Abstract: Driven by the global rise in animal protein demand, poultry farming has evolved into a highly intensive and technically complex sector. According to FAO, animal protein production increased by about 16% in the past decade, with poultry alone expanding 27% and becoming the leading source of animal protein. This intensification requires rapid, complex decisions across multiple aspects of production under uncertainty and strict time constraints. This study presents the development and evaluation of a conversational decision support system (DSS) designed to support decision-making to assist poultry producers in addressing technical queries across five key domains: environmental con-trol, nutrition, health, husbandry, and animal welfare. The system combines a large language model (LLM) with retrieval-based generation (RAG) to ground responses in a curated corpus of scientific and technical literature. Additionally, it adds a reasoning component using Paraconsistent Annotated Evidential Logic Eτ, a non-classical logic designed to handle contradictory and/or incomplete information. Evaluation was conducted by comparing system responses with expert reference answers using semantic similarity (cosine similarity with SBERT embeddings). Results indicate that the system successfully retrieves and composes relevant content, while the paraconsistent inference layer makes results easier to interpret and more reliable in the presence of conflicting or insufficient evidence. These findings suggest that the proposed architecture provides a viable foundation for explainable and reliable decision support in modern poultry production, achieving consistent reasoning under contradictory and/or incomplete information where conventional RAG chatbots would fail.

Abstract: Bone is a hierarchically organized composite material with unique mechanical properties and an intrinsic capacity for regeneration. Conventional repair strategies, including autografts, allografts, xenografts, and metallic or ceramic implants, face limitations such as donor scarcity, immunogenicity, brittleness, and poor long-term integration. Tissue engineering (TE) offers a promising alternative by combining cells, scaffolds, and growth factors to restore bone structure and function. This review outlines the principles and workflow of bone TE, emphasizing scaffold design, and clinical viability. Scaffolds serve as three-dimensional, highly porous templates that support cell adhesion, nutrient diffusion, and extracellular matrix remodeling. Successful bone TE requires osteoconductive scaffolds, osteogenic progenitor cells, and osteoinductive signaling molecules to achieve physiological compatibility and functional integration. Recent advances in biomaterials, scaffold architecture, and fabrication technologies have significantly improved the ability to replicate native bone properties, positioning TE as a transformative strategy for regenerative medicine. Despite persistent challenges in achieving complete integration and mechanical stability under complex loading, ongoing research continues to optimize scaffold performance and cellular approaches, making TE a viable and cost-effective alternative to traditional bone repair techniques.

Abstract: Voice-based approaches for screening and diagnostic applications, particularly in telemedicine, often rely on patient recordings collected outside clinical environments. Establishing normative baselines is essential to advance voice analytics and clinical utility. This pilot study examined acoustic parameters in 32 healthy young adults (ages 18–24) with no history of vocal pathology, neurological disorders, or speech impediments. Participants provided paired recordings of sustained vowels (/a/, /e/, /o/, /u/) and a standardized phonetically balanced phrase (“The sun sets in Cincinnati on Saturday”). Analyses focused on features including fundamental frequency, jitter, shimmer, harmonics-to-noise ratio, formants (F1–F3), speaking rate, intensity, and spectral measures. Preliminary results revealed significant differences between healthy controls and a reference dataset of laryngitis patients, suggesting acoustic features can serve as objective markers of vocal fold inflammation. However, pathology-specific biomarker identification was constrained by the quality of available laryngitis data. Simple statistical comparisons proved insufficient, emphasizing the value of advanced measures such as cepstral peak prominence (CPP) and mel-frequency cepstral coefficients (MFCC). Challenges in non-clinical data collection highlight the need for standardized, detailed annotation of patient recordings to improve diagnostic accuracy and strengthen the predictive power of future biomarker studies.

