Search Results (232)

Fermentation is a fundamental technique that allows us to obtain high-quality cacao beans and derived products. Therefore, it is necessary to apply fermentation correctly to maximize product quality. Fermentation techniques vary by region and include piles, trays, wooden boxes, baskets, and platforms. During these processes, several factors influence the physicochemical and sensory characteristics of cacao beans. The factors that influence these characteristics are the frequency of turning, the genotype of the bean, and the duration of fermentation. This review aims to explore how the fermentation method, turning frequency, bean genotype, and fermentation duration affect the physicochemical and sensory qualities of cacao beans. To this end, an exhaustive search for recent information on the most commonly used fermentation methods in cacao-producing countries over the last 10 years was carried out. The fermentation method in wooden boxes or crates is the most commonly used method worldwide. The most common turning frequency is 24 or 48 h, which is considered the most suitable time for obtaining cacao beans with better sensory attributes, such as floral and fruity aromas, and a lower level of acidity. Finally, a relationship was found between the genotype and the optimal fermentation time of cacao: about 4 days for Criollo cacao, approximately 5 days for Forastero cacao and between 1.5 and 10 days for Trinitario cacao. Full article

Background: The vestibular Romberg test, which assesses the deterioration of balance while standing on rubber foam with closed eyes, is a well-established method in the physical neurological assessment of patients with peripheral vestibulopathy. This study aims to determine whether it can differentiate peripheral vestibulopathy from its main differential diagnosis, namely sensory ataxia, as both conditions typically present with a positive classical Romberg test. Methods: Static balance was assessed in three groups: patients with peripheral vestibulopathy, patients with pure sensory neuropathy, and healthy age-matched controls. Participants stood quietly on a force platform under varying visual and proprioceptive feedback conditions. Conventional and advanced postural sway metrics were investigated to establish a quantitative analogy to both the clinical Romberg and vestibular Romberg tests. Results: Posturographic analysis revealed that, in contrast to healthy controls, patients with vestibular disorders exhibited higher vestibular Romberg quotient values. However, the classical vestibular Romberg quotient did not show diagnostic discrimination between vestibulopathy and sensory neuropathy patients. This lack of discrimination was mainly due to the increased body sway observed in all patient groups under the “eyes open” condition. Nevertheless, a refined vestibular Romberg quotient—comparing standing on foam versus standing on firm support with eyes closed—was able to reliably distinguish vestibulopathy from sensory ataxia. This distinction was evident in both conventional linear sway and spectral postural sway metrics. Conclusions: We conclude that a refined Romberg test, performed solely under conditions of visual deprivation, offers valuable classification potential in differentiating peripheral vestibulopathy not only from healthy controls but also from patients with disequilibrium due to sensory loss. Full article

This paper introduces SmartBarrel, an innovative IoT-based sensory system that monitors and forecasts wine fermentation processes. At the core of SmartBarrel are two compact, attachable devices—the probing nose (E-nose) and the probing tongue (E-tongue), which mount directly onto stainless steel wine tanks. These devices periodically measure key fermentation parameters: the nose monitors gas emissions, while the tongue captures acidity, residual sugar, and color changes. Both utilize low-cost, low-power sensors validated through small-scale fermentation experiments. Beyond the sensory hardware, SmartBarrel includes a robust cloud infrastructure built on open-source Industry 4.0 tools. The system leverages the ThingsBoard platform, supported by a NoSQL Cassandra database, to provide real-time data storage, visualization, and mobile application access. The system also supports adaptive breakpoint alerts and real-time adjustment to the nonlinear dynamics of wine fermentation. The authors developed a novel deep learning model called V-LSTM (Variable-length Long Short-Term Memory) to introduce intelligence to enable predictive analytics. This auto-calibrating architecture supports variable layer depths and cell configurations, enabling accurate forecasting of fermentation metrics. Moreover, the system includes two fuzzy logic modules: a device-level fuzzy controller to estimate alcohol content based on sensor data and a fuzzy encoder that synthetically generates fermentation profiles using a limited set of experimental curves. SmartBarrel experimental results validate the SmartBarrel’s ability to monitor fermentation parameters. Additionally, the implemented models show that the V-LSTM model outperforms existing neural network classifiers and regression models, reducing RMSE loss by at least 45%. Furthermore, the fuzzy alcohol predictor achieved a coefficient of determination ($R^2$) of 0.87, enabling reliable alcohol content estimation without direct alcohol sensing. Full article

