Search Results (1,029)

Genealogy is a powerful analytical framework for understanding the cultural evolution of tribes, communities, and societies. This article demonstrates that the recurrent reliance on genealogical structures is a common feature of human societies, serving as a fundamental mechanism for cultural evolution through time, space, and culture. Based on comparative analysis of indigenous tribal societies (e.g., Aboriginal Australian kinship, Polynesian chiefly genealogies), agrarian civilizations (e.g., European feudal lineages, Chinese patriliny), and modern nation-states (e.g., nationalist mythmaking, DNA-based ancestry movements), this study reveals consistent patterns in genealogical functions. Drawing on an interdisciplinary perspective from anthropology, sociology, history, and evolutionary biology, it is argued that genealogical systems are not passive records of descent but dynamic forces of cultural continuity and adaptation. The evidence shows that, despite vast sociocultural differences, genealogy widely operates as a dual-purpose instrument. It preserves cultural memory and legitimizes political authority while simultaneously facilitating social adaptation and innovation in response to new challenges. The paper also critiques contemporary trends like commercial genetic genealogy, highlighting its potential for reconnecting diasporic communities alongside its risks of biological essentialism. Ultimately, the work establishes that the persistent and patterned reliance on genealogy from oral traditions to genetic data offers a critical lens for understanding the deep structures of cultural continuity and transformation in human societies. It further underscores the importance of genealogy in cultural evolution, historical persistence, societal transformation, and the construction of belonging in an increasingly globalized world. Full article

Immersive virtual reality (VR) offers promising approaches for engaging with difficult heritage and human rights issues, potentially fostering deeper emotional connections than traditional media. This paper presents a mixed-methods evaluation of Nobody’s Listening, a VR experience documenting the Yazidi genocide in Iraq (2014–2017). Employing a historical empathy framework, the study analyses pre- and post-experience surveys, interviews, and observational data from 127 non-Yazidi participants across five Iraqi cities. It contributes a replicable framework for evaluating immersive heritage experiences, assessing how VR can foster emotional engagement, raise human rights awareness, and inspire positive action. Findings reveal substantial impact across cognitive, emotional, and behavioral domains: 85% reported increased awareness of the genocide, 71% gained new knowledge of Yazidi culture, and over 80% experienced intense emotional reactions, including empathy, grief, and shock. When describing what impressed them most, 57% demonstrated historical empathy (including contextualization, perspective taking, and affective connection). Notably, 92% believed justice had not been served, with many expressing intentions to support advocacy. Our findings suggest that VR’s impact in post-conflict contexts stems not solely from immersion, but from resonance with participants’ own trauma histories—activating empathy through analogical recognition and collective memory. The study offers key design and ethical principles, including cultural specificity, survivor testimony, community consultation, and trauma-informed evaluation. These insights contribute to inclusive heritage interpretation, reconciliation, and human rights education. Full article

Reliable load forecasting is crucial for ensuring optimal dispatch, grid security, and cost efficiency. To address limitations in prediction accuracy and generalization, this paper proposes a hybrid model, GRU-MHSAM-ResNet, which integrates a gated recurrent unit (GRU), multi-head self-attention (MHSAM), and a residual network (ResNet)block. Firstly, GRU is employed as a deep temporal encoder to extract features from historical load data, offering a simpler structure than long short-term memory (LSTM). Then, the MHSAM is used to generate adaptive representations by weighting input features, thereby strengthening the key features. Finally, the features are processed by fully connected layers, while a ResNet block is added to mitigate gradient vanishing and explosion, thus improving prediction accuracy. The experimental results on actual load datasets from systems in China, Australia, and Malaysia demonstrate that the proposed GRU-MHSAM-ResNet model exhibits superior predictive accuracy to compared models, including the CBR model and the LSTM-ResNet model. On the three datasets, the proposed model achieved a mean absolute percentage error (MAPE) of 1.65% (China), 5.52% (Australia), and 1.57% (Malaysia), representing a significant improvement over the other models. Furthermore, in five repeated experiments on the Malaysian dataset, it exhibited lower error fluctuation and greater result stability compared to the benchmark LSTM-ResNet model. Therefore, the proposed model provides a new forecasting method for power system dispatch, exhibiting high accuracy and generalization ability. Full article

