Robust Approach Research Articles

Beyond assumptions of altruism: Examining nonprofit work with a job fit framework and response surface analysis

AbstractScholars frequently investigate how nonprofit employees, as a group, differ from public or for‐profit employees. However, there is less focus on how motivational profiles vary among nonprofit employees. Particularly as the nonprofit sector professionalizes, the reasons why people seek nonprofit employment are diversifying. Recent work highlights a more individualized understanding of employee motivation, which can lead to more nuanced and tailored human resource management techniques. I amplify this stream of scholarship, guided by a job fit framework and introducing a robust methodological approach from organizational psychology that accounts for interactions between an employee's work preferences and work experiences on outcomes of interest. With two waves of original data on international aid workers, I demonstrate that the experience of prosocial work (i.e., work that aims to help other people) is associated with greater job satisfaction for those with strong prosocial work preferences but can be associated with reduced job satisfaction for those without. These findings necessitate nuanced attention to employees' work preferences, moving beyond assumptions that prosocial work will universally motivate nonprofit employee performance.

Preclinical Application of CEST MRI to Detect Early and Regional Tumor Response to Local Brain Tumor Treatment.

Treating glioblastoma and monitoring treatment response non-invasively remain challenging. Here, we developed a robust approach using a drug-loaded liposomal hydrogel that is mechanically compatible with the brain, and, simultaneously, we successfully monitored early tumor response using Chemical Exchange Saturation Transfer (CEST) MRI. This CEST-detectable liposomal hydrogel was optimized based on a sustainable drug release and a soft hydrogel for the brain tumor, which is unfavorable for tumor cell proliferation. After injecting the hydrogel next to the tumor, three distinctive CEST contrasts enabled the monitoring of tumor response and drug release longitudinally at 3T. As a result, a continuous tumor volume decrease was observed in the treatment group along with a significant decrease in CEST contrasts relating to the tumor response at 3.5 ppm (Amide Proton Transfer; APT) and at -3.5 ppm (relayed Nuclear Overhauser Effect; rNOE) when compared to the control group (p < 0.05). Interestingly, the molecular change at 3.5 ppm on day 3 (p < 0.05) was found to be prior to the significant decrease in tumor volume on day 5. An APT signal also showed a strong correlation with the number of proliferating cells in the tumors. This demonstrated that APT detected a distinctive decrease in mobile proteins and peptides in tumors before the change in tumor morphology. Moreover, the APT signal showed a regional response to the treatment, associated with proliferating and apoptotic cells, which allowed an in-depth evaluation and prediction of the tumor treatment response. This newly developed liposomal hydrogel allows image-guided brain tumor treatment to address clinical needs using CEST MRI.

Secure and resilient improved image steganography using hybrid fuzzy neural network with fuzzy logic

The exponential growth in communication networks, data technology, advanced libraries, and mainly World Wide Web services has played a pivotal role in facilitating the retrieval of various types of information as needed. However, this progress has also led to security concerns related to the transmission of confidential data. Nevertheless, safeguarding these data during communication through insecure channels is crucial for obvious reasons. The emergence of steganography offers a robust approach to concealing confidential information, such as images, audio tracks, text files, and video files, in suitable media carriers. A novel technique is envisioned based on back-propagation learning. According to the proposed method, a hybrid fuzzy neural network (HFNN) is applied to the output obtained from the least significant bit substitution of secret data using pixel value differences and exploiting the modification direction. Through simulation and test results, it has been observed that the proposed methodology achieves secure steganography and superior visual quality. During the experiments, we observed that for the secret image of the cameraman, the PSNR & MSE values of the proposed technique are 61.963895 and 0.041361, respectively.

