Adaptive Integration Research Articles

Improved collaborative filtering for cross-store demand forecasting

Accurate and reliable demand forecasting is crucial for decision-making in assortment planning, store location, and various operational aspects. This study proposes a hybrid TS-SimPMF framework for multi-store multi-product demand forecasting, employing probabilistic matrix factorization (PMF) with time-series similarity measures to capture product and store heterogeneity. The heterogeneity enables the framework to achieve multi-step demand forecasting under unknown item-store combinations through an adaptive integration strategy. We first employ a hybrid dynamic regularization approach to measure the similarity of product sales simultaneously, and then the PMF framework is applied to generate latent vectors of products and stores. The performance of the proposed framework is verified by the real-world retail data, in which the dataset includes all sales records with 50 SKUs in 10 locations, totaling over 180,000 sales records. These experimental results demonstrate the superior performance of the proposed hybrid model compared to state-of-the-art methods.

Linear-Extended-State-Observer-Based Adaptive RISE Control for the Wrist Joints of Manipulators with Electro-Hydraulic Servo Systems

Manipulators are multi-rigid-body systems composed of multiple moving joints. During movement, the Coriolis force, centrifugal force, and gravity of the system undergo significant changes. The last three degrees of freedom (DOFs) of the wrist joint of a manipulator control the end attitude. Improving the command tracking accuracy of the wrist joint is a key challenge in controlling the end attitude of manipulators. In this study, a dynamics model of the mechanical arm–wrist joint is established based on the Lagrange method. An adaptive continuous robust integral of the sign of the error (ARISE) controller is designed using the reverse step method. Additionally, a linear extended state observer (LESO) is employed to estimate the time-varying interference existing in the system and compensate for it in the designed control rate. The stability of the Lyapunov function and the boundedness of the observer are proven. The proposed control method for the wrist joint is compared with other controllers on an experimental platform of multi-DOF hydraulic manipulators. The results demonstrate that the proposed method improves the control performance of hydraulic manipulators. The application of this method offers a new strategy and idea for achieving high-performance tracking control in hydraulic manipulators.

Solar Landscapes: A Methodology for the Adaptive Integration of Renewable Energy Production into Cultural Landscapes

The increasing use of solar energy is an integral step toward carbon neutrality. At the same time, outdoor solar farms are significantly altering existing cultural landscapes. This work examines the possibilities of integrating the use of solar energy into these landscapes in such a way that the unique, regional character of places is preserved and enhanced. The research project that was carried out developed a conceptual design approach that takes as its starting point landscape architectural and aesthetic analyses of existing sites in Styria, Austria, the spatial characteristics of the cultural landscapes in which they are embedded, and their suitability for generating solar energy. The comparison of a site’s characteristics with the technical possibilities evaluated from a literature review enables a responsive design practice using solar modules. The result is a method of landscape architectural design that integrates solar energy on the basis of an adaptive site-specific approach as well as a catalogue of sample cases that illustrate how designing with solar modules can honor and add value to existing places while enhancing their ecological, economic, and social functions.

A novel approach based on a modified mask R-CNN for the weight prediction of live pigs

Since determining the weight of pigs during large-scale breeding and production is challenging, using non-contact estimation methods is vital. This study proposed a novel pig weight prediction method based on a modified mask region-convolutional neural network (mask R-CNN). The modified approach used ResNeSt as the backbone feature extraction network to enhance the image feature extraction ability. The feature pyramid network (FPN) was added to the backbone feature extraction network for multi-scale feature fusion. The channel attention mechanism (CAM) and spatial attention mechanism (SAM) were introduced in the region proposal network (RPN) for the adaptive integration of local features and their global dependencies to capture global information, ultimately improving image segmentation accuracy. The modified network obtained a precision rate (P), recall rate (R), and mean average precision (MAP) of 90.33%, 89.85%, and 95.21%, respectively, effectively segmenting the pig regions in the images. Five image features, namely the back area, body length, body width, average depth, and eccentricity, were investigated. The pig depth images were used to build five regression algorithms (ordinary least squares (OLS), AdaBoost, CatBoost, XGBoost, and random forest (RF)) for weight value prediction. AdaBoost achieved the best prediction result with a coefficient of determination (R2) of 0.987, a mean absolute error (MAE) of 2.96 kg, a mean square error (MSE) of 12.87 kg2, and a mean absolute percentage error (MAPE) of 8.45%. The results demonstrated that the machine learning models effectively predicted the weight values of the pigs, providing technical support for intelligent pig farm management.