Abstract: Flexible biosensors offer rapid and low-cost diagnostics but are often limited by the mechanical and electrochemical instability of polymer-based designs in biological media. Here, we introduce a metallic flexible sensing platform that exploits the intrinsic deformability of superelastic nickel–titanium (NiTi) for label-free impedimetric detection. Mechanical bending of NiTi wires spontaneously generates martensitic-phase microcracks whose metal–gap–metal geometry forms the active transduction sites, where functional interfacial layers and captured analytes modulate the local dielectric environment and governing the impedance response. Functionalization with thiolated monolayers and Escherichia coli-specific antibodies enables these microdomains to modulate interfacial charge transfer in response to analyte binding, creating a direct coupling between mechanical deformation and resulting impedance signal. The γ-bent NiTi sensors achieved stable and quantitative detection of E. coli ATCC 25922 in sterile human urine, with a detection limit of 53 CFU mL⁻¹ within 45 minutes, without redox mediators, external labels, or amplification steps. This work establishes the first use of self-healing martensitic microcracks in a superelastic alloy as functional transduction elements, defining a new class of metallic flexible biosensors that integrate mechanical robustness, analytical reliability, and scalability for point-of-care biosensing.

Abstract: As an emerging technology, three-dimensional (3D) printing is gaining publicity among companies, manufacturers, and individuals to fabricate prototypes, parts, and samples. 3D printing can be used in various fields such as engineering, healthcare, education, etc. This study investigates the influence of FDM 3D printing parameters such as infill patterns (line, triangle, cubic, and gyroid), infill densities (20%, 60%, and 100%), layer thicknesses (0.1, 0.2, and 0.3 mm), and temperature according to the minimum and maximum manufacturer recommendations. This study investigated the most common filament used in FDM 3D printing, which is polylactic acid (PLA). Mechanical tests were performed on the 3D printed parts, which are uniaxial tensile test and 3-point bending test according to ASTM D638 Type-I and ISO 178 standards, respectively. Modulus of elasticity (E), fracture point (σ_F), maximum stress (σ_Max), and yield strength (σ_Y) were obtained from the uniaxial tensile test. And for the 3-point bending test, strain at maximum load (ɛ), modulus of elasticity (E_bending), and flexural strength (σ_fMax) were obtained. This study showed that infill density and pattern are dominant factors in mechanical performance, while layer thickness and printing temperature provide fine-tuning effects.

Abstract:

Background Disc degeneration is an increasingly common problem in modern society and is often a precursor to a herniated disc. Contributing factors include physical exertion, overuse, the natural aging process, and disease and injury. Over time, the fibrous ring of the disc develops cracks and small tears, allowing fluid from the nucleus pulposum to escape. As a result, the ability of the disc to absorb shock decreases, potentially leading to a bulging or herniated disc. In this work, previously initiated investigations are extended, and additional thermoplastic polyurethane (TPU) filaments are examined with respect to their suitability for additive manufacturing as potential disc replacement materials. Materials & Methods To remain comparable, the additive manufacturing in this work is also carried out with Fused Deposition Modeling (FDM) 3D printers and as a Ø50 mm x 10mm disc. The Gyroid was varied from 10 mm³ for the coarsest structure to 4 mm³ for the finest structure. The wall thickness of the Gyroid was also varied from 0.5 to 1.0 mm, as were the outer walls of the disc, whose wall thickness was varied from 0.4 to 0.8 mm. Four different TPU filaments (Extrudr FlexSemiSoft, GEEETECH TPU, SUNLU TPU and OVERTURE TPU) were used. This resulted in 36 different settings per filament. The 3D printed discs were analyzed using an Olympus SZ61 stereomicroscope. A tensile test according to DIN EN ISO 527-1 was performed on the 3D printed samples 5A. The aim was to investigate the difference between the different TPU filaments. To test the mechanical properties of the 3D printed discs, a uniaxial compression test was performed with at least three samples of each setting. The body was compressed to 50% of its total height and the force required was recorded as a force-deformation curve. To be comparable to a previous project, a maximum force of 4000–7500 N was used. Results Of the 36 different discs tested for each filament, only a maximum of three were within the target range of maximum force. Microscopy revealed that all wall thicknesses were within the target range with only minor variations. The tensile strengths of Geetech, SunLu, and SemiSoft were not significantly different and were in a similar range of 10-11 MPa, with Overture deviating significantly at 9 MPa. The tensile moduli exhibited a comparable distribution: 25-30 MPa for Geetech, SunLu, and SemiSoft, and 17.5 MPa for Overture. Conclusion For all of the filaments tested, it was possible to additively produce suitable discs that were within the specified range of 4000-7500 N at 50% compression. This would ensure that these discs would withstand the stresses they would be subjected to in a potential human disc replacement application. Thus, we were able to confirm the suitability of these four filaments, as well as the Gyroid structures, for use as a disc replacement.

