Dynamic Integration Research Articles

Dynamics of Social Work Approach in Childcare; An Analysis of Integration of Islamic Kafala Theory and Bowlby’s Attachment Theory and Application Thereof in Childcare in Saudi Arabia

This paper explores the dynamic integration of Islamic Kafala theory and attachment theory within the context of childcare and welfare in Saudi Arabia. Despite the growing recognition of diverse cultural and theoretical frameworks in social work practice, there is very limited research on how specific theories are integrated and applied in childcare, particularly within Islamic contexts. This study aims to fill this gap by analyzing the application of these theories in enhancing child welfare and social work practices in Saudi Arabia. Utilizing a mixed-methods approach, the research draws on qualitative data from surveys of child welfare professionals and families engaged in the Kafala system, supplemented by case study analyses in various Muslim countries. The findings reveal that the integration of Islamic Kafala theory and attachment theory offers a culturally sensitive and effective framework for improving childcare practices, promoting the emotional and psychological well-being of children in care. The study highlights practical implications for social workers, suggesting strategies for the successful incorporation of these integrated theories into child welfare practices in Saudi Arabia. It also identifies challenges and limitations within the current system, offering recommendations for policymakers to enhance child welfare services. This research contributes to the theoretical and practical knowledge of childcare and welfare, advocating for a more nuanced and culturally informed approach to social work in Saudi Arabia.

Stress-strain state of building structures taking into account possible local failure of the element

The issue of choosing a structural design and material for supporting structures is an important technical and economic task at the stage of developing a design solution. It depends on a number of factors: consequence class of the structure, reliability of the design solution, savings in basic building materials. The publication provides a classification of design schemes and types of load-bearing structures used in the corresponding solutions. The disadvantages and advantages of using prefabricated and monolithic structures for the installation and subsequent operation of the structural scheme of buildings and structures are considered. Modern design requirements include ensuring the ability of a damaged structure to adapt to new conditions while continuing to function while ensuring the integrity of human life, property and equipment. New conditions mean the consequences of the occurrence of a certain emergency situation, accompanied by weakening or overloading of the load-bearing structures of a structure or soil foundation: a change in the structural design, a combination of new existing loads and a redistribution of internal forces. The publication reflects the results of assessing the redistribution of the stress-strain state of the elements of the “base - foundations - load-bearing structures” system as a result of the implementation of a hypothetical emergency situation with the exclusion of the load-bearing structure from operation. The case of the collapse of one of the vertical load-bearing elements (local failure of the pylon) of an underground floor, which can be used as a dual-use structure, is considered. Calculations for the stability of the structure against progressive collapse were carried out by numerical modeling in the LIRA SAPR-2019 software using a quasi-static calculation and the method of direct integration of dynamics over time. It has been demonstrated that the method of numerical modeling the joint work of a building with a soil base affects the results of a calculation of the progressive collapse of the building frame. The influence of local collapse of a vertical load-bearing element on the redistribution of stresses and strains in the foundation structures of a building section is assessed. The load on the piles under the pylons around the element removed under the local failure scenario is expected to increase by 15...25%.

Poetic Metaphors Related to Birds: A Conceptual Blending in Puisi-Puisi Cinta, WS. Rendra

Metaphors are commonly used to vividly convey deeply abstract human emotions. This is because human emotions are indeed an abstract – conceptual. In the poem of Puisi-Puisi Cinta, the poet employs a great deal of emotional poetic metaphors relating to birds in order to communicate the variety of feelings that are experienced by the characters, such as features, roles, and visual aspects. This paper aims to study the emotional poetic metaphors related to birds in Puisi-Puisi Cinta using conceptual blending theory. The research data in the study consists the poetic metaphors related to Birds in Puisi – Pusi Cinta by WS Rendra. The data are conducted qualitatively using the blending theory. The blending theory’s G.Fauconnier and M.Turner (2002) came to the conclusion that metaphorizing is not limited by the projection from “the source domain” to “the target domain”, but includes complex dynamic integration processes, creating new blended mental spaces, which are able to create the meaning structure. The findings reveal that these vivid poetic metaphors in Puisi-Puisi Cinta by W.S Rendra are substantial through the use of imagery depicting aspects of the birds' appearances and its features to human beings. The metaphorical process, which projects from the birds to human beings, can be seen as a process of conceptual blending. In addition, each and every poetic metaphor related to birds of the poem has its characteristics in a certain cognitive model.