Clinical Supervision for Quality Education Delivery in Public Schools in Nigeria

This paper discussed clinical supervision for quality education delivery in public schools in Nigeria. Clinical supervision is a formal process of professional support and learning that enables individual practitioners to develop knowledge and competence, assume responsibility for their own practice, and enhance consumer protection and safety of care in complex clinical situations. Clinical supervision stands as the cornerstone of educational excellence in Nigerian public schools, offering a dynamic and transformative approach to elevate the quality of education delivery. The paper emphasized that clinical supervision can bring about quality education delivery in several key aspects which include: Professional Growth and Development, Contextual Adaptation, Innovative Pedagogies and Technology Integration, Quality Assurance and Standardization, Professional Collaboration and Peer Learning, Emotional and Social Well-being, Observation and Feedback, Assessment of Teacher Competencies and Reflective practices. Also, identified the impacts of clinical supervision on quality education delivery, which includes: Enhanced Teaching Practices, Professional Development, Increased Teacher Self-Efficacy, Alignment with Standards, Retention and Job Satisfaction, Technology Integration, Positive Student Outcomes, Promotion of Reflective Practice, Enhanced Collaboration, Leadership Development and Quality Assurance. It therefore concluded that clinical supervision emerges not merely as a pedagogical process but as a transformative force poised to elevate the landscape of education delivery in public schools across Nigeria. The paper likewise made some outstanding suggestions that; School Administrators should clearly define the objectives of clinical supervision, outlining specific goals for professional development, teaching improvement, and student outcomes. This clarity provides a roadmap for both supervisors and educators. Finally, Federal ministry of education should offer regular professional development opportunities aligned with the needs identified through clinical supervision. Provide access to workshops, training sessions, and resources to enhance educators' skills and knowledge.

The Development of the Social Intelligence when exploring Culture in efl learning

This article reveals the effectiveness of students’ communication, social adaptation and social integration determined by Social Intelligence (SQ) as an essential intellectual ability. When exploring culture in EFL setting the teacher of English needs to focus on fostering the social awareness and social comfort of the students. While SQ stresses the external behavior of the students, Emotional Intelligence (EQ) as an internal structure focuses on internal personality that directly impacts the effective communication with other students. EQ is built of emotions that are complex affective reactions, unexpected and momentary, accompanied by physiological disturbances, which mirror the attitude of the individual towards reality. EQ plays a crucial part in students’ processes of shaping their learning since they perceive the environment, comprehend the circumstances, and respond to them. EQ is characterized as a transient emotional experience that associates a particular feature of human interaction to an experience or a circumstance, constantly shaping and restructuring SQ.

Reviewing the role of AI in fraud detection and prevention in financial services

This review explores the pivotal role of Artificial Intelligence (AI) in revolutionizing fraud detection and prevention within the realm of financial services. As financial crimes become increasingly sophisticated, traditional methods of detection fall short, necessitating the integration of advanced technologies. AI emerges as a transformative force, employing machine learning algorithms, predictive analytics, and anomaly detection to fortify the defenses against fraudulent activities. The review provides an in-depth examination of the historical context, tracing the evolution of fraud detection from manual methods to the contemporary AI-driven approaches. It delves into the diverse AI models utilized in fraud prevention, including supervised and unsupervised learning, deep learning, and natural language processing. The nuanced analysis encompasses the effectiveness of AI in identifying intricate patterns indicative of fraudulent behavior, demonstrating its superiority in discerning anomalies within vast and dynamic datasets. Moreover, the review elucidates the real-world implications of AI in fraud detection, spotlighting instances where the technology has successfully thwarted fraudulent schemes. The ethical considerations inherent in AI-driven fraud prevention are also scrutinized, emphasizing the importance of responsible and transparent practices to mitigate biases and ensure fairness in decision-making processes. As the financial landscape navigates an era of digital transformation, the review sheds light on the future trends and innovations in AI-driven fraud detection. Anticipated developments include the integration of Explainable AI (XAI), federated learning, and continuous adaptation to emerging threats. The discussion extends to the collaborative efforts between financial institutions, regulatory bodies, and technology providers to create a robust ecosystem capable of staying ahead of evolving fraudulent tactics. In conclusion, this review encapsulates the dynamic landscape of AI in fraud detection and prevention within financial services. The analysis underscores the transformative impact of AI, not only in bolstering security measures but also in fostering a proactive and adaptive approach to counter the ever-evolving nature of financial fraud. The synthesis of historical perspectives, current applications, and future trajectories provides a comprehensive understanding of how AI is reshaping the paradigm of fraud detection in the financial domain.

