Search Results (124)

This study proposes a novel non-destructive approach to assessing and predicting the quality of stored date fruits using a composite quality index (Qi) modeled via visible–near-infrared (VIS–NIR) spectroscopy and artificial neural networks (ANNs). Two leading cultivars, Sukkary and Khlass, were stored for 12 months using three temperature regimes (25 °C, 5 °C, and −18 °C) and five types of packaging. The samples were grouped into six moisture content categories (4.36–36.70% d.b.), and key physicochemical traits, namely moisture, pH, hardness, total soluble solids (TSSs), density, color, and microbial load, were used to construct a normalized, dimensionless Qi. Spectral data (410–990 nm) were preprocessed using second-derivative transformation and modeled using partial least squares regression (PLSR) and the ANNs. The ANNs outperformed PLSR, achieving the correlation coefficient (R²) values of up to 0.944 (Sukkary) and 0.927 (Khlass), with corresponding root mean square error of prediction (RMSEP) values of 0.042 and 0.049, and the relative error of prediction (REP < 5%). The best quality retention was observed in the dates stored at −18 °C in pressed semi-rigid plastic containers (PSSPCs), with minimal microbial growth and superior sensory scores. The second-order Qi model showed a significantly better fit (p < 0.05, AIC-reduced) over that of linear alternatives, capturing the nonlinear degradation patterns during storage. The proposed system enables real-time, non-invasive quality monitoring and could support automated decision-making in postharvest management, packaging selection, and shelf-life prediction. Full article

It is critical to provide proper environmental conditions in poultry houses to maintain birds’ health, boost productivity, and improve the overall economic viability of the poultry industry. Among the myriad of environmental elements, indoor air quality has been a determining factor that directly affects poultry well-being. Elevated concentrations of harmful gases—in particular Carbon Dioxide ($C{O}_2$), Methane ($C{H}_4$), and Ammonia ($N{H}_3$)—decomposition products of poultry litter, feed wastage, and biological processes have draconian effects on bird health, feed efficiency, the growth rate, reproduction efficiency, and mortality rate. Despite their importance, traditional air quality monitoring systems are often operated manually, labor intensive, and cannot detect sudden environmental changes due to the lack of real-time sensing. To overcome these limitations, this paper presents an interdisciplinary approach combining cloud computing, Artificial Intelligence (AI), and Internet of Things (IoT) technologies to measure real-time poultry gas concentrations. Real-time sensor feeds are transmitted to a cloud-based platform, which stores, displays, and processes the data. Furthermore, a machine learning (ML) model was trained using historical sensory data to predict the next-day gas emission levels. A web-based platform has been developed to enable convenient user interaction and display the gas sensory readings on an interactive dashboard. Also, the developed system triggers automatic alerts when gas levels cross safe environmental thresholds. Experimental results of $C{O}_2$ concentrations showed a significant diurnal trend, peaking in the afternoon, followed by the evening, and reaching their lowest levels in the morning. In particular, $C{O}_2$ concentrations peaked at approximately 570 ppm during the afternoon, a value that was significantly elevated (p < 0.001) compared to those recorded in the evening (~560 ppm) and morning (~555 ppm). This finding indicates a distinct diurnal pattern in $C{O}_2$ accumulation, with peak concentrations occurring during the warmer afternoon hours. Full article

This study investigated the flesh quality, shelf life, and sensory freshness of sea bream (Sparus aurata) reared in the REMEDIA Life IMTA system, which incorporates bioremediator organisms—sponges, polychaetes, bivalves, and macroalgae—supported by artificial vertical collectors to enhance the settlement of sessile macroinvertebrates and improve environmental quality. A total of 96 fish (18 months old) were analysed, 48 farmed within the IMTA system and 48 in the conventional offshore system. Both groups received the same commercial feed. For each group, 16 fish were analysed after 1, 7, and 14 days of storage at 2 ± 1 °C to evaluate physical features, chemical and fatty acid composition, and sensory freshness. The total weight was markedly greater for fish in the IMTA group (p < 0.05), which showed a significantly (p < 0.05) longer tail. For all the storage times, the content of total saturated fatty acids was markedly higher in the control group, along with a lower concentration of polyunsaturated fatty acids (p < 0.05). The quality index method showed better results for the IMTA group (p < 0.05), particularly after 2 weeks of storage in ice. In conclusion, sea bream reared in the IMTA system showed better flesh quality, extended shelf life, and prolonged sensory freshness. Full article

