This paper investigates the integration of SHM within the frame of Industry 4.0 technologies, highlighting the potential for intelligent infrastructure management through the utilization of Big Data analytics, Machine Learning, and the Internet of Things (IoT). The study also presents a success case focused on a novel SHM methodology for detecting and locating damages in metallic aircraft structures, employing dimensional reduction techniques such as Principal Component Analysis (PCA). By analyzing strain data collected from a network of sensors and comparing it to a baseline pristine condition, the methodology aims to identify subtle changes in local strain distribution indicative of damage. Through extensive Finite Element Analysis (FEA) simulations and a PCA contribution analysis, the research explores the influence of various factors on damage detection, including sensor placement, noise levels, and damage size and type. The findings demonstrate the effectiveness of the proposed methodology in detecting cracks and holes as small as 2mm in length, showcasing the potential for early damage identification and targeted interventions in diverse sectors such as aerospace, civil engineering, and manufacturing. Ultimately, this paper underscores the synergistic relationship between SHM and Industry 4.0, paving the way for a future of intelligent, resilient, and sustainable infrastructure.

Questionnaires for self-report of food-elicited emotions are an established practice in consumer studies. However, most tools are originally developed in English and translated into other languages, which limits their use. The aim of this study was to develop the first Greek emotion lexicon and measurement tool for consumers, using standard methodology and extending it. The lexicon was compiled using linguistic sources and extended using the World Wide Web and a popular social media platform for the collection of authentic language and usage data. Terms were also collected from consumers using questionnaires and a variety of stimuli. Terms collected from consumers only comprise: healthy, distrustful, curious, childish, impressed, tolerant, impatient It was validated by application in various case studies, providing discrimination across and within food categories, and by comparison to the Greek translation of a standard tool in English, the EsSense Profile. The comparison was conducted by measuring emotions elicited by six food categories and demonstrated that it is preferable to use emotion measurement tools developed in the linguistic and cultural context of intended use, as they convey consumers’ emotions naturally and more accurately. This tool can be used as a Check- or Rate- All-That-Apply questionnaire to measure consumer responses to food categories and products. The methodology and validation presented can be applied for the compilation of emotion lexicons in languages less studied than English.

Background. The Stanford Health Assessment Questionnaire-Disability Index (HAQ) is widely used to measure functional ability in persons with Rheumatoid Arthritis (RA). The instrument was developed with limited involvement from persons with RA, and their perception of the instrument has not been studied in depth. The aim of this study was to explore how persons with RA experience the use of the HAQ in care. Methods. Forty persons with RA were purposefully recruited to participate in semi-structured interviews. The interviews were then analyzed qualitatively using thematic analysis. Results. The participants questioned the relevance of the HAQ but nevertheless experienced that the instrument had a profound effect on their understanding of health and how care is delivered. The analysis resulted in three themes: Problems with individual items, meaning of the summative score, and effects on care and health perceptions. Conclusions. To make the HAQ relevant to persons with RA, it needs to be revised or to include an option to select items most meaningful to the respondent. To ensure relevance, the HAQ update should preferably be co-created by researchers, clinicians and persons with RA.

The process involved in preparing driving simulator tests is a labor intensive one, and the nature of this complexity depends on the sophistication of the tests to be carried out, as well as the number of participants. Which has an impact on the duration of the research itself. The very solution of using driving simulators is a research tool that has been in use for many years and with great success. They are used in a very wide range of areas of human functioning. They replicate in a very realistic way the conditions that can occur in the external environment, offer the possibility of repetition of particular conditions and insight into certain behaviors that could be very difficult to initiate in real life. This article also describes the dis- ease of using simulators to conduct research activities, along with the workflow from assumptions to finalizing the research. An algorithm is also presented for the study of individual participants, with examples for separate research scenarios concerning driver behavior in the vicinity of pedestrian crossings.

The present grievous situation of the tuberculosis disease can be improved by efficient case management and timely follow-up evaluations. With the advent of digital technology this can be achieved by quick summarization of the patient-centric data. The aim of our study was to assess the effectiveness of the ODK Scan paper-to-digital system during testing period of three months. A sequential, explanatory mixed-method research approach was employed to elucidate technology use. Training, smartphones, application and 3G enabled SIMs were provided to the four field workers. At the beginning, baseline measures of the data management aspects were recorded and compared with endline measures to see the impact of ODK Scan. Additionally, at the end, users’ feedback was collected regarding app usability, user interface design and workflow changes. 122 patients’ records were retrieved from the server and analysed for quality. It was found that ODK Scan recognized 99.2% of multiple choice bubble responses and 79.4% of numerical digit responses correctly. However, the overall quality of the digital data was decreased in comparison to manually entered data. Using ODK Scan, a significant time reduction is observed in data aggregation and data transfer activities, however, data verification and form filling activities took more time. Interviews revealed that field workers saw value in using ODK Scan, however, they were more concerned about the time consuming aspects of the use of ODK Scan. Therefore, it is concluded that minimal disturbance in the existing workflow, continuous feedback and value additions are the important considerations for the implementing organization to ensure technology adoption and workflow improvements.

Diffuse optical tomography (DOT) technology enables a differentiation between oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the sensory and motor cerebral gyri, resulting in greater sensitivity for cerebral activation compared to functional magnetic resonance imaging (fMRI). Here, we introduce a novel approach where functional regions of interest (ROIs) are created based on the specific signal behavior observed in DOT measurements, in contrast to the conventional use of structural-ROIs obtained from anatomical information. The generation of cerebral activation maps involves using the general linear model (GLM) to compare the outcomes obtained from both the functional and structural ROI approaches. DOT-derived maps are then compared with maps derived from fMRI datasets considered the gold standard for assessing functional brain activity. The results obtained demonstrate the effectiveness of employing functional ROIs to improve the spatial location of functional activations in the sensory and motor cerebral gyri by leveraging the neural synchronization data provided by DOT. Furthermore, this methodology simplifies data processing in animal models, where anatomical differences compared to the human head can pose challenges. By incorporating functional ROIs prior to GLM application, this study offers enhancements to DOT analysis techniques and broadens its applicability in both human and animal models.

Despite the existing effective treatment methods, tuberculosis (TB) is the second most deadly infectious disease globally, its carriers in the latent and active phases accounting for more than 20% of the world population. An effective method to control TB and reduce the mortality is regular population screening and diagnosis of the latent form of TB in order to take preventive and curative measures. Numerous methods allow diagnosing TB and directly detecting Mycobacterium tuberculosis (M.tb) biomarkers, including M.tb DNA, proteins, and specific metabolites, as well as antibodies produced by the host immune system in response to M.tb. PCR, ELISA, immunofluorescence and immunochemical analyses, flow cytometry, and other methods allow the detection of M.tb biomarkers or the host immune response to M.tb by recording the optical signal of fluorescent or colorimetric dyes included in the diagnostic tools. Current research in biosensors is aimed at increasing the sensitivity of detection, which can be achieved by using brighter and more photostable optical tags containing fluorescent quantum dots. Here, we review current methods for detection of M.tb biomarkers using optical sensor systems, primarily quantum dot–based nanosensors, and summarize M.tb biomarkers whose detection can be made significantly more sensitive by using quantum dot–based nanosensors.

This manuscript explores unmanned aerial vehicle DC motor control performance efficacy of deterministic artificial intelligence in comparison to model-following adaptation, particularly a direct self-tuner with filtering. The deterministic artificial intelligence model made use of self-awareness statements to overcome error in response to permutations of the multi-duty cycle square wave that served as the system input. It can be seen that (despite equivalently powerful estimation techniques) deterministic artificial intelligence provided far superior results: a reduction in peak initial transient error of 55%, and a mean error reduction over 81% with over 65% reduction in error standard deviation compared to a state-of-the-art nonlinear adaptive control method. Deterministic artificial intelligence also was able to very closely track at the switching of the input control, while the benchmark nonlinear adaptive control failed to respond as quickly at these points.

Objective: The purpose of this study was to investigate the effects of Crocin on brain neuroterophins, cognition, sensory and motor dysfunction and compare to fingolimod effects in experimental model of demyelination with Ethidium Bromide EB in female Wistar rats. Methods: Animals were assigned in to 8 groups; Sham, Sham operated (ShOp), EAE, crocin treated (Cr5,10,20 mg/kg), Vehicle, Fingolompd (Fing) and fingolimod + crocin (Cr+Fing). Demyelination was induced by single dose injection of 10 μl of EB 0.1% into the fourth ventricle of the brain. Crocin and fingolimod were applied for 21 days, daily, oral gavage. BDNF, NGF1, nerve conduction velocities, tail flake latency, balance and behavioral variables were sampled and analyzed by paired t-test and ANOVA test with repeated post hoc measurements. Results: The results showed that crocin improves all studied factors, but remarkable imrovments were observed in dosage of 10 mg/kg. Crocin (10mg/kg) and fingolimod (1mg/kg) significantly improved cognition variables in open field test, sensory and motor nerve conduction velocity, tail flick latency and clinical signs (p<005). In addition, applying of crocin co-administered with fingolimod led to significant increases in all assessed factors, greater than crocin or fingolimod intervention alone (α≤0.001). Conclusion: Based on the current findings, crocin can improve the level of brain neurotrophins, exploratory behavior and nerve conduction after demyelination as close as fingolimod results. So, crocins can be considered as a neuro supportive agent in the management of degenerative diseases maybe similar to fingolimod mechanism.

With the exponential growth of internet usage and mobile devices, the volume of user behavior data has surged, offering significant opportunities and challenges for businesses. This paper explores user behavior analysis in the context of big data, highlighting its pivotal role in driving innovation and strategic decision-making. We begin by discussing the importance of user behavior data and reviewing technological advancements such as machine learning, natural language processing, and data mining that enable deep insights into user behavior. Through a case study on linguistic analysis in livestreaming e-commerce, we demonstrate how text-mining techniques can correlate linguistic characteristics with sales performance, providing actionable marketing insights. We further explore the broader business applications, including personalized recommendations, user portraits, product design, and strategic decision-making. Technical and ethical challenges, such as data integration, privacy concerns, and algorithmic bias, are also addressed. The paper concludes with a discussion on future trends, such as AI and ML advancements, edge computing, IoT proliferation, and ethical AI frameworks, poised to shape user behavior analysis. Our findings underscore the transformative potential of user behavior data in enhancing business operations and user experiences, providing recommendations for future research focused on real-time analytics, privacy-preserving techniques, and ethical implications of data-driven decisions.

Objective. To test the hypothesis that a session of unilateral tSMS over the dominant primary motor cortex (M1) in healthy subjects produces a transient decrease in manual dexterity. Methods. A triple-blinded randomized control trial was conducted in 40 healthy participants who were allocated to real- or sham-tSMS group. Each participant underwent one session of tSMS and were assessed before, immediately after, 15 minutes, and 40 minutes after stimulation. Outcome measures were Finger Tapping Test (FTT), 5-seconds FTT, nine Hole Peg Test, and the hand grip assessment. Results: Significant differences were found between groups 15 minutes after stimulation in favor of the tSMS group in the FTT (2.2 ± 0.99; CI= .18-4.22; d= .84p=.034), and 5-second FTT (4.2 ± 1.98; CI= .199-8.2, d= .68; p=.04). In the intragroup analysis, a significantly lower number of taps 15 minutes after stimulation was observed in sham group (3.3 ± 1.01; CI= .47-6.13; d= .42; p=.015), but not in the real stimulation group. Conclusion: A unilateral session of tSMS in the M1 of the dominant hemisphere of healthy individuals does not reduce manual dexterity or grip strength. This technique could have benefits on the motor performance of the arm contralateral to the stimulated motor cortex

The origin of a product, consumer familiarity, and purchasing identity are factors that affect perception of cheese consumption. The present study aims at identifying consumers’ conceptualizations and attitudes towards local Greek cheeses of the North-Aegean Sea islands, such as Ladotyri, Graviera, Kasseri, Kaskavali, Melichloro, and Kalathaki, some of which have a Protected Designation of Origin (PDO) status, as opposed to cheeses of non-Greek origin, such as Cheddar, Regatto, and Gouda. Sensory and emotional attributes of local, local PDO, and imported cheeses, and drivers associated with consumers’ choice and acceptance above and beyond their sensory attributes were studied, using three methods: a) Flash Profile to gain insight into the sensory positioning of products and description of samples, b) qualitative analysis of focus groups to pinpoint consumer knowledge, preference, and consumption criteria, and c) a new methodology for natural language processing and sentiment analysis of social media posts to determine consumer conceptualizations. Social media posts have proven to be a valuable source of linguistic and cultural data for cheeses. Local cheeses, including PDO products, were found to be linked to village life and family gatherings, home, tradition, and childhood memories, with saltiness and hardness being their main sensory attributes. Imported cheeses were linked to fast food, pizza, and snacking, with elasticity and gumminess as prominent sensory qualities. The main criterion of purchase was intended usage and versatility, taste and texture, price, and familiarity. The findings provide key sensory attributes, information about consumer purchasing criteria, and relevant vocabulary for the promotion of cheeses as agri-food and gastronomic identity key products.