In addition to axial motor complications such as abnormal posture, instability, falls, and gait variability, neurodegenerative diseases like Parkinsonian syndromes include executive dysfunction, Parkinson’s disease dementia, and neuropsychiatric symptoms. These motor disorders significantly affect mobility, quality of life, and well-being. Recently, physical activity of various intensities monitored both remotely and face-to-face via digital health technologies, mobile platforms, or sensory cues has gained relevance in managing idiopathic and atypical Parkinson’s disease (PD and APD). Remote monitoring solutions, including home-based digital health assessments using semi-structured activities, offer unique advantages. Real-world gait parameters like walking speed can now be continuously assessed with body-worn sensors. Developing effective strategies to slow pathological aging and mitigate neurodegenerative progression is essential. This study presents outcomes of using digital health technologies (DHTs) for remote assessment of motor function, physical activity, and daily living tasks, aiming to reduce disease progression in PD and APD. In addition to wearable inertial sensors, clinical rating scales and digital biomarkers enhance the ability to characterize and monitor motor symptoms. By reviewing recent literature, we identified emerging trends in quantifying and intervening in neurodegeneration using tools that evaluate both remote and face-to-face physical activity. Our findings confirm that DHTs offer accurate detection of motor fluctuations and support clinical evaluations. In conclusion, DHTs represent a scalable, effective strategy for improving the clinical management of PD and APD. Their integration into healthcare systems may enhance patient outcomes, support early intervention, and help delay the progression of both motor and cognitive symptoms in aging individuals. Full article

This work presents the electro-mechanical design, physical implementation, and experimental testing of an intermediate-sized mobile robotic platform, Hygrobot, which is powered by the hybridization of a PEMFC fuel cell and LiPo batteries. Future mobile robots are anticipated to require sustainable and cost-effective energy solutions to increase their working time and adhere to ecological guidelines. Although substantial advancements have been achieved in creating marketable mobile robot designs, including improvements to their physical composition, sensory systems, navigation algorithms, and operational management, their widespread integration is still hindered. This obstacle is mainly attributable to the lack of robust power systems capable of functioning in varied and often unpredictable settings. Hydrogen, as an energy carrier, offers a promising solution to overcome these obstacles. Experimental tests reveal that the Hygrobot platform can achieve a linear speed of 17 km/h, a continuous operating time of over 8 h, and power consumption below 324 WA. Its mechanical design means that Hygrobot is able to overcome obstacles, facilitating movement in the three XYZ axes, and can perform tasks over rough terrain efficiently. Full article

The relentless drive to meet global demand for poultry products has pushed for rapid intensification in chicken farming, dramatically boosting efficiency and yield. Yet, these gains have exposed a host of complex welfare challenges that have prompted scientific scrutiny and ethical reflection. In this review, I critically evaluate recent innovations aimed at mitigating such concerns by drawing on advances in behavioral science and digital monitoring and insights into biological adaptations. Specifically, I focus on four interconnected themes: First, I spotlight the complexity of avian sensory perception—encompassing vision, auditory capabilities, olfaction, and tactile faculties—to underscore how lighting design, housing configurations, and enrichment strategies can better align with birds’ unique sensory worlds. Second, I explore novel tools for gauging emotional states and cognition, ranging from cognitive bias tests to developing protocols for identifying pain or distress based on facial cues. Third, I examine the transformative potential of computer vision, bioacoustics, and sensor-based technologies for the continuous, automated tracking of behavior and physiological indicators in commercial flocks. Fourth, I assess how data-driven management platforms, underpinned by precision livestock farming, can deploy real-time insights to optimize welfare on a broad scale. Recognizing that climate change and evolving production environments intensify these challenges, I also investigate how breeds resilient to extreme conditions might open new avenues for welfare-centered genetic and management approaches. While the adoption of cutting-edge techniques has shown promise, significant hurdles persist regarding validation, standardization, and commercial acceptance. I conclude that truly sustainable progress hinges on an interdisciplinary convergence of ethology, neuroscience, engineering, data analytics, and evolutionary biology—an integrative path that not only refines welfare assessment but also reimagines poultry production in ethically and scientifically robust ways. Full article

