Search Results (13,668)

Given the importance of turbidity as a key indicator of water quality, this study investigates the use of a convolutional neural network (CNN) to classify water samples into five turbidity-based categories. These classes were defined using ranges inspired by Mexican environmental regulations and generated from 33 laboratory-prepared mixtures with varying concentrations of suspended clay particles. Red, green, and blue (RGB) images of each sample were captured under controlled optical conditions, and turbidity was measured using a calibrated turbidimeter. A transfer learning (TL) approach was applied using EfficientNet-B0, a deep yet computationally efficient CNN architecture. The model achieved an average accuracy of 99% across ten independent training runs, with minimal misclassifications. The use of a lightweight deep learning model, combined with a standardized image acquisition protocol, represents a novel and scalable alternative for rapid, low-cost water quality assessment in future environmental monitoring systems. Full article

Memory is a fundamental neurological function, essential for animal survival. Over the course of vertebrate evolution, elaborations in the forebrain telencephalon create new memory mechanisms, meaning basal vertebrates such as amphibians must have a less sophisticated system of memory acquisition, storage, and retrieval than the well-known hippocampal-based circuitry of mammals. Personality also appears to be a fundamental vertebrate trait and is generally defined as consistent individual behavior over time and across life history stages. In anuran amphibians (frogs), personality studies generally ask whether adult frogs retain the personality of their tadpole stage or whether personality shifts with metamorphosis, an idea behavioral ecologists term adaptive decoupling. Using a multidisciplinary perspective and recognizing there are ~7843 species of frogs, each with some molecular, morphological, physiological, or behavioral feature that makes it unique, we review, clarify, and provide perspective on what we collectively know about memory and personality and their mechanisms in anuran amphibians. We propose four working hypotheses: (1) as tadpoles grow, new telencephalic neurons become integrated into functional networks, producing behaviors that become more sophisticated with age; (2) since carnivores tend to be more bold/aggressive than herbivores, carnivorous anuran adults will be more aggressive than herbivorous tadpoles; (3) each amphibian species, and perhaps life history stage, will have a set point on the Shy–Bold Continuum; and (4) around this set point there will be a range of individual responses. We also suggest that several factors are slowing our understanding of the variety and depth of memory and personality possibilities in anurans. These include the scala natura approach to comparative studies (i.e., the idea that one frog represents all frogs); the assumption that amphibians are no more than simple reflex machines; that study species tend to be chosen more for convenience than taxonomic representation; and that studies are designed to prove or disprove a construct. This latter factor is a particular hindrance because what we are really seeking as scientists is not the confirmation or refutation of ideas, but rather what those ideas are intended to produce, which is understanding. Full article

Our study aimed to identify the overall and cluster-specific characteristics of Czech beef cattle breeding systems. We used data from an online survey to ascertain farmers’ preferences in breeding objectives. Considering various evaluation criteria and clustering approaches in 41 farms, three beef systems were defined according to herd size, management, marketing, breeding strategies and structures, and farmer age. Breeding values and performance were jointly used as the primary information in all three systems. Cow temperament and calf viability, maternal fertility and longevity, and animal health were found to be the most important traits. Cluster 1 represents pure-breeding farms that specialize in producing breeding animals. Farms in clusters 2 and 3 combined pure- and crossbreeding strategies with production, which was partially (cluster 2) and fully (cluster 3) diversified for all beef categories. Farms also prioritized calving performance and calf growth (clusters 1 and 2) and exterior traits (cluster 3). Production type scores significantly (p < 0.05) differed in clusters 3 (4.12) and 2 (3.25). The proportion of production, functional, and exterior trait categories was 12:37:51, with low variability among clusters (±1 to 2 percentage points). The inter-cluster comparison showed that specific characteristics were compatible with certain breeding goal trait preferences. Full article