This study examines the contribution of early executive functions (EFs) in the association between fluency in reading and arithmetic. Kindergarten children (N = 1,185) were assessed on Executive Functions skills and on reading and arithmetic fluency in Grade 1 and Grade 3. The analysis revealed that beyond the connection within each domain there is a unidirectional effect between fluency measures, with Grade 1 reading fluency significantly influencing the development of arithmetic fluency in Grade 3. Furthermore, the findings indicate that kindergarten EFs significantly contribute to arithmetic fluency at both time points and to reading fluency in the first grade. Early EF skills also emerged as a mediator of associations between fluency performance in reading and arithmetic, suggesting that the influence of EFs extends beyond individual academic domains. These findings have implications for understanding the cognitive mechanisms that underlie the relations between these academic skills.

This research investigates the application of Fused Filament Fabrication (FFF) in producing WC-10Co hardmetals, focusing binder formulations and understanding its influence on extrusion force and the influence of printing variables on green density of the samples. By examining the interplay between different binder compositions and backbone contents, the study aims to enhance the mechanical properties of the sintered parts while reducing defects inherent in the printing process. Evidence suggested that formulated feedstocks influenced the hardness of the sintered hardmetal, not due to microstructural changes but to macrostructural responses like macro defects introduced during printing, debinding and sintering of the samples. The results demonstrate the critical role of PP-MA content in improving part density and sintered hardness, suggesting the need for specific thermal debinding protocols tailored to each feedstock. The study provides insights into the formulation of feedstocks for hardmetal FFF, proposing a path toward refining manufacturing processes for better quality and performance of 3D printed hardmetal components.

Learning dental procedures is a complex task involving the development of fine motor skills. The reported use of theories and/ or evidence for designing learning activities to develop the fine motor skills needed for dental practice is limited. The aim of this review is to explore the available body of knowledge related to learning fine motor skills in dentistry. Evidence from studies investigating motor skill learning highlights the negative impact of self-focus and self-regulation on learning outcomes, particularly during the early stages of learning. The development of activities and schedules that enable novices to demonstrate characteristics similar to experts, without the reported long period of ‘deliberate practice’, is clearly of value. Outcomes of learning implicitly are important in dentistry because working under stressful conditions is common, either during undergraduate study or in practice. It is suggested that learning implicitly in the simulation stage can reduce disrupted performance when transitioning to clinical settings. Therefore, further investigation of effective methods for learning dental fine motor skills is indicated, using approaches that result in robust performance, even under stressful conditions.

The ability to decode words accurately at an early age strongly predicts reading competence later on. Decoding-challenged children may find it difficult to understand texts, which might impede their overall academic development. Strong word decoding skills accelerate the process of learning vocabulary and grammar rules in a variety of languages. The paper aimed to collect relevant literature regarding word decoding ability in the world of the language acquisition process. Policymakers and educators will be greatly impacted by realizing how crucial word decoding is to language learning. Providing targeted interventions for struggling readers, encouraging a positive reading culture, and explicitly teaching phonological and orthographic abilities are all essential components of effective reading education. Promoting decoding proficiency also requires making sure that people have access to excellent reading materials and tools. In order to enable learners to become great readers and communicators across linguistic and cultural boundaries, educators and policymakers should prioritize phonemic awareness from an early age and use effective teaching methodologies.

Employing phonemic awareness in language development can lead to transformative educational outcomes. Having mastered this skill, students have a good foundation in language comprehension, writing, and reading, which increases their chances of academic achievement. This change can affect entire classrooms and schools, fostering a culture of literacy and academic performance, rather than just a select few students. The aim of the study is to gather literature that offers substantial information about this topic based on the facts presented. The overwhelming body of research points to the benefits of teaching phonemic awareness in early childhood education. The foundation of transformative education is phonemic awareness, which fosters fundamental language and reading skills. This profound effect emphasizes how crucial phonemic awareness is to the teaching process. Educators can use phonemic awareness to boost students' language development and create a transformative learning environment by putting these guidelines into practice.

AI tools provide individualized learning experiences tailored to specific language learners' requirements. Artificial intelligence-driven programs can customize lessons, exercises, and feedback by evaluating a learner's progress and determining strengths and shortcomings. This individualized method aids students in concentrating on their areas of weakness, thus improving their total language competency. This narrative literature review aims to gather relevant literature discussing AI tools as supplementary support in aiding a learner's language acquisition. Relevant studies stated that there are a lot of chances to improve learning outcomes when AI tools are used in language acquisition. Teachers who want to give their students more resources and help should consider including these tools in their lessons. To preserve the interpersonal and cultural components of language learning, it is imperative to strike a balance between the application of AI and conventional teaching techniques. AI tools are always accessible, providing assistance to learners whenever needed. This is especially helpful for individuals with unpredictable schedules or restricted access to conventional language learning materials.

Stroke is a major leading cause of chronic disability, often affecting patients’ motor and sensory functions. Functional magnetic resonance imaging (fMRI) is the most commonly used method of functional neuroimaging and allows the non-invasive study of brain activity. The time-dependent coactivation of different brain regions at rest is described as resting-state activation. As a non-invasive task-independent functional neuroimaging approach, resting-state fMRI (rs-fMRI) may provide therapeutically useful information on both the focal vascular lesion and the connectivity-based reorganization and subsequent functional recovery in stroke patients. Considering the role of prompt and accurate prognosis in stroke survivors along with the potential of rs-fMRI in identifying patterns of neuroplasticity in different post-stroke phases, this review provides a comprehensive overview of the latest literature regarding the role of rs-fMRI in stroke prognosis in terms of motor and sensory outcome. Our comprehensive review suggests that with the advancement of MRI acquisition and data analysis methods, rs-fMRI emerges as a promising tool to study the motor and sensory outcome in stroke patients and evaluate the effect of different interventions.

Abstract Introduction: In the last thirty years, brain neuromodulation techniques have been used as an alternative to pharmacological treatment of neurological disorders. Parkinson's disease (PD) is a neurodegenerative disorder leading to bradykinesia, rest tremor, postural changes, and non-motor symptoms such as depression, anxiety, sleep disorders, pain, and cognitive decline that compromises executive functions (EFs), responsible for the orderly execution of behaviors and tasks of daily life and intentional and directed actions. To this date, a few studies with transcranial direct current stimulation (tDCS) have shown beneficial effects in PD patients concerning specific motor and non-motor symptoms, targeting the motor cortex and/or prefrontal regions. Objective: The main objective of this study was to evaluate the effects of left prefrontal tDCS across a broad spectrum of motor and non-motor symptoms of PD using established validated scales. Method: Single-blind randomized clinical trial with 18 volunteers with PD, aged between 45 and 80 years (66.1 ± 9.65), who met inclusion and exclusion criteria. Participants were submitted to assessments of motor and non-motor functions employing psychometric scales and tests to evaluate EFs and were randomly divided into two groups: control (sham stimulation) and experimental (active stimulation). All participants were involved in three separate tDCS sessions. The anode was positioned over the left dorsolateral prefrontal cortex and the cathode over the right supraorbital region, with a direct current intensity of 2mA, lasting 20 minutes. At the end of the three sessions, all participants were reassessed. Results: Significant effects of tDCS on non-motor functions were observed for cognition (verbal fluency of actions, clock copy test, appointment by visual confrontation, and verbal memory with immediate free recall) and subjective assessment of sleep quality (overall restlessness and discomfort in the arms and legs at night, leg and arm cramps at night and distressing dreams). There was also an improvement in the rate of errors and successes for congruent and incongruent stimuli of the Stroop Test. The beneficial effects on motor function were decreased rigidity, improved gait, and greater agility in the finger-tapping test. Conclusion: Three tDCS sessions showed positive results for participants with PD, producing significant improvements in various motor and non-motor functions, including sleep quality, cognition, and EFs. Additionally, the present results indicate that tDCS neuromodulation of the left dorsolateral prefrontal cortex region is feasible, safe, and provides significant objective benefits for PD patients.

Antisense oligonucleotides (ASOs) are short oligodeoxynucleotides designed to bind to specific regions of target mRNA. ASOs can modulate pre-mRNA splicing, increase levels of functional proteins and decrease levels of toxic proteins. ASOs are being developed for the treatment of several diseases, including motor neuron diseases (MNDs), a group of neurodegenerative disorders characterised by the loss of motor neurons, including spinal muscular atrophy (SMA), amyotrophic lateral sclerosis (ALS) and spinal and bulbar muscular atrophy (SBMA). The biggest success has been the ASO known as nusinersen, the first effective therapy for SMA, able to improve symptoms and slow disease progression. Another success is tofersen, an ASO designed to treat ALS patients with SOD1 gene mutations. Both ASOs have been approved by the FDA and EMA. On the other hand, ASO treatment in ALS patients with the C9orf72 gene mutation did not show any improvement in disease progression. The aim of this review is to provide an up-to-date overview of ASO research in MND, from preclinical studies to clinical trials and, where available, regulatory approval. We highlight the successes and failures, underline the strengths and limitations of the current ASO research, and suggest possible approaches that could lead to more effective treatments.

Locomotor impairment is a high-prevalent and significant source of disability and significantly impacts a large population’s quality of life. Despite decades of research in human locomotion, the challenges of simulating human movement to study the features of musculoskeletal drivers and clinical conditions remain. Most recent efforts in utilizing reinforcement learning (RL) techniques are promising to simulate human locomotion and reveal musculoskeletal drives. However, these simulations often failed to mimic natural human locomotion because most reinforcement strategies have yet to consider any reference data regarding human movement. To address these challenges, in this study, we designed a reward function based on the trajectory optimization rewards (TOR), and bio-inspired rewards, which includes the rewards obtained from reference motion data captured by a single Intertial Moment Unit (IMU) sensor. The sensor was equipped on the participants’ pelvis to capture reference motion data. Also, we adapted the reward function by leveraging previous research in walking simulation for TOR. The experimental results showed that the simulated agents with the modified reward function performed better in mimicking the collected IMU data from participants, which means the simulated human locomotion was more realistic. Also, as this bio-inspired defined cost, IMU data enhanced the agent’s capacity to converge during the training process. As a result, the models’ convergence is faster than those developed without reference motion data. Consequently, human locomotion can be simulated more quicker and in a broader range of environments with a better simulation performance.

Keywords: olfactory rehabilitation; chronic pain; fMRI;

Background: The accurate diagnosis of regional lymph node metastasis is critical for guiding treatment decisions in head and neck cancer patients. Despite advances in imaging techniques, detecting nodal metastasis remains challenging, leading to potential undertreatment or overtreatment. This systematic review aims to identify molecular markers associated with occult metastasis in Head and Neck Squamous Cell Carcinoma (HNSCC) patients. Methods: The present research was conducted following PRISMA guidelines. Prospero registration nr. CRD42024522985. Results: Through a comprehensive literature search and review process, 634 articles were considered, 103 of them were finally included. Conclusions: Several promising markers were identified, including miR-205, DSG3, pan-CK AE1/AE3, HPV-16, Activin-A, Cyclin D1, and NPL that demonstrated effectiveness across multiple studies. Future research should focus on exploring combination scoring systems to improve diagnostic precision and optimize treatment selection in HNSCC patients.

Daytime napping offers benefits for motor memory learning and is used as habitual countermeasure to improve daytime functioning. A single napping is shown to ameliorate motor memory learning, although the effect of consecutive napping on motor memory consolidation remains unclear. This study aimed to explore the effect of daytime napping over multiple days on motor memory learning. Twenty university students were divided into the napping group and no-nap (awake) group. Napping group performed motor adaption tasks before and after napping for three consecutive days, whereas no-nap group performed the task on the similar time schedule as the napping group. In addition, a subsequent retest was conducted one week after the end of the intervention. The speed to complete the task of the napping group was significantly shorter than that of the awake group in the retention test. No significant difference was confirmed for trajectory length to complete the task. Thus, consecutive napping may facilitate motor learning and motor memory consolidation in the long term.