Assessing the Economic Viability of Climate-Resilient Crop Varieties for Smallholder Farmers in Sub-Saharan Africa

The adoption of climate-resilient crop varieties holds immense promise as a strategy to enhance the resilience of agricultural systems in the face of changing climatic conditions. This research delves into the intricate dynamics of farmer decision-making, socioeconomic impacts, and the interplay of policy and market contexts in the context of adopting climate-resilient crop varieties. Through a comprehensive analysis, this study unveils the multifaceted factors that influence farmers' choices, ranging from economic considerations and risk perceptions to access to resources and market dynamics. The socioeconomic impacts resulting from the adoption of climate-resilient crop varieties transcend conventional productivity gains. Improved incomes, heightened food security, resilience to climate shocks, reduced environmental pressures, gender empowerment, and expanded market opportunities collectively contribute to a more holistic and sustainable agricultural landscape. Furthermore, the research underscores the inseparable relationship between effective policy frameworks and responsive market dynamics. Policies that promote research and development, support extension services, and provide incentives are aligned with market demand, enhancing the potential for successful adoption. The findings of this research have far-reaching implications for policy development, agricultural extension services, and development programs aimed at fostering climate resilience. By recognizing the diverse motivations and constraints that farmers face, policymakers and practitioners can tailor interventions that resonate with local realities. The integration of climate adaptation and resilience strategies into national policies and alignment with broader climate agendas is underscored as a crucial pathway toward sustainable agriculture.

Online learning in Ukraine: Challenges and tools for identifying difficulties in adaptation and socialization of Chinese students in a university environment: A case study

This research addressed the critical topic of Chinese students' adaptation at Simon Kuznets Kharkiv National Economic University, focusing on online learning. The study assessed the effectiveness of support tools using structured questionnaires to measure professional and academic adaptation levels of Chinese students, revealing associated challenges. The research methodology integrates surveys, observation, and statistical data analysis to objectively assess comfort levels, identify critical issues, and evaluate the effectiveness of support tools, contributing to enhanced professional and academic standards. In research was employing surveys, observations, documentary analysis, and a review of official documents, and explored various facets of adaptation, including general issues and social interactions in the online environment. Data analysis uncovers key challenges during adaptation, leading to specific recommendations for improving online learning. Challenges faced by students adapting to remote education in a Ukrainian higher education institution are identified, including language barriers hindering language learning and communication for Chinese students, and cultural differences complicating adaptation and technology integration. The results highlight aspects complicating Chinese students' adaptation to the online environment, such as language barriers, cultural differences, and technology adaptation. Despite high technological literacy, it remains crucial to tailor educational materials and platforms to the specific needs of Chinese students. The study addresses significant challenges, providing recommendations as a foundation for support programs and optimizing conditions for online learning among Chinese students in a university setting, ultimately enhancing their professional and academic success. The findings contribute significantly to the scientific discourse, offering valuable insights to inform the development of strategies aimed at improving conditions for students' adaptation to the university environment, especially within the online format

Leveraging adaptive spatial shifts multi-layer perceptron with parameterized spatial offsets for hyperspectral image classification

ABSTRACT The architectures based on Multi-Layer Perceptron (MLP) have attracted great attention in hyperspectral image (HSI) classification recently, due to their simplified and efficient architectures. However, such architectures are qualified by the rigid positional relationships between weights and feature elements, inhibiting their capacity to effectively extract diversified features. To address these challenges, An adaptive spatial-shift MLP (AS2MLP) is presented to dynamically modify spatial features by parameterizing learnable spatial offsets. In this way, the AS2MLP can facilitate sample-specific spatial shifts, aligning spatial structures more effectively. Then, An innovative adaptive spatial-shift block (AS2block) is designed to adaptively shift spatial features along distinct spatial axes, enabling the extraction of diversified features separately. It also implements a re-weighting strategy to mitigate redundant features. Building on this foundation, the proposed adaptive spatial-shift network (AS2Net) is for HSI classification. The dual-path AS2Net employs AS2blocks and MLPs for channel mixing, facilitating an adaptive integration of dynamic spatial contextual information dispersed across a range of spectra. The effectiveness of this model is demonstrated using five widely used HSI datasets.

