Search Results (69)

Corn drying is a critical post-harvest process to ensure product quality and compliance with moisture standards. Traditional optimization approaches often overlook dynamic interactions between operational parameters and environmental factors in industrial continuous flow drying systems. This study integrates statistical analysis and deep learning to predict outlet moisture content, leveraging a dataset of 3826 observations from an operational dryer. The effects of inlet moisture, target air temperature, and material discharge interval on thermal behavior of the system were evaluated through linear regression and t-test, which provided interpretable insights into process dependencies. Three neural network architectures (LSTM, GRU, and TCN) were benchmarked for multivariate time-series forecasting of outlet corn moisture, with hyperparameters optimized using grid search to ensure fair performance comparison. Results demonstrated GRU’s superior performance in the context of absolute deviations, achieving the lowest mean absolute error (MAE = 0.304%) and competitive mean squared error (MSE = 0.304%), compared to LSTM (MAE = 0.368%, MSE = 0.291%) and TCN (MAE = 0.397%, MSE = 0.315%). While GRU excelled in average prediction accuracy, LSTM’s lower MSE highlighted its robustness against extreme deviations. The hybrid methodology bridges statistical insights for interpretability with deep learning’s dynamic predictive capabilities, offering a scalable framework for real-time process optimization. By combining traditional analytical methods (e.g., regression and t-test) with deep learning-driven forecasting, this work advances intelligent monitoring and control of industrial drying systems, enhancing process stability, ensuring compliance with moisture standards, and indirectly supporting energy efficiency by reducing over drying and enabling more consistent operation. Full article

This paper presents the development and structure of a geospatial (work in progress), architectural heritage database designed to document, interpret, and valorize Second World War military fortifications in Sardinia. Currently hosting over 1800 georeferenced entries—including bunkers, artillery posts, underground shelters, and camouflage systems—the database constitutes the analytical core of an interdisciplinary research framework that interprets these remnants as a coherent wartime palimpsest embedded in the contemporary landscape. By integrating spatial data, archival sources, architectural features, conservation status, camouflage typologies, and both analog and digital graphic representations, the system operates as a central infrastructure for multiscale heritage analysis. It reveals the interconnections between dispersed military structures and the wider territorial fabric, thereby laying the groundwork for landscape-based interpretation and site-specific reactivation strategies. More than a cataloging tool, the database serves as an interpretive and decision-making interface—supporting the generation of cultural itineraries, the identification of critical clusters, and the design of adaptive reuse scenarios. While participatory tools and community engagement will be explored in a second phase, the current methodology emphasizes landscape-oriented reuse strategies based on the perception, spatial storytelling, and contextual reading of wartime heritage. The methodological synergy between GIS, 3D modeling, traditional drawing, and archival research (graphic and photographic documents) contributes to a holistic vision of Sardinia’s wartime heritage as both a system of knowledge and a spatial–cultural resource for future generations. Full article

Coseismic landslides are geological events in which slopes, either on the verge of instability or already in a fragile state, experience premature failure due to seismic shaking. On 5 September 2022, an Ms 6.8 earthquake struck Luding County, Sichuan Province, China, triggering numerous landslides that caused severe casualties and property damage. This study systematically interprets 13,717 coseismic landslides in the Luding earthquake’s epicentral area, analyzing their spatial distribution concerning various factors, including elevation, slope gradient, slope aspect, plan curvature, profile curvature, surface cutting degree, topographic relief, elevation coefficient variation, lithology, distance to faults, epicentral distance, peak ground acceleration (PGA), distance to rivers, fractional vegetation cover (FVC), and distance to roads. The analytic hierarchy process (AHP) was improved by incorporating frequency ratio (FR) to address the subjectivity inherent in expert scoring for factor weighting. The improved AHP, combined with the Pearson correlation analysis, was used to identify the dominant controlling factor and assess the landslide susceptibility. The accuracy of the model was verified using the area under the receiver operating characteristic (ROC) curve (AUC). The results reveal that 34% of the study area falls into very-high- and high-susceptibility zones, primarily along the Moxi segment of the Xianshuihe fault and both sides of the Dadu river valley. Tianwan, Caoke, Detuo, and Moxi are at particularly high risk of coseismic landslides. The elevation coefficient variation, slope aspect, and slope gradient are identified as the dominant controlling factors for landslide development. The reliability of the proposed model was evaluated by calculating the AUC, yielding a value of 0.8445, demonstrating high reliability. This study advances coseismic landslide susceptibility assessment and provides scientific support for post-earthquake reconstruction in Luding. Beyond academic insight, the findings offer practical guidance for delineating priority zones for risk mitigation, planning targeted engineering interventions, and establishing early warning and monitoring strategies to reduce the potential impacts of future seismic events. Full article

