Longer Response Times Research Articles

Influence of Layer’s Annealing Temperature on Sensing Properties of Spin-Coated SBS Layer Exposed to Alcohol Vapor

QCM-based alcohol vapor sensors have been widely researched, and various materials have been deposited onto the sensor surface to serve as a functional layer. However, non-conductive polymers such as SBS as a functional layer for alcohol sensors are still minimally reported. In this study, the SBS was deposited on 1 side of the QCM using the spin coating technique. After deposition, the layer was annealed for 1 h. The annealing temperatures are 100, 150 and 200 °C. The concentrations of exposed alcohol vapor were 5, 10, 15, 20, 25, 50, 75 and 100 ppm. This study demonstrated that the SBS layer has the potential to be a functional layer of alcohol sensors. SBS 200 °C is an optimum functional layer for alcohol vapor sensors due to its high ∆f and sensitivity. Meanwhile, SBS 100 °C has poor ∆f and sensitivity. This is due to the morphology of 200 °C SBS, which consists of more valleys that perform as interaction sites for SBS and alcohol molecules. However, one of the limitations of SBS as a functional sensor layer for alcohol sensors is its low selectivity, which is a characteristic of rubbery polymers. HIGHLIGHTS The SBS layer annealed at higher temperatures has more valleys than the SBS layer annealed at low temperatures. The valleys in the morphology of the SBS layer act as interaction sites for alcohol molecules, where each site interacts with an alcohol molecule. It is based on the sensor response and proved by fitting the Langmuir model. The SBS layer annealed at 200 ᵒC has a high ∆f, sensitivity, and longer response time. The selectivity of SBS was relatively low, a characteristic of rubbery polymers. The interaction between SBS and methanol is physisorption. GRAPHICAL ABSTRACT

Exploring the flexibility of word position encoding in Chinese reading: the role of transposition effects

ABSTRACT Previous research in the alphabetic writing system has demonstrated that transposition distance (adjacent vs. non-adjacent) modulates word position encoding during sentence reading. To examine the generality of this pattern within a more holistic model of sentence processing, we investigated this effect in the logographic Chinese writing system. We manipulated the number of words intervening between the transposed words, creating four conditions: none, one, two, and three intervening words. Participants performed a rapid grammaticality judgment task. Results showed longer response latencies and higher error rates when words were transposed adjacent to each other compared to non-adjacent transpositions. Furthermore, more errors and longer response times were observed when one word intervened between the transposed words compared to two or three intervening words. However, no significant differences emerged between the two- and three-word interval conditions. These findings suggest that word position encoding exhibits graded, flexible tolerance to transposition distance, constrained by proximity.

Dynamic analysis for ultra-supercritical boiler water wall considering uneven heat flux distribution and combustion instability

The dynamic characteristics of water-cooled wall are of great significance to the evaluation of the safety and flexibility for the boiler during peak shaving process. However, few dynamic studies considered the uneven heat flux distribution on the water-cooled wall, let alone the variable heat load caused by the combustion instability. In this paper, a segmented dynamic model is developed for the water-cooled wall of a 660 MW ultra supercritical boiler. The model is validated under steady and dynamic working conditions, respectively. Then, the step responses of the steam temperature in different flow loops under different load conditions are comparatively studied under the disturbances of single-parameter changes. In the cases of 10 % step decrease of either the feedwater temperature, the heat load or the feedwater mass flowrate, the outlet steam temperature is influenced greatly by the step decreased feedwater temperature under 100 % BMCR condition, while it is impacted most by the step decreased feedwater mass flow under 50 % THA condition. Under these disturbances, the outlet steam temperature of the tube with higher heat loads has larger variation and longer response time. In addition, the simulated combustion instability by the sinusoidal fluctuating heat load on the water wall is employed to explore the influences of the flame fluctuation amplitude and frequency to the water wall. The maximum steam temperature rises with the increase in amplitude but drops with the frequency of flame fluctuation. The thresholds for the flame fluctuation amplitude and frequency are obtained under different load conditions.

