Search Results (197)

This study investigates the effectiveness of the Xsens motion capture system in performing ergonomic analysis compared to traditional manual assessments by experts in the specific environment of assembly lines. A comprehensive literature review emphasizes the need to investigate the reliability of new, promising high-tech systems. The main objective was therefore to compare the Ergonomic Assessment Worksheet (EAWS) assessment approach performed with Xsens motion capture technology and Process Simulate V16 software with the manual method using EAWS digital prepared by experts in the controlled workflow. The greatest value of the research conducted lies in the novel integration of the state-of-the-art Xsens motion capture technology with the Process Simulate V16 software environment and the use of the licensed EAWS ergonomic method and Methods-Time Measurement Universal Analyzing System (MTM-UAS). The results are presented in the form of a case study. The results show a large similarity between the whole-body results and a large difference in the upper limb results, confirming the initial benefits of the Xsens equipment but also pointing to the need to verify its reliability on larger samples. The study highlights the potential of integrating Xsens motion capture data into ergonomic assessments and tuning of the assembly line to increase productivity and worker safety. Full article

This study provides a detailed dataset from two modern homes constructed inside an environmentally controlled chamber. These data are used to carefully calibrate a dynamic thermal simulation model of these homes. The calibrated models show good agreement with measurements taken under controlled conditions. The two case study homes, “The Future Home” and “eHome2”, were constructed within the University of Salford’s Energy House 2.0, and high-quality data were collected over eight days. The calibration process involved updating U-values, air permeability rates, and modelling refinements, such as roof ventilation, ground temperatures, and sub-floor void exchange rates, set as boundary conditions. Results demonstrated a high level of accuracy, with performance gaps in whole-house heat transfer coefficient reduced to 0.5% for “The Future Home” and 0.6% for “eHome2”, falling within aggregate heat loss test uncertainty ranges by a significant amount. The study highlights the improved accuracy of calibrated dynamic thermal simulation models, compared to results from the steady-state Standard Assessment Procedure model. By providing openly accessible calibrated models and a clearly defined methodology, this research presents valuable resources for future building performance modelling studies. The findings support the UK’s transition to dynamic modelling approaches proposed in the recently introduced Home Energy Model approach, contributing to improved prediction of energy efficiency and aligning with goals for zero carbon ready and sustainable housing development. Full article

Shaking off the forgetfulness towards the methodological power of inosine-mediated error-prone PCR (epPCR), this study reintroduces an often-underappreciated method as a considerably powerful approach for generating aptamer libraries from a single decameric ATCG-repeat-oligonucleotide. The aim was to demonstrate that this simple way of creating sequence diversity was suitable for delivering functional starting libraries for a set of ten whole-cell-SELEX (Systematic Evolution of Ligands by Exponential Enrichment) processes. This epPCR method uses inosine to introduce targeted mutations, avoiding the need for commercial oligo pools or large-scale synthesis. We applied this method to a “universal aptamer” and subjected the three resulting libraries to two rounds of selection against ten diverse targets including probiotic and pathogenic bacteria (Gram-negative and -positive) as well as human cell lines. The enriched aptamers exhibited new binding specificities, demonstrating that the approach supports functional selection. Much like dusting off an old tool and finding it perfectly suited for a modern task, this work shows that revisiting established techniques can address current challenges in aptamer development. Our main finding is that epPCR provides a robust, cost-effective strategy for generating starting libraries and lowers the barrier for initiating successful SELEX campaigns. Full article