Oligo(ethylene glycol) methyl ether methacrylate based hydrogel with intrinsic antibacterial activity against Pseudomonas aeruginosa as a model of a major wound infecting human pathogen

Wound dressings represent the first important defense line against potentially life-threatening infections. Successful materials should unify efficiency, simplicity of production with the ease of application. Cationic polymers based on quaternary ammonium salt moieties along the polymer chain promise high efficacy against growth of relevant pathogens including drug-resistant strains. We present a novel and easy-to-handle wound dressing material based on the well-defined copolymers of oligo(ethylene glycol) methyl ether methacrylate (OEGMA) and 2-(dimethylamino)ethyl methacrylate (DMAEMA), using quaternization with 1,6-dibromohexane for hydrogel formation. The material obtained from this robust approach was perfectly biocompatible with human dermal fibroblasts and revealed an auspicious degree of intrinsic antibacterial activity against P. aeruginosa as a model pathogen for hospital-acquired infections. Formation of bacterial biofilms was prevented in early- and late-stages and even planktonic cells were killed upon gel contact. The rheological properties of the hydrogel materials were comprehensively characterized in both dry and swollen states. Excellent biological performance and appropriate viscoelastic properties qualify the hydrogels from our production approach as truly promising novel wound dressing materials with long-lasting and contact-active bactericidal activities. We believe that this charmingly simple procedure and the mechanical properties of the hydrogel may open new avenues towards cost-effective next-generation first-line wound-care materials.

Harnessing the power of transformers and data fusion in smart irrigation

In recent years, IoT sensors have enabled smart agriculture to grow rapidly with many compelling real-world applications. One such application is the case of smart irrigation. A particular interest exists in forecasting soil water potential to allow for the establishment of more efficient irrigation systems. Nonetheless, forecasting soil moisture is a complex task and depends on various information sources. Most existing work relies on local approaches, which are less effective at leveraging shared information across different data sources. Therefore, this paper presents a robust global approach for soil water potential forecasting, combining various environmental factors through the cutting-edge Temporal Fusion Transformer. Our proposed approach outperforms established baselines for forecasting soil water potential. As such, this work contributes to the growing body of research on data fusion in real-world applications.

Robust control and experimental validation of trajectory tracking for permanent magnet linear motors based on constraint-following under uncertainty

AbstractThis paper proposes a robust control approach to achieve high-precision trajectory tracking for permanent magnet linear motor (PMLM) system containing uncertainties by describing the dynamic model of PMLM based on the Udwadia-Kalaba equation combined with constraint-following method. First, the system of PMLM is described as a constraint-following system by adding the generalized constraint force to the unconstrained Udwadia-Kalaba equation of PMLM system. Second, the robust constraint-following controller is designed based on the proposed model after uncertainty analysis. Moreover, the proposed controller is verified to guarantee deterministic performance for uncertain systems: uniformly bounded and uniformly ultimately bounded. Third, the numerical simulation and experimental validation demonstrate the effectiveness of proposed controller. Finally, the design approach of constraint-following can be applied to other systems with uncertainties.

Homozygous substitution of threonine 191 by proline in polymerase η causes Xeroderma pigmentosum variant

DNA polymerase eta (Polη) is the only translesion synthesis polymerase capable of error-free bypass of UV-induced cyclobutane pyrimidine dimers. A deficiency in Polη function is associated with the human disease Xeroderma pigmentosum variant (XPV). We hereby report the case of a 60-year-old woman known for XPV and carrying a Polη Thr191Pro variant in homozygosity. We further characterize the variant in vitro and in vivo, providing molecular evidence that the substitution abrogates polymerase activity and results in UV sensitivity through deficient damage bypass. This is the first functional molecular characterization of a missense variant of Polη, whose reported pathogenic variants have thus far been loss of function truncation or frameshift mutations. Our work allows the upgrading of Polη Thr191Pro from ‘variant of uncertain significance’ to ‘likely pathogenic mutant’, bearing direct impact on molecular diagnosis and genetic counseling. Furthermore, we have established a robust experimental approach that will allow a precise molecular analysis of further missense mutations possibly linked to XPV. Finally, it provides insight into critical Polη residues that may be targeted to develop small molecule inhibitors for cancer therapeutics.