Making Moral Decisions With Artificial Agents As Advisors. An fNIRS Study

Artificial Intelligence (AI) is on the verge of impacting every domain of our lives. It is increasingly being used as an advisor to assist in making decisions. The present study aimed at investigating the influence of moral arguments provided by AI-advisors (i.e., decision aid tool) on human moral decision-making and the associated neural correlates. Participants were presented with sacrificial moral dilemmas and had to make moral decisions either by themselves (i.e., baseline run) or with AI-advisors that provided utilitarian or deontological arguments (i.e., AI-advised run), while their brain activity was measured using an fNIRS device. Overall, AI-advisors significantly influenced participants. Longer response times and a decrease in right dorsolateral prefrontal cortex activity were observed in response to deontological arguments than to utilitarian arguments. Being provided with deontological arguments by machines appears to have led to a decreased appraisal of the affective response to the dilemmas. This resulted in a reduced level of utilitarianism, supposedly in an attempt to avoid behaving in a less cold-blooded way than machines and preserve their (self-)image. Taken together, these results suggest that motivational power can led to a voluntary up- and down- regulation of affective processes along moral decision-making.

PDSMV3-DCRNN: A novel ensemble deep learning framework for enhancing phishing detection and URL extraction

Phishing is a cyber-attack that exploits victims' technical ignorance or naivety and commonly involves a Uniform Resources Locator (URL). As a result, it is beneficial to examine URLs before accessing them to spot a phishing assault. Several algorithms based on machine learning have been presented to detect phishing attempts. However, these approaches often suffer from lower performance outcomes, such as lower accuracy, longer response times, and higher false positive rates. Furthermore, many existing methods rely heavily on predefined feature sets, which may limit their adaptability and robustness. In contrast, our proposed method leverages a more dynamic feature selection process, which includes the Conditional Wasserstein Generative Adversarial Network (CWGAN) for addressing data imbalance and the Binary Grey Goose Optimization Algorithm (BGGOA) for optimal feature selection. This dynamic approach enhances the model's ability to adapt to varying data characteristics, improving detection performance. The proposed solution is divided into two stages: pre-deployment and deployment. During the pre-deployment stage, the dataset is preprocessed, including data transformation, handling irrelevant and redundant data, and ensuring data balancing. Minority samples are increased using CWGAN to avoid class imbalance. Features are then selected using BGGOA, resulting in a feature-reduced dataset used for training and testing ensemble deep learning classifiers, specifically the Novel Pyramid Depth-wise Separable-MobileNetV3 (PyDS-MV3) and Deformable Convolutional Residual Neural Network (DCRNN), termed PDSMV3-DCRNN. During the deployment phase, the Boosted ConvNeXt approach extracts URL features fed into the trained classifier to predict "phishing" or "benign". According to experimental findings, the proposed solution outperforms all other tested approaches, displaying a faster training time of 0.11 s and achieving an optimal accuracy of 99.21%.

"It's Different for Girls!" The Role of Anxiety, Physiological Arousal, and Subject Preferences in Primary School Children's Math and Mental Rotation Performance.

(1) Background: This study examines the role of subjective anxiety (mathematics and spatial anxiety), along with physiological responses, in mathematics or math and mental rotation performance in 131 German primary school students (65 girls, 66 boys; Mean age = 8.73 years). (2) Method: Students' preference for math vs. German and their subjective anxiety were assessed using standardized questionnaires. Emotional reactivity was measured using the Galvanic Skin Response (GSR). Math performance was evaluated via percentage scored and completion times on number line estimation, word problems, and missing terms tasks. Spatial skills were assessed using a novel mental rotation task (nMRT) incorporating gender-congruent and -neutral stimuli. (3) Results: Girls outperformed boys on percentage scored on the math task but took longer to complete this. No gender differences were found in performance on the nMRT. Girls demonstrated higher math anxiety and were less likely to prefer math over German. Math anxiety predicted math scores and accuracy on the nMRT while gender predicted math performance and mental rotation response time. Subject preference was associated with longer completion times and emotional reactivity with longer response times. Girls' preference for math and lower emotional reactivity was linked to shorter completion times, while lower math anxiety predicted higher scores. In contrast, these factors did not affect boys' math performance. Additionally, subjective anxiety, emotional reactivity, or subject preference did not impact spatial performance for either gender. (4) Conclusions: Supporting mathematical self-efficacy and emotional regulation, especially in girls, is crucial for enhancing STEM outcomes in primary education. Gender-fair assessment in mental rotation reveals equitable spatial performance and reduces the impact of anxiety.