Traditionally, Helicobacter pylori (HP) gastritis has been diagnosed by pathologists through the examination of gastric biopsies using optical microscopy with standard hematoxylin and eosin (H&E) staining. However, with the adoption of digital pathology, the identification of HP faces certain limitations, particularly due to insufficient resolution in some scanned images. Moreover, interobserver variability has been well documented in the traditional diagnostic approach, which may further complicate consistent interpretation. In this context, deep convolutional neural network (DCNN) models are showing promising results in the automated detection of this infection in whole-slide images (WSIs). The aim of the present article is to detect the presence of HP infection from our own institutional dataset of histopathological gastric biopsy samples using different pretrained and recognized DCNN and AutoML approaches. The dataset comprises 100 H&E-stained WSIs of gastric biopsies. HP infection was confirmed previously using immunohistochemical confirmation. A total of 45,795 patches were selected for model development. InceptionV3, Resnet50, and VGG16 achieved AUC (area under the curve) values of 1. However, InceptionV3 showed superior metrics such as accuracy (97%), recall (100%), F1 score (97%), and MCC (93%). BoostedNet and AutoKeras achieved accuracy, precision, recall, specificity, and F1 scores less than 85%. The InceptionV3 model was used for external validation, and the predictions across all patches yielded a global accuracy of 78%. In conclusion, DCNN models showed stronger potential for diagnosing HP in gastric biopsies compared with the auto ML approach. However, due to variability across pathology applications, no single model is universally optimal. A problem-specific approach is essential. With growing WSI adoption, DL can improve diagnostic accuracy, reduce variability, and streamline pathology workflows using automation. Full article

The Sustainable Development Goals commit states to Universal Health Coverage (UHC) for all; yet displaced populations—including asylum seekers, refugees, internally displaced persons (IDPs), and undocumented migrants—remain systematically excluded from national health systems across southern and eastern Africa. This paper applies a whole-of-route, rights-based framework to examine how legal status, policy implementation, and structural governance shape healthcare access for displaced populations across South Africa, Kenya, Somalia, and the Democratic Republic of Congo (DRC). Drawing on 70 key informant interviews and policy analysis conducted between 2020 and 2025, the study finds that despite formal commitments to health equity, access remains constrained by restrictive legal regimes, administrative discretion, and fragmented service delivery models. Critical gaps persist in migration-sensitive planning, gender-responsive care, and mental health integration. The findings highlight the limitations of rights-based rhetoric in the absence of legal coherence, intersectoral coordination, and political will. To realise UHC in displacement contexts, health systems must move beyond citizen-centric models and embed migration-aware, inclusive, and sustainable approaches across all stages of displacement. Without such structural reforms, displaced populations will remain at the margins of national health agendas—and the promise of health for all will remain unmet. Full article

Building Information Modeling (BIM) concepts are permeating the approach to the design of buildings and landscapes for the architectural, engineering, and construction sectors. Recent regulations require that even medium–small-size public works are managed through BIM-driven design. These circumstances have led to an increase in research on the topic. The expansion of the demand of BIM-skilled professionals urges higher education institutions to re-engineer their design programs. The aim of this paper is to evaluate this academic education transition in the Department of Agricultural Sciences at the University of Sassari, Italy. The method consists of a BIM academic education assessment framework based on ten criteria clustered into three macro-issues. The application of this framework to the assessment of three diploma final theses signals that some actions have been undertaken (i.e., introducing BIM basic concepts in rural building and landscape design, stimulating interest in students, clarifying the dimensions of BIM, and promoting the concept of 3D object design and management), but still, much work must be carried out. The work confirms typical barriers to the implementation of BIM concepts in the core curriculum and the need to mobilize the whole educational ecosystem to achieve satisfactory progress toward effective innovation in contemporary BIM-led design teaching. This work represents the first attempt to evaluate the progress of the Department of Agricultural Sciences, University of Sassari, toward the integration of BIM concepts in its courses and to position this transition in an international panorama. Full article

Background/Objectives: Dyslexia, a learning disability affecting reading, has been extensively studied using eye movements. This study aimed to examine in the same design the effects of different psycholinguistic variables, i.e., grammatical category, lexical frequency, word length and orthographic consistency on eye movement patterns during reading in adults. Methods: We compared the eye movements of forty university students, twenty with and twenty without dyslexia while they read aloud a meaningful and a meaningless text in order to examine whether semantic context could enhance their reading strategy. Results: Dyslexic participants made more reading errors and had longer reading time particularly with the meaningless text, suggesting an increased reliance on the semantic context to enhance their reading strategy. They also made more progressive and regressive fixations while reading the two texts. Similar results were found when examining grammatical categories. These findings suggest a reduced visuo-attentional span and reliance on a serial decoding approach during reading, likely based on grapheme-to-phoneme conversion. Furthermore, in the whole text analysis, there was no difference in fixation duration between the groups. However, when examining word length, only the control group exhibited a distinction between longer and shorter words. No significant group differences emerged for word frequency. Importantly, multiple regression analyses revealed that orthographic consistency predicted fixation durations only in the control group, suggesting that dyslexic readers were less sensitive to phonological regularities—possibly due to underlying phonological deficits. Conclusions: These findings suggest the involvement of both phonological and visuo-attentional deficits in dyslexia. Combined remediation strategies may enhance dyslexic individuals’ performance in phonological and visuo-attentional tasks. Full article

