Adaptive Integration Research Articles

CONCEPTUAL FOUNDATIONS OF ORGANIZATIONAL CULTURE OF MANUFACTURING ENTERPRISES IN THE WARTIME PERIOD

The article is devoted to the study of the conceptual foundations of organizational culture of manufacturing enterprises in wartime. The author generalizes approaches to the concept of “organizational culture” and presents the author’s own definition, which is understood as an integrated system of values, norms and behavioral models, which in wartime acts as an adaptive and protective mechanism, providing the enterprise with the ability to quickly restructure and strengthening corporate solidarity by enhancing the socio-psychological stability of the team; as a strategic tool, it ensures cohesion and motivation of employees, activating their ability to dedication and support each other, which is a once-in-a-lifetime opportunity. The article highlights the peculiarities of organizational culture in wartime, in particular: strategic direction; values that form a unique socio-psychological environment at an enterprise, thereby shaping its competitive advantages; specific components - implicit (socio-psychological) and explicit (regulatory and image elements). The characteristics of modern models of organizational culture are identified: spiritual, socio-psychological, and material. It is proved that the organizational culture of an enterprise is formed under the influence of external and internal factors, and the factors from each group are specified. The functions of the organizational culture of enterprises are studied and the functions of internal integration and functions of external adaptation are allocated. The functions of internal integration include organizational, integrating, regulatory, substitution, adaptive, educational and cognitive, motivating, communicative, quality management, and recreational. External adaptation is ensured by the following functions: innovation, formation of a positive image, customer orientation, management of partnership relations, and adaptation of the enterprise to the needs of society. The key aspects of the conceptual foundations of building the organizational culture of production enterprises are defined: process orientation; hierarchical organizational structure; physical work; safety and risks; innovation and adaptation; relationship with suppliers and consumers. New requirements for the formation of the organizational culture of enterprises during the war period were considered. The experience of foreign countries regarding the formation of organizational culture in conditions of war and constant armed conflicts, in particular Germany, South Korea, and Israel, is studied. It is proposed to introduce as elements of organizational culture specific for production enterprises under conditions of war, including: adaptability to risks and crises, support for safety and protection of personnel, patriotic aspect and values of sustainability, as well as humanitarian and social responsibility.

Cyber security of unmanned aviation complexes and features of protection against interception.

The article considers the key aspects of cyber security of unmanned aerial systems (UAS) in the conditions of modern military conflicts and technological progress, unmanned systems have become important tools for gathering information, conducting intelligence and performing combat missions. However, their widespread use is accompanied by new cyber threats that require specialized measures to ensure security. The article analyzes the main types of cyberthreats that can affect UAS, including the interception of communications, unauthorized access to management systems and the breaking of cryptographic protections. The article also discusses modern protection methods and technologies, such as cryptographic algorithms, authentication mechanisms and data encryption. In addition, existing standards and practical solutions to ensure the protection of communication channels are analyzed. The last part of the article is devoted to the latest trends in the field of cyber security of UAS and forecasting future threats. Possible solutions and approaches for improving the protection of unmanned systems in the rapidly changing cyber environment are considered, as well as recommendations are provided for improving the security systems of UAS, which include the integration of new technologies and adaptation to new threats

Color crowding considered as adaptive spatial integration.

Crowding is the inability to recognize an object in clutter, classically considered a fundamental low-level bottleneck to object recognition. Recently, however, it has been suggested that crowding, like predictive phenomena such as serial dependence, may result from optimizing strategies that exploit redundancies in natural scenes. This notion leads to several testable predictions, such as crowding being greater for nonsalient targets and, counterintuitively, that flanker interference should be associated with higher precision in judgements, leading to a lower overall error rate. Here we measured color discrimination for targets flanked by stimuli of variable color. The results verified both predictions, showing that although crowding can affect object recognition, it may be better understood not as a processing bottleneck, but rather as a consequence of mechanisms evolved to efficiently exploit the spatial redundancies of the natural world. Analyses of reaction times of judgments shows that the integration occurs at sensory rather than decisional levels.