The growing demand for high-power DC Fast Charging (DCFC) stations for Electric Vehicles (EVs) is expected to lead to increased peak power demand and reduction of grid power quality. To maximize the economic benefits and station utilization under practical constraints set by regulatory authorities and DCFC station operators, this study explores and provides methods for connecting DCFC stations to the grid employing low power interconnection rules and Distributed Energy Resources (DERs). The system uses automotive Second Life Batteries (SLBs) and Photovoltaic (PV) systems as energy buffers and local energy resource to support EV charging and improve the station techno-economic feasibility through load shifting and charge sustaining. The optimal sizing of the DERs and the selection of the grid interconnection topology is achieved by means of a Design Space Exploration (DSE) by means of exhaustive search approach to maximize the economic benefits of the charging station and to mitigate high-power demand to the grid. Without loosing of generality, this study considers a range of DER sizes, grid interconnection specifications, and related electricity tariffs of American Electric Power (AEP) Ohio and the Public Utility Commission of Ohio (PUCO). Various scenarios and strategies have been defined to account for the interconnection requirements of the grid to the DCFC with DERs. The system’s techno-economic performance of different scenarios has been analyzed and compared using a multitude of metrics.

Motor intention is a high-level brain function related to planning for movement. Although studies have shown that motor intentions can be decoded from brain signals before movement execution, it was unclear whether intentions relating to mental imagery of movement could be decoded. Here, we investigated whether differences in spatial and temporal patterns of brain activation were elicited by intentions to perform different types of motor imagery and whether the patterns could be used by a multivariate pattern classifier to detect such differential intentions. The results showed that it is possible to decode intentions before the onset of different types of motor imagery from functional MR signals obtained from fronto-parietal brain regions, such as the premotor cortex and posterior parietal cortex, while controlling for eye movements and for muscular activity of the hands. These results highlight the critical role played by the aforementioned brain regions in covert motor intentions. Moreover, they have substantial implications for rehabilitating patients with motor disabilities.

Childhood is an obvious period for motor learning since children’s musculoskeletal and nervous systems are still in development. Adults adapt movements based on reward feedback about success and failure but it is less established whether school-age children also exhibit such reward-based motor learning. We designed a new ‘circle-drawing’ task suitable for assessing reward-based motor learning in both children and adults. Participants drew circles with their unseen hand on a tablet. They received binary reward feedback after each attempt based on the proximity of the average radius of their drawing to a target radius set as double the radius of their baseline drawings. We rewarded about 50% of the trials based on a performance-dependent reward criterion. Both children (7-17 years old) and adults increased the radius of their drawings in the direction of the target radius. We observed no difference in learning between children and adults. Moreover, both groups changed the radius less following reward than following reward absence, which is a sign of reward-based motor learning. We conclude that school-age children, like adults, exhibit reward-based motor learning.

We describe a model of neurological disease based on dysfunctional brain oscillators. This is not a new model, but it is not one that is generally appreciated by clinicians. The value of this model lies in the predictions it makes and the utility it provides in translational applications, in particular for neuromodulation devices. We provide a perspective on the difference between neuromodulation devices that enforce an externally administered stimulus with devices that provide input to sensory receptors and thus stimulate endogenous sensory networks. Current forms of clinically applied neuromodulation are of the former type, including devices such as (implanted) deep brain stimulators (DBS) and various, noninvasive methods such as transcranial magnetic stimulation (TMS) and transcranial current methods (tACS, tDCS). The challenge with these methods is that they are not sensitive to underlying neuronal dynamics and work by applying an empirically derived electrical current waveform to affect dynamical patterns. Neuromodulation of a sensory organ accesses the same pathways that natural environmental stimuli do and, importantly, the modulatory signal will be transformed as it travels through the brain, allowing the modulation input to be consistent with regional dynamics. We present specific examples of devices that rely on sensory neuromodulation and evaluate the translational potential of these approaches. We argue that sensory neuromodulation is well suited to probe and, ideally, repair dysfunctional brain oscillators, thus providing a novel therapeutic approach for neurological diseases.

Objective A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if the model has been trained accurately, it should be able to identify a class and label it accordingly. The CNN model will then be restored and used to try and identify the same class of motor imagery data using much smaller sampled data in an attempt to simulate live data. Approach PyCharm, a Python platform, will be used to house and process the CNN. The raw data used for the training of the CNN will be sourced from the PhysioBank website. The EEG signal data will then be pre-processed using Brainstorm software that is a toolbox used in conjunction with MATLAB. The sample data used to validate and test the trained CNN, will be also be extracted from Brainstorm but in a much smaller size compared to the training data which is comprised of thousands of images. The sample size would be comparable to a person wearing a Brain Computer Interface (BCI), offering approximately 20 seconds of motor imagery signal data. Results The raw EEG data was successfully extracted and pre-processed. The deep learning model was trained using the extracted image data along with their corresponding labels. After training, it was able to accurately identify the T1 class label at 100 percent. The python coding was then modified to restore the trained model and feed it test sample data in which it was found to recognise 6 out of 10 lines of T1 signal image data. The result suggested that the initial training of the model required a different, more varying approach, so that it would be able to detect varying sample signal image data. The outcome of which could mean that the model could be used in applications for multiple patients wearing the same BCI hardware to control a device or interface.

The electrical motor design requires a lot of work and resources. In the field, motor designers need to decide on competitive motor sizing quickly to compete with other motor suppliers. Generally, it requires expensive FEM S/W to get accurate motor sizing results, but buying and maintaining many licenses is costly. Moreover, it needs some patience to get the result from FEM analysis. In this paper, the motor sizing technique using an existing production motor is presented to respond to urgent requests from customers without using FEM analysis. In order to enhance the fidelity of the result, magnetic flux loss by magnet coating and non-linear characteristics are considered during the motor sizing process. The proposed sizing method was verified by comparison to the FEM analysis and test result and showed reasonable confidence in them. Also, this method has the substantial advantage of outputting accurate results instantly without taking any time. It helps with the modification design of electrical motors without using expensive FEM S/W and a certain level of skill or experience.

Manual material handling (MMH) significantly impacts worker health and productivity, often leading to musculoskeletal disorders (MSDs) primarily in the lower back. As a novel assistive technology, exoskeletons may serve as ergonomic tools to mitigate these work-related MSDs. It is essential to examine exoskeletons from the users’ perspectives before their widespread implementation in occupational settings. This study investigates the effectiveness of a passive back-support exoskeleton (BExo) in reducing perceived physical exertion and improving ergonomic safety in manufacturing context. Twenty-two college students were recruited to perform MMH tasks in a controlled lab environment, both with and without the BExo, followed by completing a survey questionnaire on various aspects of the BExo. Using ANOVA, the study analyzed biomechanical exertion across various body parts and tasks. The findings indicate that the BExo substantially alleviates discomfort in the low back, shoulders and knees, thereby, enhancing ergonomic posture and reducing fatigue. These results underscore the potential of passive exoskeletons to boost workers’ safety and efficiency, providing valuable insights for future ergonomic strategies in industrial setting.

The term `motor control´ encompasses a wide range of mechanisms thought to be implicated in the organization of movements. Their study, like that of any other scientific field, must use specific notions to get to grips with them. This review is therefore organized along a series of notions that are frequently used in motor control, and we will discuss them with particular emphasis on neurological conditions, which may disrupt normal motor functioning. We will start with a short description of the roles of space and time, in which movements take place. Subsequently we will deal with kinematics and kinetics (dynamics) of movements. Then, we will list the inputs to motoneurons (MNs), which, in different forms, convey signals from the central nervous system (CNS) to skeletal muscles and muscle spindles, including central pattern generators (CPGs), sensory inputs and supraspinal descending fiber systems. Some helpers in movement organization will be introduced, such as internal models and neuronal network models. A relatively brief section on sensory-motor learning will follow because learning abilities are required to endow the sensory-motor system with flexibility and adaptability. The major emphasis is placed on neurological processes, which result from genetic modifications, autoimmune diseases, neurodegenerative diseases, infections in the nervous system as well as vascular and traumatic lesions. These neurological diseases lead to a variety of impairments and symptoms, particularly movement derangements including cerebellar ataxia, spasticity, and (other) movement disorders such as Parkinson’s syndromes. The conclusion is that there is no unique top controller that controls ´motor control`.

Depression, anxiety, and pain are part of a common phenomenon that requires to be studied together when evaluating patients with chronic pain. The aim of the study was to examine the correlations between depression, anxiety and pain intensity in patients with chronic pain and how they change when a depressive episode was added. A sample of 120 patients with chronic pain (age: 51.90±11.94; 81.7% female; 18.3% male) was studied in two stages with a three-month period between them. Patients with a depressive episode (n=61) were treated with antidepressants and analgesics, while a patient without depression (n=59) was treated only with analgesics. The scales used to achieve the objectives were: HAM-D-17, Spielberger‘s STAI and VAS. Insignificant correlations were found between depression, state and trait anxiety, and pain intensity (p&lt;0,05) in the group without depression, except for a significant one between depression and state anxiety (p&lt;0,01) in the second stage. Significant correlations were identified between all indicators (p&lt;0,01) in the group with depression at the two stages. Those in the second stage were stronger. The interrelations between sensory and affective aspects of pain were stronger when a depressive episode occurs, the severity of which determines their dynamics.

High production and consumption of bananas produce large amounts of banana waste. However, the perception of banana as waste depends on the degree of ripeness, and is therefore subjective, depending on the sensorial perception, emotional reactions, and cultural differences of consumers. Understanding consumer perception of different levels of banana ripeness could contribute to better management of the supply chain, leading thus to a decrease of waste or better utilization of its waste as a byproduct. The aim of this study was to compare European and Chinese consumers’ sensorial and emotional responses to different stages of banana ripeness, using facial expression analysis, liking, and emotion measurement by self-report. One hundred and thirty-five adults of European and Chinese origin watched a video of banana consumption at five different degrees of ripeness. Participants’ facial expressions were analyzed with regard to six emotions (neutral, happy, sad, surprised, angry, disgusted, scared). Emotional responses and consumer acceptance of different degrees of banana ripeness were assessed using pictures of bananas as reference. Liking on a 9-point Likert scale and emotion measurement using PrEmo® followed, to evaluate emotional responses and acceptability of different degrees of banana ripeness. Results showed significant differences in facial reactions between cultures and among ripeness degrees. The European participants were more positively inclined towards all ripeness degrees, compared to the Chinese. Cultural expectations and level of familiarity seem to affect consumer acceptability greatly. Finally, explicit methods can give a more accurate picture of consumer preference and acceptance of different banana ripeness stages. A combination of liking and emotion measurement can be very effective in understanding consumer preference.

In the design of real-world networks, researchers evaluate various structural parameters to assess vulnerability, including connectivity, toughness, and tenacity. Recently, the tightness metric has emerged as a potentially superior vulnerability measure, although many related theorems remain unknown due to its novelty. Harary graphs, known for their maximum connectivity, are an important class of graph models for network design. Prior work has evaluated the vulnerability of three types of Harary graphs using different parameters, but the tightness metric has not been thoroughly explored. This article aims to calculate the tightness values for all three types of Harary graphs. First, it will attempt to calculate the lower bound for the value of the tightness parameter in Harary graphs using existing lemmas and theorems. Then, by presenting new lemmas and theorems, we will try to find the exact value or upper bound for this parameter in Harary graphs. For the first type of Harary graph, the tightness is precisely determined, while for the second and third types, upper bounds are provided due to structural complexity. The lemmas, theorems, and proof methods presented in this research may be used to calculate other graph and network parameters. However, the newness of the tightness parameter means that further research is needed to fully characterize its properties.

Reinforcement learning (RL) is an extensively applied control method for the purpose of designing intelligent control systems to achieve high accuracy as well as better performance. In the present article, the PID controller is considered as the main control strategy for brushless DC (BLDC) motor speed control. For better performance, the fuzzy Q-learning (FQL) method as a reinforcement learning approach is proposed to adjust the PID coefficients. A comparison with the adaptive PID (APID) controller is also performed for the superiority of the proposed method, and the findings demonstrate the reduction of the error of the proposed method and elimination of the overshoot for controlling the motor speed. MATLAB/SIMULINK has been used for modeling, simulation, and control design of the BLDC motor.

The persistence of symptoms following COVID-19 infection represents a significant challenge in healthcare management. During the outbreak, tele-rehabilitation emerged as a new tool to support healthcare structures in providing rehabilitation services. This study assessed the effectiveness and the feasibility of a 3-week home-based motor and respiratory rehabilitation program for long COVID-19 individuals after traditional rehabilitation. Twenty-three patients completed the program and underwent functional tests at different time points (i.e. baseline, after hospital discharge, and after tele-rehabilitation). Motor function was evaluated using the instrumented Six-Minutes Walking Test (i6MWT), with monitored heart rate and oxygen saturation. Conversely, respiratory function was measured via forced vital capacity (FVC) and maximal voluntary ventilation (MVV) tests. Significant improvements (p &lt; 0.05) in motor and respiratory function were observed throughout the intervention, including an 18.3% increase in walked distance from the baseline. The findings suggest that a home-based tele-rehabilitation can enhance motor and respiratory function in post-COVID patients. Despite limitations such as sample size and lack of control group, the positive outcomes seem to support the feasibility of the proposed tele-rehabilitation program in managing long COVID symptoms and promoting functional recovery. Further research is needed to validate these findings and explore tele-rehabilitation’s potential in broader patient populations.