Background: In 2022, there were an estimated 20 million new cancer cases and 9.7 million deaths. The number of new cancer cases is expected to rise to over 35 million by 2050, marking a 75% increase from 2022 levels. Twenty to eighty-six percent of cancer patients suffer from taste disorders (TD), which are associated with an increased risk of malnutrition. Cachectic syndrome is linked to the presence and growth of tumors and leads to systemic inflammation. Synsepalum dulcificum is a plant whose berries contain miraculin, a glycoprotein that transforms sour tastes into sweet and can ameliorate TD. Objectives: To evaluate the effect of the regular intake of dried miracle berries (DMBs), a novel food containing miraculin, on biomarkers of inflammation and cachexia in malnourished patients with cancer and TD receiving systemic antineoplastic therapy. Methods: we conducted a triple-blind, randomized, placebo-controlled pilot clinical trial. Thirty-one patients with cancer of various etiologies who received chemotherapy were enrolled in this pilot study and divided into three groups. The first group received a tablet containing 150 mg of DMB (standard dose), the high-dose group received a tablet of 300 mg of DMB, and the third group received a tablet with 300 mg of the placebo for three months before each main meal. The plasma levels of several molecules associated with inflammation and cancer cachexia were measured using the X-MAP Luminex multiplexing platform. Results: We found decreased plasma levels of IFN-γ in the standard-dose group. In addition, our results suggest a downtrend of IL-1β levels in the three groups after three months of intervention (p = 0.093). Moreover, the three groups showed a reduction in tumor-derived molecule proteolysis-inducing factor/dermcidin (p = 0.021). It is important to highlight the positive correlation between IL-6 and IL-10 in the standard group, which suggests a better balance between proinflammatory and anti-inflammatory cytokines. Regardless of DMB consumption, soluble TNF receptor type II tended to decrease with treatment in patients who responded well to the antineoplastic treatment (p = 0.011). We did not find significant correlations between cytokines and sensory variables or dietary and nutritional status. Conclusions: Our results suggest that the regular consumption of a standard dose of DMB along with a systemic antineoplastic treatment could contribute to reducing inflammation and cachexia biomarkers in malnourished patients with cancer exhibiting TD. In this sense, nutritional support is crucial in the treatment of cancer cachexia. In our view, it should be considered a coadjuvant of therapeutics. Future studies on the molecular signaling pathways and specific mechanisms of action of bioactive compounds within food supplements, such as miraculin, will allow them to be used to target pathogenic mechanisms of cancer cachexia and malnutrition: NCT05486260. Full article

Multimodal interaction technology has become a key aspect of remote education by enriching student engagement and learning results as it utilizes the speech, gesture, and visual feedback as various sensory channels. This publication reflects on the latest breakthroughs in multimodal interaction and its usage in remote learning environments, including a multi-layered discussion that addresses various levels of learning and understanding. It showcases the main technologies, such as speech recognition, computer vision, and haptic feedback, that enable the visitors and learning portals to exchange data fluidly. In addition, we investigate the function of multimodal learning analytics in order to measure the cognitive and emotional states of students, targeting personalized feedback and refining instructional strategies. Though multimodal communication may bring a historical improvement to the mode of online education, the platform still faces many issues, such as media synchronization, higher computational demand, physical adaptability, and privacy concerns. These problems demand further research in the fields of algorithm optimization, access to technology guidance, and the ethical use of big data. This paper presents a systematic review of the application of multimodal interaction in remote education. Through the analysis of 25 selected research papers, this review explores key technologies, applications, and challenges in the field. By synthesizing existing findings, this study highlights the role of multimodal learning analytics, speech recognition, gesture-based interaction, and haptic feedback in enhancing remote learning. Full article

This paper comprehensively reviews the latest advances in hydrogel-based continuum soft robots. Hydrogels exhibit exceptional flexibility and adaptability compared to traditional robots reliant on rigid structures, making them ideal as biomimetic robotic skins and platforms for constructing highly accurate, real-time responsive sensory interfaces. The article systematically summarizes recent research developments across several key dimensions, including application domains, fabrication methods, actuator technologies, and sensing mechanisms. From an application perspective, developments span healthcare, manufacturing, and agriculture. Regarding fabrication techniques, the paper extensively explores crosslinking methods, additive manufacturing, microfluidics, and other related processes. Additionally, the article categorizes and thoroughly discusses various hydrogel-based actuators responsive to solute/solvent variations, pH, chemical reactions, temperature, light, magnetic fields, electric fields, hydraulic/electro-osmotic stimuli, and humidity. It also details the strategies for designing and implementing diverse sensors, including strain, pressure, humidity, conductive, magnetic, thermal, gas, optical, and multimodal sensors. Finally, the paper offers an in-depth discussion of the prospective applications of hydrogel-based continuum soft robots, particularly emphasizing their potential in medical and industrial fields. Concluding remarks include a forward-looking outlook highlighting future challenges and promising research directions. Full article