Biomimetic robotics aims to replicate biological movement, perception, and cognition, drawing inspiration from nature to develop robots with enhanced adaptability, flexibility, and intelligence. The integration of artificial intelligence has significantly advanced the control mechanisms of biomimetic robots, enabling real-time learning, optimization, and adaptive decision-making. This review systematically examines AI-driven control strategies for biomimetic robots, categorizing recent advancements and methodologies. First, we review key aspects of biomimetic robotics, including locomotion, sensory perception, and cognitive learning inspired by biological systems. Next, we explore various AI techniques—such as machine learning, deep learning, and reinforcement learning—that enhance biomimetic robot control. Furthermore, we analyze existing AI-based control methods applied to different types of biomimetic robots, highlighting their effectiveness, algorithmic approaches, and performance compared to traditional control techniques. By synthesizing the latest research, this review provides a comprehensive overview of AI-driven biomimetic robot control and identifies key challenges and future research directions. Our findings offer valuable insights into the evolving role of AI in enhancing biomimetic robotics, paving the way for more intelligent, adaptive, and efficient robotic systems. Full article

Sweet taste plays a pivotal role in human dietary behavior and metabolic regulation. With the increasing incidence of metabolic disorders linked to excessive sugar intake, the development and accurate evaluation of new sweeteners have become critical topics in food science and public health. However, the structural diversity of sweeteners and their complex interactions with sweet taste receptors present major challenges for standardized sweetness detection. This review offers a comprehensive and up-to-date overview of sweet taste transmission mechanisms and current detection methods. It outlines the classification and sensory characteristics of both conventional and emerging sweeteners, and explains the multi-level signaling pathway from receptor binding to neural encoding. Key detection techniques, including sensory evaluation, electronic tongues, and biosensors, are systematically compared in terms of their working principles, application scope, and limitations. Special emphasis is placed on advanced biosensing technologies utilizing receptor–ligand interactions and nanomaterials for highly sensitive and specific detection. Furthermore, an intelligent detection framework integrating molecular recognition, multi-source data fusion, and artificial intelligence is proposed. This interdisciplinary approach provides new insights and technical solutions to support precise sweetness evaluation and the future development of healthier food systems. Full article

Background/Objectives: Neurological disorders often result in a broad spectrum of disabilities that impact mobility, communication, cognition, and sensory processing, leading to significant limitations in independence and quality of life. Assistive technologies (ATs) offer tools to compensate for these impairments, support daily living, and improve quality of life. The World Health Organization encourages the adoption and diffusion of effective assistive technology (AT). This narrative review aims to explore the integration, benefits, and challenges of assistive technologies in individuals with neurological disabilities, focusing on their role across mobility, communication, cognitive, and sensory domains. Methods: A narrative approach was adopted by reviewing relevant studies published between 2014 and 2024. Literature was sourced from PubMed and Scopus using specific keyword combinations related to assistive technology and neurological disorders. Results: Findings highlight the potential of ATs, ranging from traditional aids to intelligent systems like brain–computer interfaces and AI-driven devices, to enhance autonomy, communication, and quality of life. However, significant barriers remain, including usability issues, training requirements, accessibility disparities, limited user involvement in design, and a low diffusion of a health technology assessment approach. Conclusions: Future directions emphasize the need for multidimensional, user-centered solutions that integrate personalization through machine learning and artificial intelligence to ensure long-term adoption and efficacy. For instance, combining brain–computer interfaces (BCIs) with virtual reality (VR) using machine learning algorithms could help monitor cognitive load in real time. Similarly, ATs driven by artificial intelligence technology could be useful to dynamically respond to users’ physiological and behavioral data to optimize support in daily tasks. Full article