Ensuring precise electric locomotive stopping is pivotal for achieving intelligent and unmanned auxiliary transportation systems. Currently, human drivers' expertise plays a central role in the stopping process, posing challenges for automation and cargo location accuracy, particularly in coal mining. This paper focuses on achieving accurate electric locomotive stopping under diverse conditions by utilizing permanent magnet synchronous motor-driven locomotives. This technique introduces a novel stopping control method that combines a fuzzy Proportional-Integral-Derivative (F-PID) controller and a vector control model for permanent magnet synchronous motors (PMSM). After this, we design the F-PID using the PMSM technique based on the new fuzzy rules for each sub-system. In the end, extensive simulations and real-world experiments are conducted on an electric locomotive stopping test bed, which demonstrate the effectiveness of the proposed control method. From the simulation results, it confirming that the ability to achieve precise stopping under various working conditions.

Two major sample configurations are adopted in all the instruments for fabric hand measurement, flat sample as in KES and FAST types machines, and wrinkled sample as in PhabrOmeter. This paper compares the two sample types to examine which one if any offers a better coverage and reflection of the fabric sensory attributes. Fabrics have unique behaviors of drape, wrinkle and tactile sense which are entirely due to the simultaneous occurrence of both in-plane membrane deformation and out-of-plane bending deformation in multiple curvature. Such singular deformation mode cannot be detected by any machines using flat sample, whereas during a PhabrOmeter test, the fabric sample genuinely produces drape, wrinkle in addition to other related deformations. This paper then introduced the theoretical research pertaining to the measurement. Then a split sample experiment is conducted to demonstrate the importance of the internal connections in fabric during drape and wrinkle processes. As such fabric interconnection will be barely disclosed during tests using flat samples, another important advantage of PhabrOmeter is hence clearly shown.

The lack of adequate knowledge of the culinary and sensory properties of most indigenous and wild foods hampers their promotion in human diets and the market. In the present study, 80 Burkinabe volunteers evaluated the sensory appeal and attributes of three selected Senegalia seed species (Zamnè, pseudoZamnè or Kumatiya, and S. erythrocalyx) and their food formulas (traditionally cooked, harvested as green and fresh legumes, and fermented as tempeh) using the 9-point hedonic scale and check-all-that-apply questionnaire. They found that the traditionally cooked Zamnè, pseudoZamnè or Kumatiya, and derived tempeh had good sensory appeal (scoring between 5-7) and subtle alkaline and nutty tastes, which will facilitate their blending in cuisines. Yet, an appreciable number (32%) of the participants, as unaccustomed to tempeh, scored all the tempeh products very low (2.5-3.4), suggesting food neophobia. In contrast, the traditionally cooked seeds of Senegalia erythrocalyx and the green and fresh Zamnè evoked bitter and sour off-tastes, respectively, and were not much appreciated (scoring 4). The present study provides unprecedented insight into consumers’ non-sensory perceptions and the culinary and sensory properties of Senegalia seed foods – which will be essential for their valorization, branding, and marketing.

Sensory descriptive analysis was used to evaluate the aged Feng-flavored baijiu. The results indicated that there are notable differences in the samples of different ages. The samples with shorter storage times and fresh distilled Baijiu present bran flavor, and fresh green flavor. with the increase of storage time, the fragrance of honey, sweetness, flowers, and aging gradually increases. The samples of Feng-flavored baijiu were dected by Headspace solid-phase microextraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC–TOFMS) , in total, 496 sustances were identified in all samples, mainly containing 14 types of substances, such as esters, aldehydes etc., of which 42 substances were found in Feng-flavored baijiu for the first time. Chemometrics was used to analyze the key differential compounds. 143 differential compounds closely related to aging were preliminarily screened, and principal component analysis revealed that these samples were separated by year. 65 differential compounds were selected out by partial least squares discriminant analysis(PLS-DA). Furthermore, 43 key differential compounds were selected from the aged Feng-flavored baijiu combined with variable importance in projection and Pearson correlation coefficients. Partial least squares regression (PLSR) was used to research the correlation of sensory properties and key differential compounds, and the results indicated that most compounds were closely related to the aged Baijiu . This study afford theoretical basis and reference for flavor research of Feng-flavored baijiu.

Sensory integration is an essential function of human life, and its decline causes difficulties in the quality of life, especially in older adults. In this study, we proposed an arm-reaching task based on a virtual reality head-mounted display (VR-HMD) system to assess sensory integration in daily life, and examined whether reaching task performance was associated with age and resting-state functional connectivity (rsFC) between the brain regions of interest involved in sensory integration. We hypothesized that performance during a reaching task with multiple cognitive loadings would be affected by aging. The gap angle between the reaching hand and target positions was compared between older and middle-aged adults as a measure of sensory integration function. In addition, rsFC, which was significantly correlated with the gap angles of individual participants in the older adult group, was identified using resting-state functional magnetic resonance imaging data. The mean gap angle was significantly larger in the older adults than in the middle-aged group and was also significantly correlated with connections between the left primary motor area and the left inferior temporal gyrus, temporo-occipital part. Because the connectivity between the areas is known to be related to visuomotor integration, the results suggest the involvement of visuomotor integration in the decline of sensory integration function in old age and the validity of the gap angle during the proposed VR-HMD task as an index for the decline in function.

BACKGROUND: Qu-aroma is a critical quality indicator for medium-high temperature Daqu (MT-Daqu), significantly influencing the aroma profile of strong-aroma Baijiu (SAB). SAB is produced across various regions in China, each with unique environmental conditions and production techniques. These regional differences are likely to cause variations in the Qu-aroma of MT-Daqu. Despite this, comprehensive research on the variations in Qu-aroma across different regions has been lacking. This study aims to address this gap by utilizing Gas Chromatography-Olfactometry-Mass Spectrometry (GC-O-MS) and electronic nose (E-nose) technology, combined with sensory evaluation, to analyze the differences in Qu-aroma characteristics and volatile compound profiles of MT-Daqu from seven distinct production areas.RESULTS: Sensory analysis identified seven broad aroma categories and 25 specific aromas characterizing the Qu-aroma of MT-Daqu samples. Chen aroma and roasted aroma were notably more prevalent in MT-Daqu samples from Sichuan compared to those from other regions (P < 0.05), while the fermentation aroma was less prominent in Sichuan samples. In total, 123 volatile compounds were detected in MT-Daqu, with 46 classified as aroma-active compounds. Among these, 21 aroma-active compounds were identified as markers distinguishing Sichuan MT-Daqu from those produced in non-Sichuan regions, showing significant differences in their concentrations (P < 0.05). Additionally, six major aroma-active compounds-ethyl isovalerate, ethyl heptanoate, hexanal, 1-nonanal, (E)-2,4-decadienal, and 2,3-dimethyl-5-ethylpyrazine-were found to significantly contribute to the aroma differences between Sichuan and non-Sichuan MT-Daqu.CONCLUSION: This study delineates the distinct Qu-aroma characteristics of MT-Daqu from various SAB production areas and identifies the aroma-active compounds responsible for these differences. The insights gained from this research offer valuable guidance for optimizing flavor profiles in MT-Daqu production, potentially enhancing the overall quality and regional distinctiveness of the product.

(1) Background: Alzheimer’s Disease (AD) accounts for 70% of dementia cases and with no effective pharmacological treatments, new rehabilitation methods are needed. Motor activities and transcranial direct current stimulation (tDCS) have shown promise in stabilizing and enhancing cognitive functions. Objective: We want to investigate the effects of combined motor or cognitive activity with tDCS on cognitive functions in AD patients. (2) Methods: Patients with mild or moderate AD were randomized into four groups: motor activity or cognitive activity associated with anodic or sham tDCS (MotA, MotS, CogA, CogS). They received two weeks of treatment (45 minutes, 5 days/week), with the first 15 minutes using 2mA tDCS on the dorsolateral prefrontal cortex. Cognitive assessments were conducted pre-treatment (T0), post-treatment (T1), and one week after (T2). (3) Results: Twenty patients were included. Mann-Whitney test showed significant improvements in anodic groups (MotA+CogA) in the global cognitive status (p=.042), in selective attention (p=.012), and sustained attention (p=.012) at T1, but not at T2. In addition, MotA improved in the Picture Recognition Test (p=.027) and in sustained attention (p=.047) at T2. (4) Conclusion: Combined motor or cognitive activity with anodic tDCS could slow cognitive decline in AD patients, with notable improvements in global cognitive state and attention at T1.

In the proposed article, the issues of increasing the synchronizing ability of the system of coordinated rotation of asynchronous motors are considered. Based on the T-shaped equivalent circuit, electromechanical relationships were obtained with changes in the value of the total rotor resistance and the supply voltage of individual motors. An algorithm for calculating the torques, stator and rotor currents of the motors of the system has been compiled. Based on the calculations obtained, the mechanical characteristics of the system asynchronous motors are constructed for various values of total resistance, supply voltages and rotor positions. The obtained data and graphs confirm the adequacy of the theory obtained in an analytical way. Functional and structural diagrams in small scales have been compiled, which allow simulating transient operation modes of multi-motor asynchronous electric drives in the Matlab application package. Multi-motor asynchronous electric drives with adjustable supply voltages, as well as with preliminary synchronization and synchronous braking, have been developed. Compilation of a program for the stability of a nonlinear system of a multi-motor asynchronous electric drive (MAEDs) with a thyristor voltage converter (TVC) in Matlab. Also, the modes of soft start, load shedding and surge on the motor shaft are simulated, confirming the stability of the system.

The aging ability of dry white wines has been increasingly recognised. The present work aimed to understand which sensory factors drive their quality evaluation by experienced tasters. Individuals assessed several aesthetic, synthetic, and analytic flavour attributes, using dark tasting glasses. Wines originated from the grape varieties Alvarinho, Arinto, Sauvignon Blanc and Pinot Bianco with a wide range of ages. Basic physical-chemical analysis, browning (Abs 420 nm), elemental composition and a partial volatile fraction were also determined. The overall quality scores were a function of complexity and balance and were negatively influenced by the perception of faultiness. The different ages could be associated with a continuous sensory space characterised by a decreasing perception of freshness and an increase in the mellowed flavours. All wines shared an austere in-mouth perception elicited by their acidity, saltiness, bitterness, smoothness and dryness. Nevertheless, quality scores were similar from the youngest to the oldest-tasted wines (17 years old). The exception was a Sauvignon Blanc wine from a recent vintage that was judged as faulty due to the perception of earthiness. Overall, the tasted wines displayed an unexpected aging ability as demonstrated by the difference between the predicted and real wine ages. To improve the recognition of old wines, the metaphor “mellowed by age” is proposed to describe flavours resulting from beneficial aging instead of “oxidised by age”.

The popular appreciation of dry white wines is most frequently directed to young wines. However, present consumption trends comprise the valorisation of aged dry white wines. Therefore, the present work was aimed at the sensory analysis of these wines with different ages to define their sensory space and to understand which factors drive their quality evaluation by experienced tasters (critics, oenologists and students). Individuals were asked to evaluate several synthetic and aesthetic attributes and to characterise the analytic sensory profile through a Check-All-That-Apply (CATA) methodology. The quality evaluations were differently correlated with wine synthetic parameters according to the taster group. For both critics and oenologists, overall quality scores were driven by persistence and complexity. Moreover, quality was also highly correlated with power for critics and with balance for oenologists. Quality scores were highly correlated with wine browning (absorbance at 420 nm) for critics. The tasting panel showed a homogeneous analytic description of aroma, taste and mouthfeel consistent with wine age. The different ages could be associated to a continuous sensory space characterised by a decreasing perception of freshness and increase in mature and mellowed descriptors. All wines shared an austere in-mouth perception elicited by their acidity, saltiness, bitterness, smoothness and dryness. The age prediction showed that most tasters failed to guess the age wines with more than roughly 13 years old, indicating that tasters were not familiar with the sensory features of white wines with 17 to 46 years old. In conclusion, experienced tasters consistently described the sensory space and recognised the high quality of aged dry white wines. Education programs may use the defined sensory spaces according to aging and to expand the range of quality perception by consumers.