A survey on the role of artificial intelligence in managing Long COVID.

In the last years, several techniques of artificial intelligence have been applied to data from COVID-19. In addition to the symptoms related to COVID-19, many individuals with SARS-CoV-2 infection have described various long-lasting symptoms, now termed Long COVID. In this context, artificial intelligence techniques have been utilized to analyze data from Long COVID patients in order to assist doctors and alleviate the considerable strain on care and rehabilitation facilities. In this paper, we explore the impact of the machine learning methodologies that have been applied to analyze the many aspects of Long COVID syndrome, from clinical presentation through diagnosis. We also include the text mining techniques used to extract insights and trends from large amounts of text data related to Long COVID. Finally, we critically compare the various approaches and outline the work that has to be done to create a robust artificial intelligence approach for efficient diagnosis and treatment of Long COVID.

Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature.

To develop and assess a radiomics-based prediction model for distinguishing T2/T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) METHODS: A total of 118 patients with pathologically proven LHSCC were enrolled in this retrospective study. We performed feature processing based on 851 radiomic features derived from contrast-enhanced CT images and established multiple radiomic models by combining three feature selection methods and seven machine learning classifiers. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the models. The radiomic signature obtained from the optimal model and statistically significant morphological image characteristics were incorporated into the predictive nomogram. The performance of the nomogram was assessed by calibration curve and decision curve analysis. Using analysis of variance (ANOVA) feature selection and logistic regression (LR) classifier produced the best model. The AUCs of the training, validation, and test sets were 0.919, 0.857, and 0.817, respectively. A nomogram based on the model integrating the radiomic signature and a morphological imaging characteristic (suspicious thyroid cartilage invasion) exhibited C-indexes of 0.899 (95% confidence interval (CI) 0.843-0.955), fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram. The nomogram based on the radiomics model derived from contrast-enhanced CT images had good diagnostic performance for distinguishing T2/T3 staging of LHSCC. Accurate T2/T3 staging assessment of LHSCC aids in determining whether laryngectomy or laryngeal preservation therapy should be performed. The nomogram based on the radiomics model derived from contrast-enhanced CT images has the potential to predict the T2/T3 staging of LHSCC, which can provide a non-invasive and robust approach for guiding the optimization of clinical decision-making. • Combining analysis of variance with logistic regression yielded the optimal radiomic model. • A nomogram based on the CT-radiomic signature has good performance for differentiating T2 from T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma. • It provides a non-invasive and robust approach for guiding the optimization of clinical decision-making.

An analytic approach for the moment-of-fluid interface reconstruction method on tetrahedral meshes

This article presents an original fast and robust approach for solving the moment-of-fluid interface reconstruction problem on tetrahedral meshes. We establish an explicit parametrization of the locus of the centroids under volume constrains using the spherical coordinates on a reference tetrahedron. This parametrization allows to write the moment-of-fluid objective function and its gradient by means of analytic formulas. Then, we show that the minimization problem on arbitrary tetrahedra can be effectively solved on the reference tetrahedron by using an affine transformation. Based on this transformation, we propose a improved technique for obtaining an initial condition for the minimization algorithm. We then describe the Gauss-Newton and the BFGS minimization algorithms, both combined with a trust-region method, to solve this reconstruction problem. Finally, we perform a verification and validation process on a series of increasingly complex test cases. These tests show that the proposed analytic method, paired with the Gauss-Newton algorithm, outperforms the other methods that were tested.

Combining Liquid Chromatography and Cryogenic IR Spectroscopy in Real Time for the Analysis of Oligosaccharides.