Flavor compounds are key determinants of food sensory quality, originating from natural sources, processing, or artificial additives. Although physical and chemical methods can effectively enhance food flavor, microbial fermentation and enzyme catalysis technology possess good potential in food flavor regulation due to their mild reaction conditions and high safety. In addition, the high efficiency and specificity of enzymes help to shorten the production cycle and accurately regulate food flavor. This review focuses on the application and regulation mechanism of bacteria, yeast, other fungi, and mixed microbe fermentation systems in flavor production. The utilization and catalytic reaction schemes of oxidoreductases, transferases, and hydrolases in flavor regulation are also deeply explored, and suggestions for the application of microbial fermentation and enzyme catalysis technology in flavor regulation are discussed. Full article

The growth in the adoption of circular economy principles in the construction industry has given rise to material passports as a critical implementation tool. Given the existing problems of high resource use and high waste generation in the construction industry, there is a pressing need to adopt novel strategies and tools to mitigate the adverse impacts of the built environment. However, research on the application of material passports in the context of construction waste management remains limited. The aim of this paper is to identify the contextual uses, stakeholders, requirements, and challenges in the application of material passports for managing waste generated from building construction and demolition processes through a systematic review approach. Comprehensive searches in Scopus and the Web of Science databases are used to identify relevant papers and reduce the risk of selection bias. Thirty-five (35) papers are identified and included in the review. The identified key contexts of use included buildings and cities as material banks, waste management and trading, and integrated digital technologies. Asset owners, waste management operators, construction and deconstruction teams, technology providers, and regulatory and sustainability teams are identified as key stakeholders. Data requirements related to material, components, building stock data, lifecycle, environmental impact data, and deconstruction and handling data are critical. Moreover, the key infrastructure requirements include modeling and analytical tools, collaborative information exchange systems, sensory tracking tools, and digital and physical storage hubs. However, challenges with data management, costs, process standardization, technology, stakeholder collaboration, market demand, and supply chain logistics still limit the implementation. Therefore, it is recommended that future research be directed towards certification and standardization protocols, automation, artificial intelligence tools, economic viability, market trading, and innovative end-use products. Full article

Motion perception is a fundamental function of biological visual systems, enabling organisms to navigate dynamic environments, detect threats, and track moving objects. Inspired by the mechanisms of biological motion processing, we propose an Unsupervised Artificial Visual System for motion direction detection. Unlike traditional supervised learning approaches, our model employs unsupervised learning to classify local motion direction detection neurons and group those with similar directional preferences to form macroscopic motion direction detection neurons. The activation of these neurons is proportional to the received input, and the neuron with the highest activation determines the macroscopic motion direction of the object. The proposed system consists of two layers: a local motion direction detection layer and an unsupervised global motion direction detection layer. For local motion detection, we adopt the Local Motion Detection Neuron (LMDN) model proposed in our previous work, which detects motion in eight different directions. The outputs of these neurons serve as inputs to the global motion direction detection layer, which employs a Gaussian Mixture Model (GMM) for unsupervised clustering. GMM, a probabilistic clustering method, effectively classifies local motion detection neurons according to their preferred directions, aligning with biological principles of sensory adaptation and probabilistic neural processing. Through repeated exposure to motion stimuli, our model self-organizes to detect macroscopic motion direction without the need for labeled data. Experimental results demonstrate that the GMM-based global motion detection layer successfully classifies motion direction signals, forming structured motion representations akin to biological visual systems. Furthermore, the system achieves motion direction detection accuracy comparable to previous supervised models while offering a more biologically plausible mechanism. This work highlights the potential of unsupervised learning in artificial vision and contributes to the development of adaptive motion perception models inspired by neural computation. Full article