Development of a Dynamically Re-Configurable Radio-Frequency Interference Detection System for L-Band Microwave Radiometers.

Real-Time RFI Detection and Flagging (RT-RDF) for microwave radiometers is a versatile new FPGA algorithm designed to detect and flag Radio-Frequency Interference (RFI) in microwave radiometers. This block utilizes computationally-efficient techniques to identify and analyze RF signals, allowing the system to take appropriate measures to mitigate interference and maintain reliable performance. With L-Band microwave radiometry as the main application, this RFI detection algorithm focuses on the Kurtogram and Spectrogram to detect non-Gaussian behavior. To gain further modularity, an FFT-based filter bank is used to divide the receiver's bandwidth into several sub-bands within the band of interest of the instrument, depending on the application. Multiple blanking strategies can then be applied in each band using the provided detection flags. The algorithm can be re-configured in the field, for example with dynamic integration times to support operation in different environments, or configurable thresholds to account for variable RFI environments. A validation and testing campaign has been performed on multiple scenarios with the ARIEL commercial microwave radiometer, and the results confirm the excellent performance of the system.

Four dimensions of naturalistic language production in aphasia after stroke.

There is a rich tradition of research on the neuroanatomical correlates of spoken language production in aphasia using constrained tasks (e.g., picture naming), which offer controlled insights into the distinct processes that govern speech and language (i.e., lexical-semantic access, morphosyntactic construction, phonological encoding, speech motor programming/execution). Yet these tasks do not necessarily reflect everyday language use. In contrast, naturalistic language production (also referred to as connected speech or discourse) more closely approximates typical processing demands, requiring the dynamic integration of all aspects of speech and language. The brain bases of naturalistic language production remain relatively unknown, however, in part because of the difficulty in deriving features that are salient, quantifiable, and interpretable relative to both speech-language processes and the extant literature. The present cross-sectional observational study seeks to address these challenges by leveraging a validated and comprehensive auditory-perceptual measurement system that yields four explanatory dimensions of performance-Paraphasia (misselection of words and sounds), Logopenia (paucity of words), Agrammatism (grammatical omissions), and Motor speech (impaired speech motor programming/execution). We used this system to characterize naturalistic language production in a large and representative sample of individuals with acute post-stroke aphasia (n = 118). Scores on each of the four dimensions were correlated with lesion metrics, and multivariate associations among the dimensions and brain regions were then explored. Our findings revealed distinct yet overlapping neuroanatomical correlates throughout the left-hemisphere language network. Paraphasia and Logopenia were associated primarily with posterior regions, spanning both dorsal and ventral streams, which are critical for lexical-semantic access and phonological encoding. In contrast, Agrammatism and Motor speech were associated primarily with anterior regions of the dorsal stream that are involved in morphosyntactic construction and speech motor planning/execution respectively. Collectively, we view these results as constituting a brain-behavior model of naturalistic language production in aphasia, aligning with both historical and contemporary accounts of the neurobiology of spoken language production.

Cleft Lip and Palate: Examining Quality of Life, Family Dynamics, and Social Integration

Cleft Lip and Palate: Examining Quality of Life, Family Dynamics, and Social Integration

Static and dynamic glazing integration for enhanced building efficiency and indoor comfort with thermochromic and electrochromic windows

Effective building energy efficiency requires understanding fenestration’s role in regulating indoor temperatures. Therefore, this study investigates the impact of integrating static glazing with dynamic coatings on building energy efficiency and indoor comfort in a lightweight structure situated in a semi-arid climate. Employing a comprehensive numerical model developed using EnergyPlus and Radiance tools, various static commercial glass window configurations are evaluated to assess their effects on energy consumption, thermal and visual comfort, and economic and environmental considerations. This analysis includes assessments of thermal comfort using PMV and PPD indicators and evaluations of visual comfort based on daylighting and glare metrics. The findings highlight the advantages of incorporating thermochromic and electrochromic films, demonstrating significant improvements in building energy efficiency and interior thermal and visual comfort. Notably, double glazing emerges as the most economically efficient and environmentally viable option, resulting in a reduction of emissions by 1522.38 kgCO2/year, with a payback period of 12.86 years. Furthermore, combining thermochromic and electrochromic coatings with optimal static glazing leads to a remarkable 26 % reduction in energy consumption. These results underscore the potential of dynamic coatings to enhance building energy performance while ensuring occupant comfort. This research approach provides valuable insights into sustainable building design, emphasizing the integrated impact of glazing solutions on energy use, comfort, and environmental sustainability.

