National University of Distance Education

Olduvai Gorge, nestled between the East African Rift Valley and the Mozambique Belt, is key to understanding human evolution. Even though extensive archaeological and palaeoanthropological findings have been unearthed here since the 1930s, the Middle Stone Age in this area has nonetheless received less attention than the Oldowan or the Acheulean. This paper presents the lithic technology analysis of Dorothy Garrod Site (DGS), a newly-documented MSA site located at the junction of the main gorge and the side gorge at Olduvai. DGS provides valuable additional knowledge to our understanding of the MSA groups that inhabited the region, offering insights into the mobility and settlement patterns of human groups in East Africa during MIS 4. This study focuses on the techno-typological characterization of the DGS lithic assemblage through an analysis of the raw material management strategies and knapping methods employed. The presence of discoid and Levallois methods, as well as single platform methods shows DGS to be a ‘typical’ MSA archaeological site, together with its associated fauna. The retouched pieces are scarce and characterized by a high presence of denticulates, retouched flakes and notches, as well as by the low presence of heavy-duty tools and total absence of points. All of these features make of DGS an exceptional MSA site at the heart of Olduvai.

Agriculture continues to be one of the world’s main sources of income and provides great environmental, territorial and social value. However, frost is a recurring problem for farmers each year, representing a significant threat to agricultural production. In a matter of hours, temperatures below the freezing point can result in the loss of nearly the entire crop from a producer. In this article, we have analyzed and compared the application of a set of machine learning algorithms to predict the occurrence of frost events in the next 24 hours. The prediction process covers several challenges, such as data capture, processing, extracting each relevant parameter and finally building different prediction models to compared their performance. Furthermore, we have employed the Synthetic Minority Oversampling Technique (SMOTE) methodology to address the issue of imbalanced datasets, given the natural scarcity of frost events during the data sampling period. Our results show that among the machine learning algorithms we compared, the most efficient in terms of Recall score is K-Nearest Neighbor (KNN), while using the Area Under Curve (AUC) criteria, the highest score belongs to the Extra Trees algorithm, with 0.9909. Moreover, by applying the SMOTE balancing process, the AUC score of our models increased 13%, while the Recall score increased from 55% to 82%.

The coronavirus pandemic posed a major challenge to mental health. Existing evidence shows that COVID-19 is related to poor emotional well-being, particularly among women. However, most work on the subject uses single-country samples, limiting the ability to generalize the disparity or explain it as a function of societal variables. The present study investigates the expression of positive and negative emotions during the pandemic as a function of gender and across 24 countries (N = 49,637). Strong gender differences emerged across countries, with women reporting more negative emotions (anxious, depressed, nervous, exhausted) and less positive emotions (calm, content, relaxed, energetic) than men. The gender gap in positive emotions was significantly wider in countries higher in individualism and narrower in countries higher in power distance. For instance, differences in emotions were larger in Western countries high in individualism, such as the USA, the UK, Italy, and France, and smaller in countries with higher collectivism and power distance, such as China, Malaysia, and South Korea, with a few exceptions like Japan and Brazil. These gender differences across countries were not explained by country-level gender inequalities indicators (GGGI and GII). Interestingly, the national severity of the pandemic, an epidemiological factor, reduced gender differences in positive emotions. These results underscore the importance of considering cultural and national factors when assessing gender differences in well-being.

The Maslach Burnout Inventory-Student Survey (MBI-SS) is a widely used instrument to assess burnout levels, which provides valuable insight into their psychological well-being. Accurate measurement of burnout is crucial for developing interventions aimed at reducing stress and promoting mental health among students. This study aims to validate the MBI-SS when applied among Thai university students and to examine whether the psychometric properties of the scale are consistent with the original conceptual framework. A total of 413 undergraduate students from Thailand participated in the study, with 57.63% females and 42.37% males, and a mean age 21.75 years (SD = 2.40). The MBI-SS was translated into Thai by following rigorous procedures to maintain accuracy and cultural relevance. The factorial structure of the MBI-SS Thai version was evaluated using confirmatory factor analysis (CFA) for both a three-factor model and second-order factor model. The Thai version of the MBI-SS demonstrated a three-dimensional structure consistent with the original inventory, with excellent model fit indices. All item factor loadings exceeded the recommended threshold, and the instrument showed high internal consistency, establishing it a valuable tool for future research and practical application in educational settings aimed at addressing and reducing student burnout.