Behavioral lateralization in animals is a well-known phenomenon; however, it has only rarely been studied in unicellular organisms. A groundbreaking study found lateralized movement in T-mazes in the formless plasmodia of the slime mold Physarum polycephalum. In this work, a replication of that study was conducted in a specially designed, elaborated T-maze system. Considering the amoeboid organism’s diverse sensory capabilities, we further comprehensively investigated the influence of light, artificial magnetic fields, the magnetic field of the Earth, and vibration on movement direction. Two different clonal lines were tested to assess genetic diversity, encompassing over 1600 individual plasmodia. Our results show that no general lateralized behavior exists in the absence of stimuli in both clonal lines. On the other hand, Physarum’s sensitivity to strong magnetic fields and vibration induces significant true lateralization in previously nonlateralized plasmodia (37.6% right and 62.4% left, respectively). Possible mechanisms behind this induced lateralization are discussed. We conclude that previous findings showing lateralization are likely to have been influenced by unknown external stimuli. Full article

Molecular imaging probes play a pivotal role in assaying molecular events in various physiological systems. In this study, we demonstrate a new genre of bioluminescent probes for imaging protein–protein interactions (PPIs) in mammalian cells, named the molecular association assay (MAA) probe. The MAA probe is designed to be as simple as a full-length marine luciferase fused to a protein of interest with a flexible linker. This simple fusion protein alone surprisingly works by recognizing a specific ligand, interacting with a counterpart protein of the PPI, and developing bioluminescence (BL) in mammalian cells. We made use of an artificial intelligence (AI) tool to simulate the binding modes and working mechanisms. Our AlphaFold-based analysis on the binding mode suggests that the hinge region of the MAA probe is flexible before ligand binding but becomes stiff after ligand binding and protein association. The sensorial properties of representative MAA probes, FRB-ALuc23 and FRB-R86SG, are characterized with respect to the quantitative feature, BL spectrum, and in vivo tumor imaging using xenografted mice. Our AI-based simulation of the working mechanisms reveals that the association of MAA probes with the other proteins works in a way to facilitate the substrate’s access to the active sites of the luciferase (ALuc23 or R86SG). We prove that the concept of MAA is generally applicable to other examples, such as the ALuc16- or R86SG-fused estrogen receptor ligand-binding domain (ER LBD). Considering the versatility of this conceptionally unique and distinctive molecular imaging probe compared to conventional ones, we are expecting the widespread application of these probes as a new imaging repertoire to determine PPIs in living organisms. Full article

Food quality assessment is a critical aspect of food production and safety, ensuring that products meet both regulatory and consumer standards. Traditional methods such as sensory evaluation, chromatography, and spectrophotometry are widely used but often suffer from limitations, including subjectivity, high costs, and time-consuming procedures. In recent years, the development of electronic nose (e-nose) and electronic tongue (e-tongue) technologies has provided rapid, objective, and reliable alternatives for food quality monitoring. These bio-inspired sensing systems mimic human olfactory and gustatory functions through sensor arrays and advanced data processing techniques, including artificial intelligence and pattern recognition algorithms. The e-nose is primarily used for detecting volatile organic compounds (VOCs) in food, making it effective for freshness evaluation, spoilage detection, aroma profiling, and adulteration identification. Meanwhile, the e-tongue analyzes liquid-phase components and is widely applied in taste assessment, beverage authentication, fermentation monitoring, and contaminant detection. Both technologies are extensively used in the quality control of dairy products, meat, seafood, fruits, beverages, and processed foods. Their ability to provide real-time, non-destructive, and high-throughput analysis makes them valuable tools in the food industry. This review explores the principles, advantages, and applications of e-nose and e-tongue systems in food quality assessment. Additionally, it discusses emerging trends, including IoT-based smart sensing, advances in nanotechnology, and AI-driven data analysis, which are expected to further enhance their efficiency and accuracy. With continuous innovation, these technologies are poised to revolutionize food safety and quality control, ensuring consumer satisfaction and compliance with global standards. Full article