The reality of rurality: Understanding the impact of remoteness on out-of-hospital cardiac arrest in Western Australia - A retrospective cohort study.

Western Australia (WA) spans a large, sparsely-populated area of Australia, presenting a challenge for the provision of Emergency Medical Service (EMS), particularly for time-critical emergencies such as out-of-hospital cardiac arrest (OHCA). To assess the impact of rurality on the epidemiology, incidence and survival of OHCA in WA. We conducted a retrospective cohort study of EMS-attended OHCA in WA from 2015 to 2022. Incidence was calculated on all OHCAs, but the study cohort for the multivariable regression analysis of rurality on survival outcomes consisted of OHCAs of medical aetiology with EMS resuscitation attempted. Rurality was categorised into four categories, derived from the Australian Standard Geographic Classification - Remoteness Areas. The age-standardised incidence of EMS-attended OHCA per 100 000 population increased with increasing remoteness: Major Cities = 104.9, Inner Regional = 123.3, Outer Regional = 138.0 and Remote = 103.9. Compared to Major Cities, the adjusted odds for return of spontaneous circulation (ROSC) at hospital were lower in Inner Regional (aOR = 0.71, 95%CI 0.53-0.95), Outer Regional (aOR = 0.62, 95%CI 0.45-0.86) and Remote areas (aOR = 0.52, 95%CI 0.35-0.77) but there was no statistically significant difference for 30-day survival. Relative to Major Cities, Regional and Remote areas had longer response times, shorter transport-to-hospital times, and higher rates of bystander CPR and automated external defibrillator use. Out-of-hospital cardiac arrest in rural areas had lower odds of ROSC at hospital compared to metropolitan areas, despite adjustment for known prognostic covariates. Despite WA's highly sparse regional population, these differences in ROSC are consistent with those reported in other international studies.

Long-range hydrological drought forecasting using multi-year cycles in the North Atlantic Oscillation

With global temperatures, populations and ecological stressors expected to rise, hydrological droughts are projected to have progressively severe economic and environmental impacts. As a result, hydrological drought forecasting systems have become increasingly important water resource management tools for mitigating these impacts. However, high frequency behaviours in meteorological or atmospheric conditions often limit the lead times of hydrological drought forecasts to seasonal timescales, either through poorer performance of multi-year meteorological forecasts or the lack of multi-year lags in atmosphere-hydrology systems. By contrast, low frequency behaviours in regionally important teleconnection systems (such as the North Atlantic Oscillation, NAO) offer a novel way to forecast hydrological drought at longer lead times. This paper shows that, by using a data-driven modelling approach, long-term behaviours within the NAO can be skilful predictors of hydrological drought conditions at a four-year forecasting horizon. Multi-year semi-periodic patterns in the NAO were used to forecast regional groundwater drought coverage in the UK (proportion of groundwater boreholes in drought), with the greatest forecast performance achieved for longer duration droughts, and for hydrogeological regions with longer response times. Model errors vary from 14 % (proportion of boreholes, (MAE)) in flashy hydrological regions or short droughts (<3 months), to 2 % for longer duration droughts (>8 months). Model fits of r2 up to 0.8 were produced between simulated and recorded regional drought coverage. As such our results show that teleconnection indices can be a skilful predictor of hydrological drought dynamics at multi-year timescales, opening new opportunities for long-lead groundwater drought forecasts to be integrated within existing drought management strategies in Europe and beyond.

Psoriasis with Leg Involvement as the New Difficult-To-Treat Area: A Cohort Study of Patients Treated With Risankizumab.