The implementation of Building Information Modelling (BIM) in public design processes enhances efficiency, transparency, and sustainability. However, public agencies often encounter significant barriers, particularly regarding organisational and managerial readiness. This study develops a BIM implementation framework tailored to the specific needs of an Italian public agency. The research adopts a qualitative approach, combining 15 semi-structured interviews with process mapping Using (Business Process Modeling Notation) BPMN. The current as-is workflows were analysed and validated by internal stakeholders. Based on this analysis, strategic objectives were defined, relevant (Building Information Modelling) BIM uses were selected, and revised to-be processes were proposed, integrating new roles and responsibilities according to the standards. The framework addresses both technical and organisational dimensions of BIM adoption, highlighting the need for training, coordination, and stakeholder engagement. The main outcomes include a structured process model, a priority-based selection of BIM uses, and a role matrix supporting organisational transformation. The added value for researchers lies in the replicable methodology that combines empirical process mapping with implementation planning. For practitioners, especially consultants in sustainable design, the study offers a practical roadmap for aligning BIM adoption with project goals, regulatory compliance, and environmental performance targets in complex public sector contexts. Full article

This paper presents an interpretable and modular framework for energy management in smart buildings based on fuzzy logic and the IEEE Std 1855-2016. The proposed system builds upon the JFML-IoT library, enabling the integration and execution of fuzzy rule-based systems on resource-constrained IoT devices using a lightweight and extensible architecture. Unlike conventional data-driven controllers, this approach emphasizes semantic transparency, expert-driven control logic, and compliance with fuzzy markup standards. The system is designed to enhance both operational efficiency and user comfort through transparent and explainable decision-making. A four-layer architecture structures the system into Perception, Communication, Processing, and Application layers, supporting real-time decisions based on environmental data. The fuzzy logic rules are defined collaboratively with domain experts and encoded in Fuzzy Markup Language to ensure interoperability and formalization of expert knowledge. While adherence to IEEE Std 1855-2016 facilitates system integration and standardization, the scientific contribution lies in the deployment of an interpretable, IoT-based control system validated in real conditions. A case study is conducted in a realistic indoor environment, using temperature, humidity, illuminance, occupancy, and CO${}_2$ sensors, along with HVAC and lighting actuators. The results demonstrate that the fuzzy inference engine generates context-aware control actions aligned with expert expectations. The proposed framework also opens possibilities for incorporating user-specific preferences and adaptive comfort strategies in future developments. Full article

Understanding the interactions between specific cell types within tissue environments is essential for elucidating key biological processes, such as immune responses, cancer progression, inflammation, and development, in both physiological and pathological studies. The predominant methods for analyzing cell–cell interactions (CCI) rely primarily on statistical inference using mapping or network-based techniques. However, these approaches often struggle to prioritize meaningful interactions owing to the high sparsity and heterogeneity inherent in single-cell RNA sequencing (scRNA-seq) data, where small but biologically important differences can be easily overlooked. To overcome these limitations, we developed PriorCCI, a deep-learning framework that leverages a convolutional neural network (CNN) alongside Grad-CAM++, an explainable artificial intelligence algorithm. This study aims to provide a scalable, interpretable, and biologically meaningful framework for systematically identifying and prioritizing key ligand–receptor interactions between defined cell-type pairs from single-cell RNA-seq data, particularly in complex environments such as tumors. PriorCCI effectively prioritizes interactions between cancer and other cell types within the tumor microenvironment and accurately identifies biologically significant interactions related to angiogenesis. By providing a visual interpretation of gene-pair contributions, our approach enables robust inference of gene–gene interactions across distinct cell types from scRNA-seq data. Full article

