American University of Science and Technology

Geographic information systems have traditionally focused on the representation of visual and geometric aspects of geographic phenomena, while auditory information has largely been ignored or reduced to simple numerical attributes. Although numerous spatio-temporal data models have been proposed to describe dynamic geographic processes, few of them provide explicit semantic constructs for representing sound and its evolution over space and time. This paper extends the Modeling of Application Data with Spatio-Temporal Features (MADS) conceptual model by introducing an explicitly defined acoustic semantic dimension. The extension separates source-intrinsic acoustic emission (soundfeature), source–receiver propagation and exposure (AFFECTS and associated EXPOSUREZONE instances), and receiver-centered contextual perception (PERCEPTIONASSESSMENT). These complementary constructs enable acoustic emission, propagation, temporal dynamics, spatial exposure, and perceptual assessment to be represented within a unified spatio-temporal semantic framework. The applicability of the proposed approach is illustrated through a proof-of-concept scenario and representative database queries, demonstrating how the proposed acoustic constructs can be mapped, stored, and queried using standard GIS database infrastructure.

This study examines whether integrating program and performance budgeting (PPB) with artificial intelligence (AI) technologies improves the quality of financial decision-making. PPB links appropriations to programmes, objectives, outputs, outcomes, and performance indicators, whereas AI can strengthen forecasting, anomaly detection, reporting, scenario analysis, and decision support. The proposed model treats integration as an organisational, informational, and analytical capability rather than the mere acquisition of software. Five dimensions constitute the independent variable: data readiness and integration, intelligent analytics, AI-supported financial forecasting, budget-cycle automation, and programme evaluation and intelligent decision support. Financial decision-making quality is assessed through timeliness, accuracy, evidence use, allocation efficiency, expenditure rationalisation, risk detection, traceability, transparency, and responsiveness. A quantitative, cross-sectional, descriptive-analytical design was used with 64 eligible respondents and a 45-item questionnaire. The findings show a strong positive relationship between overall integration and financial decision-making quality, while the integration dimensions overlap substantially. The results support treating integration as a coherent capability bundle while retaining human oversight and accountability.

Artificial intelligence (AI) is changing the future of sustainable tourism marketing, and with it offers a degree of potential on hyper-personalization, predictive analytics, and responsible management of resources such as has not been seen before. This chapter examines how AI can be used in a multidimensional context to advance sustainability objectives in the tourism sector. The paper looks at utilization scenarios of AI-based recommendation engines to promote carbon-neutral itineraries, to applications of generative AI that can support awe-inspiring eco-branding material. The chapter. sets besides research in consumer behavioural reactions to AI-mediated sustainable travel publicity, and in addition evaluates the ethical implications of the usage of the technologies including the possibility of information privacy violation, algorithm favouritism and the environmental cost of the AI infrastructure itself. Through the UN SDGs, the analysis suggests a combined approach to the ethical aspects of the implementation of the artificial intelligence in tourism marketing.

Lebanon has historically experienced political, sectarian, ideological, and international divisions that shape public debates and national identity. Among the most prominent issues is the role of the resistance movement, particularly Hezbollah, in its conflict with Israel. This study employs a qualitative interpretive methodology to analyze binary divisions within Lebanese society concerning support for or opposition to the resistance movement. The analysis synthesizes academic literature, public discourse, political narratives, media coverage, and recent research to illustrate how societal factors shape divergent viewpoints. The findings identify several key factors influencing Lebanese perspectives, including resistance versus sovereignty, security versus instability, nationalism versus transnationalism, protection versus provocation, and communal survival versus authority. Supporters of the resistance movement frequently regard it as a legitimate means of defending the nation against Israeli aggression, whereas critics perceive it as a threat to state sovereignty and a source of persistent conflict. The study demonstrates that both perspectives are informed by distinct historical experiences, collective traumas, identity formation processes, and security concerns. Overall, the research indicates that binarism functions as a social and political mechanism in Lebanon, shaping public opinion and reinforcing divisions.

The chemosteometric regression models proposed by Gonçalves et al. (2020) based on data obtained through Fourier-transform infrared spectroscopy in attenuated total reflectance mode provided encouraging results regarding the prediction of heat-induced metric changes in human bone. In this study, the potential of chemosteometry was further explored by adding powder X-ray diffraction (XRD) data. Bones from six experimentally burnt human skeletons from the 21st Century Identified Skeletal Collection (four females and two males with ages at death between 62 and 88years) were measured before and after burning. Cortical bone was sampled from each bone and analysed through FTIR-ATR and XRD. The resulting data were used to build regression models to predict the percentage of heat-induced metric changes. Results were then compared to the actual metric changes. Chemometric data that most significantly correlated with such changes were the infrared band at 3572cm-1(hydroxyapatite OH stretch) and the XRD crystallite size. The infrared crystallinity index also provided relevant information for the regression models. Adjusted R2 of the regression models ranged between 0.20 and 0.68 and mean absolute error (MAE) values ranged between 2.59 and 4.22 percentage points. Models based only on the maximum length of clavicles, metacarpals and metatarsals provided even better predictions, suggesting that results vary according to bone type. In contrast, correction factors of 10%, 12% and 15% resulted in higher MAE of 5.50, 6.34 and 8.20 percentage points in the same sample, respectively. The added benefit of adding the XRD crystallite size to models including FTIR data was ambiguous, appearing to be irrelevant for the overall sample, but improving estimations in the subsample that only comprised bones with diaphysis. The results suggest that the prediction of heat-induced shrinkage in epiphyseal measurements is to some extent undermined by using FTIR and XRD data obtained from cortical bone samples. This study concluded that chemosteometry provides an objective case-by-case prediction of heat-induced shrinkage, having clear advantages over the shrinkage correction factor approach.