Some Solutions to Enhance the Quality of English Language Education at the Secondary Level in the Foreign Language Department of Tan Trao University

This study aims to provide a comprehensive assessment of the English language teaching and learning process at the A2 level among students at Tan Trao University, with the primary goal of enhancing students' language proficiency to meet the objectives of the National Foreign Language Project 2020. Utilizing two survey instruments—one for students and one for instructors—the research offers an insightful and in-depth examination of both sides of the teaching and learning process. The survey results indicate that students highly appreciate the curriculum used in their English language learning journey at the university, while also holding positive perceptions of their instructors' teaching abilities. The enthusiastic, dynamic teaching approach and integration of information technology demonstrated by the instructors underscore the university's commitment to improving the quality of English language instruction. However, despite the effective efforts of instructors, a majority of students continue to approach English learning passively. There is a need for students to cultivate a more proactive approach to self-study and research. This highlights the necessity for new strategies and interventions to stimulate students' self-directed learning and development in English language acquisition. It is imperative to consider and implement measures such as active teaching methodologies and creating conducive environments for self-directed learning, alongside promoting student engagement and autonomy in the learning process. These efforts will significantly contribute to enhancing the quality of English language education at Tan Trao University.

Bridging traditions: The synergy of historical wisdom and modern sustainable practices in architecture

This paper explores the dynamic integration of traditional architectural wisdom with contemporary green building technologies, highlighting a holistic approach to sustainable development in the built environment. Traditional architecture, characterized by its intrinsic sustainability, leverages local materials, climatic conditions, and cultural practices, presenting a rich repository of ecological knowledge. Modern green technologies further these sustainability goals through innovations in energy efficiency, waste reduction, and the use of renewable resources. Through quantitative analyses, case studies, and a comprehensive review of advancements in energy-efficient technologies, water management, and sustainable materials, this study illustrates the significant potential of marrying historical practices with modern sustainability principles. It underscores the importance of policy frameworks, urban planning, and community engagement in facilitating this integration, aiming to preserve cultural heritage while addressing contemporary environmental challenges. The findings advocate for a nuanced understanding of both traditional and modern methods, suggesting that the future of sustainable architecture lies in the thoughtful amalgamation of past wisdom and future innovations.

Dynamic Nonlinear Spatial Integrations on Encoding Contrasting Stimuli of Tectal Neurons.

Animals detect targets using a variety of visual cues, with the visual salience of these cues determining which environmental features receive priority attention and further processing. Surround modulation plays a crucial role in generating visual saliency, which has been extensively studied in avian tectal neurons. Recent work has reported that the suppression of tectal neurons induced by motion contrasting stimulus is stronger than that by luminance contrasting stimulus. However, the underlying mechanism remains poorly understood. In this study, we built a computational model (called Generalized Linear-Dynamic Modulation) which incorporates independent nonlinear tuning mechanisms for excitatory and inhibitory inputs. This model aims to describe how tectal neurons encode contrasting stimuli. The results showed that: (1) The dynamic nonlinear integration structure substantially improved the accuracy (significant difference (p < 0.001, paired t-test) in the goodness of fit between the two models) of the predicted responses to contrasting stimuli, verifying the nonlinear processing performed by tectal neurons. (2) The modulation difference between luminance and motion contrasting stimuli emerged from the predicted response by the full model but not by that with only excitatory synaptic input (spatial luminance: 89 ± 2.8% (GL_DM) vs. 87 ± 2.1% (GL_DMexc); motion contrasting stimuli: 87 ± 1.7% (GL_DM) vs. 83 ± 2.2% (GL_DMexc)). These results validate the proposed model and further suggest the role of dynamic nonlinear spatial integrations in contextual visual information processing, especially in spatial integration, which is important for object detection performed by birds.