Background After breast cancer (BC), women may face other severe symptoms such as sleep problems. The use of simple, fast, and reliable scales is necessary in the clinic to improve patient benefits, and sleep is an important aspect to be addressed. Objective This study was conducted to adapt and validate the Spanish version of the satisfaction, alertness, timing, efficiency, and duration (SATED) scale for measuring sleep health in women who have completed treatment for BC in Spain (SATED-BC). Design Cross-sectional study. Methods The adaptation process involved adding a sixth item to the SATED-BC scale: “the impact of symptoms experienced after completing breast cancer treatment on sleep” item was not considered for scoring. The SATED-BC score ranged from 0 (poorest sleep health) to 10 (best sleep health). A validation analysis was performed using the Pittsburgh Sleep Quality Index, the Consensus Sleep Diary, and actigraphy, and the results were compared with those obtained using the SATED-BC scale. Results The SATED-BC scale was reliable in terms of its internal consistency (Cronbach’s α = 0.70; McDonald’s ω = 0.72), showed high intrasubject reliability (r = 0.90), and was shown to be valid for use in women who have completed treatment for breast cancer. Conclusion The SATED-BC scale is a reliable and valid tool for comprehensively evaluating sleep health in women who have completed treatment for breast cancer.

Disparities in access to opportunities and services often accumulate in peripheral rural areas, contributing to a sense of being left out of prosperity. To bridge this gap, regular commuting to cities where jobs and resources are concentrated has partly replaced emigration, making the private car the key to settling and living in the countryside. However, car dependency reveals important fractures within the rural population. Our work examines the relationship between mobility capabilities and life opportunities by analysing the Spanish case. The results underscore the link between automobility deficits and the risk of exclusion. In a context where rural depopulation and gentrification, hypermobility and mobility deprivation coexist, car dependency needs to be addressed beyond a transport problem as a variable of social peripheralization. The conclusions highlight the need for rural policies to address the challenges posed by this issue in the current transitions to greener mobility paradigms and ageing societies.

Populism is usually understood as a complex multi-dimensional phenomenon that encompasses different manifestations. However, most studies on the demand-side adopt a parsimonious minimal definition approach that hinders the ability to capture different forms of populism and the variable weight of its components. This article tests a new multi-dimensional strategy to measure and compare populist and pluralist attitudes in the context of Brexit Britain. We explore the relationship between populism and Britons' socio-political views-on borders, democracy, governance, identity, and the European Union-and psychological traits-such as conspiracy belief, social alienation, justification of political violence, and meaning in life-. Our new Multi-dimensional Populist Attitudes Scale (MPAS) reveals two varieties of populism, 'aspirational/ subversive' and 'identitarian/protective', and a non-populist 'moderate/pluralist' archetype. The new items introduced in the MPAS can complement (or become an alternative to) extant scales especially in contexts where populist movements do not fully fit narrow conceptualisations of populism.

This article focuses on the study of the specific social vulnerability of migrant minors during their arrival and the corresponding integration processes in the host countries. The analysis focuses on identifying risks of social vulnerability using a conceptual framework based on the notion of social exclusion. Using a multidimensional, processual approach, the construction of vulnerability in households with migrant minors is analysed in comparison with households with non‐migrant minors (in the EU, using Spain as a case study). Despite having an intermediate‐level mean income, and despite economic development in the country, Spain has seen a re‐emergence of child poverty that has had a significant impact on households with minors. The lack of targeted programmes and low levels of investment contributes to one in four minors living below the at‐risk‐of‐poverty line. The economic crisis of 2008 and the COVID‐19 pandemic have worsened the situation, especially in households with migrant minors.