This paper presents an alarm system and teleoperation control framework, comparing ROS 1 and ROS 2 within a local network to mitigate the risk of robots failing to reach their goals during autonomous navigation. Such failures can occur when the robot moves through irregular terrain, becomes stuck on small steps, or approaches walls and obstacles without maintaining a safe distance. These issues may arise due to a combination of technical, environmental, and operational factors, including inaccurate sensor data, sensor blind spots, localization errors, infeasible path planning, and an inability to adapt to unexpected obstacles. The system integrates a web-based graphical interface developed using frontend frameworks and a joystick for real-time monitoring and control of the robot’s localization, velocity, and proximity to obstacles. The robot is equipped with RGB-D and tracking cameras, a 2D LiDAR, and odometry sensors, providing detailed environmental data. The alarm system provides sensory feedback through visual alerts on the web interface and vibration alerts on the joystick when the robot approaches walls, faces potential collisions with objects, or loses stability. The system is evaluated in both simulation (Gazebo) and real-world experiments, where latency is measured and sensor performance is assessed for both ROS 1 and ROS 2. The results demonstrate that both systems can operate effectively in real time, ensuring the robot’s safety and enabling timely operator intervention. ROS 2 offers lower latency for LiDAR and joystick inputs, making it advantageous over ROS 1. However, camera latency is higher, suggesting the need for potential optimizations in image data processing. Additionally, the platform supports the integration of additional sensors or applications based on user requirements. Full article

Traditional cereals, recognised for their adaptability, high nutritional value, and unique sensory characteristics, have largely been excluded from global food supply chains. Recent shifts in consumption patterns, particularly in urban areas, indicate a growing demand for high-quality bread, creating new opportunities for farmers interested in sustainable production techniques and traditional varieties. However, challenges such as seed availability, regulatory constraints, marketing strategies, and logistical barriers persist. This study, conducted within the framework of the CERTRA project—Development of Traditional Cereal Value Chains for Sustainable Food in Portugal—aims to enhance the traditional cereal value chain in Portugal by identifying key challenges and opportunities and proposing effective development strategies. The research employs a mixed-method approach, including documentary research, a SWOT analysis based on the scientific literature and stakeholder insights, and a case study methodology examining twelve successful European initiatives. The findings highlight strengths such as seed sovereignty, resilience under low-input farming, and market potential through certification and short food supply chains. However, weaknesses such as lower yields, mechanisation challenges, and seed access restrictions remain critical obstacles. Our analysis suggests that participatory breeding programs, farmer-led seed networks, and hybrid distribution models integrating direct sales, online platforms, and local partnerships can support the revitalisation of traditional cereals. Future research should focus on consumer preferences, branding strategies, and technological innovations that enhance processing efficiency while preserving the ecological and cultural value of traditional varieties. Full article

The exploitation of immersive simulation platforms to improve traditional training techniques in the agricultural industry sector would enable year-round accessibility, flexibility, safety, and consistent high-quality training for agricultural operators. An innovative workflow in virtual simulations for training and educational purposes includes an immersive environment in which the operator can interact with plants through haptic interfaces, following instructions imparted by a non-playing character (NPC) instructor. This study allows simulating the pruning of a complex case study, a hazelnut tree, reproduced in very high detail to offer agricultural operators a more realistic and immersive training environment than those currently existing. The process of creating a multisensorial environment starts with the integrated survey of the plant with a laser scanner and photogrammetry and then generates a controllable parametric model from roots to leaves with the exact positioning of the original branches. The model is finally inserted into a simulation, where haptic gloves with tactile resistance responsive to model collisions are tested. The results of the experimentation demonstrate the correct execution of this innovative design simulation, in which branches and leaves can be cut using a shear, with immediate sensory feedback. The project therefore aims to finalize this product as a realistic training platform for pruning, but not limited to it, paving the way for high-fidelity simulation for many other types of operations and specializations. Full article