The strong correlation between evoked potentials (EPs) and American Spinal Injury Association (ASIA) scores in individuals with spinal cord injury (SCI) suggests that EPs may serve as reliable predictive markers for rehabilitation progress. Numerous studies have confirmed a relationship between variations in somatosensory evoked potentials (SSEPs) and ASIA scores, especially in the early stages of SCI. Machine learning’s (ML’s) increasing importance in medicine is driven by the growing availability of health data and improved algorithms. It enables the creation of predictive models for disease diagnosis, progression prediction, personalized treatment, and improved healthcare efficiency. Data-driven approaches can significantly improve patient care, reduce costs, and facilitate personalized medicine. The meticulous analysis of medical data is crucial for timely disease identification, leading to effective symptom management and appropriate treatment. This study applies artificial intelligence to identify predictors of SCI progression, as measured by the disability index, ASIA impairment scale (AIS), and final motor recovery. We aim to clarify the prognostic role of electrophysiological testing (SSEPs, MEPs, and nerve conduction studies (NCSs)) in SCI. We analyzed data from a medical database of 123 records. We developed an ML-based intelligent system, utilizing ensemble algorithms combining decision trees and neural network approaches, to predict SCI recovery. Our evaluation showed SEP accuracies of 90% for motor recovery prediction and 80% for AIS scale determination, comparable to full electrophysiology evaluation accuracies of 93% and 89%, respectively, and generally superior results compared to MEP and NCS results. EPs emerged as the best predictors, comparable to a comprehensive electrophysiology assessment, significantly improving accuracy compared to clinical findings alone. An electrophysiological assessment, when available, increased overall accuracy for final motor recovery prediction to 93% (from a maximum of 75%) and, for ASIA score determination, to 89% (from a maximum of 66%). Further validation is needed with a larger dataset. Future research should validate that sensory electrophysiology assessment is a less expensive, portable, and simpler alternative to other prognostic tests and more effective than clinical assessments, like the AIS, biomarker for SCI, and personalized rehabilitation planning. Full article

The rapid adoption of artificial intelligence (AI) systems, such as predictive AI, generative AI, and explainable AI, is in contrast to the slower development and uptake of robotic AI systems. Dynamic environments, sensory processing, mechanical movements, power management, and safety are inherent complexities of robotic intelligence capabilities that can be addressed using novel AI approaches. The current AI landscape is dominated by machine learning techniques, specifically deep learning algorithms, that have been effective in addressing some of these challenges. However, these algorithms are subject to computationally complex processing and operational needs such as high data dependency. In this paper, we propose a computation-efficient and data-efficient framework for robotic motion intelligence (RMI) based on vector symbolic architectures (VSAs) and blockchain-based smart contracts. The capabilities of VSAs are leveraged for computationally efficient learning and noise suppression during perception, motion, movement, and decision-making tasks. As a distributed ledger technology, smart contracts address data dependency through a decentralized, distributed, and secure transactions ledger that satisfies contractual conditions. An empirical evaluation of the framework confirms its value and contribution towards addressing the practical challenges of robotic motion intelligence by significantly reducing the learnable parameters by 10 times while preserving sufficient accuracy compared to existing deep learning solutions. Full article

Fermentation represents a pivotal bioconversion process that enhances foodstuffs’ nutritional and sensory attributes while playing a crucial role in global food systems. Nevertheless, concerns about safety issues associated with microbial contamination and the production of biogenic amines are often understated. This review appraised recent advancements in fermentation technology, emphasising their association with the health and safety of fermented foods. Key advances include predictive microbiology models, in some cases achieving up to 95% accuracy in predicting microbial behaviour, and high-throughput sequencing (HTS) for microbial enrichment. In addition, advanced detection methods such as biosensors and PCR-based assays enable the rapid identification of contaminants, improving manufacturing processes and preserving product integrity. Advanced bioreactor technologies equipped with real-time monitoring systems have been shown to increase fermentation efficiency. Moreover, innovative packaging, artificial intelligence, machine learning models, and sensor technologies have optimised fermentation processes and contributed to tracking quality and safety in the blockchain technology supply chain, potentially reducing spoilage rates and showing a decrease in production times. This study also addresses regulatory frameworks essential for establishing robust safety protocols. Integrating advanced fermentation technologies is imperative to meet the growing global demand for safe fermented foods. Continuous research and innovation are needed to address safety challenges and promote industry practices prioritising health and quality, ensuring public safety and building consumer confidence in fermented products. Full article