Follow-up supportive policies and risk perception influence livelihood adaptation of anti-poverty relocated households in ethnic mountains of southwest China

The poverty alleviation relocation program (PAR) is a milestone project in breaking spatial poverty traps in ethnic mountainous regions and provides a key solution for eradicating global absolute poverty. As direct participants in the restructuring of environmental and livelihood systems, the livelihood adaptation of relocated households is fundamental to the sustainable development of human-land systems in resettlement sites and the consolidation of the success of poverty alleviation. This research constructs an adaptive integration analysis framework of “external disturbance-adaptation process-adaptation outcome”, by utilizing questionnaire data from relocated households in the Liangshan Yi minority area of Southwest China, and employing a formative structural equation model to investigate the transmission mechanisms and heterogeneous effects of PAR on livelihood adaptation across different resettlement characteristics. The results indicate that: (1) External disturbance of PAR facilitates positive adaptation outcomes, with follow-up supportive policy as the most significant determinant, which contributes directly to adaptation outcomes and indirectly through improved learning and self-organization capacity. Conversely, risk perception has a negative impact on adaptation outcomes and further exacerbates adverse impacts by reducing buffering capacity. (2) The pathways influencing subjective and objective adaptation outcomes demonstrate heterogeneity. PAR has a positive impact on subjective adaptation, but a non-significant effect on objective adaptation, self-organization capacity and buffering capacity are the most important direct drivers and mediating variables affecting both, respectively. (3) Resettlement characteristics exert a moderating effect on livelihood adaptation. The positive effect of follow-up supportive policy on learning capacity and the marginal contribution of learning capacity to adaptative strategies being greater in mid-to-long-term resettlements. Buffering capacity of village resettlements and subjective adaptation of town resettlements are more beneficial from follow-up supportive policy.

Improving prediction accuracy in agricultural markets through the CIMA-AttGRU model.

In the Chinese futures market, agricultural product futures play a crucial role. While previous studies have primarily relied on historical price data and fundamental financial indicators of agricultural product futures, there is a growing recognition of the value that lies within the vast amounts of textual data generated in the financial domain. Our study specifically focuses on the limitations of existing methods in capturing the complex relationships and rich semantic information embedded in these textual sources. This article designs a CIMA AttGRU (CIMA-AttGRU) model for soybean futures, which is a forecasting method for the agricultural product market. This model uniquely integrates Collective Intrinsic Mode Analysis (CIMA) with an Attention-Gated Recurrent Unit (AttGRU), leveraging the strengths of both techniques to enhance predictive accuracy and adaptability. The rationale behind employing the CIMA-AttGRU model lies in its ability to effectively tackle the inherent challenges of financial market analysis. By incorporating CIMA, the model adeptly filters out market noise, directly addressing the challenge of high volatility. Additionally, with its attention mechanism, the CIMA-AttGRU targets the issue of non-linear patterns by allowing dynamic adjustment to temporal dependencies, offering differential learning capabilities crucial for capturing the nuanced fluctuations in futures prices. Complementing the CIMA and AttGRU, the integration of Class-wise Adversarial Domain Adaptation (CADA) further refines the model's robustness, addressing the critical challenge of domain adaptivity. This aspect is particularly vital for edamame futures, where price determinants can vary significantly over time and across regions. Our empirical results demonstrate a significant improvement in forecasting precision, with the CIMA-AttGRU model achieving a Mean Absolute Error (MAE) reduction of 15% and a Mean Squared Error (MSE) reduction of 20% compared to conventional models. This superior performance, especially in terms of prediction accuracy and handling market fluctuations, highlights the improve of the model compared to existing methods and has made significant explorations in agricultural market forecasting.

Psychosis and the self: How spontaneous discussions of subjective experiences compare in the clinical high-risk and first-episode psychosis populations.

Prior qualitative studies show that individuals with psychoticlike experiences express difficulties concerning their identity. However, previous work has studied individuals at clinical high risk for psychosis (CHR) and individuals with first-episode psychosis (FEP) separately. Here, we compare the experiences of individuals at CHR, individuals with FEP, and healthy individuals. Participants included 70 individuals at CHR (57% female, Mage = 19.2 ± 3.0), 50 individuals with FEP (56% female, Mage = 20.4 ± 2.9), and 70 healthy individuals (67% female, Mage = 21.1 ± 2.8). Participants completed 30-45-min open-ended interviews. Trained research staff reviewed interview transcripts and conducted an iterative thematic analysis to identify major themes. Themes related to distress, social difficulties, trauma, gratitude, and communication were spontaneously described by both individuals at CHR and individuals with FEP, with individuals at CHR describing suspiciousness more than individuals with FEP (42% vs. 22%), χ²(1, 120) = 4.95, p = .03, and individuals with FEP discussing adaptive integration of mental health struggles into their identity more than individuals at CHR (56% vs. 36%), χ²(1, 120) = 4.87, p = .03. Healthy individuals endorsed future orientation more than individuals at CHR (69% vs. 49%), χ²(1, 140) = 5.77, p = .02, and individuals with FEP (69% vs. 48%) χ²(1, 120) = 5.14, p = .02. All groups endorsed essentialist beliefs: CHR (31%), FEP (43%), and healthy controls (44%). Individuals at CHR and with FEP spontaneously discuss issues related to their identities in ways that are informed and shaped by the social milieu. The bidirectional nature of these struggles reinforces a need for integrated care through psychiatric rehabilitation, with a specific focus on identity development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Strategic Integration of Climate Change Adaptation and Mitigation to support Climate Village Program in Rural Indonesia