Historically, difficult-to-treat areas in psoriasis included face, scalp, folds, genitalia, nails, and palmoplantar region. Recent studies have found that lower limbs behave like a "new" difficult-to-treat area as they can be the only site of residual disease even in patients undergoing biologic therapies. We aimed to evaluate whether legs had different response rates and response times to treatment with a new biologic drug, risankizumab, compared to other body sites. We conducted a real-life, observational, retrospective, multicenter study including patients affected by moderate-to-severe psoriasis with leg involvement and undergoing biological therapy with risankizumab for more than 16 weeks. The Psoriasis Area Severity Index (PASI) and Leg-PASI were collected at T0 and at weeks 16, 28, 40, 52, 64, and 76. Statistical analysis using Student's t test and linear regression analysis were performed. A total of 124 patients were included. The difference between the improvement percentage compared to baseline was statistically significant at weeks 16 and 28, demonstrating that Leg-PASI improved less than PASI. From the linear regression it was deduced that the slope is statistically less steep for Leg-PASI than for overall PASI, confirming that this site responds more slowly to the therapy. Leg response to risankizumab appears to differ significantly from other body sites in the first weeks of treatment, even if after 28 weeks, statistical significance is lost. Our preliminary finding suggests that risankizumab can be considered an effective treatment for leg psoriasis but with longer response times than other areas, demonstrating the relative nature of resistance to treatment of this district.

Study on Symbol Selection Ability According to AAC Symbol Types for Patients with Dementia of Alzheimer’s Type

Objectives: This study aimed to establish evidence for effective (Augmentative and Alternative Communication) AAC intervention approaches for patients with dementia of Alzheimer’s type (DAT) by examining whether the severity of dementia and the symbol type used in AAC tools affect symbol selection. Additionally, we aimed to investigate the relationship between symbol selection ability and language and cognitive abilities. Methods: Twenty-one normal elderly individuals, 17 patients diagnosed with mild DAT, and 20 patients with moderate to severe DAT were recruited. A symbol selection task was conducted using the Augmentative and Alternative Communication Symbol Assessment Tool (AAC-SAT). Participants selected target symbols on the screen consisting of graphic symbols, photos, and written words in response to auditory prompts given by the examiner. Data from the tasks were statistically analyzed for accuracy and response time, and correlation analyses were conducted for cognitive and language abilities. Results: The moderate to severe DAT group showed significantly lower accuracy and longer response times in symbol selection compared to the mild DAT group and normal elderly group. Upon examining each group separately, both mild and moderate to severe DAT groups showed no symbol type that was advantageous for symbol selection. However, the rapid response to photos observed in mild DAT group disappeared in the moderate to severe DAT group. Additionally, the symbol selection ability of DAT patients significantly deteriorated as language and cognitive abilities declined. Conclusion: Appropriate symbol types for AAC aimed at facilitating effective communication considering the language and cognitive abilities of DAT patients were suggested, and the findings of this study were compared with the claims made by previous research.

Implementation of the Fp-Growth Algorithm on Spare Parts Supply Requests

Manufacturing companies rely on machines for operational activities to produce finished goods. Common factors constraining the demand and supply of spare parts are the high number of spare parts managed and irregular patterns of demand for spare parts. These varying quantities also require investment in spare parts inventory and longer response times than predicted. The research aims to apply the FP-Growth algorithm approach to find association rules and produce patterns of demand and supply of spare parts in lightweight brick manufacturing companies based on transaction data on demand and supply of spare parts from January – March 2023. The approach used is associated with the applied algorithm. In this research, the primary process of the FP-Growth algorithm is to create a combination of each item until no more combinations are formed using minimum support and minimum confidence parameters. Based on the results of making association rules using spare parts demand data from the machine maintenance department, it is stated that the regulations formed from processing the RapidMiner application with a confidence value of 100% recommend FD Regular Bolt spare parts, then the next rating with a confidence value of 94% is Steel Nuts, seven rules recommend Nuts. Steel. Therefore, it is recommended that FD Regular Bolts and Steel Nuts carry out safety stock to maintain stock availability and place them on shelves included in the fast-moving inventory category.