Chemical communication of predation risk has evolved multiple times in fish species, with the conspecific alarm substance (CAS) contemporaneously being the most well understood mechanism. CAS is released after epithelial damage, usually when prey fish is captured by a predator, and elicits neurobehavioral adjustments in conspecifics which increase the probability of avoiding predation. As such, CAS is a partial predator stimulus, eliciting risk assessment-like and avoidance behaviors, and disrupting the predator sequence. The present paper reviews the distribution and putative composition of CAS in fish, and presents a model for the neural processing of these structures by the olfactory and the brain aversive systems. Applications of CAS in the behavioral neurosciences and neuropharmacology are also presented, exploiting the potential of model fish (e.g., zebrafish, guppies, minnows) on neurobehavioral research.

Based on a constant volume combustion chamber, the initial ambient pressure effect on combustion performance and flame propagation of the active and passive pre-chamber ignition systems were studied. Compared with the passive pre-chamber, the active pre-chamber can significantly expand the lean combustion limit, enhance the ignition and combustion performance, and speed up the combustion of the main combustion chamber. At slightly lean combustion conditions, the heat release performance has a positive correlation with the initial ambient pressure. As the premixed λ increases, the heat release performance and the initial ambient pressure begin to become negatively correlated. Ignition delay can be minimized by controlling the excess air coefficient in the pre-chamber close to the stoichiometric ratio. The high ambient pressure in the constant volume chamber greatly reduces the mixture entrainment ability of the jet flame at lean combustion conditions, thereby reducing the combustion speed and peak heat release rate.

Considering the health benefits of the kombucha, already widely studied, the objective of the study was to development of the beverage with the addition of infusion of special arabica coffee from the first fermentation varying the proportion of special coffee (2% a 13%) and evaluate the pH, volatile acidity, degree of alcohol, centesimal composition, sodium contents, colour parameters as like acceptability of the beverages. With respect to the pH, all the formulations conformed with the kombucha identity standard, but K3 and K4 were below the established minimum for volatile acidity. Except for K4, all the other formulations were classified as alcoholic kombuchas, although the values were very close to the limit for non-alcoholic. The formulations presented low sugar and sodium content, which corroborated the low caloric value. Therefore, coffee-flavored kombucha fermented for 18 days becomes an option for consumers looking for low-calorie, refreshing and healthy drinks. The luminosity of the beverages decreased as the proportion of coffee increased, and consequently the values for a* and b* increased, indicating a strong tendency for a yellowish red colour. Sensorially, the formulations K4 and K3 were the most well accepted in all respects and can therefore be considered formulations with high commercialization potential.

Understanding user challenges with light dosimeters is crucial for designing more acceptable devices and advancing light exposure research. We systematically evaluated the usability and acceptability of the lido (light dosimeter) by 29 participants who wore the dosimeter near the corneal plane of the eye for 5 days. Common reasons for not wearing the dosimeter included exercise, recharging, wet environments, public settings, and discomfort. Despite these issues, participants adhered to using the dosimeter, with high compliance (89% of recording time). Our findings reveal a significant discrepancy between mean melanopic Equivalent Daylight Illuminance (300 lxmEDI) and median values (51 lxmEDI). This discrepancy indicates that, for most of the time, participants experienced significantly lower light levels. Specifically, participants were exposed to light levels above 250 lxmEDI for only 14% of their wearing time. This highlights the need for increased exposure to recommended light levels. In the evening, participants' light exposure was below the recommended 10 lxmEDI for 58% of the wear time, which is in line with the guidelines for reducing light exposure before sleep. This study highlights the critical need for strategies to enhance daily light exposure, aligning better with circadian health recommendations.

This work proposes a new theoretical approach for the next generation of extremely efficient motors, whose impact will be substantial for future sustainable technological development. Building on well-known physical concepts, this approach introduces the concept of equilibrium of mutual retention, upon which a device remains in an equilibrium state between movement and attraction-repulsion. This new theoretical approach showed promising results in computer simulation experiments, indicating that the equilibrium of mutual retention can allow for a new way of modeling an extremely efficient motor. Finally, a theoretical efficiency analysis showed the need to expand the limits of some physical concepts already established.

Parkinson’s disease (PD) is a common multisystem neurodegenerative disorder affecting 1% of the population above 60 years. The main neuropathological features of PD are the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the presence of alpha synuclein (Syn)-rich Lewy bodies both manifesting with classical motor signs. Syn has emerged as a key protein in PD pathology as it can spread through synaptic networks to reach several anatomical regions of the body contributing to the appearance of non-motor symptoms (NMS) considerate prevalent among individuals before PD diagnosis and persisting throughout the patient’s life. NMS mainly include loss of taste and smell, constipation, psychiatric disorders, dementia, rapid eye movement (REM) sleep behavior impairment, urogenital dysfunction, and cardiovascular impairment. This review summarizes the more recent findings showing the impact of Syn deposits on several prodromal NMS and emphasizes the importance of early detection of Syn toxic species in biofluids and peripheral biopsies as prospective biomarkers in PD.

To improve efficacy and safety of sunscreen emulsions, UV‐filter skin permeation could be kept as low as possible by reducing UV‐filter content or changing emulsion composition. In this work, different lipid phase ratio (12, 14 and 16% w/w) were assessed for their ability to affect the technological and sensory properties of O/W emulsions in which bemotrizinol (BMTZ), a broad spectrum sunscreen agent, was incorporated free or loaded into nanostructured lipid nanocarriers (NLC) to reduce its release from the vehicle and, hence, its skin permeation. The following technological properties were evaluated in vitro by suitable methodologies: spreadability, viscosity, pH, occlusion factor, BMTZ release, sun protection factor (SPF). Sensory attributes were assessed by trained panelists in three different phases: before/during pick‐up, rub‐in, after application. Raising the lipid phase ratio led to an increase of viscosity and to a corresponding drop of spreadability while the incorporation of BMTZ‐loaded NLC determined a decrease of occlusion factor and an increase of SPF. No BMTZ release was observed from all emulsions. Sensory attributes were mainly affected by lipid phase ratio. These results suggest that lipid phase ratio and BMTZ incorporation into NLC could contribute to determine technological and sensory properties of O/W emulsions.

This study involved a comprehensive examination of sensory attributes in Bísaro dry-cured loins, including odor, androsterone, scatol, color, fat color, hardness, juiciness, chewiness, flavor intensity, and flavor persistence. Analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveil distinct peaks associated with significant components such as protein, lipid, and water. Support Vector Regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization, and radial base kernel (non-linear SVR model). The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616 - 0.9955) and low RMSE values (0.0400 – 0.1031). The prediction set's relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig aroma, hardness, fat color, and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment.

Background: Vestibular Neuritis (VN) induces Acute Vestibular Syndrome (AVS). This study aimed to identify predictive factors of VN recovery by healing and/or compensation. Methods: In this longitudinal and prospective study, 40 patients were included. The first assessment (T0) included medical history taking (general and specific), for cardiovascular risk factors (CVRFs), a battery of diagnostic tests, bithermal caloric test (BCT), video Head Impulse Test (VHIT) and skull vibration induced nystagmus (SVIN). All patients also completed a Dizziness Handicap Inventory (DHI). All assessments were repeated 90±15 days (T3) later. Subjective criteria compensation were based on DHI total score and objective criteria compensation on the instrumental test results. Four groups of patients (A, B, C, D) were delineated by combining patients with normal vs abnormal vestibular tests and patients with normal vs abnormal DH. Results: CVRFs were associated with a poorer recovery of symptoms but not the age or BMI. Vestibular tests mainly BCT (canal paresis %), VHIT (lateral semicircular canal gain) and SVINT (nystagmus slow phase velocity) recovered within normal values in 20%, 20%, 26% of cases respectively at T3 for the 40-patient group. Conclusions: Vascular risk factors (hypercholesterolemia) are correlated with patients who do not recover their symptoms either by healing or compensation. The evolution did not show any significant difference between high or low frequency vestibular tests in patients recovering from their symptoms. However, some patients with objective recovery may continue to show persisting subjective symptoms.

Background: The hub-and-spoke model (HSM), offers a framework for efficient healthcare service delivery. This scoping review seeks to establish the implementation and effectiveness of the HSM in Antimicrobial Stewardship (AMS) programs. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) reporting guideline was followed. A systematic search was conducted in four electronic databases (PubMed, Medline, Cochrane library and Google scholar) from inception until January 2024. Studies where the HSM was used for delivering any AMS activity, intervention or action, were included. No study specific filters were applied and all populations, study designs and health settings were included. Data screening and selection was achieved using Rayyan. Three authors independently screened studies, with conflicts resolved by a fourth author. Data were narratively synthesized. Standard appraisal tools were impractical; however, critically evaluation of data collection and outcome reporting was ensured. Results: Out of 1,438 articles, three were included in the scoping review. The primary interventions utilising the HSM in AMS involved reducing antibiotic misuse, training healthcare professionals, case-based learning, establishing AMS programs, developing antibiograms, and formulating policies or guidelines pertinent to AMS. The studies demonstrated significant clinical improvements in AMS. Clinical outcomes from the studies include a significant reduction in antimicrobial usage and improved antibiotic management, with a notable decrease in days on antimicrobial therapy and increased antibiotic de-escalation. Key facilitators for AMS program success: tailored education, collaborative learning, strong leadership, strategic practices, and data-driven decisions. Key barriers: leadership challenges, change resistance, knowledge gaps, inadequate data systems, resource limitations, and technological constraints. Conclusion: The review identified a literature gap in HSM use for AMS programs. Further studies are needed to assess HSM's effectiveness, feasibility, and cost-effectiveness in AMS contexts.

External motivational stimuli have been shown to improve athletic performance. However, the neurophysiological mechanisms underlying this improvement remain poorly understood. This randomized crossover study investigated the effects of music and verbal encouragement on measures of muscle excitation and myoelectric manifestations of fatigue in the biceps brachii (BB) and brachioradialis (BR) muscles during an endurance task. Fifteen untrained (mean age 29.57 ± 2.77 years) and 13 trained individuals (mean age 32.92 ± 2.90 years) were included. The endurance task, performed to exhaustion, consisted of keeping the dominant arm flexed to 90 degrees while holding a dumbbell loaded to 80% of 1RM with a supine grip in three randomized conditions: standard, with self-selected music, and with verbal encouragement. The untrained subjects showed an increase in task duration of 15.26% (p < 0.003) with music and 15.85% (p < 0.002) with verbal encouragement compared to the condition without external stimuli. There were no significant differences in the myoelectric manifestations of fatigue between the dif-ferent conditions. Regarding the muscle excitation metrics, although the mean amplitude, peak value and area under the curve remained unchanged across conditions, a significant reduction in the trend coefficient, indicating motor unit recruitment over time, was observed with both music (BB: -10.39%, p < 0.001; BR: -9.40%, p < 0.001) and verbal encouragement (BB: -7.61%, p < 0.001; BR: -6.51%, p < 0.001) compared to the standard condition. For the trained participants, no significant differences were observed between conditions in terms of task duration and outcome measures related to muscle excitation and myoelectric manifestations of fatigue, suggesting the possible presence of a ceiling effect on motivation. These results highlight the significant role of external motivational stimuli, such as music and verbal encouragement, in improving task performance in untrained subjects, probably through more ef-fective and efficient recruitment of motor units.

Background: Despite recent advancements, medical technology has not yet reached its peak. Precision medicine is growing rapidly, thanks to machine learning breakthroughs powered by increased computational capabilities. This article explores a deep-learning application for computer-aided diagnosis in dermatology. Methods: Using a custom model based on EfficientNetB3 and deep learning, we propose an approach for skin lesion classification that offers superior results with smaller, cheaper, and faster inference times compared to other models. The skin images dataset used for this research includes 8,222 files selected from the authors' collection and the ISIC2019 archive, covering six dermatological conditions. Results: The model achieved 95.4% validation accuracy on four categories—melanoma, basal cell carcinoma, benign keratosis-like lesions, and melanocytic nevi—using an average of 1,600 images per category. Adding two categories with fewer images (about 700 each)—squamous cell carcinoma and actinic keratoses—reduced the validation accuracy to 88.8%. The model maintained accuracy on new clinical test images taken under the same conditions as the training dataset. Conclusions: The custom model demonstrated excellent performance on the diverse skin lesions dataset, with significant potential for further enhancements.

Human development encompasses the integration of neuromotor, psychological, social-emotional, and cognitive processes across time. However, a better understanding of the interconnectedness of motor and cognitive development throughout the lifespan is needed. The integrative and dynamic nature of short-term motor skill acquisition and long-term developmental processes may foster executive functions. Unfortunately, assessments of motor competence have traditionally focused on skillfulness from a movement perspective, using restrictive task protocols that decontextualize performance and limit cognitive involvement. Additionally, traditional motor-cognitive dual-task assessments centered on cognitive involvement during specific movement protocols, have minimized impacts of task complexity and motor competence on cognitive performance. To gain a more ecologically valid understanding of the relation between motor and cognitive development, a novel assessment paradigm using an integrative approach to motor-cognitive dual-tasking is needed. The purpose of the present paper is to: 1) provide a conceptual bridge, based on multidisciplinary evidence, to effectively link the concurrent development of motor competence and executive functions via learning-related and exercise-related neurotrophic mechanisms, and 2) use this conceptual bridge to inform the development of novel motor-cognitive dual-task assessments that account for the role of task complexity, current levels of motor competence, and the continuous decision-making inherent in “real world” performance environments.