This study was related to climate change adaptation and mitigation in the rural community. Its primary objective was to determine a strategy for community empowerment through the integration of mitigation and adaptation actions to support the Climate Village program (PROKLIM). The Participatory Rural Appraisal (PRA) and FGD were adopted to facilitate community in action plan development. SWOT analysis and Analytical Hierarchy Process (AHP) were used to identify and select the most viable strategies based on their weighted scores. From the SWOT analysis, eight alternative strategies were formulated including: (1) optimizing the use of local natural resources (SO1), (2) enhancing community capacity in adaptation and mitigation (knowledge, attitudes and skills) (SO2), (3) strengthening collaborations with both governmental and non-governmental organizations (WO1), (4) augmenting financial support from village funds for adaptation and mitigation efforts (WO2), (5) reinforcing continuous training of local groups (ST1), (6) enhancing natural resource management to increase community income (ST2), (7) increasing the capacity of the local village government (WT1) and (8) promoting the benefits of Climate Village program awareness and the importance of environmental protection (WT2). Through the AHP, three strategies were found to have the highest weight as follows: (1) SO2, (2) WO2 and, (3) WO1.

MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification

BackgroundThe integration of multi-omics data through deep learning has greatly improved cancer subtype classification, particularly in feature learning and multi-omics data integration. However, key challenges remain in embedding sample structure information into the feature space and designing flexible integration strategies.ResultsWe propose MoAGL-SA, an adaptive multi-omics integration method based on graph learning and self-attention, to address these challenges. First, patient relationship graphs are generated from each omics dataset using graph learning. Next, three-layer graph convolutional networks are employed to extract omic-specific graph embeddings. Self-attention is then used to focus on the most relevant omics, adaptively assigning weights to different graph embeddings for multi-omics integration. Finally, cancer subtypes are classified using a softmax classifier.ConclusionsExperimental results show that MoAGL-SA outperforms several popular algorithms on datasets for breast invasive carcinoma, kidney renal papillary cell carcinoma, and kidney renal clear cell carcinoma. Additionally, MoAGL-SA successfully identifies key biomarkers for breast invasive carcinoma.

Decomposing Future Exposure from Increasing Flood Risk and Forecast Population Changes Across Shared Socioeconomic Pathways (SSPs) in the United States

Extreme weather events, like flooding, are expected to become more severe due to climate change and increasingly impact populations across the US. Adding to this challenge, populations have concurrently settled in risky areas that were previously thought to have low, or no, exposure. Objective: This research seeks to understand the unique contribution of population growth and climate change as independent components of future risk levels in the US. To do so, future population level forecasts are coupled with future flood projections along all five Shared Socioeconomic Pathways (SSPs) at the block group level across the US. The results indicate that, across the five SSPs, the most increase in exposure will occur in SSP5 (+470,719), and the least will occur in SSP 3 (+57,189). By decomposing the contributions from flood and population growth, we find that the population growth-induced effect contributed to an increase in the population exposure for all of the SSPs except for SSP3. This research, and these results, provide a foundation for understanding future risks of flood exposure in an isolated framework and lay the groundwork for the development and integration of planning, adaptation, and mitigation efforts that may be used to address the growing risk of flooding in the context of the population forecasts provided here.

Adaptive fuzzy gain-scheduling robust control for stability of quadrotors

To solve the instability problem in quadrotor control system subject to parametric uncertainties and external disturbances, an attitude control approach integrated with adaptive fuzzy gain-scheduling desired model compensation robust integral of the sign of the error is proposed. Firstly, the original cascade model of attitude motion is transformed into a strict feedback form with lumped disturbances. Secondly, a desired model compensation robust integral of the sign of the error controller is designed to regulate attitude motion for its invariant properties to lumped disturbances, replacing the velocity state in model-based feedforward control with expected value to ensure the robustness of the system against uncertainties without velocity information. To mitigate the chattering caused by continuous switching control, the fuzzy logic system is used to construct the proposed robust control algorithm, in which robust integral feedback gains associated with the sign function based on fuzzy rules are adaptive scheduled, with the filter tracking error and its derivative as inputs to the fuzzy logic system and robust integral feedback gains as outputs. The stability of the designed closed-loop system is verified by analyzing the convergence of output errors. Finally, the effectiveness and robustness of the proposed method in dealing with the stability of the control system are fully demonstrated by extensive simulations and platform experiments.