The dissociation between conscious and unconscious perception is one of the most relevant issues in the study of human cognition. While there is evidence suggesting that some stimuli might be unconsciously processed up to its meaning (e.g., high-level stimulus processing), some authors claim that most results on the processing of subliminal stimuli can be explained by a mixture of methodological artefacts and questionable assumptions about what can be considered non-conscious. Particularly, one of the most controversial topics involves the method by which the awareness of the stimuli is assessed. To address this question, we introduced an integrative approach to assess the extent to which masked hierarchical stimuli (i.e., global shapes composed of local elements) can be processed in the absence of awareness. We combined a priming task where participants had to report global or local shapes, with the use of subjective and objective awareness measures collected either in a separate block (offline), or trial-by-trial during the main task (online). The unconscious processing of the masked primes was then evaluated through two different novel model-based methods: a Bayesian and a General Recognition Theory modeling approach. Despite the high correlation between awareness measures, our results show that the use of alternative approaches based on different theoretical assumptions leads to diverging conclusions about the extent of the unconscious processing of the masked primes.

The development of schedule‐induced drinking depends on different variables affecting the food delivered at the end of the interfood interval. There are mixed results concerning the effects of varying magnitude and/or preference of different reinforcers in the development of schedule‐induced drinking, with some studies showing higher levels and other studies showing lower levels of drinking. The purpose of this study was to observe how differences in preference for a flavor of equally nutritious food pellets influence the development and maintenance of schedule‐induced drinking. Using the operant demand framework, four flavors of food pellets were compared to form two groups: one in which subjects would receive their most preferred flavor and another in which subjects would receive their least preferred flavor. In general, licking rates were lower and magazine‐entering rates were higher when the preferred flavor was delivered regardless of the fixed‐time schedule used. It is suggested that the value of the reinforcer has a larger influence on the immediately preceding behaviors, which will determine the distribution of competing responses in the interreinforcement intervals. These results are relevant to developing public policies that manipulate the taste of healthy food to increase its consumption.

This study investigated how exposure to Caucasian and Chinese faces influences native Mandarin-Chinese speakers’ learning of emotional meanings for English L2 words. Participants were presented with English pseudowords repeatedly paired with either Caucasian faces or Chinese faces showing emotions of disgust, sadness, or neutrality as a control baseline. Participants’ learning was evaluated through both within-modality (i.e., testing participants with new sets of faces) and cross-modality (i.e., testing participants with sentences expressing the learned emotions) generalization tests. When matching newly learned L2 words with new faces, participants from both groups were more accurate under the neutral condition compared to sad condition. The advantage of neutrality extended to sentences as participants matched newly learned L2 words with neutral sentences more accurately than with both disgusting and sad ones. Differences between the two groups were also found in the cross-modality generalization test in which the Caucasian-face Group outperformed the Chinese-face Group in terms of accuracy in sad trials. However, the Chinese-face Group was more accurate in neutral trials in the same test. We thus conclude that faces of diverse socio-cultural identities exert different impacts on the emotional meaning learning for L2 words.

The growth of Open Educational Resources and Open Courseware are a result of a prolonged process of e-learning industry growth based on standards development, social resource production and consolidation of e-learning model of teaching. However, despite this considerable number of educational resources available, and variety of approaches for finding and search resources, there is still not many approaches to combine search of labelled -either based on metadata or in semantic relationships- and non-labelled material. This paper describes a method for search and retrieval of open educational resources and open courseware combining these both kind of retrieval approaches, regardless of its representation, their metadata, or their management system. The method retrieves resources and cluster the results according to specific search terms combining web crawling with automatic semantic labelling using Linked Open Data. The experimentation conducted shows the feasibility of the method to respond to diverse queries about different topics of educational resources.

This study examines the impact of learning style and study habits alignment on the academic success of engineering students. Over a 16-week semester, 72 students from the Process Engineering and Electronic Engineering programs at Universidad de Los Llanos participated in this study. They completed the Learning Styles Index questionnaire on the first day of class, and each week, teaching methods and class activities were aligned with one of the four learning dimensions of the Felder-Silverman Learning Styles Model. Lesson 1 focused on one side of the learning dimension, lesson 2 on the opposite side, and the tutorial session incorporated both. Quizzes and engagement surveys assessed short-term academic performance, while midterm and final exam results measured long-term performance. Paired t-tests, Cohen's effect size, and two-way ANOVA showed that aligning teaching methods with learning styles improved short-term exam scores and engagement. However, multiple regression analysis indicated that study habits (specifically, time spent studying, frequency, and scores on a custom-developed study quality survey) were much stronger predictors of midterm and final exam performance. Several machine learning models, including Random Forest and Voting Ensemble, were tested to predict academic outcomes using study behavior data. Voting Ensemble was found to be the strongest model, explaining 83% of the variance in final exam scores, with a mean absolute error of 3.18. Our findings suggest that while learning style alignment improves short-term engagement and comprehension, effective study habits and time management play a more important role in long-term academic success.