In high-stakes decision-making environments, predictive models must deliver not only high accuracy but also reliable uncertainty estimations and transparent explanations. This study explores the integration of probability calibration techniques with Conformal Prediction (CP) within a predictive process monitoring (PPM) framework tailored to healthcare analytics. CP is renowned for its distribution-free prediction regions and formal coverage guarantees under minimal assumptions; however, its practical utility critically depends on well-calibrated probability estimates. We compare a range of post-hoc calibration methods—including parametric approaches like Platt scaling and Beta calibration, as well as non-parametric techniques such as Isotonic Regression and Spline calibration—to assess their impact on aligning raw model outputs with observed outcomes. By incorporating these calibrated probabilities into the CP framework, our multilayer analysis evaluates improvements in prediction region validity, including tighter coverage gaps and reduced minority error contributions. Furthermore, we employ SHAP-based explainability to explain how calibration influences feature attribution for both high-confidence and ambiguous predictions. Experimental results on process-driven healthcare data indicate that the integration of calibration with CP not only enhances the statistical robustness of uncertainty estimates but also improves the interpretability of predictions, thereby supporting safer and robust clinical decision-making. Full article

Background: Excessive alcohol consumption constitutes a serious cause of death worldwide. Fatty acid ethyl esters, as metabolites of the non-oxidative elimination pathway of ethanol, have been recognized as mediators of alcohol-induced organ damage. These metabolites serve as potential biomarkers for the assessment of ethanol intake and might be also used in post-mortem studies. Methods: In this study, the development and optimization of a simple, fast, precise, accurate, and cost-effective method with the use of gas chromatography coupled with tandem mass spectrometry for quantitative analysis of six fatty acid ethyl esters, namely ethyl laurate, myristate, palmitate, linoleate, oleate, and stearate, were conducted. Results: The optimized method was fully validated according to ICH guidelines. Additionally, identification of critical method parameters was possible by using the quality by design approach. By carrying out analyses according to the Plackett–Burman plan (design of experiments methodology), the robustness of the analytical method developed was confirmed for four (ethyl palmitate, linoleate, oleate, and stearate) ethyl esters. In the case of ethyl myristate, the variable significantly affecting the results appeared to be the temperature of solvent evaporation after the deproteinization step. Conclusions: Biochemical interpretation of the obtained results with available medical records suggests that plasma concentrations of selected fatty acid ethyl esters are valuable indicators of pre-mortem alcohol consumption and may be one of the key factors helpful in determining the cause and mechanism of death. Full article