While the combination of liquid chromatography (LC) and mass spectrometry (MS) serves as a robust approach for oligosaccharide analysis, it has difficulty distinguishing the smallest differences between isomers. The integration of infrared (IR) spectroscopy within a mass spectrometer as an additional analytical dimension can effectively address this limitation by providing a molecular fingerprint that is unique to each isomer. However, the direct interfacing of LC-MS with IR spectroscopy presents a technical challenge arising from the mismatch in the operational time scale of each method. In previous studies, this temporal incompatibility was mitigated by employing strategies designed to slow down or broaden the LC elution peaks of interest, but this workaround is applicable only for a few species at a time, necessitating multiple LC runs for comprehensive analysis. In the current work, we directly couple LC with cryogenic IR spectroscopy by acquiring a spectrum in as little as 10 s. This allows us to generate an orthogonal data dimension for molecular identification in the same amount of time that it normally takes for LC analysis. We successfully demonstrate this approach on a commercially available human milk oligosaccharide product, acquiring spectral information on the eluting peaks in real time and using it to identify both the specified constituents and nonspecified product impurities.

Recent Advancements in Pyrolysis of Halogen-Containing Plastics for Resource Recovery and Halogen Upcycling: A State-of-the-Art Review.

Plastic waste has emerged as a serious issue due to its impact on environmental degradation and resource scarcity. Plastic recycling, especially of halogen-containing plastics, presents challenges due to potential secondary pollution and lower-value implementations. Chemical recycling via pyrolysis is the most versatile and robust approach for combating plastic waste. In this Review, we present recent advancements in halogen-plastic pyrolysis for resource utilization and the potential pathways from "reducing to recycling to upcycling" halogens. We emphasize the advanced management of halogen-plastics through copyrolysis with solid wastes (waste polymers, biomass, coal, etc.), which is an efficient method for dealing with mixed wastes to obtain high-value products while reducing undesirable substances. Innovations in catalyst design and reaction configurations for catalytic pyrolysis are comprehensively evaluated. In particular, a tandem catalysis system is a promising route for halogen removal and selective conversion of targeted products. Furthermore, we propose novel insights regarding the utilization and upcycling of halogens from halogen-plastics. This includes the preparation of halogen-based sorbents for elemental mercury removal, the halogenation-vaporization process for metal recovery, and the development of halogen-doped functional materials for new materials and energy applications. The reutilization of halogens facilitates the upcycling of halogen-plastics, but many efforts are needed for mutually beneficial outcomes. Overall, future investigations in the development of copyrolysis and catalyst-driven technologies for upcycling halogen-plastics are highlighted.

Automated classification of MRI images for mild cognitive impairment and Alzheimer’s disease detection using log gabor filters based deep meta-heuristic classification

In older people, mild cognitive impairment (MCI) is a precursor to more severe forms of dementia like AD (AD). In diagnosing patients with primary AD and amnestic MCI, modern neuroimaging techniques, especially MRI, play a key role. To efficiently categorize MRI images as normal or abnormal, the research presents a machine learning-based automatic labelling system, with a focus on boosting performance via texture feature analysis. To this end, the research implements a preprocessing phase employing Log Gabor filters, which are particularly well-suited for spatial frequency analysis. In addition, the research uses Gray Wolf Optimization (GWO) to acquire useful information from the images. For classification tasks using the MRI images, the research also make use of DenseNets, a form of deep neural network. The proposed method leverages Log Gabor filters for preprocessing, Gray Wolf Optimization (GWO) for feature extraction, and DenseNets for classification, resulting in a robust approach for categorizing MRI images as normal or abnormal. When compared to earlier trials performed without optimization, the proposed systematic technique shows a significant increase in classification accuracy of 15%. For neuroimaging applications, our research emphasizes the use of Log Gabor filters for preprocessing, GWO for feature extraction, and DenseNets for classification, which can help with the early detection and diagnosis of MCI and AD.

Large-Scale Preparation of Hair Follicle Germs Using a Microfluidic Device.