Fast adaptive Fourier integration for spectral densities of Gaussian processes

Fast adaptive Fourier integration for spectral densities of Gaussian processes

Perceptions of AI Integration in the UAE’s Creative Sector

This study explores the perceptions of artificial intelligence (AI) within the creative sector of the United Arab Emirates (UAE) based on 13 semi-structured interviews and a survey with 224 participants among media professionals and their stakeholders. The findings indicate considerable enthusiasm surrounding AI’s potential to augment creativity and drive operational efficiency, a perspective that the study’s participants share. However, there are also apprehensions regarding job displacement and the necessity for strategic upskilling. Participants generally regard AI as an unavoidable technological influence that demands adaptation and seamless integration into daily workflows. The study underscores the disparity between the UAE’s government-led digital transformation objectives and the actual implementation within organizations, underscoring the urgent need for cohesive strategic alignment. The findings caution that the absence of clear directives and strategic planning may precipitate a new digital schism, impeding progress in the sector.

Adaptive integration of history variables in constrained mixture models for organ-scale growth and remodeling.

In the last decades, many computational models have been developed to predict soft tissue growth and remodeling (G&R). The constrained mixture theory describes fundamental mechanobiological processes in soft tissue G&R and has been widely adopted in cardiovascular models of G&R. However, even after two decades of work, large organ-scale models are rare, mainly due to high computational costs (model evaluation and memory consumption), especially in long-range simulations. We propose two strategies to adaptively integrate history variables in constrained mixture models to enable large organ-scale simulations of G&R. Both strategies exploit that the influence of deposited tissue on the current mixture decreases over time through degradation. One strategy is independent of external loading, allowing the estimation of the computational resources ahead of the simulation. The other adapts the history snapshots based on the local mechanobiological environment so that the additional integration errors can be controlled and kept negligibly small, even in G&R scenarios with severe perturbations. We analyze the adaptively integrated constrained mixture model on a tissue patch for a parameter study and show the performance under different G&R scenarios. To confirm that adaptive strategies enable large organ-scale examples, we show simulations of different hypertension conditions with a real-world example of a biventricular heart discretized with a finite element mesh. In our example, adaptive integrations sped up simulations by a factor of three and reduced memory requirements to one-sixth. The reduction of the computational costs gets even more pronounced for simulations over longer periods. Adaptive integration of the history variables allows studying more finely resolved models and longer G&R periods while computational costs are drastically reduced and largely constant in time.

Exploratory Evaluation of Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR) with CNS-Active Drugs in Brain Metastases Treatment

Brain metastases (BMs) affect an increasing number of cancer patients and are typically managed with stereotactic radiosurgery (SRS). Our institution advocates the use of Personalized Ultrafractionated Stereotactic Adaptive Radiotherapy (PULSAR), where radiation is delivered in high-dose pulses at extended intervals allowing for treatment adaptation and easy concurrent systemic therapy integration. We explore the integration of PULSAR with central nervous system (CNS)- active drugs (CNS-aDs). This study involved a retrospective evaluation of patients treated with PULSAR using Gamma Knife from 2018-2024. We collected demographic, clinical, and specific treatment details, outcomes such as local failure (LF) and toxicity rates. Cumulative incidence analysis for local failure and toxicity, considering death a competing risk, and Kaplan-Meier survival analysis for overall survival (OS) were conducted. Analysis included 109 lesions treated with PULSAR, predominantly in patients with lung and breast cancer. The median follow-up was 1.72. Median OS was not reached. The 1- and 2-year LF rates were 5% and 8.9%, respectively, and 3.4% and 5.5% with concurrent CNS-aDs (cCNS-aDs). BMs > 2 cm had LF rates of 9.4% at two years. No LFs were observed in BMs > 2 cm treated with the combined PULSAR+CNS-aDs approach at 2.5 years. Univariate analysis indicated CNS-aD and radioresponsive histologies were associated with decreased LR rates. The two-year grade 3+ toxicity rate for PULSAR was 8.7%, with no increase in toxicity with cCNS-aDs. The integration of PULSAR with CNS-aDs appears to offer excellent local control for larger brain metastases with limited toxicity. These promising results merit further prospective investigation to validate the findings and potentially establish new treatment protocols.