Medical education is the paradigm of 21st century education and the current changes involve the adoption of integrative and comprehensive patient-centered teaching and learning approaches. Thus, curricular developers from George Emil Palade University of Medicine, Pharmacy, Sciences, and Technology of Targu Mures (G.E. Palade UMPhST of Targu Mures) have recently designed and implemented an innovative medical curriculum, as well as two valuable assessment tools for both theoretical knowledge and practical skills. Thus, during the first three preclinical years, the students will benefit from an organ- and system-centered block teaching approach, while the clinical years will focus on enabling students to achieve the most important practical skills in clinical practice, based on a patient bedside teaching system. In terms of theoretical knowledge assessment, the UNiX center at G.E. Palade UMPhST of Targu Mures, a recently designed center endowed with the latest next-generation technology, enables individualized, secured multiple-choice question-based assessments of the student’s learning outcomes. Moreover, an intelligent assessment tool for practical skills was also recently implemented in our branch in Hamburg, the Objective Structured Clinical Examination (O.S.C.E). This system uses direct observations for testing the student’s practical skills regarding anamnesis, clinical exams, procedures/maneuvers, the interpretation of laboratory tests and paraclinical investigations, differential diagnosis, management plans, communication, and medical counselling. The integrative, comprehensive, patient-centered curriculum and the intelligent assessment system, implemented in G.E Palade UMPhST of Targu Mures, help define innovation in education and enable the students to benefit from a high-quality medical education. Full article

Direct preference optimization (DPO) has been successfully applied to align large language models (LLMs) with human preferences. In recent years, DPO has also been used to improve the generation quality of text-to-image diffusion models. However, existing techniques often rely on a single type of reward model. They are also prone to overfitting to inaccurate reward signals. As a result, model quality cannot be continuously improved. To address these limitations, we propose xDPO. This method introduces a novel regularization approach that implicitly defines reward functions for both preferred and non-preferred samples. This design greatly enhances the flexibility of reward modeling. The experimental results show that, after fine-tuning Stable Diffusion v1.5, xDPO achieves significant improvements in human preference evaluations compared to previous DPO methods. It also improves training efficiency by approximately 1.5 times. Meanwhile, xDPO maintains image–text alignment performance that is comparable to the original model. Full article

This paper introduces a unified and systematic design methodology for robotic systems that is generalizable across a wide range of applications. It integrates rigorous mathematical formalisms such as kinematics, dynamics, control theory, and optimization with advanced simulation tools, ensuring that each design decision is grounded in provable theory. The approach defines clear phases, including mathematical modeling, virtual prototyping, parameter optimization, and theoretical validation. Each phase builds on the previous one to reduce unforeseen integration issues. Spanning from conceptualization to deployment, it offers a blueprint for developing mathematically valid and robust robotic solutions while streamlining the transition from design intent to functional prototype. By standardizing the design workflow, this framework reduces development time and cost, improves reproducibility across projects, and enhances collaboration among multidisciplinary teams. Such a generalized approach is essential in today’s fast-evolving robotics landscape where rapid innovation and cross-domain applicability demand flexible yet reliable methodologies. Moreover, it provides a common language and set of benchmarks that both novice and experienced engineers can use to evaluate performance, facilitate knowledge transfer, and future-proof systems against emerging application requirements. Full article

In recent years, the rapid development of lightweight Unmanned Aerial Vehicle (UAV) technology under 250 g has begun to challenge the validity of existing mass-based safety classifications. The commonly used 250 g threshold for defining “harmless” UAVs has become a subject requiring more detailed evaluations, especially as new models with increased speed and performance enter the market. This study aims to reassess the adequacy of the current 250 g mass limit by conducting a comprehensive analysis using human-centered injury metrics, including kinetic energy, Blunt Criterion (BC), Viscous Criterion (VC), and the Abbreviated Injury Scale (AIS). Within this scope, an extensive dataset of commercial UAV models under 500 g was compiled, with a particular focus on the sub-250 g segment. For each model, KE, BC, VC, and AIS values were calculated using publicly available technical data and validated physical models. The results were compared against established injury thresholds, such as 14.9 J (AIS-3 serious injury), 25 J (“harmless” threshold), and 33.9 J (AIS-4 severe injury). Furthermore, new recommendations were developed for regulatory authorities, including energy-based classification systems and mission-specific dynamic threshold mechanisms. According to the findings of this study, most UAVs under 250 g continue to remain below the current “harmless” threshold values. However, some next-generation high-speed UAV models are approaching or exceeding critical KE levels, indicating a need to reassess existing regulatory approaches. Additionally, the strong correlation between both BC and VC metrics with AIS outcomes demonstrates that these indicators are complementary and valuable tools for assessing injury risk. In this context, the adoption of an energy-based supplementary classification and dynamic, mission-based regulatory frameworks is recommended. Full article