Youth pastoral care is a part of the Church’s pastoral mission directed towards men and women who can be categorised as young people. Young people were the central theme of the 15th Ordinary General Assembly of the Synod of Bishops, held in Rome from 3 to 28 October 2018, under the motto Young People, the Faith, and Vocational Discernment. Based on an analysis of the preparatory and final documents of the Synod—particularly the Preparatory Document, Instrumentum Laboris, Final Document, Christus Vivit—as well as relevant scientific analyses and interpretations, we have identified the most significant societal challenges facing the youth, along with the key guidelines and recommendations of the Synod for working with them. The motivation for writing this article arises from the fact that the Synod on Youth, which took place in Rome from 3 to 28 October 2018, under the theme “Youth, Faith, and Vocational Discernment,” did not generate significant interest within the Church in Croatia. This is evidenced by several indicators: the responses of young people to the questionnaire sent to all bishops’ conferences were never published; there has been a lack of relevant commentary, articles, or academic studies on the Synod; and among the many pre- and post-synodal documents, only “Christus vivit” has been translated into Croatian. Given that youth ministry plays an important role in the pastoral activities of the Church in Croatia, we sought to explore whether, and to what extent, the core recommendations of the Synod on Youth continue to be reflected in current pastoral work with young people in the country. Our approach involved analysing the pre- and post-synodal documents from the Synod on Youth to identify key recommendations for youth ministry. We then examined the current youth pastoral programs in seven (arch)dioceses, as well as the pastoral plans of the Youth Office of the Croatian Bishops’ Conference, through the lens of the Synod’s main recommendations. Analytical, descriptive, and critical methods were used in the research. The analysis of the aforementioned youth pastoral programs confirmed that the most important guidelines and recommendations of the Synod, although some only partially, are integrated into youth pastoral care in Croatia. Full article

Background: The advent of tandem mass spectrometry (MS/MS) had an essential role in the expansion of newborn screening (NBS) for different inborn errors of metabolism (IEMs). Nowadays, almost 50 IEMs are screened in Italy. The use of second-tier tests (2-TTs) in NBS minimizes the false positive rate; nevertheless, the metabolic profile is influenced not only by the genome but also by environmental factors and clinical variables. We reviewed the MS/MS NBS data from over 37,000 newborns (of which 8% required 2-TTs) screened in the Italian Abruzzo region to evaluate the impact of neonatal and maternal variables on propionate-related primary biomarker levels. Methods: Expanded NBS and 2-TT analyses were performed using MS/MS and liquid chromatography–MS/MS methods. We set up layered cut-offs dividing all 37,000 newborns into categories. Statistical analysis was used to create alarm thresholds for NBS-positive samples. Statistically significant differences were found in both neonatal and maternal conditions based on the 2-TTs carried out. According to the stratified cut-offs, only 1.47% of the newborns would have required a 2-TT while still retaining the ability to recognize the true-positive case of methylmalonic acidemia with homocystinuria, which has been identified by NBS. To further support the clinical applicability, we performed an external evaluation considering nine positive cases from an extra-regional neonatal population, confirming the potential of our model. Interestingly, the setting of alarm thresholds and their application would allow for establishing the degree of priority/urgency for 2-TTs. Conclusions: Tailoring NBS by customized cut-offs may enhance the application of precision medicine, focusing on true-positive cases and also reducing analysis costs and times. Full article

Culinary heritage is central to preserving cultural identity. The present study analyses the content of culinary notebooks from 1946 and 1947 using large language models (LLMs) and a dedicated AI plugin, Linguistic Insight (LI-AI). The general goal was to analyse content in Granny Josie’s Notebooks (the BaJa corpus) with AI tools. The specific objective was to create and test a dedicated analytical tool, an LI-AI ChatGPT plugin for the in-depth analysis of the BaJa corpus, focusing on ingredients, techniques, and recipes. LI-AI identified keywords and main themes in the large textual dataset. It then visualised them as word clouds. Compared to manual tools, LI-AI’s semantic analysis was more precise and comprehensive. This study contributes to the analysis of historical culinary practices in post-war Poland, revealing that cooking had not only a pragmatic role but also a symbolic one as it supported social and family bonds. This finding was not evident in the word clouds. Instead, it emerged from an in-depth semantic analysis. This study has confirmed the practical value of LLMs in historical text interpretation. It also established that the parallel use of AI and manual tools is advisable for a fuller analysis of textual data. Full article