The extreme environmental issues and the resulting needs to save energy have turned the attention to electrification of energy applications. One of the key components involved in energy efficiency improvements is the appropriate conception and manufacturing of electric machines. This paper overviews the electromagnetic analysis governing the behavior of permanent magnets that enabled substantial efficiency gains in recent electric machine developments. Particular emphasis is given to model the properties and losses developed in permanent magnets in the emerging high speed applications. In addition, the investigation of properties and harmonic losses related to ferromagnetic materials constituting the machine magnetic circuits are equally analyzed and discussed. The experimental validation of the implemented methodologies and developed models with respect to the obtained precision is reported. The introduction of mixed numerical techniques based on the finite element method intended to appropriately represent the different physical phenomena encountered is outlined and discussed. Finally, fast and accurate simulation techniques including aggregated lumped parameter models considering harmonic losses associated with inverter supplies are commented.

In former times, the spinal cord was considered a hard-wired network for spinal reflexes and a conduit for long-range connections. This view has changed dramatically over the past few decades. It is now recognized as a plastic device whose structures and functions adapt to changing circumstances. While such changes also occur under physiological conditions, the most dramatic alterations take place during or after various pathological events. It is astonishing what mechanisms the musculo-skeletal system has evolved to come to grips with the damages. Many of these changes are maladaptive, but some appear to help adapt to the new conditions. Although myriads of studies, using manifold methods, have been devoted to elucidating the underlying mechanisms, in humans and animal models, the etiology and pathophysiology of various diseases are still little understood, due to a number of reasons. We will here try to summarize some results and remaining problems in a selection of diseases, in particular spinal muscular atrophy (SMA), amyotrophic laterals sclerosis (ALS), and predominantly spinal cord injury (SCI) with occasional relations to stroke. Especially the changes in SCI (and stroke) depend on the cause, site and extent of the afflicted damage and are therefore multifarious. At the end, we will briefly summarize results indicating that operant, classical and instrumental conditioning can be used to produce plastic changes in healthy people, with potentials for applications to patients with spinal cord injury. In order not to overload the article, we will not delve deeply into sub-cellular processes.

Hybrid electric vehicles (HEVs) are perceived as a first step towards the future of sustainable transport. Of course, battery electric vehicles (EVs) are currently ideal for what is wanted to stand for transport in the future. The lack of infrastructure for these vehicles makes many potential users choose hybrid vehicles in these conditions. This paper analyzes the influence of the engine and electric motor of hybrid vehicles on their performances. Three engines with slightly different power and close characteristics were considered for configured models. Also, two electric motors with very different power but having a very close maximum continuous torque have been used in models. One is an induction motor, and the other is a permanent magnet synchronous motor. ADVISOR software has been used for vehicles configuration and simulation. Series and parallel hybrid vehicles have been considered. The main dynamic performances and the fuel consumption in two driving cycles were compared for the configured models. Three conventional models with the same engines used in HEVs have also been simulated for reference. The results highlight that in the case of HEVs, the choice of combination engine/electric motor is crucial in obtaining the best report between dynamic performances and low fuel consumption, and implicitly a low negative impact on the environment.

The Eurasian grapevine (Vitis vinifera L.) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and inspection activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) and Machine Learning (ML) methods have emerged as a classification alternative to deal with the scarcity of ampelographs and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The classification pipeline of the 31 studies found in the literature was described, highlighting its pros and cons. Most of the studies used DL-based models trained with leaf images acquired in a controlled environment at a maximum distance of 1.2 metres to classify grape varieties. In addition, there is a large gap between practical applications and the datasets used: a great lack of varieties, limited data acquired in the field and a lack of tests on plants under adverse conditions. Potential directions for improving this area of research were also presented.

Sensory processing is a crucial function of the nervous system that significantly affects a large portion of the population, particularly older individuals who may be dealing with different forms of dementia. The global population of people aged 65 or older has reached a substantial count of around 703 million, representing approximately 9% of the total global population. Within this demographic, an estimated 5-8% of those aged 60, highlighting the growing need to address sensory modulation challenges for healthy aging. Sensory diet, derived from sensory integration theory, provide a tailored approach to modulating sensory experiences and triggering neuroplastic changes in the brain. Recent neuroimaging studies have revealed intricate structural and functional alterations in the brain associated with the implementation of sensory diets. Furthermore, the integration of the Metaverse, augmented reality, and virtual reality has opened up personalized experiences, fostering cognitive stimulation and emotional well-being for individuals during aging. This article offers a comprehensive exploration of the effectiveness of sensory diets in enhancing attentional control, self-regulation, and alleviating sensory-related distress among the healthy aging and dementia population. By employing precise quantitative evaluations such as sensory processing scales and advanced neuroimaging methodologies, it presents solid evidence of the tangible impact of these interventions. Emphasizing the importance of interdisciplinary cooperation and leveraging technological advancements, the article underscores the crucial need for customized sensory diets to optimize sensory processing and improve the overall well-being of older individuals contending with sensory modulation challenges and dementia.

Self-sustained learning (SSL) is considered a crucial factor in promoting students’ long-term learning and development in a sustainable society. To deepen the understanding of SSL, especially in the context of learning English as a second language, the current study examined the influence of intercultural communicative skills, language mindset, and positive L2 self on SSL in English learning among 1,238 college students in China. The results showed that students not only had a strong language mindset, positive L2 self, and intercultural communicative skills but also scored higher in long-term self-sustained English learning. Findings from the Structural Equation Modeling indicated that both language mindset and intercultural communicative skills positively predicted positive L2 self and self-sustained English learning. Furthermore, language mindset and intercultural communicative skills had an indirect impact on self-sustained English learning through positive L2 self. These findings suggest that to promote English learners’ SSL, instructors should help facilitate students’ intercultural communicative skills and foster a growth language mindset, as well as positive language learning beliefs.

Reliable motor control design is a critical part of the electric vehicle application. Control techniques and motor control devices play an important role in achieving system reliability. In this study, motor control devices have been proposed to implement control techniques in sensorless PMSM. Motor control devices including PWM signal generation by FPGA, gate driver circuit (GDC), and power inverter were developed to be integrated into single-stage motor control devices for sensorless motor control applications. Improvements in GDC design have been the focus to increase the performance of the motor control devices used. Switching losses due to the parasitic elements caused ringing switching behavior of the power inverter circuit, which reduces the performance when the system is integrated. Moreover, in the implementation of sensorless motor control using observer techniques, these conditions affect the accuracy of rotor position estimation, which uses the current or voltage information of the power converter as a variable in the observer design model. Therefore, a GDC design using IR2110 with additional ringing suppression circuit configuration was proposed for the application of motor device control topology to improve the performance of sensorless motor control system. Furthermore, the reduction of switching losses, reduction of current distortion, and accuracy of rotor position information were confirmed by experimental results.

Abstract: Windtech device is a novel tool for measuring the sensation of the ‘cold’. Cold poses numerous challenges for industrial operations, human survival, and living convenience. The impact of the cold is not possible to be quantified just based on temperatures; however other factors such as wind speed, humidity, irradiance have to be taken into consideration. Efforts have been made to develop combined indices such as wind chill temperature (WCT), AccuWeather RealFeel®, and others. The presented article discusses these along with the industrial standards that emphasize on the quantification of the ‘cold’. The following article introduces the Windtech device and its operating principle involving ‘heated temperature’, where the ‘heated temperature’ is affected by environmental parameters including ambient temperature, humidity, wind velocity, and irradiance. The discussed Windtech device is calibrated for operation according to the ISO 11079:2007 standard.

The present study aimed to investigate the effect of a motor skills training program in children with DCD considering their gender. The Movement Assessment Battery for Children (MABC-2) classified the children and assessed skill changes over time. The study was implemented at four kindergartens in the Khorezm region of Uzbekistan. In the study, all children had DCD (5.17 ± 0.70 years; 10 girls), and all indicators on MABC-2 were less than 16 percent. Participants were divided into an experimental group (n=17) receiving ten weeks of motor skills training program for 45 min twice per week, the control group (n=7) proceeded with exercises of everyday living. All children in the experimental group had a significant increase in total percentile rank of MABC-2 and concerning each domain (manual dexterity; throwing and catching; balance). In the control group, a significant decrease in the total percentile rank of MABC-2 and each domain of MABC-2 was observed. The effectiveness of the intervention program was similar across both genders. The study supports that a period of 10 weeks of a motor skills training program can increase the quality of children's motor coordination and represent a valuable procedure for physical education specialists to enhance motor skills for children with DCD.

The rapid expansion of Bangladesh's financial infrastructure, coupled with the increasing digitization of banking services, underscores the critical importance of cybersecurity. Machine learning and deep learning methodologies have emerged as potent tools in combating cyber threats such as intrusion, malware, and fraud. By harnessing artificial intelligence and data-driven analysis, these techniques enable real-time scrutiny of vast banking data, facilitating proactive defense against cyberattacks. Bangladesh's evolving financial landscape, characterized by governmental digitalization initiatives and a national financial inclusion strategy, heightens the imperative for robust cybersecurity measures. This paper explores the application of machine learning and deep learning techniques in bolstering banking cybersecurity within the context of Bangladesh's burgeoning digital economy.

In many developing countries of the world including Pakistan motorcycle steeling is one of the main issue that needs to be dealt with an iron hand as it leads to economic losses as well as crime increase in the society, according to the DOWN REPORT there were 28609 motorcycle stolen in 2019 and this number has been increased to 34908 in 2020. Now due to the increasing motorcycles stealing crime what measures can we take to reduce the number of motorcycle theft the rule of technology is enormous in this regards specially information technology (IT) , information technology can play an important role in the security of motorcycles from being theft by the thieves, Information technology provide us the GSM based control system which can be very help full to construct a security system for motorcycle in order to make it antitheft.

Molecular motors play a vital role in the transport of material within the cell. A family of motors of growing interest are burnt bridge ratchets (BBRs). BBRs rectify spatial fluctuations into directed motion by creating and destroying motor-substrate bonds. It has been shown that the motility of a BBR can be optimized as a function of the system parameters. However, the amount of energy input required to generate such motion and the resulting efficiency has been less well characterized. Here, using a deterministic model, we calculate the efficiency of a particular type of BBR, namely a polyvalent hub interacting with a surface of substrate. We find that there is an optimal burn rate and substrate concentration that leads to optimal efficiency. Additionally, the substrate turnover rate has important implications on motor efficiency. We also consider the effects of force-dependent unbinding on the efficiency and find that under certain conditions the motor works more efficiently when bond breaking is included. Our results provide guidance for how to optimize the efficiency of BBRs.

This paper is concerned with a hydraulic cylinder directly controlled by a variable speed fixed displacement pump. The considered configuration is referred to as a one-motor-one-pump (1M1P) motor-controlled hydraulic cylinder (MCC). A 1M1P MCC with a hydraulically driven passive load-holding function enabled by controlled system pressures is proposed. The proposed load-holding functionality works if there is a standstill command, a loss of power supply, or a hose rupture. Additionally, this paper conducts a comprehensive analysis of the proposed system's operation and load-holding function across four quadrants. Simulation results demonstrate the four-quadrant operation and good load-holding performance under the aforementioned scenarios. In conclusion, the proposed 1M1P MCC can be successfully used on practical applications characterized by overrunning loads and four quadrant operation.

The proliferation of fake news on social media platforms poses significant challenges to information authenticity and public trust. In this study, the impact of various word embedding techniques on the performance of both machine learning and deep learning models for fake news detection is systematically evaluated. The comprehensive TruthSeeker dataset, which spans over a decade of labeled news articles and social media posts, is utilized. Three popular word embedding techniques are analyzed: TF-IDF, Word2Vec, and FastText. These embeddings were tested with multiple machine learning classifiers, including logistic regression, naive Bayes, K-nearest neighbors, support vector machines, multilayer perceptrons, decision trees, and random forests, as well as deep learning models, specifically convolutional neural networks (CNNs). Among machine learning models, the Support Vector Machine (SVM) with TF-IDF achieved the highest accuracy of 99.03%, precision of 98.92%, recall of 99.15%, and F1 score of 99.03%. The Multilayer Perceptron (MLP) also demonstrated strong performance with Word2Vec, reaching an accuracy of 95.24%, precision of 94.95%, recall of 95.50%, and F1 score of 95.07%. For deep learning models, CNNs combined with TF-IDF embeddings showed outstanding results, achieving an accuracy of 98.99%, precision of 98.56%, recall of 99.48%, and F1 score of 99.02%. These results highlight the critical role of word embeddings in enhancing model accuracy and reliability in misinformation detection.