Objectives: Oncocyte-rich indeterminate thyroid nodules (O-ITNs) present diagnostic and management challenges due to overlapping features between benign and malignant lesions and differing cytological classifications. This study aimed primarily to assess the ultrasound (US) characteristics and US-based risk of O-ITNs using the American College of Radiology Thyroid Imaging Reporting And Data Systems (ACR TI-RADS). A secondary objective was to compare the Bethesda System for Reporting Thyroid Cytopathology (BSRTC) and Italian Consensus for the Classification and Reporting of Thyroid Cytology (ICCRTC) cytological systems regarding classification and clinical management implications for O-ITNs. Methods: A retrospective study was conducted on 177 ITNs (TIR3A and TIR3B) evaluated between June 2023 and December 2024 at CTO-Alesini, Rome (Italy). Nodules were assessed with US, cytology, and histology. Oncocyte predominance was defined as >70% oncocytes on fine-needle aspiration (FNA). US features were analyzed according to ACR TI-RADS. Nodules were reclassified by BSRTC, and potential differences in clinical case management (CCM) were analyzed. Results: O-ITNs comprised 47.5% of the sample. Compared to non-O-ITNs, O-ITNs were larger and more frequently showed low-risk US features, including a higher prevalence of ACR TI-RADS 3 nodules. However, no progressive increase in the risk of malignancy (ROM) was observed across ACR TI-RADS classes within O-ITNs. Histological malignancy was identified in 47.1% of O-ITNs, a lower proportion compared to non-O-ITNs, though the difference was not statistically significant. Classification discordance with potential management impact was lower in O-ITNs (20.2%) than in non-O-ITNs (38.7%). Conclusions: O-ITNs typically exhibit benign-appearing US features and lower classification discordance between BSRTC and ICCRTC, yet US risk stratification fails to differentiate malignancy risk within O-ITNs. A tailored approach integrating cytology and cautious US interpretation is essential for optimal O-ITN management. Full article

Post-tensioned (PT) reinforced concrete bridges are particularly vulnerable structures, as the deterioration of internal tendons is often difficult to detect using conventional inspection methods or visual assessments. This paper introduces a practical framework for ranking non-destructive testing (NDT) techniques employed to assess PT systems. The ranking is based on four performance categories: measurement accuracy, ease of use, cost, and impact of disruption to bridge operations on traffic. For each NDT technique, a score is assigned for each evaluation category, and the final ranking is determined using the weighted sum model (WSM). This approach enables the final assessment to reflect the priorities of different decision-making contexts defined by the end-user such as accuracy-oriented, cost-oriented, and impact-oriented scenarios. The proposed method is then applied to an existing bridge in order to practically demonstrate its effectiveness and the flexibility of the proposed criteria. Full article

This paper evaluates the effectiveness of driver training programmes aimed at the proper use of Advanced Driver Assistance Systems (ADASs). Participants (N = 49) were divided into the following three groups based on the type of training received: practical training, e-learning, and brief manual instruction. The effectiveness of the training methods was assessed using selected parameters obtained from driving simulator studies, including reaction times and system activation attempts. Given the large volume and nonlinear nature of the input data, a heuristic, expert-based approach was used to identify key evaluation criteria, structure the decision-making process, and define fuzzy rule sets and membership functions. This phase served as the foundation for the development of a fuzzy logic model in the MATLAB environment. The model processes inputs to generate a quantitative performance score. The results indicate that practical training (mean score = 4.0) demonstrates superior effectiveness compared to e-learning (3.09) and manual instruction (mean score = 3.01). The primary contribution of this work is a transparent, data-driven evaluation tool that overcomes the inherent subjectivity and bias of traditional trainer-based assessments. This model provides a standardised and reproducible approach for assessing driver competence, offering a significant advancement over purely qualitative, trainer-based assessments and supporting the development of more reliable certification processes. Full article

Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping of environmental dynamics enables us to define factors that trigger these processes and are crucial for our understanding of Earth system processes. In this study, a reclassification scheme of image analysis was developed for mapping the adjusted categorisation of land cover types using multispectral remote sensing datasets and Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The data included four Landsat 8–9 satellite images on 2015, 2019, 2021 and 2023. The sequence of time series was used to determine land cover dynamics. The classification scheme consisting of 17 initial land cover classes was employed by logical workflow to extract 10 key land cover types of the coastal areas of Bab-el-Mandeb Strait, southern Red Sea. Special attention is placed to identify changes in the land categories regarding the thermal saline lake, Lake Assal, with fluctuating salinity and water levels. The methodology included the use of machine learning (ML) image analysis GRASS GIS modules ‘r.reclass’ for the reclassification of a raster map based on category values. Other modules included ‘r.random’, ‘r.learn.train’ and ‘r.learn.predict’ for gradient boosting ML classifier and ‘i.cluster’ and ‘i.maxlik’ for clustering and maximum-likelihood discriminant analysis. To reveal changes in the land cover categories around the Lake of Assal, this study uses ML and reclassification methods for image analysis. Auxiliary modules included ‘i.group’, ‘r.import’ and other GRASS GIS scripting techniques applied to Landsat image processing and for the identification of land cover variables. The results of image processing demonstrated annual fluctuations in the landscapes around the saline lake and changes in semi-arid and desert land cover types over Djibouti. The increase in the extent of semi-desert areas and the decrease in natural vegetation proved the processes of desertification of the arid environment in Djibouti caused by climate effects. The developed land cover maps provided information for assessing spatial–temporal changes in Djibouti. The proposed ML-based methodology using GRASS GIS can be employed for integrating techniques of image analysis for land management in other arid regions of Africa. Full article

Background: Cyclic vomiting syndrome (CVS) is a functional gastrointestinal disorder characterized by recurrent episodes of intense nausea and vomiting. Despite increasing awareness, a standardized treatment approach remains lacking in pediatric populations. Lifestyle factors and anxiety are common triggers, yet their systematic management has not been fully incorporated into therapeutic strategies. Objective: To evaluate the effectiveness of lifestyle modifications and selective serotonin reuptake inhibitors (SSRIs) in the management of pediatric CVS and to compare their outcomes with standard cyproheptadine prophylaxis. Methods: This retrospective study included 119 patients aged 1.2–17.5 years who were diagnosed with CVS according to Rome IV criteria between September 2021 and January 2025. Clinical, psychiatric, and lifestyle data were retrieved from the university’s digital medical records. Patients were grouped according to treatment modality: cyproheptadine, SSRI, or acute attack management alone. Treatment success at 12 weeks was defined as complete cessation of vomiting episodes or absence of hospitalization, prolonged attacks, and school/work absenteeism. Results: Anxiety symptoms were present in 78.2% of patients. SSRIs were prescribed to 34 patients with moderate to severe anxiety, all of whom achieved treatment success. Lifestyle adherence was observed in 73.9% and was found to be a predictor of treatment success. Cyproheptadine was administered to 66 patients but did not provide additional benefit over effective lifestyle modification. Six patients discontinued cyproheptadine due to drowsiness or weight gain. Conclusions: Lifestyle interventions significantly improve outcomes in pediatric CVS. SSRIs represent a safe and effective prophylactic option for patients with comorbid anxiety or poor adherence to behavioral recommendations. These findings support the integration of psychosocial and lifestyle-based strategies into standard CVS treatment protocols. Full article