16 research outputs found
“Literature and academia in the former Yugoslavia are obsessed with identity issues and cultural relativism”: An Interview with Bosnian Author Nihad Hasanović
No full textAuthor Nihad Hasanovic. Bild: Adnan Hasanovic In autumn 2018, the Bosnian author Nihad Hasanović read from his novel “The Basement Man“ (Čovjek iz podruma, 2013) in Regensburg. Hasanović is a non-conformist author from the former Yugoslavia who in 2017 has received the Skender Kulenović Award for his latest novel Laki pogon [Light Engine] (2016). His characters are rooted in Bosnia and Herzegovina, but their stories are stretching through the US, France, and Mexico. The students Denise Trz..
La transition des marchés financiers vers un développement durable
No full textCet article avait pour objectif de déterminer les facteurs de la transition des marchés financiers vers un avenir durable. Cette analyse vise à comprendre et évaluer l'évolution des marchés financiers dans le contexte de la durabilité, mettant l'accent sur les changements nécessaires pour favoriser un développement économique qui soit à la fois socialement juste et respectueux de l'environnement.
Pour atteindre cet objectif, nous avons adopté une approche mixte, utilisé les données primaires, nous sommes partis d’une exploration auprès des politiques qui peuvent encourager la recherche et le développement en fournissant des subventions ou des incitations fiscales pour les entreprises qui investissent dans des technologies durables.
Les résultats montrent, que les entreprises devront prendre en compte ces coûts lors de la prise de décision d'investissement, ce qui les encouragera à investir dans des technologies durables. En intégrant ces coûts externes dans le prix des actifs financiers, les investisseurs peuvent prendre en compte l'impact environnemental et social de leurs investissements, ce qui les encourage à investir dans des technologies durables et à éviter les activités polluantes.
Les entreprises qui se distinguent par leur engagement dans ce domaine peuvent bénéficier d'une meilleure image publique, ce qui peut favoriser leur compétitivité sur le marché.
 
Explainable Spatio-Temporal Inference Network for Car-Sharing Demand Prediction
No full textEfficient resource allocation in car-sharing systems relies on precise predictions of demand. Predicting vehicle demand is challenging due to the interconnections of temporal, spatial, and spatio-temporal features. This paper presents the Explainable Spatio-Temporal Inference Network (eX-STIN), a new approach that improves upon our prior Unified Spatio-Temporal Inference Prediction Network (USTIN) model. It offers a comprehensive framework for the integration of various data. The eX-STIN model enhances the previous one by utilizing Ensemble Empirical Mode Decomposition (EEMD), which results in refined feature extraction. It uses Minimum Redundancy Maximum Relevance (mRMR) to find features that are relevant and not redundant, and Shapley Additive Explanations (SHAP) to show how each feature affects the model’s predictions. We conducted extensive experiments that use real car-sharing data to thoroughly evaluate the efficacy of the eX-STIN model. The studies revealed the model’s ability to accurately represent the relationships among temporal, spatial, and spatio-temporal features, outperforming the state-of-the-art models. Moreover, the experiments revealed that eX-STIN exhibits enhanced predictive accuracy compared to the USTIN model. This proposed approach enhances both the accuracy of demand prediction and the transparency of resource allocation decisions in car-sharing services
Quantum-Inspired Spatio-Temporal Inference Network for Sustainable Car-Sharing Demand Prediction
No full textAccurate car-sharing demand prediction is a key factor in enhancing the operational efficiency of shared mobility systems. However, mobility data often exhibit temporal, spatial, and spatio-temporal interdependencies that pose significant challenges for conventional models. These models typically struggle to capture nonlinear and high-dimensional patterns. Existing methods struggle to model entangled relationships across these modalities and lack scalability in dynamic urban environments. This paper presents the Quantum-Inspired Spatio-Temporal Inference Network (QSTIN), an enhanced approach that builds upon our previously proposed Explainable Spatio-Temporal Inference Network (eX-STIN). QSTIN integrates a Quantum-Inspired Neural Network (QINN) into the fusion module, generating complex-valued feature representations. This enables the model to capture intricate, nonlinear dependencies across heterogeneous mobility features. Additionally, Quantum Particle Swarm Optimization (QPSO) is applied at the final prediction stage to optimize output parameters and improve convergence stability. Experimental results indicate that QSTIN consistently outperforms both conventional baseline models and the earlier eX-STIN in predictive accuracy. By enhancing demand prediction, QSTIN supports efficient vehicle allocation and planning, reducing energy use and emissions and promoting sustainable urban mobility from both environmental and economic perspectives
Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning
No full textThe car-sharing system is a popular rental model for cars in shared use. It has become particularly attractive due to its flexibility; that is, the car can be rented and returned anywhere within one of the authorized parking slots. The main objective of this research work is to predict the car usage in parking stations and to investigate the factors that help to improve the prediction. Thus, new strategies can be designed to make more cars on the road and fewer in the parking stations. To achieve that, various machine learning models, namely vector autoregression (VAR), support vector regression (SVR), eXtreme gradient boosting (XGBoost), k-nearest neighbors (kNN), and deep learning models specifically long short-time memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), CNN-LSTM, and multilayer perceptron (MLP), were performed on different kinds of features. These features include the past usage levels, Chongqing’s environmental conditions, and temporal information. After comparing the obtained results using different metrics, we found that CNN-LSTM outperformed other methods to predict the future car usage. Meanwhile, the model using all the different feature categories results in the most precise prediction than any of the models using one feature category at a tim
A Unified Spatio-Temporal Inference Network for Car-Sharing Serial Prediction
No full textCar-sharing systems require accurate demand prediction to ensure efficient resource allocation and scheduling decisions. However, developing precise predictive models for vehicle demand remains a challenging problem due to the complex spatio-temporal relationships. This paper introduces USTIN, the Unified Spatio-Temporal Inference Prediction Network, a novel neural network architecture for demand prediction. The model consists of three key components: a temporal feature unit, a spatial feature unit, and a spatio-temporal feature unit. The temporal unit utilizes historical demand data and comprises four layers, each corresponding to a different time scale (hourly, daily, weekly, and monthly). Meanwhile, the spatial unit incorporates contextual points of interest data to capture geographic demand factors around parking stations. Additionally, the spatio-temporal unit incorporates weather data to model the meteorological impacts across locations and time. We conducted extensive experiments on real-world car-sharing data. The proposed USTIN model demonstrated its ability to effectively learn intricate temporal, spatial, and spatiotemporal relationships, and outperformed existing state-of-the-art approaches. Moreover, we employed negative binomial regression with uncertainty to identify the most influential factors affecting car usage
Naguib Mahfouz’s Miramar. A Mediterranean Saga
No full textDe la numerosa producción literaria del escritor egipcio Naguib Mahfouz, Miramar (1967) es la novela más destacada, ya que aborda la vida de cuatro personajes principales con diferentes antecedentes sociopolíticos y económicos, reunidos en una posada y abandonados a su suerte. Mahfouz no interpone su voz narrativa entre el personaje y el lector, sino que permite que los personajes chocan y subvierten entre sí. El lector no visualiza una única realidad presentada por el autor, sino la realidad que representa cada personaje. Hay una pluralidad de conciencias, cada personaje en su propio mundo, algo muy parecido a lo que Mikhail Bakhtin escribió en Problemas de la Poética de Dostoievski y se desarrolla como una «polifonía». A lo largo de la novela, se observa que el diálogo no tiene una posición fija ni temas determinados, sino, enunciados o géneros discursivos existentes que construyen un texto, a modo de capas narrativas de ideologías y reflexiones. La noval presenta una muestra de una ciudad mediterránea con su pasado histórico y su presente, los viejos y los jóvenes, los ricos y los pobres, los civilizados y los insólitos, todos orquestados en una única sinfonía musical llamada Miramar. Cada personaje representa un instrumento con su voz distintiva y todos tocan juntos para formar una melodía de la saga