With advancements in digital technologies such as 5G communications, big data, and cloud computing, the components of network operation systems have become increasingly complex, significantly complicating system monitoring and maintenance. Correspondingly, automated log anomaly detection has become a crucial means to ensure stable network operation and protect them from malicious attacks or failures. Conventional machine learning and deep learning methods assume consistent distributions between the training and testing data, adhering to a closed-set recognition paradigm. Nevertheless, in realistic scenarios, systems may encounter new anomalies that were not present in the training data, especially in log anomaly detection. Inspired by evidential learning, we propose a novel anomaly detector called LogEDL, which supervises training of the model through an evidential loss function. Unlike traditional loss functions, the evidential loss function not only focuses on correct classification, but also quantifies the uncertainty of predictions. This enhances the robustness and accuracy of the model in handling anomaly detection tasks, while achieving functionality similar to open-set recognition. To evaluate the proposed LogEDL method, we conduct extensive experiments on three datasets, i.e., HDFS, BGL, and Thunderbird, to detect anomalous log sequences. Experimental results demonstrate that our proposed LogEDL achieves state-of-the-art performance in anomaly detection.

Recent years have witnessed significant advances in reinforcement learning (RL), which has registered remarkable success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Although, typically, the communication protocol between agents is manually specified and not altered during training, recently, some papers have shown signs of trying to emerge a communication between agents on the one hand and, on the other hand, to understand what is exchanged between agents. So, there is a growing body of literature on this topic which includes qualitative and quantitative studies and the ones that apply mixed methods. This study presents the scoping review of the methodological strategies undertaken in a total of 16 research articles. The results present the critical appraisal of quantitative methods in terms of validity and reliability and for qualitative methods considering four trustworthiness factors. In the end, relevant insights are further explored with implications and reflections on how they can benefit one's research in the field.

Technology-based approaches for upper limb (UL) motor rehabilitation after stroke are designed mostly for severely affected patients to increase their recovery chances. However, the available randomised controlled trials (RCTs) focused on efficacy of technology-based interventions often include patients with a wide range of motor impairment. This scoping review aims at overviewing the actual severity of stroke patients enrolled in RCTs which claim to specifically address UL severe motor impairment. The literature search was conducted on Scopus and PubMed databases and included articles from 2008 to May 2024, specifically RCTs investigating the impact of technology-based interventions on UL motor functional recovery after stroke. Forty-eight studies were selected. They showed that upon patients’ enrolment the values of the UL Fugl-Meyer Assessment and Action Research Arm Test covered the whole range of both scales, thus revealing the non-selective inclusion of severe impaired patients. Heterogeneity in terms of numerosity, characteristics of enrolled patients, trial design, implementation, and reporting were present across studies. No clear difference in the severity of the included patients according to the intervention type was found. Patient stratification upon enrolment is crucial to best direct resources to those patients who will benefit the most from a given technology-assisted approach (personalised rehabilitation).

Background and Aims. Most research on Parkinson's disease (PD) focuses on describing symp-toms and movement characteristics. Studies rarely focus on the early detection of PD and the search for suitable markers of a prodromal stage. Early detection is important, so treatments that may potentially change the course of the disease can be attempted early on. While gait disturb-ances are less pronounced in the early stages of the disease, the prevalence, and severity increase with disease progression. Therefore, postural instability and gait difficulties could be identified as sensitive biomarkers. The aim was to evaluate the discriminatory power of the Trail-Walking Test (Schott, 2015) as a potential diagnostic instrument to improve the predictive power of the clinical evaluation concerning the severity of the disease and record the different aspects of walking. Methods. 20 older healthy (M = 72.4 years, SD = 5.53) adults and 46 older adults with PD and the motor phenotypes postural instability/gait difficulty (PIGD; M = 69.7 years, SD = 8.68) and tremor dominant (TD; M = 68.2 years, SD = 8.94) participated in the study. The participants performed a motor-cognitive dual task (DT) of increasing cognitive difficulty in which they had to walk a given path (condition 1), walk to numbers in ascending order (condition 2), and walk to numbers and letters alternately and in ascending order (condition 3). Results. With an increase in the cognitive load, the time to complete the tasks (seconds) become longer in all groups, F(1.23, 73.5) = 121, p < .001, ɳ2p = .670. PD-PIGD shows the longest times in all conditions of the TWT, F(2, 60) = 8.15, p < .001, ɳ2p = .214. Mutual interferences in the cognitive and motor domain can be observed. How-ever, clear group-specific patterns cannot be identified. A differentiation between the motor phenotypes of PD is especially feasible with the purely motor condition (TWT-M; AUC = . 685, p = 0.44). Conclusions. PD patients with PIGD must be identified by valid, well-evaluated clinical tests that allow a precise assessment of the disease's individual fall risk, the severity of the dis-ease, and the prognosis of progression. The TWT covers various aspects of mobility, examines the relationship between cognitive functions and walking, and enables differentiation of the motor phenotypes of PD.

The potentiality of cv. Malbec grape pomace (GP) as a functional ingredient in the formulation of bakery foods (muffins, biscuits and cereal bars) was studied. The effect of GP addition on the phenolic compounds (PCs) composition, nutritional and sensory properties were evaluated. The addition of GP increased the content of dietary fiber, proteins, ash, total phenolic content (TPC), antioxidant activity (AA), anthocyanins and non-anthocyanins PCs while decreasing the carbohydrates content. The main PCs given by the GP to supplemented foods were (-)-gallocatechin, quercetin-3-O-glucoside, rutin, caffeic acid, syringic acid and (+)-catechin. For anthocyanins, the acylated derivatives were more stable to heat treatment (baking) in food processing which was evidenced by a higher proportion of these PCs compounds when compared to the same derivatives quantified in GP. In general, when the TPC or individual concentrations of PCs were analyzed in a nutritional or functional context, one portion of the supplemented foods showed levels high enough to satisfy the recommended dose per day of these bioactive compounds. Additionally, the foods were well received by consumers during the sensory evaluation and supplemented biscuits got the highest acceptability. This study demonstrated that GP could be a viable functional ingredient in bakery foods to give nutritionally enhanced properties like TPC, PCs, AA and dietary fiber in consumer’s diets.

Salting influences microbial growth, enzymatic activity and biochemical reactions during ripening, thus contributing to the final quality of cheese. The aim of this study was to evaluate the influence of different salting methods (dry salting at 12, 24 and 48 h; in brine; and curd salting) on the chemical (moisture and salt content), physico-chemical (pH, titratable acidity and water activity) and sensory (texture profile analysis, colour and sensory attributes) characteristics of an industrial blue vein cheese. Dry salted cheeses had lower moisture content and water activity, and higher salt/moisture content and acidity than those salted in brine and curd. Dry-salted cheeses were also characterised by higher values for hardness, fracturability and instrumental gumminess. Dry-salted cheeses showed differences only in the red/green colour component (a*), with the curd-salted cheeses being less greenish. All cheeses scored high (around 7) in the tasters' overall impression, with the dry-salted cheeses at 12 and 24 h showing optimal growth and even distribution of mould, as well as better flavour and texture.

The craft beers are outlined as a distinctively flavored, brewed and distributed regionally, using top-fermenting (ale) yeast, bottom-fermenting (lager) yeast or spontaneously fermentation. Craft beers are largely consumed and produced in Brazil and presents great level of polyphenols, which would affect the consumer’s preference. In this way, we analyzed the relation between polyphenols, bitterness and composition of main different styles of craft beers and the consumer´s preference. Six different styles were analyzed according its polyphenol content, bitterness, chemical composition, sensory profile and preference. For preference, a panel with 62 non-trained assessors was used. For sensory profile, the quantitative descriptive analysis was performed, using expert assessors (n = 8). The preferred style was Classic American Pilsner and the style less preferred was Standard American Lager. The craft beer more preferred showed a decreased bitterness (9.52), polyphenol content (0.61 mg EAG/mL), total solids (6.75 ºBrix) and turbidity (7.27 NTU). This beer exhibited reduced sensory notes of malty, fruity, smoked, hoppy and phenolic, but a higher perception of floral, sweet and yeast notes. The bitter attribute has a reduced perception. This study advances understanding the sensory profile and complexity of craft beers styles from Southern Brazilian.

The advancement of autonomous driving technology is becoming increasingly vital in the modern technological landscape, promising notable enhancements in safety, efficiency, traffic management, and energy use. Despite these benefits, conventional deep reinforcement learning algorithms often struggle to navigate complex driving environments effectively. To tackle this challenge, we propose a novel network called DynamicNoise, designed to significantly boost algorithmic performance by introducing noise into the Deep Q-Network (DQN) and Double Deep Q-Network (DDQN). Drawing inspiration from the NoiseNet architecture, DynamicNoise uses stochastic perturbations to improve the exploration capabilities of these models, leading to more robust learning outcomes. Our experiments demonstrate a 57.25% improvement in navigation effectiveness within a 2D experimental setting. Moreover, by integrating noise into the action selection and fully connected layers of the Soft Actor-Critic (SAC) model in the more complex 3D CARLA simulation environment, our approach achieved an 18.9% performance gain, substantially surpassing traditional methods. These results confirm that the DynamicNoise network significantly enhances the performance of autonomous driving systems across various simulated environments, regardless of their dimensionality and complexity, by improving their exploration capabilities rather than just their efficiency.

The success of near-infrared spectroscopy (NIRS) analysis hinges on the precision and robustness of the calibration model. Shallow learning (SL) algorithms like partial least squares discriminant analysis (PLS-DA) often fall short in capturing interrelationships between adjacent spectral variables, and the analysis results are easily affected by spectral noise, which dramatically limits the breadth and depth application of NIRS. Deep learning (DL) methods, with their capacity to discern intricate features from limited samples, are progressively integrated into NIRS. In this paper, two discriminant analysis problems including wheat kernels and Yali pears were used as examples, and several representative calibration models to research the robustness and effectiveness of the model. Additionally, this article proposed a near-infrared calibration model, which is based on gramian angular difference field and coordinate attention convolutional neural networks (G-CACNN). The research results show that, compared with SL, spectral preprocessing has a smaller impact on the analysis accuracy of consensus learning (CL) and DL, and the latter has the highest analysis accuracy in the modeling results of using the original spectrum. The accuracy of G-CACNN in two discrimination tasks is 98.48% and 99.39%, respectively. Finally, This research compared the performance of various models under noise to evaluate the robustness and noise resistance of the proposed method.

When we are asleep we lose the ability to promptly respond to external stimuli and yet we spend many hours every day in this inherently risky behavioral state. This simple fact strongly suggests that sleep must serve essential functions that rely on the brain going offline, on a daily basis, and for long periods of time. If these functions did not require partial sensory disconnection, it would be difficult to explain why they are not performed during waking. Paradoxically, despite its central role in defining sleep and what sleep does, sensory disconnection during sleep remains a mystery. We have a limited understanding of how it is implemented along the sensory pathways, we do not know whether the same mechanisms apply to all sensory modalities, nor do we know to which extent these mechanisms are shared between non-rapid eye movement (NREM) sleep and REM sleep. The main goal of this contribution is to review some knowns and unknowns about sensory disconnection during sleep as a first step to fill this gap.

The detection of early biomarkers and molecular mechanisms in Parkinson’s disease (PD) remains a challenge. Recent research has pointed to potential roles for peptidylarginine deiminases (PADs), a family of calcium activated enzymes, and associated post-translational protein citrul-lination/deimination in early stages of the disease, with a focus on hippocampal and cortex re-gions of the brain. Further analysis of brain region specific changes in PAD isozymes and identi-fication of brain-region specific citrullinomes, therefore remain to be studied. This study assessed brain-region specific PAD isozyme expression (PADs 1-4; PAD6) and associated citrullinated protein targets in the 6-hydroxydopamine (6-OHDA) induced rat model of pre-motor PD. Cortex, hippocampus, striatum, midbrain, cerebellum and olfactory bulb were compared between con-trols/shams and the PD model. In cortex, a significant increase of PAD2 and PAD3 was observed in the PD model. In hippocampus, PAD3, PAD4 and PAD6 showed strongest levels, but with no significant changes observed between PD and controls. In striatum, a significant reduction in all PAD isozymes was observed in the PD model. In the olfactory bulb, PAD3 was significantly el-evated in PD. In midbrain, PAD2, PAD4 and PAD6 showed strongest signal but with no signifi-cant changes between PD and controls. In cerebellum, a significant increase was seen in PAD3, PAD4 and PAD6 in the PD model. Citrullinated protein hits were most abundant in cortex and hippocampus, while for the PD model an increase in citrullinated hits was particularly observed in cortex and cerebellum, compared to controls. For all brain regions there was a considerable difference in citrullinated protein IDs between the PD model and the controls. Citrullinome as-sociated KEGG pathways differed in the six brain regions; some were overlapping for controls and PD, some were identified for the PD model only, and some were identified in control brains only. KEGG pathways identified in PD brains onlywere: “Axon guidance”; “Spinocerebellar ataxia”; “Hippo signalling pathway”; “NOD-like receptor signalling pathway”; “Phosphatidyl-inositol signalling system”; “Rap1 signalling pathway”; “Platelet activation”; “Yersinia infection”; “Fc gamma R-mediated phagocytosis”; “Human cytomegalovirus infection”; “Inositol phosphate metabolism”; “Thyroid hormone signalling pathway”; “Progesterone-mediated oocyte matura-tion”; “Oocyte meiosis”; and “Choline metabolism in cancer”. Our findings highlight differences in PAD isozymes, citrullinated proteins and associated KEGG pathways between brain regions, in pre-motor PD.