Intact parathyroid hormone (PTHi) plays a central role in the regulation of mineral and bone metabolism. Due to post-translational modifications of the hormone, the interpretation of elevated PTHi values is challenging and may benefit from an expanded analytical panel. Within this project, additional parameters of calcium–phosphate metabolism, such as non-oxidised parathyroid hormone (noxPTH), calcidiol, vitamin D binding protein (VDBP), and fibroblast growth factor 23 (FGF23) were evaluated in a control population of 177 individuals as well as 182 patients with renal, gastroenterological, and liver diseases. While PTHi and noxPTH levels were up to 10-fold higher in dialysis patients, the proportion of noxPTH on PTHi was significantly higher for all patient groups showing signs of inflammation. However, no strong confounders for PTHi could be identified. The correlation between CRP and the proportion of oxidised PTHi in total PTHi suggests an influence of inflammatory oxidative stress on the proportion of active noxPTH. Apart from the established role of vitamin D, the addition of noxPTH and its proportion of total PTHi in the assessment of unclear PTHi elevations seems reasonable, whereas there is no evidence for the standardised analysis of further parameters such as FGF23 and VDBP. Full article

The stability analysis of rock slopes traditionally involves the evaluation of limit state conditions to determine the potential for rockslides and rockfalls. However, empirical evidence supported by experimental studies has highlighted the complex response of rock interfaces under differential loading. It is characterized by distinct pre-peak and post-peak stress–deformation relationships, which represent the deformation profile of loaded rock interfaces and, thus, capture dynamic and evolving events. The present research introduces an interpretation framework to reconcile these contradicting paradigms by interpreting empirically and explicitly the full stress–displacement relationship along active shear surfaces of rockslide events. The Complete Stress–Displacement Surface (CSDS) model was incorporated into conventional analytical solutions for a rock slope planar failure to describe the evolving stress conditions during an active rockslide event. The Ruinon rockslides (Italy), monitored and studied extensively at the turn of the century, are revisited using the adapted CSDS model to describe the evolving stress–deformation conditions. Empirical and experimental calibrations of the model are implemented and compared using the CSDS model for the description of evolving shear stresses in large rockslide events based on topographical monitoring. This paper contributes a detailed framework for correlating in situ topographical monitoring with relevant geomechanical information to develop a representative model for the evolving stress conditions during a rockslide event. Full article

Background/Objective: Subarachnoid hemorrhage (SAH) is associated with high morbidity and mortality rates, necessitating prognostic algorithms to guide decisions. Our study evaluates the use of machine learning (ML) models for predicting 1-month and 1-year mortality among SAH patients using national electronic health records (EHR) system. Methods: Retrospective cohort of 29,274 SAH patients, identified through national EHR system from January 2017 to December 2022, was analyzed, with mortality data obtained from central civil registration system in Türkiye. Variables included (n = 102) pre- (n = 65) and post-admission (n = 37) data, such as patient demographics, clinical presentation, comorbidities, laboratory results, and complications. We employed logistic regression (LR), decision trees (DTs), random forests (RFs), and artificial neural networks (ANN). Model performance was evaluated using area under the curve (AUC), average precision, and accuracy. Feature significance analysis was conducted using LR. Results: The average age was 56.23 ± 16.45 years (47.8% female). The overall mortality rate was 22.8% at 1 month and 33.3% at 1 year. One-month mortality increased from 20.9% to 24.57% (p < 0.001), and 1-year mortality rose from 30.85% to 35.55% (p < 0.001) in the post-COVID period compared to the pre-COVID period. For 1-month mortality prediction, the ANN, LR, RF, and DT models achieved AUCs of 0.946, 0.942, 0.931, and 0.916, with accuracies of 0.905, 0.901, 0.893, and 0.885, respectively. For 1-year mortality, the AUCs were 0.941, 0.927, 0.926, and 0.907, with accuracies of 0.884, 0.875, 0.861, and 0.851, respectively. Key predictors of mortality included age, cardiopulmonary arrest, abnormal laboratory results (such as abnormal glucose and lactate levels) at presentation, and pre-existing comorbidities. Incorporating post-admission features (n = 37) alongside pre-admission features (n = 65) improved model performance for both 1-month and 1-year mortality predictions, with average AUC improvements of 0.093 ± 0.011 and 0.089 ± 0.012, respectively. Conclusions: Our study demonstrates the effectiveness of ML models in predicting mortality in SAH patients using big data. LR models’ robustness, interpretability, and feature significance analysis validate its importance. Including post-admission data significantly improved all models’ performances. Our results demonstrate the utility of big data analytics in population-level health outcomes studies. Full article