The nonlocality (capable of associating target dynamics across multiple time moments) and memory properties (able to retain historical trajectories) of fractional calculus serve as the core theoretical approach to resolving the “dynamic information association deficiency” in UAV mobile target search. This paper proposes the Fractional-order Black-winged Kite Algorithm (FOBKA), which transforms the search problem into an adaptability function optimization model aimed at “maximizing target capture probability” based on Bayesian theory. Addressing the limitations of the standard Black-winged Kite Algorithm (BKA), the study incorporates fractional calculus theory for enhancement: A fractional-order operator is embedded in the migration behavior phase, leveraging the memory advantage of fractional-orders to precisely capture the temporal span, spatial position, and velocity evolution of targets, thereby enhancing global detection capability and convergence accuracy. Simultaneously, population individuals are initialized using motion-encoding, and the attack behavior phase combines alternating updates with a Lévy flight mechanism to balance local exploration and global search performance. To validate FOBKA’s superiority, comparative experiments were conducted against eight newly proposed meta-heuristic algorithms across six distinct test scenarios. Experimental data demonstrate that FOBKA significantly outperforms the comparison algorithms in convergence accuracy, operational robustness, and target capture probability. Full article

Road traffic accidents are a major global public health concern, ranking among the top three causes of death worldwide and constituting the leading cause of death among individuals aged 15–29. Monitoring traffic safety status and trends is a vital element of effective road safety management. This study investigates road traffic casualties involving young car drivers (aged 18–24) in the Republic of Serbia from 1997 to 2024, analyzing historical patterns and introducing a predictive model for casualty outcomes. The analytical framework employs machine learning techniques, specifically Long Short-Term Memory (LSTM) networks, to estimate the number of casualties (FSI = Fatal + Serious Injuries) based on various contributing factors. Accurate prediction of accident outcomes is essential for designing targeted road safety measures and reducing casualty numbers. Full article

Patenting is essential for protecting intellectual property, fostering technological innovation, and maintaining competitive advantages in the global market. In Mexico, strategic planning in science, technology, and innovation requires reliable forecasting tools. This study evaluates computational models for predicting applied and granted patents between 1990 and 2024, including statistical (ARIMA), machine learning (Regression Trees, Random Forests, and Support Vector Machines), and deep learning (Long Short-Term Memory, LSTM) approaches. The workflow involves historical data acquisition, exploratory analysis, decomposition, model selection, forecasting, and evaluation using the Root Mean Square Error (RMSE), the determination coefficient ($R^2$), and the Mean Absolute Percentage Error (MAPE) as performance metrics. To ensure generalization and robustness in the training stage, we use the cross-validation rolling origin. On the test stage, LSTM achieves the highest accuracy (RMSE = 106.91, $R^2=0.97$, and MAPE = 0.63 for applied patents; RMSE = 283.20, $R^2=0.93$, and MAPE = 2.65 for granted patents). However, cross-validation shows that ARIMA provides more stable performance across multiple scenarios, highlighting a trade-off between short-term accuracy and long-term reliability. These results demonstrate the potential of machine learning and deep learning as forecasting tools for industrial property management. Full article