Abstract:

The main objective of this study is to investigate the influence of cognitive stress (mental workload) on some physiological parameters and reactions of a set of experimental subjects. The aim is to check whether these indicators, observed simultaneously, can distinguish the state of rest from the state of mental tension and whether they can distinguish tasks of different difficulty. An assessment of the state of rest in the study protocol is also performed. The experiments implemented a multimodal, non-invasive BCI for tracking physiological responses during cognitive task performance. Five parallel measured parameters are used: electroencephalography (EEG), heart rate (HR), galvanic skin response (GSR), facial surface temperature, and oxygen saturation (SpO₂). The results show that HR is a fast and reliable marker for detecting psychological load, the normalized phase GSR is good for detecting higher loads, EEG α/θ can be used for central validation, facial temperature is shown to be a slowly changing but reliable context indicator and SpO₂ preservation can be used as a measure of stability.

Abstract: Reaction time (RT) is a key indicator of cognitive and motor processing speed, and its age-related decline has important implications for everyday activities such as driving. However, conventional Psychomotor Vigilance Tests (PVTs) assess hand responses and do not capture lower-limb reaction characteristics relevant to pedal operations. This study used a foot-response version of the PVT (Foot PVT) to compare RTs between younger and older adults and to examine the influence of height, sleep factors, and physical activity level (PAL). Twenty younger adults (24 ± 3 years) and twenty-four older adults (73 ± 5 years) performed a 10-minute Foot PVT between 11:00 and 14:00. Participants responded to visual stimuli by moving the right foot laterally from a central pedal to the left or right pedals. RT mean, RT median, RT SD, skewness, and kurtosis were calculated, and correlation and multiple regression analyses were conducted using height, five OSA Sleep Inventory factors, and PAL as predictors. RT mean was significantly slower in older adults (818 ± 105 ms) than in younger adults (700 ± 73 ms), indicating an age-related delay of approximately 120 ms. Older adults showed lower skewness and kurtosis, suggesting more homogeneous responses and a cautious response strategy. In younger adults, height correlated negatively with RT (r = −0.593), and multiple regression identified height as the only significant predictor (adjusted R² = 0.316). No significant predictors were found in older adults. In the combined sample, height and age jointly explained 37.2% of RT variance. These findings indicate that Foot PVT performance reflects both biomechanical characteristics and age-related declines in reaction speed. Height strongly influences RT in younger adults, whereas RT in older adults appears to be shaped by multifactorial age-related changes. The Foot PVT provides a practical tool for assessing lower-limb reaction capabilities relevant to driving and aging.

Abstract: Background: Intervertebral motion is a fundamental aspect of spinal biomechanics, crucial for understanding lumbar spine function, pain mechanisms and surgical out-comes. Various methods exist for measuring and interpreting it, each with its own advantages, limitations and specific applications. However, a comprehensive and standard taxonomy of study types for the measurement and interpretation of in vivo intervertebral motion in the lumbar spine is lacking. Objectives: This review aimed to systematically identify, characterize and categorize the diverse study types deposited in the literature. Eligibility criteria: Only studies in English and of lumbar spine intervertebral motion in living subjects were considered and only those that employed objective measurement of motion sequences were included. Sources of evidence: A comprehensive literature search was performed in PubMed, CINAHL and SCOPUS for articles published between January 2000 and October 2025. Charting methods: After removal of duplicates, all studies were subjected to Title and abstract screening, followed by Full-text screening of potentially eligible studies. Data selected were charted into tables under the headings: Author, year, country, purpose, technology, participants, measurement, interpretation, radiation dosage and significance of findings. Results: Forty-nine studies were abstracted and are described under 11 study types. These formed a taxonomy constituting the following 6 categories: Normal biomechanical mechanisms, Pathological and injury mechanisms, Direct kinematic measurement, Spinal stabilization, Dynamic radiography and Clinical markers. The resulting taxonomy will serve as a resource for researchers, clinicians and policymakers by facilitating a more coherent understanding of the field and promoting standardization in research design and re-porting.