Background/Objectives: Recent theories of the neurocognitive architecture of semantic memory have included a distinction between semantic control in the left inferior frontal gyrus (LIFG) and semantic representation in the left anterior temporal lobe (LATL). Support for this distinction has been found both in tasks in which high semantic selection demands have been instantiated and in tasks in which previous presentations of semantic information that compete with target information have been instantiated. Methods: In the current study, these manipulations were combined in a novel manner into a single task in which brand-name product pictures were used. Functional near-infrared spectroscopy (fNIRS) was used to measure hemodynamic activity and tensor decomposition, in addition to grand averaging, was used to analyze the fNIRS output. Results: Both analytic methods converged on the same set of findings. That is, in line with past studies, greater activity in the LIFG was observed in the competitive condition than in a repeated condition. However, unlike past studies, greater activity in the competitive condition was also observed in both the left and right anterior temporal lobes (ATLs). Conclusions: While it was possible that the novel combination of high selection and competition into a single task unlocked a semantic selection mechanism in the bilateral ATL, a number of other post-hoc explanations were offered for this unusual finding, including a re-interpretation of the high-selection task as an ad hoc categorization task. Finally, the convergence of the tensor decomposition and grand averaging approaches on the same set of findings supported tensor decomposition as a viable approach to the analysis of fNIRS data. Full article

Advanced driver assistance system (ADAS) technologies are key to improving road safety and promoting innovation in the automotive sector. The approval and analysis of ADAS systems, especially automatic emergency braking (AEB) tests, require complex procedures and in-depth data management. This work presents innovative tools developed to facilitate the post-processing of ADAS AEB test data, created in collaboration with Nardò Technical Center. The tool, called Autonomous Code Generation Intelligence (ACGI), introduces an intuitive and intelligent user interface that helps analyze and interpret ADAS test approval regulations. ACGI automates the generation of code sections within a data analytics framework, streamlining the compliance process and significantly reducing the time and programming skills required. This tool allows engineers to focus on high-value tasks, improving overall process efficiency. To achieve this objective, the tool encodes the DAART code framework (Data Analysis and Automated Report Tool) which allows users to carry out real post-processing of the tests conducted on the track. The results demonstrate that both tools simplify and automate critical steps in the ADAS automatic emergency braking test data analysis process. In fact, the tool not only improves the accuracy and efficiency of the analyses but also offers a high degree of customization, making it a flexible and adaptable tool to meet the specific needs of users. In future developments, ACGI could be extended to cover additional ADAS tests and could be equipped with artificial intelligence to suggest configurations based on new regulations. Full article