Short-term traffic flow prediction is a critical component of efficient management in Intelligent Transportation Systems (ITS), providing real-time travel guidance for commuters and supporting informed decision-making by transportation authorities. To address the current challenges of insufficient prediction accuracy and excessive reliance on time-series features, we propose a spatio-temporal feature-integrated short-term traffic flow prediction model named KAN-CNN-BiLSTM. In this model, traffic flow data from the target road segment and its two adjacent segments are jointly fed into the model to fully leverage spatio-temporal features for prediction. Subsequently, a Convolutional Neural Network (CNN) extracts spatial features from the combined traffic flow data. To overcome the limitation of traditional LSTMs, which can only process unidirectional time series, we introduce a bidirectional long short-term memory network (BiLSTM) with symmetric time series extraction capability. This enables simultaneous capture of historical and future traffic flow dependencies. Finally, we replace the conventional fully connected network with a Kolmogorov–Arnold network (KAN) to enhance the representation of complex nonlinear features. Experimental results using traffic flow data from the UK Highways Agency website demonstrate that the KAN-CNN-BiLSTM model outperforms existing mainstream methods, achieving superior prediction accuracy and minimal error. The model’s MAE, RMSE, MAPE, and R² values are 27.4696, 40.3923, 8.65%, and 0.9615, respectively. Full article

Historic urban cores face escalating pressures from climate change, rapid urbanization, and uncoordinated redevelopment, which often threaten their cultural identity and social cohesion, demanding innovative solutions that balance heritage conservation with contemporary housing needs. This study introduces an integrated evaluation framework encompassing 18 criteria across architectural, urban, and green dimensions to assess adaptive housing interventions in urban heritage contexts. Building on resilience theory, urban living, and sustainable urban futures, the paper traces the historical and contemporary design influences that have shaped urban housing design in Damascus, and investigates strategies to maintain prospective housing identity by applying the methodology of the developed framework to three representative dwellings in Damascus’s UNESCO-listed city. Considering the heritage-specific indicators, social place memory, and the cultural significance—with environmental performance and socio-economic viability—the developed compass-like tool in this research visualizes multi-criteria scores to identify leverage points for resilience. Results highlight priority zones for intervention and suggested policy incentives. Through the provision of a flexible, clear tool grounded in adaptive housing concepts, this study empowers planners, conservationists, and communities to develop sustainable, forward-thinking approaches for historic urban environments globally. Full article

Current generative AI systems, despite extraordinary progress, face fundamental limitations in temporal reasoning, contextual understanding, and ethical decision-making. These systems process information statistically without authentic comprehension of experiential time or intentional context, limiting their applicability in security-critical domains where reasoning about past experiences, present situations, and future implications is essential. We present Phase 3 of the Sophimatics framework: Super Time-Cognitive Neural Networks (STCNNs), which address these limitations through complex-time representation T ∈ ℂ where chronological time (Re(T)) integrates with experiential dimensions of memory (Im(T) < 0), present awareness (Im(T) ≈ 0), and imagination (Im(T) > 0). The STCNN architecture implements philosophical constraints through geometric parameters α and β that bound memory accessibility and creative projection, enabling neural systems to perform temporal-philosophical reasoning while maintaining computational tractability. We demonstrate STCNN’s effectiveness across five security-critical applications: threat intelligence (AUC 0.94, 1.8 s anticipation), privacy-preserving AI (84% utility at ε = 1.0), intrusion detection (96.3% detection, 2.1% false positives), secure multi-party computation (ethical compliance 0.93), and blockchain anomaly detection (94% detection, 3.2% false positives). Empirical evaluation shows 23–45% improvement over baseline systems while maintaining temporal coherence > 0.9, demonstrating that integration of temporal-philosophical reasoning with neural architectures enables AI systems to reason about security threats through simultaneous processing of historical patterns, current contexts, and projected risks. Full article