Integration of urban ecosystem-based adaptation in Nepal: A policy landscape analysis.

Ecosystem-based adaptation (EbA) is an ecologically sensitive, cost-effective, and locally adaptive climate adaptation strategy to strengthen the climate resilience of vulnerable communities. While many studies on EbA have been conducted in rural and mountainous regions or within the natural sciences realm, there is a lack of comprehensive research that assesses how urban EbA measures have been incorporated into existing policies and plans in Global South, including in Nepal. Ecosystem-based adaptation is in the early stages of its establishment as a fundamental component to address climate adaptation and sustainable development in urban environments. Accordingly, effective integration strategies, challenges, potential focal areas, and entry points have yet to be extensively studied. To address the literature gap, this paper analyses the types of EbA interventions and the extent of urban EbA integration within Nepal's climate, urban, and sectoral policies and plans. Direct content analysis and a qualitative scoring system were used to evaluate the plan components and assess the level of EbA integration. The findings indicate that the policies and plans recognise the importance of conserving, enhancing, and managing ecosystems for climate change adaptation, and EbA measures are mainly included in action-oriented sections. However, the results also reveal inadequate EbA integration, particularly in the information base, vision and objectives, and implementation aspects. The implementation component notably lacks comprehensive provisions for budget allocation, responsible authorities, definite timelines, and clear roadmaps. The breakdown of EbA integration in the policies and plans suggests that climate and urban plans substantially integrate urban EbA measures, but discrepancies exist with climate and urban policies and sectoral policies and plans. These findings collectively emphasise a pressing need to enhance the recognition and integration of urban EbA measures within policy frameworks with a view towards strengthening climate resilience and mitigating climate-related hazards in urban environments.

Critical Analysis and Ameliorative Strategies in Combating the Impact of Climate Variability on Pesticide Dynamics and Efficacy

Background: The importance of pesticides in the economy especially in agriculture, cannot be over-emphasized. It is therefore very crucial to uphold and maintain the efficacy of these pest control agents. This study seeks to explore the effect of climate variables on the potency of pesticides. Climate change has emerged as one of the most significant global challenges, disrupting weather patterns and altering ecosystems. These changes pose serious implications for pest dynamics, distribution, and behavior, thereby directly influencing the performance of pesticides. This study analyzed relevant articles related to climate change and pesticides systematically sourced from credible reference databases for a period of 21 years from 2002 to 2023, using the most suitable keywords. Recent scientific research and empirical evidence, analyzing the effects of rising temperatures, altered precipitation patterns, and changing humidity levels on pests and their interactions with pesticide applications were studied. The relevant impact of these effects is seen in reduced agricultural yield, loss of livelihood and source of income, infestation and destruction of agricultural crops, and reduced living standard in the society amongst others. The projected changes include; the development of adaptive agricultural practices and integration of climate-resilient pest management strategies that could improve the efficacy of pesticides. This involves exploring the potential of eco-friendly and biologically-based pest control approaches to reduce the destructive impacts that arise from climate change. These sustainable practices and novel technologies aimed at improving pest control under changing climatic conditions must be encouraged and upheld tenaciously.