Background: Breast cancer remains a critical health concern worldwide, with histopathological analysis of tissue biopsies serving as the clinical gold standard for diagnosis. Manual evaluation of histopathology images is time-intensive and requires specialized expertise, often resulting in variability in diagnostic outcomes. In silver in situ hybridization (SISH) images, accurate nuclei detection is essential for precise histo-scoring of HER2 gene expression, directly impacting treatment decisions. Methods: This study presents a scalable and automated deep learning framework for nuclei detection in HER2-SISH whole slide images (WSIs), utilizing a novel dataset of 100 expert-marked regions extracted from 20 WSIs collected at the University of Malaya Medical Center (UMMC). The proposed two-stage approach combines a pretrained Stardist model with image processing-based annotations, followed by fine tuning on our domain-specific dataset to improve generalization. Results: The fine-tuned model achieved substantial improvements over both the pretrained Stardist model and a conventional watershed segmentation baseline. Quantitatively, the proposed method attained an average F1-score of 98.1% for visual assessments and 97.4% for expert-marked nuclei, outperforming baseline methods across all metrics. Additionally, training and validation performance curves demonstrate stable model convergence over 100 epochs. Conclusions: These results highlight the robustness of our approach in handling the complex morphological characteristics of SISH-stained nuclei. Our framework supports pathologists by offering reliable, automated nuclei detection in HER2 scoring workflows, contributing to diagnostic consistency and efficiency in clinical pathology. Full article

With accelerating urbanization, the outdoor thermal environment has become a critical factor affecting the thermal comfort of public spaces, particularly in high-density commercial districts and pedestrian-concentrated areas. To enhance thermal comfort and livability in public outdoor space, this study proposes a thermal demand-responsive design approach that integrates thermal conditions with pedestrian flow dynamics. A commercial pedestrian mall featuring semi-open public spaces and air-conditioned interior retail areas was selected as a case study. Computational Fluid Dynamics (CFD) simulations were conducted based on design-phase documentation and field measurements to model the thermal environment. The Universal Thermal Climate Index (UTCI) was employed to assess thermal comfort levels, and thermal discomfort was further quantified using the Heat Discomfort Index (HI). Simultaneously, pedestrian density distribution (λ) was analyzed using the agent-based simulation software MassMotion (Version 11.0). A demand of thermal comfort (DTC) index was developed by coupling UTCI-based thermal conditions with pedestrian density, enabling the spatial quantification of thermal demand across the whole commercial pedestrian mall. For example, in a sidewalk area parallel to the main street, several points exhibited high discomfort levels (HI = 0.95) but low pedestrian volume, resulting in DTC values approximately 0.2 units lower than adjacent zones with lower discomfort levels (HI = 0.7) but higher foot traffic. Such differences demonstrate how DTC can reveal priority areas for intervention. Key zones requiring thermal improvement were identified based on DTC values, providing a quantitative foundation for outdoor thermal environment design. This method provides both a theoretical foundation and a practical tool for the sustainable planning and optimization of urban public spaces. Full article