The work developed aims to present an innovative methodology to execute the heritage conservation processes in a collaborative and interdisciplinary Building Information Modeling (BIM) project, with an effective management of the deterioration suffered over time, emphasizing the structures and coatings. The research begins with an architectural survey using terrestrial laser scanning (TLS) and terrestrial photogrammetry software, Structure from Motion (SfM), studying study the Duomo of Molfetta (Italy), a unique Romanesque architecture of Puglia (Italy). The methodological process is mainly aided by the precise semantic segmentation of global point clouds, a semi-automatic process assisted by classification algorithms implemented in the Cyclone 3DR post-processing software, which has allowed the classification of the unstructured information provided by the remote sensing equipment when identifying the architectural-structural systems of a building with high historical values. Subsequently, it was possible to develop an efficient Scan-to-HBIM workflow, where the Heritage BIM (HBIM) project has fulfilled the function of a database by incorporating and organizing all the information (graphic and non-graphic) to optimize the tasks of auscultation, identification, classification, and quantification and, in turn, facilitating the parametric modeling of unique structures and architectural elements. The results have shown great effectiveness in the processes of characterization of architectural heritage, focusing on the deformations and deterioration of the masonry in columns and pilasters. To make multidisciplinary conservation work more flexible, specific properties have been created for the identification and analysis of the degradation detected in the structures, with the HBIM project constituting a manager of the control and inspection activities. The restoration technician interacts with the determined 3D element to mark the “type decay”, managing the properties in the element’s own definition window. Interactive schemes have been defined that incorporate the items for the mapping of the elements, as well as particular properties of a conservation process (intervention, control, and maintenance). All listed parametric elements have links to be viewed in 2D and 3D views. Therefore, the procedure has facilitated the auscultation of the scanned element as it is semantically delimited, the parametric modeling of it, the analytical study of its materials and deterioration, and the association of intrinsic parameters so that they can be evaluated by all the intervening agents. But there are still some difficulties for the automatic interpretation of 3D point cloud data, related to specific systems of the historical architecture. In conclusion, human action and interpretation continues to be a fundamental pillar to achieve precise results in a heritage environment. Full article

In post-mortem (PM) investigations, forensic toxicologists attempt to identify legal or illegal substances present before death and determine how they contributed to the cause of death. A critical challenge is ensuring that PM sample concentrations accurately reflect those at the time of death, as postmortem redistribution (PMR) can alter these levels due to anatomical and physiological changes. The PMR phenomenon is called a ‘toxicological nightmare’. PMR significantly affects post-mortem drug concentrations, particularly for lipophilic drugs and those with a high volume of distribution. The emergence of new psychoactive substances (NPSs) has led to a growing recognition of their role as a significant public health concern, frequently associated with fatalities related to polydrug use. These substances are renowned for their ability to induce intoxication at low doses, which has led to the continuous updating of toxicological and forensic methods to improve detection and adopt new analytical standards. The comprehensive detection of NPS metabolites, some of which are still undiscovered, presents an additional analytical challenge, as do their metabolic pathways. This complicates their identification in fatal cases using standard analytical methods, potentially leading to an underestimation of their actual prevalence in toxicological results. Furthermore, the interpretation of analytical results is hindered by the absence of data on PM blood levels and the specific contributions of NPS to causes of death, exacerbated by the lack of knowledge of whether the PMR phenomenon influences them. This paper presents a comprehensive review of the literature on post-mortem cases involving various NPS, categorized according to classifications by the United Nations Office on Drugs and Crime (UNODC) and the European Union Drugs Agency (EUDA). These categories include cathinones, phenylethylamines, arylalkylamines, phencyclidine-type substances, phenmetrazines, piperazines, phenidates, aminoindanes, LSD-like NPSs, tryptamines, fentanyl analogs, designer benzodiazepines, synthetic cannabinoids, and nitazenes. This review covers not only postmortem blood levels but also the stability of the substances studied, the methods of analysis, and attempts to shed some light on the PMR phenomenon. This review used various key terms, such as PMR, NPS, and the names of previously categorized substances and drug analyses across multiple peer-reviewed journals and databases, including Scopus, Google Schoolar, Springer, PubMed, and Wiley Online Library. In addition, references from retrieved articles were examined to identify additional relevant research. Interpreting post-mortem toxicological results is complex and lacks definitive guidelines, requiring a nuanced understanding of its challenges and potential pitfalls. As a result, post-mortem toxicology is often regarded as an art. The primary aim of this review is to provide forensic toxicologists with a comprehensive framework to assist in the evaluation and interpretation of NPS analysis. This guide is intended to complement the existing knowledge and practices applied in forensic laboratories within the toxicological analysis of post-mortem cases. Full article