Eight Views is a time-honored East Asian cultural-landscape paradigm in which eight emblematic natural—cultural scenes fuse regional character, historical memory, and aesthetic ideals into a coherent narrative. It encodes the collective memory and identity of a city (or garden/region), a premodern ‘mental map’ or proto- ‘city brand’. In China, the historic Urban Eight Views are rooted in local environments and traditions and constitute significant, high-value landscape heritage today. Yet rapid urbanization has inflicted severe physical damage on these ensembles. Coupled with insufficient holistic and systemic understanding among managers and the public, this has led, during development and conservation alike, to spatial insularization, fragmentation, and even disappearance, alongside widening divergences in cultural cognition and biases in value judgment. Taking Longmen Haoyue in Chongqing, one of the historic Urban Eight Views, as a case that manifests these issues, this study develops a holistic–relational approach for the urban, historical Eight Views and explores landscape-based pathways to protect the spatial structure and cultural connotations of the heritage that has been severely damaged and is in a state of disappearance or semi-disappearance amid modernization. Methodologically, we employ decomposition analysis to extract the historical information elements of Longmen Haoyue and its internal relational structure and corroborate its persistence through field surveys. We then apply the FAHP method to grade the conservation value and importance of elements within the Eight Views, quantitatively clarifying protection hierarchies and priorities. In parallel, a multidimensional corpus is constructed to analyze online dissemination and public perception, revealing multiple challenges in the evolution and reconstruction of Longmen Haoyue, including symbolic misreading and cultural decontextualization. In response, we propose an integrated strategy comprising graded element protection and intervention, reconstruction of relational structures, and the building of a coherent cultural-semantic and symbol system. This study provides a systematic theoretical basis and methodological support for the conservation of the urban historic Eight Views cultural landscapes, the place-making of distinctive spatial character, and the enhancement of cultural meanings. It develops an integrated research framework, element extraction, value assessment, perception analysis, and strategic response that is applicable not only to the Eight Views heritage in China but is also transferable to World Heritage properties with similar attributes worldwide, especially composite cultural landscapes composed of multiple natural and cultural elements, sustained by narrative traditions of place identity, and facing risks of symbolic weakening, decontextualization, or public misperception. Full article

Previous research has shown that Two-Spirit Indigenous people may experience more trauma, interracial violence, and violent crimes than youth from other cultural backgrounds. This study aims to examine how identifying as Two-Spirit, an identity that integrates both non-cisgender and non-heterosexual identities, as well as Indigenous identities simultaneously and congruently, may allow one to feel more resilient and empowered. The sample consisted of Indigenous, sexual gender minority emerging adults (N = 91) with ages ranging from 18 to 29 with an average age of 24.78 (SD = 2.35). This sample reported perceived discrimination for being Indigenous, for their gender identity, and for their sexual orientation. The amount of discrimination for each of these categories was not significantly different, but the source was, and the predominant source for all three types was White individuals. The combined effects were related to lower self-esteem; more psychological symptoms of anxiety, depression, and somatization; and greater identity distress and higher scores on disturbed identity and lack of identity. In this study, Two-Spirit identification did not reduce the negative effects of discrimination by connecting with historical memory through this identity, but that does not necessarily mean that it cannot, only that its potential has yet to be fulfilled. Many participants did not have a full understanding of the label and its history. Further research into this idea is another area of study that might be fruitful. Full article

This article examines the processes of de-Sovietization of public spaces in Lithuania, focusing on the visual transformation of monuments after the collapse of the Soviet Union. While scholarship has primarily analyzed the dismantling of Soviet monuments as acts of iconoclasm, this study argues that de-Sovietization is a dual process involving both negative and positive dimensions: the removal of Soviet-era symbols and the creation of new monuments that articulate a post-Soviet national narrative. Drawing on Jacques Rancière’s framework of artistic regimes, the article explores how newly constructed or restored monuments embody the search for a new symbolic language of political and social communication. The analysis is based on qualitative content analysis of expert interviews with sculptors, architects, and artists involved in monument-making in Lithuania since 1990. Over the past three decades, more than 400 monuments have been erected in Lithuania, reflecting the tensions between continuity and rupture with Soviet monumentalism. While naturalistic monuments often avoided controversy, projects departing from realistic aesthetics—such as Regimantas Midvikis’ Exploded Bunker and Andrius Labašauskas’ Freedom Hill—became sites of conflict and public debate. By identifying the visual features of positive de-Sovietization, the article contributes to understanding how post-Soviet societies negotiate historical memory, identity, and aesthetic form in public space. Full article