Abstract: Intraoperative patient motion degrades static registration. We propose an adaptive AR guidance system with feedback-driven registration updates. The system employs particle filter-based motion compensation and multi-scale ICP refinement. In 10 cadaver experiments with head motion up to ±20 mm, average TRE remained under 1.5 mm, compared with 2.7 mm in static ICP. Accuracy improved by 43%, and frame rate stayed at 26 fps. The system enables real-time adaptation in dynamic surgeries.

Abstract: Various recurring medical events encourage innovative patient well-being through con-nected health strategies based on an elegant digital environment that prioritizes safety, comfort, and beneficial outcomes for both patients and medical staff. This article aims to investigate and highlight the potential of advanced, reliable, high-precision, and secure medical observation and intervention missions. These involve an intelligent digital envi-ronment integrating smart materials combined with intelligent digital monitoring. These medical implications concern robotic surgery and drug delivery through image-assisted implantation, as well as wearable observation and assistive tools. The former requires high-precision motion and positioning strategies, while the latter enables sensing, diag-nosis, monitoring, and central task assistance. Both advocate minimally invasive or non-invasive procedures and precise supervision through autonomously controlled processes with staff participation. The article analyzes the requirements and evolution of medical interventions, robotic actuation technologies for positioning actuated and self-moving in-stances, monitoring of image-assisted robotic procedures using digital twins and aug-mented digital tools, and wearable medical detection and assistance devices. A discussion including future research perspectives and conclusions terminate the article. The different themes addressed in the proposed paper, although self-sufficient, are supported by exam-ples of the literature, allowing a deeper understanding.

Abstract: Electrocardiography (ECG) is a key biosensing technique for assessing cardiac function and autonomic activity. Accurate detection of R-wave peaks and precise calculation of R-R intervals (RRIs) are essential for heart rate variability (HRV) analysis, yet automated detection algorithms remain vulnerable to misdetections caused by noise or baseline fluctuations. Conventional correction methods based on filter or threshold adjustments may introduce new errors, highlighting the need for an intuitive manual correction function. To address this issue, we developed a prototype graphical user interface (GUI)-based ECG viewer implemented in Fortran for high computational efficiency. The system enables interactive insertion or deletion of R-wave peaks with immediate recalculation of RRIs and automatic updates of related analyses, including power spectral density, histograms, Lorenz plots, and polar plots. Validation using synthetic ECG signals at four sampling frequencies (125-1000 Hz) and three time scales (2, 5, and 10 s) compared GUI-derived RRIs with gold-standard RRIs, showing correction errors below 0.7% and stable update times within 20-30 ms. When applied to real ECG recordings from the MIT-BIH Arrhythmia Database using the MLII lead from records 115 and 122, the same RRI-based comparison achieved accuracies exceeding 0.985 at ±10 ms and reaching 1.000 at ±20 ms or higher. These results confirm that the proposed system provides reliable and immediate feedback and is applicable to physiological data. The algorithm may support future applications in research, clinical, and educational domains of biosignal processing.

Abstract: This report is a deep dive into the complex world of using neuromorphic chips to help people with severe brain damage regain control of their bodies. We’ll look at the fundamental science behind neuromorphic computing, explore the current landscape of brain-computer interfaces (BCIs), and confront the biological and ethical challenges of making such a technology a reality. The main idea is to create a kind of “digital nervous system” that could bypass damaged parts of the brain to restore basic functions like movement and breathing. This isn't just a technical paper; it’s a detailed exploration of the immense hurdles and profound questions that must be answered before we can truly build a bridge between mind and machine. This document is a starting point for anyone looking to understand this fascinating and difficult field.