de una ciudad. En este trabajo, se pretende analizar la novela Miramar, de Mahfouz, como una novela polifónica ambientada en la legendaria ciudad de Alejandría en una época convulsa de la historia de Egipto, a la luz del concepto de polifonía de Mikhail Bakhtin.From the numerous literary productions of Naguib Mahfouz, is Miramar (1967). The novel tackles four main characters with different socio-political and economic backgrounds put together in an inn and left to deal with each other. Mahfouz does not place his narrative voice between the character and the reader, but rather, allows the characters to shock and subvert. The reader does not see a single reality the author presents but how reality appears to each character. There is a plurality of consciousness, each character with his own world, which resembles what Mikhail Bakhtin wrote about in Problems of Dostoevsky’s Poetics and develops as a ‘polyphony.’ The dialogue has no fixed position or subjects; its analysis emphasizes the combination of existing statements or speech genres that construct a text, which are layers of ideologies and reflections. It presents a sample of a Mediterranean city with its past and present, the old and the young, the rich and the poor, and the educated and the uneducated, which are orchestrated in one musical symphony called Miramar. Every character represents an instrument with its distinctive voice, and all play together to form a symphony of a city’s saga. The paper analyzes Mahfouz’s Miramar, as a Polyphonous novel set in Alexandria in a raging time of Egyptian history. in the light of Mikhail Bakhtin’s concept of polyphony.Área de Historia del Art
Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach
No full textBrain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders—such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury—by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user’s intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot
Processing passive constructions in arabic and english a crosslanguage priming study
No full textThe English and Arabic languages each have passive constructions, but their realizations in the two languages are quite different. We carried out a syntactic priming experiment on Arabic-English bilinguals to investigate whether analogous sentence structures might share a consistent underlying representation across languages, regardless of their different surface forms. Participants read a series of sentence stimuli, half of which were in Arabic and half in English. Sentences could be in active or passive voice. The stimuli were presented in a randomized order so that each target could be preceded by a prime matching or mismatching the target in either voice or language. Subjects needed to decide who carried out the action described in the sentence (first half of the experiment) or to whom the action happened (second half). The resulting reaction time data provide strong evidence of syntactic priming within but not across languages. Our findings are discussed in relation to the shared-syntax and separate-syntax accounts of sentence processing in bilinguals.The authors wish to express their gratitude to Amina Nihad Awartani and to Heba Jamal Taleb Al-Kababji for assistance in creating the sentence stimuli and collecting the experimental data. We also thank attendees of the "Workshop on Sentence Processing in Multilingual and Other Less Commonly Studied Populations," held at Potsdam University on August 4-5, 2016, in Potsdam, Germany, for their comments. This research was funded by a Qatar University grant awarded to the first author (QUSG-CAS-DELL-14\15-24) and by a Qatar National Research Fund award to the second author (NPRP-7-1506-3-390).Scopu
Porous Media Condensing Heat Exchanger with Integral Gas Liquid Separation for Space Flight Use
No full textExisting condensing heat exchangers utilized to condition cabin air in space vehicles and/or stations have two major obstacles to overcome. First, they must be over designed to deal with the inefficient heat transfer due to the formation of condensate file and second, additional dynamic hardware must be used to separate the condensed water vapor from the non-condensable gases in air. This paper deals with new design strategies and compares one version of a proposed porous media heat exchanger to an existing temperature and humidity control system and demonstrates a reduction in mass, volume, and power consumption of the concept. A decoupled solution based on an author\u27s modified N(tu) effectiveness method and a Reynolds analogy mass transfer calculation is proposed to simplify the sizing of the required surface for heat and mass transfer