Identification of reentry vulnerable targets during substrate mapping for ablation in scar related VT: the "visual" reentry vulnerability index

Abstract Introduction Conduction abnormalities including zones of deceleration (DZ) &amp; late activation are functionally important targets for ablation in scar-related ventricular tachycardias (VT). Repolarisation heterogeneities are equally important but are not routinely assessed. Visualisation of a combination of both conduction &amp; repolarisation metrics whilst in sinus in the form of a re-entry vulnerability index (RVI) may enable more precise identification of critical ablation targets. Rationale: To test accuracy of a novel functional substrate mapping strategy combining conduction &amp; repolarisation metrics to "visually" estimate reentry vulnerability sites (RVIv) during scar related VT ablation without the need for offline processing required for "computational" RVI. Methods VT cases using a multipolar grid-catheter for contact mapping were reviewed. DZ &amp;areas of late activation were tagged on omnipolar LAT substrate maps. The corresponding unipolar repolarisation maps depicting local repolarisation time (LRT) based on the Wyatt method (WOI over T-Wave annotating to +dV/dt, UEGM-Filter 0.05-100Hz) were then displayed side-by-side with the LAT map. Timing reference was set to onset of surface QRS. Colour scale of LRT maps was set to start at or ahead of the timing of latest LAT. "Visual RVI" zones were identified if the tagged zone of late activation was overlapping and/or directly adjacent to an area of "early" repolarisation (defined as before or within 100ms of latest LAT point) based on colour. In visually identified RVI areas, temporal (timing difference in ms) &amp; spatial relationship (distance in mm) of EGMs with earliest RT (RTe) to EGM with latest AT (ATL) separated by max. 20mm were measured &amp; RVI calculated (RTe subtracted by ATL). Number of RVIv zones per map, RVI metrics &amp; distance from closest RVI to VT Exit site (defined per activation±entrainment or pace mapping) were assessed. The lower/negative the numeric RVI, the higher the vulnerability for reentry. Results 20 patients with scar-related reentry VT were included (90% male, 63.8±11.6 years, LVEF 36±16%, 60% ischemic/40% non-ischemic). LAT &amp; LRT map point counts were 4835±2625 and 3596±2805. In 17 (85%) patients at least 1 RVIv zone was identified, with on avg. 3.3±1.7 RVIv zones/patient. Manual RVIs were calculated to 10.4±78.9ms ( "most vulnerable" RVI was -227ms) with on avg. 9.5±5.3mm distance between RTe &amp; ATL. All maps with no RVIv zone on the mapped surface were of non-ischemic etiology. The shortest distance of RVIs to functionally critical sites during VT was 8.9±7.4mm (range 0-33mm). Conclusion Visual reentry vulnerability estimation based on a combination of established conduction and novel repolarisation maps is feasible &amp; may help to identify functionally critical zones during substrate mapping. Future efforts should include dedicated automated repolarisation mapping tools to facilitate integration of repolarisation dynamics in functional VT substrate mapping workflows.Figure 1Example Visual RVI Mapping

Socioeconomic, Ethnographic and Political Integrations and Challenges of Igbo Migrants in Lafia, Nasarawa State, Nigeria

The study examines the socioeconomic, ethnographic and political integrations and challenges of Igbo migrants in Lafia. Quantitative and qualitative techniques were adopted to collect data on integration variables via the administration of questionnaires to groups of Igbos from the five states of the Southeast region, and the conduct of In-depth Interviews (IDIs) with Igbo elders. Fifty copies of questionnaires were distributed to each group, totaling 250. Data were analysed by SPSS version 25, and tables and charts were generated and the qualitative data were content analysed. On socioeconomic integration, the results showed that the majority (88.1%) of Igbos were first generation migrants, who arrived to start business in Lafia over 40 years ago, whereby, almost three-fifths (59.5%) of them started their current occupation. Majority (37.6%) of Igbo migrants’ present occupation was facilitated by ‘master’, ‘family’ (66.8%) facilitated their accommodation, close to 70 per cent of them possessed their personal houses, and over two-fifths (43.1%) of them got landed property through ‘friend’ while close to three-quarters (74.8%) got married to Lafia indigenes. On ethnographic integration, three-quarters were proficient in Hausa language, with half rated their proficiency in Hausa language ‘excellent,’ while Gwandara language became the best secondary proficient language. As par political integration, over half (52.0%) of Igbo migrants held political office within their association politics but few with the wider politics. In summary, Igbo migrants have been experiencing dynamic and positive integrations but facing challenges of ethnic divide and nepotism.