Hair follicle morphogenesis during embryonic development is driven by the formation of hair follicle germs (HFGs) via interactions between epithelial and mesenchymal cells. Bioengineered HFGs are potential tissue grafts for hair regenerative medicine because they can replicate interactions and hair follicle morphogenesis after transplantation. However, a mass preparation approach for HFGs is necessary for clinical applications, given that thousands of de novo hair follicles are required to improve the appearance of a single patient with alopecia. In this study, we developed a microfluidics-based approach for the large-scale preparation of HFGs. A simple flow-focusing microfluidic device allowed collagen solutions containing epithelial and mesenchymal cells to flow and generate collagen microbeads with distinct Janus structures. During the 3 days of culture, the collagen beads contracted owing to cellular traction forces, resulting in collagen- and cell-dense HFGs. The transplantation of HFGs into nude mice resulted in highly efficient de novo hair follicle regeneration. This method provides a scalable and robust tissue graft preparation approach for hair regeneration.

Abstract B013: A quest for deeper understanding of BRCA2 Variants of Uncertain Significance (VUS)

Abstract Deleterious mutations in homology-directed repair pathway genes impact both future cancer risk and therapeutic intervention options for cancer diagnosis and prognosis. Homology-directed repair pathway is a high-fidelity DNA repair pathway for resolving double-strand breaks (DSBs) throughout the human genome. BRCA2, as a homology-directed repair factor, is essential to ensure genome integrity through an error-free process. Deleterious BRCA2 mutations increase the lifetime risk of breast, ovarian, melanoma, prostate, and pancreatic cancers. Deleterious BRCA2 mutations cover only 25% of the total BRCA2 mutations identified in the population by genetic testing whereas 60% are mutations with unknown connection to the disease called Variants of Uncertain Significance (VUS). The majority of BRCA2 VUS are missense mutations with one point mutation which could potentially alter BRCA2 cellular localization and/or nuclear functions. Thus, evaluating BRCA2 cellular localization and function in BRCA2 missense carriers and tumors is urgently needed to provide clinical guidance regarding cancer risk and treatment options. We have identified a paradoxical relationship between BRCA2 nuclear import and treatment. Deleterious missense mutations in the DNA binding domain of BRCA2 direct the protein to the cytosol which increase sensitivity to PARP inhibitors (PARPi) and platinum drugs. Therefore, although BRCA2 mislocalization might be a cause of cancer, keeping it out of the nucleus allows for much better treatment outcomes. Here, we present how BRCA2 cancer-driver missense mutations lead to cytosolic mislocalization and how BRCA2 localization can be used as a diagnostic tool to stratify patients. Our robust multi-step molecular approach will guide future clinical decision rules surrounding BRCA2 VUS. By providing reliable and accurate functional assays to correctly evaluate the pathogenicity of BRCA2 VUS, our goal is to improve personalized medicine by providing actionable information to patients and their physicians. Citation Format: Cole Niedmann, Judit Jimenez Sainz. A quest for deeper understanding of BRCA2 Variants of Uncertain Significance (VUS) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Damage Repair: From Basic Science to Future Clinical Application; 2024 Jan 9-11; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2024;84(1 Suppl):Abstract nr B013.

Probabilistic feature selection for improved asset lifetime estimation in renewables. Application to transformers in photovoltaic power plants

The increased penetration of renewable energy sources (RESs) as an effective mechanism to reduce carbon emissions leads to an increased weather dependency for power and energy systems. This has created dynamic operation and degradation phenomena, which affect the lifetime estimation of the assets operated with RESs. For the reliable and efficient operation of RES it is crucial to monitor the health of its constituent components and feature selection is a crucial step for building robust and accurate health monitoring approaches. In this context, this paper presents a probabilistic feature selection approach, which probabilistically weights and selects features through a heuristic and iterative process for an improved asset lifetime estimation. Power transformers are key power grid assets and they are used to demonstrate the validity and impact of the proposed approach. The approach is tested on two different photovoltaic power plants operated in Spain and Australia. Results consistently show that the proposed feature-selection approach reduces the prediction error and consistently selects relevant features. The approach has been applied to transformer lifetime estimation, but it can be generally applied to assist in the lifetime estimation of other components operated in RESs. Part of the studies presented here as well as source codes are all open-source under the GitHub repository https://github.com/iramirezg/FeatureSelection.