Mental rotation abilities of gymnasts and soccer players: a comparison of egocentric and object-based transformations. An exploratory and preliminary study.

The experience obtained from motor expertise may contribute to and enhance the development of particular visuo-spatial abilities. This exploratory and preliminary study compares the response times of a mental rotation task with egocentric and object-based transformation instructions between soccer players of varying performance levels and gymnasts. Fifty-six male participants were grouped based on their sports experience. Soccer-specific novices (SS-N: n = 19; age = 15.9 ± 0.87), soccer-specific experts (SS-E: n = 17; age = 16.4 ± 0.70), gymnastic-specific experts (GS-E: n = 10; age = 16.6 ± 1.71), and gymnastic-specific novices (GS-N: n = 10; age = 16.0 ± 1.63) were recruited to perform a perceptual task (recognition of soccer-specific poses) and mental rotation tasks with different stimuli (soccer-specific poses, cubes, line-drawings of hands, letters). During the perceptual task with instructions on egocentric transformation and soccer-specific poses, we observed that gymnasts had longer response times than soccer players. Our findings also suggest that experts correctly identified most of the poses in terms of accuracy. In the mental rotation task with object-based transformation, gymnasts processed all stimuli, even the soccer-specific poses, more accurately than both soccer groups. Our results suggest that gymnasts' motor expertise plays a role in their performance on mental rotation tasks involving both egocentric and object-based transformations, regardless of the stimuli presented.

One suitcase, two grammars: what can we conclude about Australian Turkish heritage speakers’ divergent processing of evidentiality?

Abstract This study investigates the processing of evidentiality using an auditory sentence verification task in heritage speakers of Turkish residing in Sydney, Australia. Evidentiality is a grammatical category that marks the sources of information through which the speaker comes to know information regarding an event. Turkish obligatorily marks two distinct forms of direct and indirect evidentials. We compare the sensitivity to evidentiality-information source mismatches of the speakers of Turkish as a heritage language to Turkish speakers who were late arrivals to Australia. The results show that the heritage language speakers perform less accurately and with longer response times than late arrivals, and both the groups’ response accuracy is largely predicted by amount of exposure to Turkish during their development. The data suggest that heritage speakers of Turkish show insensitivity to evidentiality. Moreover, diminishing exposure to Turkish throughout heritage speakers’ development appears to be an important trigger for divergent attainment of evidentiality in Turkish heritage grammar.

Construction of Logistics Information Management of Small and Medium-sized Enterprises Relying on Recursive Algorithm

With the rapid development of information technology, the application of information technology in logistics management is more and more, which makes traditional logistics gradually move towards modern logistics, and realizes the optimal allocation of logistics management and resources; at the same time, the logistics industry is also faced with huge data flow. , the low level of informatization and many other challenges, these problems restrict the development of enterprises. In order to solve many problems existing in the construction of logistics management informatization, this paper analyzes logistics management and its informatization application. By understanding the current situation of logistics management in small and medium-sized enterprises, it studies how to construct the informatization application of logistics management in small and medium-sized enterprises, and adds recursive algorithm to the informatization application, and finally constructs a logistics management informatization system. The system is realized through the architecture of B/S model, and the function and performance of the system are tested. The test results show that the system functions well, the maximum load capacity of the system is 600, and the longest response time does not exceed about 20s. The test results are in line with expectations, indicating that the recursive algorithm constructed in this paper is effective in the application of logistics management informatization.

Development and validation of a nonverbal consensus-based semantic memory paradigm in patients with epilepsy.