Empirical Assessment of Maize Price Dynamics and Market Integration in Selected Regional Markets of Benue State, Nigeria.

This study investigated how maize prices move in selected regional markets in Benue State, Nigeria. The researchers looked at monthly maize price data from January 2008 to December 2018 to determine if the price series in these different markets were connected. Specific tests were used to determine if the prices influenced each other over time (market integration) and, if so, which market (rural or urban) tended to lead the price changes in the state. The result revealed that maize prices eventually stabilized over time (became stationary) and that rural and urban markets were indeed connected. There was also evidence of some level of market integration in the short term. Interestingly, the price changes in one market didn't always directly cause changes in the other market. In some cases, price changes seemed to flow in both directions. Based on these results, this study therefore recommends that the all stakeholders provide information centers and use media channels to improve communication between maize producers and consumers in the state; as this would allow actors to make more informed decisions about maize marketing.

On‐line machine‐learning forecast uncertainty estimation for sequential data assimilation

AbstractQuantifying forecast uncertainty is a key aspect of state‐of‐the‐art numerical weather prediction and data assimilation systems. Ensemble‐based data assimilation systems incorporate state‐dependent uncertainty quantification based on multiple model integrations. However, this approach is demanding in terms of computations and development. In this work, a machine‐learning method is presented based on convolutional neural networks that estimates the state‐dependent forecast uncertainty represented by the forecast error covariance matrix using a single dynamical model integration. This is achieved by the use of a loss function that takes into account the fact that the forecast errors are heteroscedastic. The performance of this approach is examined within a hybrid data assimilation method that combines a Kalman‐like analysis update and the machine‐learning‐based estimation of a state‐dependent forecast error covariance matrix. Observing system simulation experiments are conducted using the Lorenz'96 model as a proof‐of‐concept. The promising results show that the machine‐learning method is able to predict precise values of the forecast covariance matrix in relatively high‐dimensional states. Moreover, the hybrid data assimilation method shows similar performance to the ensemble Kalman filter, outperforming it when the ensembles are relatively small.

The dynamic integration of computational approaches and machine learning for cutting-edge solutions in photonics

The dynamic integration of computational approaches and machine learning for cutting-edge solutions in photonics

Analysis of unmanned aerial vehicle communication performance with optic sensor integration

This paper presents a novel algorithmic framework to enhance unmanned aerial vehicle (UAV) communication performance through the adaptive integration of optical sensor data. The framework features a dynamic sensor integration algorithm (DSIA) that optimizes sensor bitrates based on real-time bandwidth availability and mission-driven priorities. An adaptive compression scheme works alongside the DSIA to maintain high data fidelity and resolution at lower bitrates. Together, these algorithms provide an efficient and scalable solution for managing high-volume UAV sensor data under varying conditions. The efficiency gains are demonstrated through simulations highlighting the framework’s ability to reduce packet loss and latency while maximizing image quality for a given communication bandwidth constraint. The scalable architecture ensures robustness across diverse UAV platforms, sensor types, and operational scenarios. Simulation results validate up to a 75 % reduction in latency and a 45 % improvement in image resolution compared to conventional fixed bitrate approaches. By optimizing sensor data relevancy and transmission fidelity, this framework enables more effective UAV communication and enhances mission performance.