The effect of extracellular matrix on the precision medicine utility of pancreatic cancer patient-derived organoids.

The use of patient-derived organoids (PDOs) to characterize therapeutic sensitivity and resistance is a promising precision medicine approach, and its potential to inform clinical decisions is now being tested in several large multiinstitutional clinical trials. PDOs are cultivated in the extracellular matrix from basement membrane extracts (BMEs) that are most commonly acquired commercially. Each clinical site utilizes distinct BME lots and may be restricted due to the availability of commercial BME sources. However, the effect of different sources of BMEs on organoid drug response is unknown. Here, we tested the effect of BME source on proliferation, drug response, and gene expression in mouse and human pancreatic ductal adenocarcinoma (PDA) organoids. Both human and mouse organoids displayed increased proliferation in Matrigel compared with Cultrex and UltiMatrix. However, we observed no substantial effect on drug response when organoids were cultured in Matrigel, Cultrex, or UltiMatrix. We also did not observe major shifts in gene expression across the different BME sources, and PDOs maintained their classical or basal-like designation. Overall, we found that the BME source (Matrigel, Cultrex, UltiMatrix) does not shift PDO dose-response curves or drug testing results, indicating that PDO pharmacotyping is a robust approach for precision medicine.

Comparing Methods for Induction of Insulin Resistance in Mouse 3T3-L1 Cells.

Insulin resistance (IR) is a cellular condition linked to metabolic disorders. Despite the utility of cell culture in IR research, questions persist regarding the suitability of various models. This study seeks to evaluate these models' efficiency in inducing IR and their ability to mimic in vivo conditions. Insights gained from this research could enhance our understanding of model strengths and limitations, potentially advancing strategies to combat IR and related disorders. 1- Investigate the technical differences between existing cell culture models used to study molecular mediators of insulin resistance (IR). 2- Compare the effectiveness of present in vitro models in inducing insulin resistance (IR). 3- Assess the relevance of the existing cell culture models in simulating the in vivo conditions and environment that provoke the induction of insulin resistance (IR). In vitro, eight sets of 3T3-L1 cells were cultured until they reached 90% confluence. Subsequently, adipogenic differentiation was induced using a differentiation cocktail (media). These cells were then divided into four groups, with four subjected to normal conditions and the other four to hypoxic conditions. Throughout the differentiation process, each cell group was exposed to specific factors known to induce insulin resistance (IR). These factors included 2.5nM tumor necrosis factor-alpha (TNFα), 20 ng/ml interleukin-6 (IL-6), 10 micromole 4-hydroxynonenal (4HNE), and high insulin (HI) at a concentration of 100nM. To assess cell proliferation, DAPI staining was employed, and the expression of genes associated with various metabolic pathways affected by insulin resistance was investigated using Real-Time PCR. Additionally, insulin signaling was examined using the Bio-plex Pro cell signaling Akt panel. We induced insulin resistance in 3T3-L1 cells using IL-6, TNFα, 4HNE, and high insulin in both hypoxic and normoxic conditions. Hypoxia increased HIF1a gene expression by approximately 30% (P<0.01). TNFα reduced cell proliferation by 10-20%, and chronic TNFα treatment significantly decreased mature adipocytes due to its cytotoxicity. We assessed the impact of insulin resistance (IR) on metabolic pathways, focusing on genes linked to branched-chain amino acid metabolism, detoxification, and chemotaxis. Notably, ALDH6A1 and MCCC1 genes, related to amino acid metabolism, were significantly affected under hypoxic conditions. TNFα treatment notably influenced MCP-1 and MCP-2 genes linked to chemotaxis, with remarkable increases in MCP-1 levels and MCP-2 expression primarily under hypoxia. Detoxification-related genes showed minimal impact, except for a significant increase in MAOA expression under acute hypoxic conditions with TNFα treatment. Additional genes displayed varying effects, warranting further investigation. To investigate insulin signaling's influence in vitro by IRinducing factors, we assessed phospho-protein levels. Our results reveal a significant p-Akt induction with chronic high insulin (10%) and acute TNFα (12%) treatment under hypoxia (both P<0.05). Other insulin resistance-related phospho-proteins (GSK3B, mTOR, PTEN) increased with IL-6, 4HNE, TNFα, and high insulin under hypoxia, while p-IRS1 levels remained unaffected. In summary, different in vitro models using inflammatory, oxidative stress, and high insulin conditions under hypoxic conditions can capture various aspects of in vivo adipose tissue insulin resistance (IR). Among these models, acute TNFα treatment may offer the most robust approach for inducing IR in 3T3-L1 cells.