Recent research on fractional order control laws has introduced the fractional calculus concept into the field of control engineering. As described herein, we apply fractional order LQR control to a current-controlled attractive-force type magnetic levitation system, which is a strongly nonlinear and unstable system, to investigate its control performance through experimentation. First, to design the controller, a current-controlled attractive-force type magnetic levitation system expressed as an integer-order system is extended to a fractional order system expressed using fractional order derivatives. Then target value tracking control of levitated objects is achieved by adding states, described by the integrals of the deviation between the output and the target value, to the extended system. Next, a fractional order LQR controller is designed for the extended system. For state-feedback control such as fractional order servo LQR control, which requires information of all states, a fractional order state-observer is configured to estimate fractional order states. Simulation results demonstrate that fractional order servo LQR control can achieve equilibrium point stabilization and enable target-value tracking. Finally, to verify the fractional order servo LQR control effectiveness, experiments using the designed fractional order servo LQR control law are done with comparison to conventional integer-order servo LQR control.

Multimodal remote sensing data classification can enhance the model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks is the focus of researchers. Inspired by human feedback learning mechanisms, causal reasoning mechanisms, and knowledge induction mechanisms, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposed the causal meta-reinforcement learning (CMRL). First, based on feedback learning mechanisms, we have overcome the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we help them understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information. Second, based on the causal inference mechanism we designed causal distribution prediction action, classification reward, and causal intervention reward, capturing pure causal factors in multimodal data and cutting off false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets show that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

The increasing adoption of student-centred learning environs by the institutions of higher education in business, has led to new mechanisms and methodologies such as the Simulation-Based Learning (SBL), Problem-Based Learning (PBL) and Challenge-Based Learning (CBL). Literature has shown evidence of raising interest in these methodologies towards a more experiential paradigm with regard to upcoming events from the surrounding real-world context. This paper presents a Systematic Bibliographic Literature Review (SBLR) with respect to the integration and application of these innovative and collaborative methodologies in higher education. It aimed to identify its relevant advantages, challenges, and consequences for the future of business education. Bibliographic databases were searched for documents published up to May 2024 to categorize key topics discussed in the literature on SBL, PBL and CBL. The review process identified 89 empirical and non-empirical papers on SBL, PBL and CBL, business related paperwork, which resulted from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method. PRISMA is a framework with evidence-based data designed to help authors perform a systematic literature review. Data analysis revealed diverse subthemes for those methodologies uses in business higher education institutions. The research questions “What are the advantages, challenges and implications of SBL, PBL and CBL altogether, in the training of business students?” guided the study. The central output of the research is the discovery of the varying uses of SBL, PBL and CBL overall, to cope with diverse demands arising within environmental context. Future research directions are suggested.

Renal cell carcinoma (RCC) accounts for approximately 90-95% of all kidney cancers in adults, with the clear cell RCC (ccRCC) the most frequently identified subtype. RCC is known for its responsiveness to immunotherapy, making it an area of significant research interest. Immune checkpoint (IC) molecules, which regulate immune surveillance, are established therapeutic targets in RCC. The aim of this study was to analyze the influence of polymorphisms in genes HVEM and CD160 on susceptibility to ccRCC and patient overall survival (OS) with over a ten-year period of observation. We genotyped three HVEM single nucleotide polymorphisms (SNPs): rs1886730, rs2234167, and rs8725, and two CD160 SNPs: rs744877 and rs2231375, in 238 ccRCC patients and 521 controls. Our findings indicated that heterozygosity within rs2231375 and/or rs2234167 increases ccRCC risk. Furthermore, in women, heterozygosity within additional HVEM SNPs, rs8725 and rs1886730, is associated with an increased ccRCC risk. The presence of a minor allele for rs1886730, rs2234167, rs8725, and rs2231375 was also correlated with certain clinical features of the disease. Moreover, the rs1886730 SNP was found to be associated with patients' OS. In conclusion, our study highlights an association between HVEM and CD160 polymorphisms and the risk of developing ccRCC as well as patient’s OS.

Motor imagery requires mental representation of the body, which is obtained by the integration of exteroceptive and interoceptive information. The aim of the study was to investigate the role of interoceptive sensitivity in motor imagery performed through visual and kinesthetic modalities in participants with low (lows, N = 26), medium (mediums, N = 11) and high hypnotizability scores (highs, N = 16), measured by the Stanford Hypnotic Susceptibility Scale: Form A. These groups display different motor imagery abilities and interoceptive sensitivity. The efficacy of motor imagery was measured by chronometric index and self-reported experience, and interoceptive sensitivity by the Multisensory Assessment of Interoceptive Awareness (MAIA) questionnaire. EEG alpha and beta power spectral densities were extracted from recordings acquired during baseline, actual movement, visually and kinesthetically imagined movements. The chronometric indices did not reveal significant differences among groups and between imagery modalities. In contrast, the self-reported imagery efficacy indicated better kinesthetic imagery in highs and mediums than in lows, and no modality difference among lows. MAIA dimensions sustained the differences in sub-jective experience and almost all the EEG differences, slightly different in highs, mediums and lows. This is the first report of a major role played by interoceptive sensitivity in motor imagery.

The number of artificially ripened pineapples is outnumbered than the naturally ripened pineapples. However, there is a lack of understanding between artificially ripened and naturally ripened pineapples. Thus the inquiry was anticipated to explore the physicochemical changes and organoleptic acceptability of the naturally ripened and artificially ripened pineapples. Farmers used different chemicals such as calcium carbide, ethylene, besides growth hormones to reduce production loss. Here we evaluated the content of moisture, ash, protein, fat, crude fiber, reducing sugar, total sugar, titratable acidity, sucrose, and vitamin C in both naturally ripened and artificially ripened pineapples. Artificially ripened pineapples showed a significantly lower vitamin C than naturally ripened ones, but arsenic content was nil in both samples. In the case of color and appearance, there was no significant difference between the two samples, but in case of the other organoleptic properties, such as flavor, sweetness, sourness, the natural one was more acceptable. Thus naturally ripened pineapples are more beneficial to consumers than artificially ripened ones.

Applying cold to a bone injury can aid healing, though its mechanisms are complex. This study investigates how cold therapy impacts bone repair to optimize healing. Cold was applied to a rodent bone model, with physiological responses analyzed. Vasoconstriction was mediated by an increase in TRP channels, TRPA1 (p=0.012) and TRPM8 (p&lt;0.001), within cortical defects enhancing sensory response and blood flow regulation. Cold exposure also elevated hypoxia (p &lt; 0.01) and VEGF expression (p &lt; 0.001), promoting angiogenesis vital for bone regeneration. Increased expression of osteogenic proteins PGC-1α (p = 0.039) and RBM3 (p&lt;0.008) suggests stimulated reparative processes. Enhanced osteoblast differentiation and ALP presence at days 5 (three-fold, p=0.014) and 10 (two-fold, p=0.010) were observed, along with increased osteocalcin (OCN) at day 10 (two-fold, p = 0.010) indicating presence of mature osteoblasts capable of mineralization. These findings highlight cold therapy's multifaceted effects on bone repair, offering insights for therapeutic strategies.

Arthrospira platensis, which is known as the most important food supplement of the future due to its high nutritional value, was the focus of this study. The main objective of this study was to develop a functional cracker enriched with proteins, essential amino acids, and polyunsaturated fatty acids with the incorporation of A. platensis. Therefore, this research aimed to improve the texture (including moisture and firmness) and sensory properties (such as color, taste, and smell) of crackers by adding A. platensis at three different levels (0%, 2.5%, and 5.0%). The sensory attributes of crackers were evaluated by twenty taste testers and thirty consumers, assessing color, odor, taste, crispness, and overall preference. The highest hardness value among the studied trials was observed in trial 14, which contained 5% A. platensis with 577 N. According to the results of the panel test, among the crackers with 5% A. platensis addition, trial 14 had the highest color value. To obtain the optimum formula, dough with 50 mg/g A. platensis, 625 mg/g flour, and 220 mg/g water was recommended. The addition of A. platensis has great potential to improve the nutritional, textural, and sensory properties of crackers.

The popularity of functional gummies has increased, which is evident from the growing line of functional gummies from almost every nutraceutical companies. Sensory evaluation serves the purpose of determining which brand of functional gummy would capture the largest market share. Texture profile analysis was used to determine the mechanical properties of functional gummies. The brands of functional gummies that came under the scope of this study were denoted as Brand A, B, C, D, E, F and G. Fourier Transform Infrared Spectroscopy was utilised to detect organic material and functional groups in the functional gummies. Texture profile analysis gave valuable insights into the gummies’ mechanical properties which are cohesiveness, springiness, hardness, gumminess, and chewiness. Amongst the gummies that were studied, Brand F gummy has the highest value of cohesiveness of 0.92. Brand A gummy has a high springiness value of 1.0. Brand B gummy possesses the highest value of hardness, gumminess and chewiness of 12 532.2 g, 7617.6 N, and 6256.8 J, respectively. Qualitative sensory evaluation reveals that Brand G gummy has the best aesthetic qualities in terms of colour and appearance. Brand B gummy tastes the best while brand A gummy claims the top spot for gumminess and chewiness. Overall, the respondents in this study preferred brand A gummy over other brands.

Sustainable innovation in the agro-food system has become a strategy increasingly used by companies as a means to increase their competitiveness and position themselves in the market. In this context, the objective of this work is to identify the attitudes and sensory perceptions of consumers towards sustainable food technology through two scales (Food Technology Neophobia and Domain Specific Innovativeness) and hedonic tastings. For this, a new product was selected in the market: powder to prepare rice with milk. Most consumers have attitudes toward low neophobia to products with food technology, but there is also some caution, situation that is corroborated by the moderate predisposition towards innovations. Color and flavor attributes can make the difference in positive perceptions. It should not be forgotten that there is a segment of innovative sustainable consumers that represent a key market.

Discretization is the process of converting a continuous function or model or equation into discrete steps. In this work, adaptive and learning methods are implemented to control DC motors that are used for actuating control surfaces of unmanned underwater vehicles. Adaptive control is a method in which the controller is designed to adapt the system with parameters which vary or are uncertain. Parameter estimation is the process of computing the parameters of a system using a model &amp; measured data. Adaptive methods have been used in conjunction with different parameter estimation techniques. Deterministic artificial intelligence, a learning-based approach that uses the process dynamics defined by physics, is also applied to control the output of the DC motor to track a specified trajectory. This work goes further to evaluate the performance of the adaptive &amp; learning techniques based on different discretization methods. The results are evaluated based on the absolute error mean between the output &amp; the reference trajectory and the standard deviation of the error. The first order-hold method of discretization and surprisingly large sample time of seven tenths of a second yields over sixty percent improvement over the results presented in the prequel literature.

In a recent paper discussing Newton’s third law in the framework of special relativity for charged bodies, it was suggested that one can construct a practical relativistic motor provided high enough charge and current densities are available. As on the macroscopic scale charge density is limited by the phenomena of dielectric breakdown, it was suggested to take advantage of the high charge densities which are available on the microscopic scale. A preliminary analysis of this option denoted "nano relativistic scale" is studied in the current paper.

This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected was merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port's activities.

Autonomous electronic area, territory, or space protection is getting more important. One of the areas of application of the system is to monitor the quarantine zones created during epidemics. The idea to create a system of mobile sensory units for defending quarantine zones was born during one of the phases of the Covid epidemic in 2020. The paper contains a brief technical description of a mobile sensor units and their network. This network surrounds an area of interest. The work includes a mathematical and data model of the protected area. In addition, the paper presents a method for distributing mobile sensor units in areas of interest.