Deep-Reinforcement-Learning-Based Vehicle-to-Grid Operation Strategies for Managing Solar Power Generation Forecast Errors

This study proposes a deep-reinforcement-learning (DRL)-based vehicle-to-grid (V2G) operation strategy that focuses on the dynamic integration of charging station (CS) status to refine solar power generation (SPG) forecasts. To address the variability in solar energy and CS status, this study proposes a novel approach by formulating the V2G operation as a Markov decision process and leveraging DRL to adaptively manage SPG forecast errors. Utilizing real-world data from the Korea Southern Power Corporation, the effectiveness of this strategy in enhancing SPG forecasts is proven using the PyTorch framework. The results demonstrate a significant reduction in the mean squared error by 40% to 56% compared to scenarios without V2G. Our investigation into the effects of blocking probability thresholds and discount factors revealed insights into the optimal V2G system performance, suggesting a balance between immediate operational needs and long-term strategic objectives. The findings highlight the possibility of using DRL-based strategies to achieve more reliable and efficient renewable energy integration in power grids, marking a significant step forward in smart grid optimization.

Wireless Charging System for Electric Vehicles

This paper presents a comprehensive review of wireless electric vehicle (EV) charging technologies, focusing on the advancements, challenges, and prospects in this domain. With the increasing adoption of electric vehicles globally, the demand for efficient and convenient charging solutions has grown significantly. Wireless charging offers promising opportunities to enhance the user experience by eliminating the need for physical connections between the charger and the vehicle. This review discusses the underlying principles of various wireless charging technologies, including inductive charging, resonant charging, and capacitive charging. Furthermore, it examines the key components and system architectures involved in wireless EV charging systems. The paper also addresses the technical challenges such as efficiency, alignment, safety, and interoperability that must be overcome to realize the widespread deployment of wireless charging infrastructure. Additionally, the regulatory and standardization efforts governing wireless EV charging are discussed, along with their implications for market adoption. Finally, the review provides insights into future research directions and potential applications of wireless EV charging technology, including dynamic charging systems and integration with smart grid networks. Overall, this paper serves as a valuable resource for researchers, engineers, and policymakers interested in understanding the current state and future prospects of wireless electric vehicle charging.

Multi-perspective option price forecasting combining parametric and non-parametric pricing models with a new dynamic ensemble framework

This article introduces a dynamic ensemble framework that integrates parametric and non-parametric pricing models. Within this framework, we propose a time-varying parametric pricing model optimized using artificial intelligence algorithms. Additionally, we construct a non-parametric pricing model using a 2-dimensional convolutional neural network (2D-CNN) to capture the interactions among options, enhancing the existing non-parametric pricing model. Validation using China's SSE 50 ETF options trading data reveals several key findings: Firstly, the dynamic integration method proposed in this study not only improves prediction accuracy but also enhances stability. Secondly, previous parametric pricing models do not effectively utilize their pricing performance, while our proposed time-varying parametric pricing model significantly enhances accuracy. Lastly, the 2D-CNN model, which considers interactions among options trades, proves to be reasonable and effective, outperforming common non-parametric pricing models. The dynamic ensemble framework proposed in this study effectively combines the strengths of both parametric and non-parametric pricing models. This research serves as an important reference for risk managers, institutional investors, and other stakeholders. Furthermore, it provides valuable research ideas for future scholars in the field.

Risk management and HR practices in supply chains: Preparing for the future

This study explores the strategic integration of risk management and Human Resources (HR) practices within the context of supply chain management, aiming to identify how such integration enhances organizational resilience and sustainability. Employing a systematic literature review and content analysis, the study examines scholarly articles from key databases, focusing on the period from January 2000 to the present. The methodology involves a comprehensive search strategy, stringent inclusion and exclusion criteria, and a detailed selection process to distill insights from relevant literature. Key findings reveal that strategic HR practices, such as talent management, leadership development, and fostering a risk-aware culture, significantly contribute to mitigating supply chain risks and bolstering resilience. Furthermore, the study highlights the pivotal role of technological advancements, including artificial intelligence, blockchain, and the Internet of Things, in supporting the dynamic integration of risk management and HR practices. This integration not only addresses operational efficiencies but also aligns with strategic objectives towards sustainability and adaptability in the face of global disruptions. The study concludes that the strategic importance of integrating risk management with HR practices cannot be overstated, emphasizing its necessity for building agile, resilient, and sustainable supply chains. As organizations navigate the complexities of modern supply chains, this integrated approach offers a pathway to not only survive but thrive amid uncertainties, underscoring a blueprint for sustainable competitive advantage in evolving global markets. This review contributes to the existing body of knowledge by providing a comprehensive overview of current practices and future directions for integrating risk management and HR practices in supply chain management.