Multi-well clustering and inverse modeling-based approaches for exploring geometry, petrophysical, and hydrogeological parameters of the Quaternary aquifer system around Debrecen area, Hungary.

This research aims to explore the application of an unsupervised machine learning and inverse modeling-based methods to map the aquifers geometry and investigate the petrophysical and hydrogeological parameters of the Quaternary aquifer system around the Debrecen area, Eastern Hungary. The study utilized a limited well-logs, including spontaneous potential, natural gamma ray, and normal resistivity logs. The k-means clustering technique is applied to identify the distribution of lithological facies within the formerly identified basin-scale hydrostratigraphical units of coarsening upward, alluvial, valley incision, and Late Miocene deposits. Based on the mathematical and geological considerations, the analysis revealed three main clusters (C1, C2, and C3), representing different lithofacies of shale, shaly sand, and sand and gravel. The result of the cluster analysis is further validated with a surface geophysical survey using the vertical electrical sounding (VES) technique. Furthermore, an inverse-modeling-based approach using Csókás method is employed to detect the vertical and horizontal distribution of the hydraulic conductivity along these units. Csókás model is empirically modified from the Kozeny-Carman equation that suites the unconsolidated freshwater-bearing units. The results indicated that the petrophysical and hydrogeological parameters widely ranged with the hydraulic conductivity between almost zero in the shaly layers to more than 21.5 m/d in the sandy and gravely layers. However, the valley incision unit aquifers showed a more uniform distribution of hydraulic conductivity. Based on these results, the Quaternary aquifers in the Debrecen area are classified as moderate to highly productive and ideal for groundwater development. The applied methodology contributed to understanding the complexity of the hydrogeological conditions, providing a robust approach to characterize the heterogeneous groundwater systems.

Probabilistic models of dynamic increase factor (DIF) for reinforced concrete structures: A Bayesian approach

The response of structures under rapidly varying loads can be affected by strain rate sensitivity generally expressed using Dynamic Increase Factor (DIF). Current models for estimating the DIF in Reinforced Concrete (RC) structures are generally deterministic and have restricted applicability due to their dependence on limited experimental data resulting in bias. This paper overcomes these limitations by proposing three probabilistic models that quantify compressive and tensile concrete and steel DIF, accounting for the relevant uncertainties. The proposed models are based on existing deterministic models with the addition of probabilistic correction terms. Bayesian updating is employed to estimate the unknown model parameters using observational data from a large collection of experimental observations. The models incorporate model uncertainties stemming from assumed model form and (potential) missing variables through a model error term. The proposed probabilistic models are used to evaluate the reliability of RC structures under dynamic loads. As an illustration, the proposed probabilistic models are used to estimate the reliability of an example RC column under combined dynamic axial force and moment, and a RC column or beam under dynamic bending moments resulting in cracking. In the two examples, we consider the ACI 318-19 requirements for Ultimate Limit State (ULS) and Serviceability Limit States (SLS). In comparison to deterministic DIF models, the proposed probabilistic models yield enhanced predictive accuracy, presenting a practical and robust approach to assess the structural reliability under impact and blast loads.