Life-cycle cost assessment has gained increasing relevance across sectors related to urban and building development. In real estate and public procurement decision-making, it offers a comprehensive view of property costs beyond the initial investment, which aligns with European Sustainable Development policies and new taxonomies in sustainable investment. Life-cycle cost assessment supports sustainable design decisions by integrating multiple perspectives and methodologies, including Whole Life Costing and Net Present Value calculations. This approach enables a comprehensive evaluation of long-term costs and benefits, assessing their impact on economic viability and profitability throughout the investment life cycle. However, several challenges persist in standardizing methodologies, developing comprehensive data inventories, and ensuring consistency in result interpretation. The absence of universally accepted frameworks and guidelines introduces additional limitations for practitioners, including estimation inaccuracies, biased assessments, unreliable probability judgments, and the neglect of indirect consequences in decision-making. This review particularly emphasizes the need for interdisciplinary research to advance the integration of costs and benefits of externalities and intangibles associated with social and environmental criteria. Full article

This paper discusses the development of an Integrated Building Energy Model (IBEM) in MATLAB (R2024b) for a university campus building. In the general context of the development of integrated energy district models to guide the evolution and planning of smart energy grids for increased efficiency, resilience, and sustainability, this work describes in detail the development and use of an IBEM for a university campus building featuring a heat pump-based heating/cooling system and PV generation. The IBEM seamlessly integrates thermal and electrical aspects into a complete physical description of the energy performance of a smart building, thus distinguishing itself from co-simulation approaches in which different specialized tools are applied to the two aspects and connected at the level of data exchange. Also, the model, thanks to its physical, white-box nature, can be instanced repeatedly within the comprehensive electrical micro-grid model in which it belongs, with a straightforward change of case-specific parameter settings. The model incorporates a heat pump-based heating/cooling system and photovoltaic generation. The model’s components, including load modeling, heating/cooling system simulation, and heat pump implementation are described in detail. Simulation results illustrate the building’s detailed power consumption and thermal behavior throughout a sample year. Since the building model (along with the whole campus micro-grid model) is implemented in the MATLAB Simulink environment, it is fully portable and exploitable within a large, world-wide user community, including researchers, utility companies, and educational institutions. This aspect is particularly relevant considering that most studies in the literature employ co-simulation environments involving multiple simulation software, which increases the framework’s complexity and presents challenges in models’ synchronization and validation. Full article

Background/Objectives: Breast cancer diagnosis heavily relies on histopathological assessment, which is prone to subjectivity and inefficiency, especially with whole-slide imaging (WSI). This study addressed these limitations by developing an automated breast cancer cell classification algorithm using an information-extreme machine learning approach and universal cytological features, aiming for objective and generalized histopathological diagnosis. Methods: Digitized histological images were processed to identify hyperchromatic cells. A set of 21 cytological features (10 geometric and 11 textural), chosen for their potential universality across cancers, were extracted from individual cells. These features were then used to classify cells as normal or malignant using an information-extreme algorithm. This algorithm optimizes an information criterion within a binary Hamming space to achieve robust recognition with minimal input features. The architectural innovation lies in the application of this information-extreme approach to cytological feature analysis for cancer cell classification. Results: The algorithm’s functional efficiency was evaluated on a dataset of 176 labeled cell images, yielding promising results: an accuracy of 89%, a precision of 85%, a recall of 84%, and an F1-score of 88%. These metrics demonstrate a balanced and effective model for automated breast cancer cell classification. Conclusions: The proposed information-extreme algorithm utilizing universal cytological features offers a potentially objective and computationally efficient alternative to traditional methods and may mitigate some limitations of deep learning in histopathological analysis. Future work will focus on validating the algorithm on larger datasets and exploring its applicability to other cancer types. Full article