The aviation industry operates as a complex, dynamic system generating vast volumes of data from aircraft sensors, flight schedules, and external sources. Managing this data is critical for mitigating disruptive and costly events such as mechanical failures and flight delays. This paper presents a comprehensive application of predictive analytics and machine learning to enhance aviation safety and operational efficiency. We address two core challenges: predictive maintenance of aircraft engines and forecasting flight delays. For maintenance, we utilise NASA’s C-MAPSS simulation dataset to develop and compare models, including one-dimensional convolutional neural networks (1D CNNs) and long short-term memory networks (LSTMs), for classifying engine health status and predicting the Remaining Useful Life (RUL), achieving classification accuracy up to 97%. For operational efficiency, we analyse historical flight data to build regression models for predicting departure delays, identifying key contributing factors such as airline, origin airport, and scheduled time. Our methodology highlights the critical role of Exploratory Data Analysis (EDA), feature selection, and data preprocessing in managing high-volume, heterogeneous data sources. The results demonstrate the significant potential of integrating these predictive models into aviation Business Intelligence (BI) systems to transition from reactive to proactive decision-making. The study concludes by discussing the integration challenges within existing data architectures and the future potential of these approaches for optimising complex, networked transportation systems. Full article

Feature selection in high-dimensional datasets presents significant computational challenges, particularly in domains with large feature spaces and limited sample sizes. This paper introduces FL-SBA, a novel metaheuristic algorithm integrating fractional calculus enhancements with Laguerre operators into the Secretary Bird Optimization Algorithm framework for binary feature selection. The methodology incorporates fractional opposition-based learning utilizing Laguerre operators for enhanced population initialization with non-local memory characteristics, and a Laguerre-based binary transformation function replacing conventional sigmoid mechanisms through orthogonal polynomial approximation. Fractional calculus integration introduces memory effects that enable historical search information retention, while Laguerre polynomials provide superior approximation properties and computational stability. Comprehensive experimental validation across ten high-dimensional gene expression datasets compared FL-SBA against standard SBA and five contemporary methods including BinCOA, BAOA, BJSO, BGWO, and BMVO. Results demonstrate FL-SBA’s superior performance, achieving 96.06% average classification accuracy compared to 94.41% for standard SBA and 82.91% for BinCOA. The algorithm simultaneously maintained exceptional dimensionality reduction efficiency, selecting 29 features compared to 40 for competing methods, representing 27% improvement while achieving higher accuracy. Statistical analysis reveals consistently lower fitness values (0.04924 averages) and stable performance with minimal standard deviation. The integration addresses fundamental limitations in integer-based computations while enhancing convergence behavior. These findings suggest FL-SBA represents significant advancement in metaheuristic-based feature selection, offering theoretical innovation and practical performance improvements for high-dimensional optimization challenges. Full article

Nonlocal models offer a unified framework for describing long-range spatial interactions and temporal memory effects. The review briefly outlines several representative physical problems, including anomalous diffusion, material fracture, viscoelastic wave propagation, and electromagnetic scattering, to illustrate the broad applicability of nonlocal systems. However, the intrinsic global coupling and historical dependence of these models introduce significant computational challenges, particularly in high-dimensional settings. From the perspective of algorithmic strategies, the review systematically summarizes high-dimensional numerical methods applicable to nonlocal equations, emphasizing core approaches for overcoming the curse of dimensionality, such as structured solution frameworks based on FFT, spectral methods, probabilistic sampling, physics-informed neural networks, and asymptotically compatible schemes. By integrating recent advances and common computational principles, the review establishes a dual “problem review + method review” structure that provides a systematic perspective and valuable reference for the modeling and high-dimensional numerical simulation of nonlocal systems. Full article