a systematic literature review following PRISMA guidelines to identify, classify, and synthesize existing prompt enhancement strategies. The review leads to the development of a taxonomy distinguishing task-alignment techniques (e.g., one-shot and few-shot prompting) from reasoning-transparency techniques (e.g., Chain-of-Thought prompting). Building on this taxonomy, we design a controlled experimental study in which knowledge workers evaluate LLM-generated outputs across analytical and summarization tasks. Using linear mixed-effects modeling, we assess the impact of prompting techniques and the moderating role of Generative AI usage frequency. Results indicate that structured prompting significantly increases perceived usefulness compared to baseline approaches, with the combination of example-based conditioning and explicit reasoning scaffolding yielding the highest evaluations. The moderating effect of usage frequency is not statistically significant, suggesting that the benefits of structured prompt design are robust across different experience levels. These findings position prompt structure as a practical cognitive interface mechanism and provide evidence-based guidelines for enhancing human–AI interaction in cognitive computing environments. Full article

The growing deployment of green hydrogen technologies is increasing pressure on freshwater resources, motivating the exploration of alternative water sources that do not compete with human consumption. In this work, the direct use of untreated produced water from the Shushufindi 78 oil well (Ecuador) as an electrolyte for the hydrogen evolution reaction (HER) was experimentally evaluated. A comprehensive physicochemical characterization combined with electrochemical techniques, electrochemical impedance spectroscopy (EIS), and gas chromatography (GC-TCD) was performed to correlate electrolyte composition with electrochemical performance. Despite the high salinity and complex composition of the electrolyte, hydrogen production was achieved without pretreatment. Quantitative GC-TCD analysis yielded 10.29 µmol of H₂ after 4 h of electrolysis under non-optimized laboratory conditions, corresponding to a faradaic efficiency of 43.8%. These results demonstrate the feasibility of direct hydrogen generation from untreated produced water under realistic operating conditions. Additional experiments conducted in a membrane separated H-type electrolyzer evaluated mixtures of produced water and KOH, the electrolyte commonly employed in alkaline water electrolysis. Hydrogen production increased significantly under alkaline conditions, with the PW 10% + KOH 90% electrolyte exhibiting the highest hydrogen yield and faradaic efficiency among the investigated systems. Electrochemical impedance spectroscopy revealed that KOH addition reduced solution resistance and improved ionic transport, while differences in interfacial behavior were observed depending on electrolyte composition. The combined electrochemical and chromatographic results demonstrate that untreated produced water can be directly utilized for hydrogen production and can also be partially integrated into alkaline electrolysis systems without compromising electrochemical performance. These findings highlight the potential of produced water as a non-conventional water resource for sustainable hydrogen generation and industrial wastewater valorization. Full article

Dog parks are widely used recreational spaces for human–dog interaction, yet there is little empirical data about how surface materials influence dogs’ behavior and welfare. This study evaluated dogs’ behavior on three surfaces commonly used in outdoor dog recreation areas. Ten dogs (N = 10) participated in ten structured play sessions in an experimental area with unrestricted access to all surfaces. The testing area consisted of a 12.2 m² playground divided into nine plots of equal size. Each plot was randomly assigned a surface material (i.e., natural grass, stabilized grass, or artificial turf) in a 3 × 3 block design. Environmental and surface temperatures were recorded in each session. Dog behavior was recorded during a pre- and post-play period and measured using a 10 s scan sampling technique. In addition, surface characteristic measures were collected throughout the study to evaluate differences in their tolerance to weather conditions and usage. Artificial turf consistently reached a higher temperature (25.2 °C; 95% CI: 24.5–25.8 °C) than natural (19.4 °C; 95% CI: 18.7–20.1 °C) and stabilized (20.0 °C; 95% CI: 19.3–20.6 °C). In this study, the artificial turf reached temperatures as high as 63.8 °C while, under the same environmental conditions, the surface temperature of both natural turfgrass treatments remained below 40 °C. During the pre-play period, dogs showed more active than passive behaviors on the stabilized surface (35.03%; 95% CI: 30.58–39.80%) compared to the natural (27.86%; 95% CI: 23.94–32.10%) and artificial turf (23.31%; 95% CI: 19.81–27.20%). During the post-play period, activity levels decreased across all surfaces, while the occurrence of passive behaviors increased and was observed more frequently on the natural turfgrass surfaces (27.30% on stabilized and 15.52% on natural) than on artificial turf (2.41%). Artificial turf was less affected by dog traffic and seasonal changes; however, its surface was harder than both natural turfgrass treatments. The addition of the stabilizing grid failed to reduce soil compaction as anticipated. Overall, dogs spent more time on both natural turfgrass surfaces than artificial turf. However, a potential confounding effect of location could have influenced dog behavior. Nonetheless, our findings show that dog owners should be cautious when using artificial turf areas when environmental temperatures are above 25 °C when the turf temperature is above the safety threshold for burn injuries. These findings highlight the importance of carefully selecting surface materials for outdoor dog spaces to ensure dogs’ safety and comfort. Full article

With the introduction of Navarixin in clinical trials, the role of squaric acid derivatives as bioisosteres gained popularity. Because of their distinctive electronic properties and hydrogen-bonding capacity, these compounds hold considerable promise for medicinal chemistry applications. In this study, a series of novel furan- and thiophene-containing squaric acid derivatives was synthesized via base-catalyzed nucleophilic substitution and characterized by spectroscopic techniques. The structures of three compounds were additionally confirmed by X-ray crystallography. Density functional theory calculations showed good agreement with the experimental vibrational spectra. In silico evaluation predicted favorable drug-like characteristics, including compliance with Lipinski’s rule of five and high gastrointestinal absorption. The cytotoxic activity of the synthesized compounds was assessed against HeLa, HT-29, HL-60, A-549, and MCF-7 cancer cell lines, as well as the non-cancerous CCL-1 cell line. Several derivatives displayed moderate to strong antiproliferative activity with selectivity toward malignant cells. Compound 3d exhibited the most pronounced improvement (five-fold) over Navarixin in HL-60 cells 5.81 µM, while compounds 3a and 3c demonstrated superior potency and selectivity in A-549 cells (10.33 µM and 9.65 µM). These findings identify squaric acid derivatives as promising candidates for further anticancer drug development and structure–activity relationship studies. Full article

Hydrated lime is widely incorporated into cement-based mortars to improve workability and fresh-state properties; however, its influence on hydration products and mechanical performance remains insufficiently understood. This study investigates the effect of hydrated lime content on the mechanical behavior and microstructural development of cement-based mortars after 28 days of curing. Eight mortar formulations, ranging from lime-free (1:0:6) to lime-rich (1:5:6) mixtures, including intermediate and modified proportions, were evaluated through compressive strength, flexural tensile strength, and consistency tests. The microstructural evolution was investigated using complementary techniques, including X-ray fluorescence (XRF), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TG/DSC), and scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM/EDS). Increasing hydrated lime content improved mortar workability but was generally associated with reduced compressive strength under the curing conditions investigated. The combined characterization techniques indicated progressive modifications in the hydration products and phase assemblage, with increased calcium-rich phases, greater evidence of carbonation, and reduced continuity of the hydraulic matrix as the hydrated lime content increased. The observed microstructural changes were qualitatively consistent with the mechanical behavior of the mortars. The conclusions of this study are restricted to the 28-day curing period investigated, and further research is required to evaluate the long-term influence of hydrated lime on carbonation and durability-related properties. These findings contribute to a better understanding of the role of hydrated lime in cement-based mortars and provide experimental evidence for the optimization of mortar formulations. Full article