Background/Objectives: This study aims to identify genetic variants associated with early-onset inflammatory bowel disease (IBD) and to improve diagnostic and therapeutic approaches. In selected monogenic IBD cases, treatment included colchicine, interleukin-1 inhibitors, and hematopoietic stem cell transplantation. Methods: This study included patients with early-onset IBD, defined as IBD diagnosed before the age of 10, who were under follow-up at the Department of Pediatric Gastroenterology, Hacettepe University, and agreed to participate between December 2018 and April 2021. Whole-exome sequencing (WES) was performed prospectively in patients without a prior diagnosis of monogenic disease, while clinical and laboratory data were reviewed retrospectively. Identified variants were evaluated for pathogenicity using standard bioinformatics tools. Results: A total of 47 patients were enrolled, including 33 boys (70.2%) and 14 girls (29.8%). The median age at symptom onset was 36 months (IQR: 10–72), and the median age at diagnosis was 3.7 years (IQR: 1.5–7.6). Crohn’s disease was diagnosed in 53.2% (n = 25), ulcerative colitis in 38.3% (n = 18), and unclassified IBD in 8.5% (n = 4). Monogenic IBD was identified in 36.2% (n = 17) of patients, including nine with Familial Mediterranean Fever and others with glycogen storage disease type 1b (n = 2), XIAP deficiency, chronic granulomatous disease, DOCK8 deficiency, IL10 receptor alpha defect, LRBA deficiency, and NFKB2 deficiency (n = 1 each). A novel SLC29A3 gene variant (c.480_481delTGinsCA, p.V161I) (transcript ID: ENST00000479577.2) was identified in 76.6% (n = 36) of patients. Conclusions: This study underscores the importance of genetic variants in early-onset IBD, particularly MEFV and the novel NFKB2. The frequent detection of the SLC29A3 variant may suggest its potential involvement in the pathogenesis of the disease. Full article

Semantic segmentation of medical images holds significant potential for enhancing diagnostic and surgical procedures. Radiology specialists can benefit from automated segmentation tools that facilitate identifying and isolating regions of interest in medical scans. Nevertheless, to obtain precise results, sophisticated deep learning models tailored to this specific task must be developed and trained, a capability not universally accessible. Segment Anything Model (SAM) 2 is a foundational model designed for image and video segmentation tasks, built on its predecessor, SAM. This paper introduces a novel approach leveraging SAM 2’s video segmentation capabilities to reduce the prompts required to segment an entire volume of medical images. The study first compares SAM and SAM 2’s performance in medical image segmentation. Evaluation metrics such as the Jaccard index and Dice score are used to measure precision and segmentation quality. Then, our novel approach is introduced. Statistical tests include comparing precision gains and computational efficiency, focusing on the trade-off between resource use and segmentation time. The results show that SAM 2 achieves an average improvement of 1.76% in the Jaccard index and 1.49% in the Dice score compared to SAM, albeit with a ten-fold increase in segmentation time. Our novel approach to segmentation reduces the number of prompts needed to segment a volume of medical images by 99.95%. We demonstrate that it is possible to segment all the slices of a volume and, even more, of a whole dataset, with a single prompt, achieving results comparable to those obtained by state-of-the-art models explicitly trained for this task. Our approach simplifies the segmentation process, allowing specialists to devote more time to other tasks. The hardware and personnel requirements to obtain these results are much lower than those needed to train a deep learning model from scratch or to modify the behavior of an existing one using model modification techniques. Full article

Energy storage systems (ESSs) and active power filters (APFs) are key power electronic technologies for FACTS (Flexible AC Transmission Lines). Battery energy storage has a structure similar to a shunt active power filter, i.e., a storage element and a voltage source inverter (VSI) connected to the grid using a PWM filter and/or transformer. This similarity allows for the design of an ESS with the ability to operate as a shunt APF. One of the key milestones in ESS or APF development is the DC-link design. The proper choice of the capacitance of the DC-link capacitors and their equivalent resistance ensures the proper operation of the whole power electronic system. In this article, it is proposed to estimate the required minimum DC-link capacitance using a spectral analysis of the DC-link current for different operating modes, battery charge mode and harmonic compensation mode, for a nonlinear load. It was found that the AC component of the DC-link current is shared between the DC-link capacitors and the rest of the DC stage, including the battery. This relation is described analytically. The main advantage of the proposed approach is its universality, as it only requires calculating the harmonic spectrum using the switching functions. This approach is demonstrated for DC-link capacitor estimation in two-level and three-level NPC inverter topologies. Moreover, an analysis of the AC current component distribution between the DC-link capacitors and the other elements of the DC-link stage was carried out. This part of the analysis is especially important for battery energy storage systems. The obtained results were verified using a simulation model. Full article