Objective: Mental fatigue (MF) induced by prolonged cognitive tasks poses significant risks to postural stability, yet its effects on multi-sensory integration remain poorly understood. Method: This study investigated how MF alters sensory reweighting and postural control in 27 healthy young males. A 45 min incongruent Stroop task was employed to induce MF, validated via subjective Visual Analog Scale (VAS) scores and psychomotor vigilance tests. Postural stability was assessed under four sensory perturbation conditions (O-H: no interference; C-H: visual occlusion; O-S: proprioceptive perturbation; C-S: combined perturbations) using a Kistler force platform. Center of pressure (COP) signals were analyzed through time-domain metrics, sample entropy (SampEn), and Discrete Wavelet Transform (DWT) to quantify energy distributions across sensory-related frequency bands (visual: 0–0.1 Hz; vestibular: 0.1–0.39 Hz; cerebellar: 0.39–1.56 Hz; proprioceptive: 1.56–6.25 Hz). Results: MF significantly reduced proprioceptive energy contributions (p < 0.05) while increasing vestibular reliance under O-S conditions (p < 0.05). Time-domain metrics showed no significant changes in COP velocity or displacement, but SampEn decreased under closed-eye conditions (p < 0.001), indicating reduced postural adaptability. DWT analysis highlighted MF’s interaction with visual occlusion, altering cerebellar and proprioceptive energy dynamics (p < 0.01). Conclusion: These findings demonstrate that MF disrupts proprioceptive integration, prompting compensatory shifts toward vestibular and cerebellar inputs. The integration of nonlinear entropy and frequency-domain analyses advances methodological frameworks for fatigue research, offering insights into real-time sensor-based fatigue monitoring and balance rehabilitation strategies. This study underscores the hierarchical interplay of sensory systems under cognitive load and provides empirical evidence for optimizing interventions in high-risk occupational and clinical settings. Full article

The acquisition of depth information sensorial data is critically important in medical applications, such as the monitoring of the elderly or the extraction of human biometrics. In such applications, compressing the stream of depth video data plays an important role due to bandwidth constraints on transmission channels. This paper introduces a novel lightweight compression system that encodes the semantics of the input depth video and can operate in both lossless and L-infinite near-lossless compression modes. A quantization technique that targets the L-infinite norm for sparse distributions and a new L-infinite compression method that sets bounds on the quantization error is proposed. The proposed codec enables the control of the coding error on every pixel in the input video data, which is crucial in medical applications. Experimental results show an average improvement of 45% and 17% in lossless mode compared to standalone JPEG-LS and CALIC codecs, respectively. Furthermore, in near-lossless mode, the proposed codec achieves superior rate-distortion performance and reduced maximum error per frame compared to HEVC. Additionally, the proposed lightweight codec is designed to perform efficiently in real time when deployed on an embedded depth-camera platform. Full article

The integration of artificial intelligence (AI) with additive manufacturing (AM) is driving breakthroughs in personalized rehabilitation and physical therapy solutions, enabling precise customization to individual patient needs. This article presents the current state of knowledge and perspectives of using personalized solutions for rehabilitation and physiotherapy thanks to the introduction of AI to AM. Advanced AI algorithms analyze patient-specific data such as body scans, movement patterns, and medical history to design customized assistive devices, orthoses, and prosthetics. This synergy enables the rapid prototyping and production of highly optimized solutions, improving comfort, functionality, and therapeutic outcomes. Machine learning (ML) models further streamline the process by anticipating biomechanical needs and adapting designs based on feedback, providing iterative refinement. Cutting-edge techniques leverage generative design and topology optimization to create lightweight yet durable structures that are ideally suited to the patient’s anatomy and rehabilitation goals .AI-based AM also facilitates the production of multi-material devices that combine flexibility, strength, and sensory capabilities, enabling improved monitoring and support during physical therapy. New perspectives include integrating smart sensors with printed devices, enabling real-time data collection and feedback loops for adaptive therapy. Additionally, these solutions are becoming increasingly accessible as AM technology lowers costs and improves, democratizing personalized healthcare. Future advances could lead to the widespread use of digital twins for the real-time simulation and customization of rehabilitation devices before production. AI-based virtual reality (VR) and augmented reality (AR) tools are also expected to combine with AM to provide immersive, patient-specific training environments along with physical aids. Collaborative platforms based on federated learning can enable healthcare providers and researchers to securely share AI insights, accelerating innovation. However, challenges such as regulatory approval, data security, and ensuring equity in access to these technologies must be addressed to fully realize their potential. One of the major gaps is the lack of large, diverse datasets to train AI models, which limits their ability to design solutions that span different demographics and conditions. Integration of AI–AM systems into personalized rehabilitation and physical therapy should focus on improving data collection and processing techniques. Full article