Despite the rise of robotics and automation in industrial applications, the widespread adoption of collaborative robotics still needs to be improved due to the lack of interoperability between robots and the low adaptability of existing systems. Solving this problem would mean a significant advance in robotics and industrial automation. Under this context, an open-source MultiRobot Framework based on ROS2 was developed in the present research to effectively communicate and coordinate robotics agents and sensors in closed collaborative environments. A simulation-based control and software design was performed using the Gazebo tool.Acentralized architecturewas obtained with an autonomous navigation module for the planning and robot routes monitoring, a computer vision module for the location and management of uncertainties, and a task controller module to assign mobilization mission objects. In conclusion, using ROS2 to communicate and coordinate various mechatronic systems effectively results in a robust, flexible, and scalable solution critical to industrial processes.

In this essay, we aim to provide a comprehensive overview of the various stances in the contemporary debate on the sources of political normativity. Besides, we describe some consequences of this debate for several related areas of philosophical discussion. We believe this overview may help readers navigate and connect the numerous works within the expanding literature on political normativity, as well as the controversies between advocates of political realism and so-called political moralists, including the articles featured in Topoi’s collection Political Normativity and Ethics.

The state of health (SOH) of a Li-ion battery is determined by complex interactions among its internal components and external factors. Approaches leveraging deep learning architectures have been proposed to predict the SOH using convolutional networks, recurrent networks, and transformers. Recently, Mamba selective state space models have emerged as a new sequence model that combines fast parallel training with data efficiency and fast sampling. In this paper, we propose SambaMixer, a Mamba-based model for predicting the SOH of Li-ion batteries using multivariate time signals measured during the battery’s discharge cycle. Our model is designed to handle analog signals with irregular sampling rates and recuperation effects of Li-ion batteries. We introduce a novel anchor-based resampling method as an augmentation technique. Additionally, we improve performance and learn recuperation effects by conditioning the prediction on the sample time and cycle time difference using positional encodings. We evaluate our model on the NASA battery discharge dataset, reporting MAE, RMSE, and MAPE. Our model outperforms previous methods based on CNNs and recurrent networks, reducingMAEby 52%, RMSE by 43%, and MAPE by 7%.

The detrimental effects of inequality have been demonstrated. However, although tax-based wealth redistribution can reduce it, part of the population rejects them due to partially inaccurate and erroneous beliefs disseminated through misinformation campaigns. This research explores the mechanisms that explain the opposition to the inheritance tax, a controversial tax figure. Through an experimental study, we analysed whether exposing anti-tax participants (37.8% women, M age = 39.65, SD age = 14.27) to an exaggerated and absurd message aligned with their tax beliefs — paradoxical thinking strategy — influenced participants’ perceptions and behavioural intentions. Contrary to predictions from paradoxical thinking, the results revealed that participants exposed to the paradoxical thinking message exhibited more extreme anti-tax beliefs and behavioural dispositions than those who received a control message. Furthermore, participants who perceived the sender as trustworthy polarized their position against the tax. These results highlight the factors contributing to the rejection of certain redistributive policies, expose the detrimental impact of misinformation campaigns, question the applicability of paradoxical thinking in some contexts and emphasize the influence of perceived credibility on message acceptance.

This report presents the archaeological excavations carried out in the fort of Lahore, Pakistan, just a few months before the COVID -19 pandemic lockdown. The Lahore Fort archaeological field-training programme was organised by Aga Khan University, the Aga Khan Cultural Service Pakistan, the Walled City of Lahore Authority, and the Department of Pakistani Antiquities. The main structure excavated was a Mughal hammam built at the location of the first Mughal palace when the emperor Akbar rebuilt the old fort of the city (r. 1556–1605). Our excavations indicated that this hammam was built in the second part of the 16th century and that it can be attributed to Akbar.