Abstract: Objective: The aim of this preliminary study was to recognize motion patterns by classifying time series features obtained from electromyography (EMG) data of the upper limb muscles. Methods: In this study we tested six models Machine Learning (ML) classification models (Decision Trees, Support Vector Machines, Linear Discriminant, Quadratic Discriminant, K-Nearest Neighbors, and Efficient Logistic Regression) to classify time series features extracted from processed EMG data that were acquired from eight superficial muscles of two upper limbs over performing given physical activities in two main stages (supination and neutral forearm configuration) in initial and target (isometric) positions. Results: Findings indicate that aiming to classify stages of the upper limb with the highest performance following ML models should be used: 1) K-NN cityblock (F1 equals 0.973/0.992), K-NN seuclidean (0.971/0.996), K-NN minkowski (0.966 /0.992), K-NN co-sine (0.962/0.969) for the left limb; 2) K-NN euclidean (0.970 /0.989), K-NN cityblock (0.966 /0.986), K-NN seuclidean (0.959/0.985), K-NN minkowski (0.957/0.986) for the right limb. Conclusion: Motion patterns tested in this study can be recognized with the highest performance by applying following ML models to classify EMG data: K-NN city-block, K-NN seuclidean, and K-NN minkowski models.

Abstract: Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder typically diagnosed through behavioral assessments and subjective reports. Electroencephalography (EEG) offers a cost-effective, non-invasive alternative for capturing neural activity patterns associated with the disorder. However, EEG-based ADHD classification remains challenged by overfitting, dependence on extensive preprocessing, and limited interpretability. Here, we propose T-GARNet, a novel neural architecture that integrates transformer-based temporal attention with Gaussian-mixture functional connectivity modeling and a cross-entropy loss regularized through α-Rényi mutual information. The multi-scale Gaussian kernel functional connectivity leverages parallel Gaussian kernels to identify complex spatial dependencies, which are further stabilized and regularized by the α-Rényi term. This design enables the direct modeling of long-range temporal dependencies from raw EEG while enhancing spatial interpretability and reducing feature redundancy. We evaluate T-GARNet on a publicly available ADHD EEG dataset using both leave-one-subject-out (LOSO) and stratified group k-fold cross-validation (SGKF-CV), where groups correspond to control and ADHD, and compare its performance against classical and modern state-of-the-art methods. Results show that T-GARNet achieves competitive or superior performance (88.3% accuracy), particularly under the more challenging SGKF-CV setting, while producing interpretable spatial attention patterns consistent with ADHD-related neurophysiological findings. These results underscore T-GARNet’s potential as a robust and explainable framework for objective EEG-based ADHD detection.

Abstract: Neuroscience adopts a multidimensional approach to decode thoughts and actions originating inside the brain, aka the Brain Computer Interface (BCI). However, achieving high accuracy in these decodings remains a challenge and an open research topic in BCI research. This study aims to enhance the accuracy of signal classification for identifying human emotional states. We utilized the publicly available EEG-Audio-Video (EAV) dataset that comprises EEG recordings from 42 subjects across five emotional categories. Our key contribution is to exploit the 2-dimensional contrast enhancement applied to the spectrogram for feature extraction, followed by classification using the EEGNet model. As a result, 12.5% improvement in classification accuracy over the baseline was achieved. This contribution demonstrates a potential advancement in BCI-based EEG signal processing in neuroscientific research.

Abstract: With the rising global population and increasing energy demands, sustainable bioproducts such as bioethanol offer essential alternatives to fossil fuels. Unlike first-generation bioethanol derived from food crops like corn, second-generation bioethanol is produced from lignocellulosic (LC) biomass, a non-food resource that addresses sustainability concerns. Consolidated Bioprocessing (CBP) integrates enzyme production, hydrolysis, and fermentation in a single step, using either microbial consortia or engineered microorganisms, reducing costs and simplifying the process compared to separate hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF). However, CBP systems are complex due to the dynamic interactions between microbial consortia, metabolic pathways, and process conditions. Addressing these complexities requires advanced modeling techniques that effectively capture non-linear relationships and optimize process parameters. Machine learning-based models have the potential to advance the field of CBP by enabling data-driven approaches to capture complex bioprocess dynamics, improve prediction accuracy, and optimize bioproduct production in CBP systems, thus paving the way toward commercial viability. This review gives an actual overview of relevant key processes CBP, the current state of modeling CBP, its limitations, and the emerging role of machine learning (ML) as a solution to CBP’s modeling challenges. It details recent modeling techniques for CBP, including polynomial models, response surface methodologies, with detailed discussions on regression models and neural network models. In this paper, a summarized review of first-order principle-based modeling approaches as well as data-driven modeling approaches is included, emphasizing advancements that contribute to the scalability and efficiency of CBP for bioproduct production. This review provides new perspectives and insights on the modeling of consolidated bioprocessing for utilizing low-cost lignocellulosic biomass in bioproduction.