Brain areas implicated in semantic memory can be damaged in patients with epilepsy (PWE). However, it is challenging to delineate semantic processing deficits from acoustic, linguistic, and other verbal aspects in current neuropsychological assessments. We developed a new Visual-based Semantic Association Task (ViSAT) to evaluate nonverbal semantic processing in PWE. The ViSAT was adapted from similar predecessors (Pyramids & Palm Trees test, PPT; Camels & Cactus Test, CCT) comprised of 100 unique trials using real-life color pictures that avoid demographic, cultural, and other potential confounds. We obtained performance data from 23 PWE participants and 24 control participants (Control), along with crowdsourced normative data from 54 Amazon Mechanical Turk (Mturk) workers. ViSAT reached a consensus >90% in 91.3% of trials compared to 83.6% in PPT and 82.9% in CCT. A deep learning model demonstrated that visual features of the stimulus images (color, shape; i.e., non-semantic) did not influence top answer choices (p = 0.577). The PWE group had lower accuracy than the Control group (p = 0.019). PWE had longer response times than the Control group in general and this was augmented for the semantic processing (trial answer) stage (both p < 0.001). This study demonstrated performance impairments in PWE that may reflect dysfunction of nonverbal semantic memory circuits, such as seizure onset zones overlapping with key semantic regions (e.g., anterior temporal lobe). The ViSAT paradigm avoids confounds, is repeatable/longitudinal, captures behavioral data, and is open-source, thus we propose it as a strong alternative for clinical and research assessment of nonverbal semantic memory.

Transformer Versus LSTM: A Comparison of Deep Learning Models for Karst Spring Discharge Forecasting

AbstractKarst springs are essential drinking water resources, however, modeling them poses challenges due to complex subsurface flow processes. Deep learning models can capture complex relationships due to their ability to learn non‐linear patterns. This study evaluates the performance of the Transformer in forecasting spring discharges for up to 4 days. We compare it to the Long Short‐Term Memory (LSTM) Neural Network and a common baseline model on a well‐studied Austrian karst spring (LKAS2) with an extensive hourly database. We evaluated the models for two further karst springs with diverse discharge characteristics for comparing the performances based on four metrics. In the discharge‐based scenario, the Transformer performed significantly better than the LSTM for the spring with the longest response times (9% mean difference across metrics), while it performed poorer for the spring with the shortest response time (4% difference). Moreover, the Transformer better predicted the shape of the discharge during snowmelt. Both models performed well across all lead times and springs with 0.64–0.92 for the Nash–Sutcliffe efficiency and 10.8%–28.7% for the symmetric mean absolute percentage error for the LKAS2 spring. The temporal information, rainfall and electrical conductivity were the controlling input variables for the non‐discharge based scenario. The uncertainty analysis revealed that the prediction intervals are smallest in winter and autumn and highest during snowmelt. Our results thus suggest that the Transformer is a promising model to support the drinking water abstraction management, and can have advantages due to its attention mechanism particularly for longer response times.

The dynamics of experiencing Gestalt and Aha in cubist art: pupil responses and art evaluations show a complex interplay of task, stimuli content, and time course.

Gestalt perception refers to the cognitive ability to perceive various elements as a unified whole. In our study, we delve deeper into the phenomenon of Gestalt recognition in visual cubist art, a transformative process culminating in what is often described as an Aha moment. This Aha moment signifies a sudden understanding of what is seen, merging seemingly disparate elements into a coherent meaningful picture. The onset of this Aha moment can vary, either appearing almost instantaneously, which is in line with theories of hedonic fluency, or manifesting after a period of time, supporting the concept of delayed but more in-depth meaningful insight. We employed pupillometry to measure cognitive and affective shifts during art interaction, analyzing both maximum pupil dilation and average dilation across the trial. The study consisted of two parts: in the first, 84 participants identified faces in cubist paintings under various conditions, with Aha moments and pupil dilation measured. In part 2, the same 84 participants assessed the artworks through ratings in a no-task free-viewing condition. Results of part 1 indicate a distinctive pattern of pupil dilation, with maximum dilation occurring at both trial onset and end. Longer response times were observed for high-fluent, face-present stimuli, aligning with a delayed but accurate Aha-moment through recognition. Additionally, the time of maximum pupil dilation, rather than average dilation, exhibited significant associations, being later for high-fluent, face-present stimuli and correct detections. In part 2, average, not the time of maximum pupil dilation emerged as the significant factor. Face-stimuli and highly accessible art evoked stronger dilations, also reflecting high clearness and negative valence ratings. The study underscores a complex relationship between the timing of recognition and the Aha moment, suggesting nuanced differences in emotional and cognitive responses during art viewing. Pupil dilation measures offer insight into these processes especially for moments of recognition, though their application in evaluating emotional responses through artwork ratings warrants further exploration.