Adaptive knowledge distillation and integration for weakly supervised referring expression comprehension

Weakly supervised referring expression comprehension (REC) aims to ground target objects in images according to given referring expressions, while the mappings between image regions and referring expressions are unavailable during the model training phase. Existing models typically reconstruct the multimodal relationships to ground targets by utilizing off-the-shelf information, and ignore to further exploit helpful knowledge to enhance the model performance. To address this issue, we propose an adaptive knowledge distillation architecture to enrich the predominant pattern of weakly supervised REC and transfer the target-aware and interaction-aware knowledge from a pre-trained teacher grounder to enhance the grounding performance of the student model. Specifically, in order to encourage the teacher to impart more reliable knowledge, we present a Knowledge Confidence-Based Adaptive Temperature (KCAT) learning approach to learn optimal temperatures to transfer the target-aware and interaction-aware knowledge with higher prediction confidence. Moreover, to urge the student to absorb more helpful knowledge, we introduce a Student Competency-Based Adaptive Weight (SCAW) learning strategy to dynamically integrate the distilled target-aware and interaction-aware knowledge to enhance the student’s grounding certainty. We conduct extensive experiments on three benchmark datasets, RefCOCO, RefCOCO+, and RefCOCOg, to validate the proposed approach. Experimental results demonstrate that our approach achieves superior performance over state-of-the-art methods with the aid of adaptive knowledge distillation and integration. The code and trained models are available at: https://github.com/dami23/WREC_AdaptiveKD.

Combining jumping knowledge into traffic forecasting: An attention-based spatial-temporal adaptive integration gated network

Traffic forecasting has become a core component of Intelligent Transportation Systems. However, accurate traffic forecasting is very challenging, caused by the complex traffic road networks. Most existing forecasting methods do not fully consider the topological structure information of road networks, making it difficult to extract accurate spatial features. In addition, spatial and temporal features have different impacts on traffic conditions, but the existing studies ignore the distribution of spatial-temporal features in traffic regions. To address these limitations, we propose a novel graph neural network architecture named Attention-based Spatial-Temporal Adaptive Integration Gated Network (AST-AIGN). The originality of AST-AIGN is to obtain a spatial feature that more accurately reflects the topological structure of the road networks by embedding Graph Attention Network (GAT) into Jumping Knowledge Net (JK-Net). We propose a data-dependent function called spatial-temporal adaptive integration gate to process the diversity of feature distribution and highlight features in road networks that significantly affects traffic conditions. We evaluate our model on two real-world traffic datasets from the Caltrans Performance Measurement System (PEMS04 and PEMS08), and the extensive experimental results demonstrate the proposed AST-AIGN architecture outperforms other baselines.

An extended phase‐field approach for the efficient simulation of fatigue fracture processes

AbstractFatigue fracture simulations with the phase‐field method (PFM) lack efficiency due to the fine meshes required, especially when each load cycle is simulated explicitly. Recent developments in combining the phase‐field method for brittle fracture with the extended/generalised finite element method (XFEM/GFEM) show a remarkable reduction of the number of degrees of freedom and thus a reduction of computational effort while retaining or even improving accuracy. In this paper, the combined extended phase‐field method (XPFM) is expanded to fatigue processes in a two‐dimensional setting. A new stabilisation of the phase‐field transformation function is proposed and a convergence criterion for the nested staggered solution process and enrichment scheme update procedure as well as an adaptive integration technique for the non‐polynomial ansatz functions is incorporated. Several numerical examples emphasise the benefits and the efficiency of the newly developed method.

Adaptive Spatial–Temporal Aware Graph Learning for EEG-Based Emotion Recognition

An intelligent emotion recognition system based on electroencephalography (EEG) signals shows considerable potential in various domains such as healthcare, entertainment, and education, thanks to its portability, high temporal resolution, and real-time capabilities. However, the existing research in this field faces limitations stemming from the nonstationary nature and individual variability of EEG signals. In this study, we present a novel EEG emotion recognition model, named GraphEmotionNet, designed to enhance the accuracy of EEG-based emotion recognition through the incorporation of a spatiotemporal attention mechanism and transfer learning. The proposed GraphEmotionNet model can effectively learn the intrinsic connections between EEG channels and construct an adaptive graph. This graph’s adaptive nature is crucial in optimizing spatial–temporal graph convolutions, which in turn enhances spatial–temporal feature characterization and contributes to the process of emotion classification. Moreover, an integration of domain adaptation aligns the extracted features across different domains, further alleviating the impact of individual EEG variability. We evaluate the model performance on two benchmark databases, employing two types of cross-validation protocols: within-subject cross-validation and cross-subject cross-validation. The experimental results affirm the model’s efficacy in extracting EEG features linked to emotional semantics and demonstrate its promising performance in emotion recognition.