Abstract: Achieving effective tissue integration depends mainly on the biological performance of polymeric implants. Poly-ether-ether-ketone (PEEK) is widely used as an implant material; however, its inert nature results in limited biological interactions. Various surface modification techniques have been investigated to enhance its bioactivity and overall biological performance. In this study, the PEEK surface was activated using low-pressure oxygen plasma and functionalized with phosphate and calcium ions. Comprehensive surface characterization by contact angle, scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and Fourier Transform Infrared (FT-IR) confirmed the effect of plasma and the ionic surface incorporation. The biological response was evaluated through cell viability, adhesion, and proliferation in NIH/3T3 fibroblasts and HOS osteoblasts, and the results indicated the efficacy of the surface modifications. Therefore, the proposed treatments provide an efficient strategy to improve the biological performance of PEEK-based implants.

Abstract: Digital twin applications for water resource recovery facilities require frequent model recalibration to maintain predictive accuracy under dynamic operational conditions. Current calibration methodologies face critical limitations: manual protocols demand extensive expert intervention and iterative parameter adjustments spanning weeks to months, while automated optimization algorithms impose elevated computational burdens that struggle to converge within practical timeframes. This study introduces Expert Systems with Neuro-Evolution of Augmenting Topologies (ES-NEAT), integrating genetic algorithms, artificial neural networks, and transfer learning to preserve and transfer calibration knowledge across recalibration scenarios. Application to the full-scale Eindhoven WRRF over six months, calibrating 33 parameters across multiple temporal scenarios, demonstrated 72.1% and 49.0% Kling-Gupta Efficiency improvement over manual calibration for tank-in-series and compartmental model structures, respectively. Transfer learning reduced subsequent recalibration computational time by 50-70% while maintaining prediction accuracy, transforming initial 10-12 hour optimizations into 3-6 hour recalibrations through knowledge preservation. Performance degradation analysis established 2-month optimal recalibration intervals under observed operational variability. The methodology enables practical digital twin implementation by transforming recalibration from episodic expert-dependent burden into continuous, automated learning processes operating at timescales matching operational decision-making needs.

Abstract: Over the past two decades, robotic surgery has witnessed a rapid and widespread adoption across almost all surgical specialties, with the da Vinci Surgical System (Intuitive Surgical) emerging as the dominant platform worldwide. Its technological advantages—such as improved ergonomics, precision, and minimally invasive access—have contributed to its success. However, the substantial costs associated with acquisition, maintenance, and disposable instruments represent a significant limitation, especially in public healthcare systems. In recent years, new robotic platforms have entered the market with the explicit goal of improving cost-effectiveness while maintaining comparable clinical performance. This study aims to conduct a comprehensive economic evaluation of three robotic surgical platforms currently in use at ASST Grande Ospedale Metropolitano Niguarda Hospital, through a cost-minimization analysis and break-even point calculation. Cost data were collected from the hospital's internal management system and supplemented with published literature to ensure a realistic and robust estimation of direct and indirect costs. A deterministic sensitivity analysis was applied, varying key parameters—such as number of surgeons, number of instruments, and procedure duration—within predefined ranges to assess the variability of outcomes under different assumptions. This allowed the identification of threshold values and critical cost drivers that influence the economic sustainability of each system. The results of this analysis provide valuable insights into the comparative cost-efficiency of the platforms evaluated and may guide hospital administrators and policymakers in making informed decisions regarding the adoption and allocation of robotic surgical technologies. By integrating real-world data with economic modelling, this study contributes to the growing body of evidence aimed at optimizing the value of innovation in surgical practice.