Research on multi-objective parameter optimization of split flow dual flash thermodynamic cycle

In addition to considering the intermediate pressure variables, the split flow double flash thermodynamic cycle also needs to consider the operating conditions and split flow coefficient of the cycle. Traditional algorithms require too much computation and have a longer response time. Faced with sudden operating conditions or changes in production conditions, it is difficult to quickly re-optimize the intermediate parameters of the split flow double flash thermal cycle and carry out corresponding system control. This article uses intelligent optimization algorithms instead of global search methods to optimize multi-objective parameters in a split flow double flash thermodynamic cycle. By summarizing and analyzing the characteristics of algorithms such as SIO, EA, and ANN, it is believed that the GWO algorithm in the SIO class is the most suitable for multi-objective parameter optimization of the split flow double flash thermal cycle. It is combined with the SA optimization algorithm to propose the SA-GWO algorithm with faster optimization speed and higher optimization accuracy, taking the initial position of the wolf pack as the optimization objective. Combined with the cycle thermodynamic model, a multi-objective parameter optimization model for the split flow double flash thermal cycle is established.

Word or pseudoword? The lexicality effect in naming and lexical decision tasks during advanced aging.

Although there is evidence that recognizing pseudowords is more difficult than recognizing words during childhood, adulthood, and early old age (60-75 years), it is not yet clear what happens during advanced aging or the fourth age, a stage when the decline of fluid intelligence strongly affects processing speed, but a good performance of crystallized intelligence is described through an increase in vocabulary and knowledge. The objective of this study was to determine the lexicality effect in advanced aging, specifically exploring how the ability to recognize words and pseudowords (ortho-phonologically plausible for Spanish) is affected during the third and fourth-ages. The lexicality effect was measured using naming and lexical decision tasks. Response time and accuracy were compared between a fourth-age group (80+ years) and two third-age groups (60-69 and 70-79 years) through linear regression models. The results showed that, in general, the fourth-age group had longer response times and reduced accuracy when recognizing words and pseudowords. Moreover, they showed a significant lexicality effect (which increases from the third- age onwards), reflected in higher costs during pseudoword recognition, especially when the task required more cognitive effort (lexical decision task). These results were consistent with the impact of the deterioration of fluid intelligence on the speed of lexical recognition and with the better performance that crystallized intelligence can generate on accuracy, especially in the early stages of old age. Additionally, this study supports the fact that pseudoword recognition resists cognitive decline, as accentuated deterioration is visualized only after 80 years.

Development of a Position Control System for Wheeled Humanoid Robot Movement Using the Swerve Drive Method Based on Fuzzy Logic Type-2

A humanoid robot is capable of mimicking human movements, which poses a challenge for researchers. This has led some to utilise wheels to facilitate its motion. However, achieving smooth and accurate movements at desired positions remains a challenge, necessitating the development of an optimal control system and movement method. In this study, solutions to address these challenges include the use of type-2 fuzzy logic controller (FLC) and the swerve drive method. During the steering rotation movement testing, type-1 FLC exhibits the fastest response time of 0.8 seconds, but oscillations occur, reaching up to 117 degrees to achieve the set point of 90 degrees. Additionally, type-1 FLC cannot reach the set point of -90 degrees. On the contrary, type-2 FLC aligns successfully with both set points of 90 and -90 degrees. In coordinate movement testing, type-1 FLC still shows an error between 1 cm and 2 cm compared to type-2 FLC, particularly with 3 and 5 members, which are equal to the given set point. The results of the tests indicate that type-2 FLC is reliable, showing a small steady-state error, stability, and no overshoot, despite its longer response time and processing duration compared to type-1 FLC.