Innovation, Entrepreneurship, Higher Education: Integrated and Transversal Basic Sciences

Peru possesses vast natural wealth in forestry, energy, water, maritime, agricultural, and mineral resources, yet its consumption of derived goods and services is low compared to Mexico and Brazil, where collaboration between universities, industry, and government has driven innovation and sustainable technological development. In this work, we address the transversal interrelation of scientific disciplines such as chemistry, physics, biology, and mathematics. Thus, through an interdisciplinary didactic model, we propose a conceptual framework that fosters understanding and synergy among these disciplines, allowing students to integrate and apply knowledge holistically. This model is structured into five stages: identification of key concepts, classification of transversal interactions, design of didactic activities, assessment of comprehension, and feedback for continuous improvement. This approach aims to develop critical skills and facilitate the practical application of knowledge in complex problems and real-life situations. By integrating these basic natural science disciplines, the quality of learning would be improved, promoting a deeper understanding of natural phenomena. This proposal would be validated through indicators measuring its effectiveness in promoting interdisciplinary integration, critical skills, practical application, and creative, collaborative problem-solving. The objective is to prepare students for the 21st century by fostering creative problem-solving, interdisciplinary collaboration, and critical thinking, with the involvement of teachers and administrators in curricular adaptation and basic science integration. Implementing this educational model could represent a significant evolution in higher education in Peru, enhancing academic and professional training and contributing to economic and social development.

Unleashing the Potential of Large Language Models for Knowledge Augmentation: A Practical Experiment on Incremental Sheet Forming

As the influence of Incremental Sheet Forming (ISF) grows in manufacturing sectors, so does the demand for precise and updated knowledge construction in this domain. In this research, we evaluate the capability of Large Language Models (LLMs) to capture domain-specific knowledge, using ISF as a case study. Recognizing common LLMs’ limitations such as potential inaccuracies and outdated information reliance, we propose a comprehensive approach involving automated and adaptive knowledge extraction, enrichment, and integration into an ISF-specific dataset. We then fine-tune the LLMs for ISF-related text classification and prompt response tasks. Our results reveal a significant enhancement in LLMs’ performance within the ISF domain, with a domain knowledge acquisition rate exceeding that of GPT-3.5 by 10.4%, achieved by the fine-tuned Alpaca-33B model. Additionally, we introduce a novel conversational prototype designed to refine the accuracy and relevance of LLMs in the ISF domain. Our findings will guide future efforts in downstream tasks such as ISF-domain knowledge graph construction and quality prediction.

Adaptive Iterative Explicit Time Integration for Nonlinear Heat Conduction Problems

Adaptive Iterative Explicit Time Integration for Nonlinear Heat Conduction Problems

СТРАТЕГИИ СОЦИАЛЬНОЙ АДАПТАЦИИ: ЖИЗНЕННЫЕ ИСТОРИИ ТРЕТЬЕГО ПОКОЛЕНИЯ КОРЕЙЦЕВ КАЗАХСТАНА

In Kazakhstan, the local ethnic Korean population is identified as an ethnic group that assimilates well within the country. Considering this, the authors of this article posit the following questions: What adaptation strategies and life values do the third generation of ethnic Koreans adhere to? What adaptive behaviors may be anticipated from the Korean ethnic group in the future? This paper analyses major integration trends of the third generation of Korean diaspora in Kazakhstan. Three strategies for adaptation (integration) are proposed in the context of post-Soviet independent Kazakhstan: education and professionalism; ethnic leadership; and transnational ties with South Korea. The authors test and analyze hypotheses regarding the influence of role models on the integration process into Kazakhstani society. This is achieved through content analysis of materials derived from journalistic articles alongside interviews with third and fourth generation ethnic Koreans. Analysis is conducted on materials gathered from the urban population of Koreans in Kazakhstan. The concept of the “ethnic enclave” was corroborated by the evidence of ethnic population concentration with high social and economic activity outside of geographically delineated borders. The concept of strong upward assimilation was supported by the life histories of third and fourth generation of ethnic Koreans in Kazakhstan. Overall, this article contributes significantly to the academic understanding of ethnic adaptation, integration, and assimilation in the context of post-Soviet Kazakhstan and the broader migration process.