DDoS-attacks prevention using MinE-DT an adaptive security and energy optimization integration of NIPS in wireless sensor networks

DDoS-attacks prevention using MinE-DT an adaptive security and energy optimization integration of NIPS in wireless sensor networks

Adaptive RISE-based tracking control of uncertain nonlinear systems: A FAS approach

A fully actuated system (FAS) approach integrated with adaptive robust integral of the sign of the error (ARISE) feedback control strategy is proposed for multi-input multi-output nonlinear systems in the presence of both external disturbances and parametric uncertainties. Owing to an inability to eliminate unmeasured disturbances and model inaccuracies simultaneously, the existing results based on the FAS approaches are typically limited to the uniformly ultimate boundedness of the tracking errors. To achieve the asymptotic tracking performance confronted with parametric uncertainties and time-varying disturbances, an ARISE feedback controller with desired compensation is synthesized to suppress the adverse effects arising from nonlinearity and uncertainty of the system. The improvements compared to the traditional RISE feedback control are attributed to two aspects: (i) the feedback gains in the RISE term are adjustable-online without having to know the prior bounds of disturbances and their time derivatives; (ii) a desired compensation-based adaptive feedforward term, primarily employing the desired trajectories in place of the measured states, could weaken the underlying interaction between the adaptive compensation and robustness part. A rigorous stability analysis is provided to demonstrate that the system state can asymptotically track a bounded desired trajectory in spite of bounded disturbances and parametric uncertainties. Comparative simulations on an under-actuated planar manipulator, possessing an equivalent multi-order FAS model, have been conducted to verify the effectiveness and merits of the developed controller. Experimental validation on a two-wheeled self-balancing robot is also provided to show the feasibility of the proposed approach.

Cross-Cultural Communication Strategies of the Thai Advertising Industry and Their Implications for the International Communication of Chinese Advertising

Based on the theory of cultural adaptation, this paper explores the cross-cultural communication strategies and successes of Thailand’s advertising industry, analyzing their implications for China’s international advertising efforts. Through case studies and empirical analysis, the paper identifies key cross-cultural strategies in Thailand’s advertising industry, such as language adaptation, symbol interpretation, and cultural element integration, which effectively enhance advertising impact and audience acceptance. The study suggests that China’s advertising industry should draw from Thailand’s successes by aligning with target market cultural characteristics while maintaining Chinese cultural identity, achieving a balance of localization and internationalization. The paper also provides specific strategies for China’s advertising industry to enhance its cultural adaptation capabilities, addressing the challenges posed by globalization. The research aims to offer theoretical support and practical guidance for cultural adaptation strategies in China’s international advertising.

The Interplay between Population Growth and Climate Change: A Critical Analysis

This paper critically examines the interrelationship between population growth and climate change, emphasizing the profound environmental and socio-economic impacts on human populations. Population growth exacerbates environmental degradation, contributing to resource depletion, greenhouse gas emissions, and biodiversity loss, which in turn intensifies climate change. Conversely, climate change directly affects populations by threatening health, livelihoods, food security, and prompting large-scale migration, particularly in vulnerable regions. The disproportionate impact on developing countries and marginalized communities highlights the need for climate justice and equitable solutions. This paper explores the health risks, economic impacts, and migration patterns driven by climate change, and evaluates the socio-economic inequalities exacerbated by both phenomena. It concludes by offering a range of recommendations, including the integration of climate adaptation into development plans, increased investment in climate resilience, sustainable population growth through education and family planning, and global cooperation to ensure climate justice. Urgent, coordinated action is required to mitigate the environmental and societal impacts of population growth and climate change, fostering a sustainable future for all.

Assessing the Alignment and Implementation Challenges of the Sustainable Development Goals (SDGS) 2030 in Morocco: A Relevant Inquiry

Morocco, a North African nation known for its rich cultural heritage and diverse landscapes, has been actively pursuing the Sustainable Development Goals (SDGs) as a framework to guide its development efforts1. The country has made significant progress in aligning its national development strategies with the SDGs, particularly prioritizing goals such as quality education2, clean water and sanitation, and clean energy3. This paper aims to evaluate the effectiveness of SDGs implementation in Morocco, highlighting achievements, challenges, and future prospects. (Smith, 2020). The SDGs, adopted by the United Nations in 20154, represent a global commitment to address pressing challenges including poverty, inequality, climate change, environmental degradation, peace, and justice. This review provides an analysis of the SDGs' effectiveness in driving positive change across various dimensions5. By examining academic literature, reports, it evaluates progress towards achieving the SDGs, identifies key implementation challenges, and assesses potential for successful realization. The article explores how effectively Morocco aligns its policies and addresses implementation challenges to achieve the SDGs by 20306, considering factors such as cultural diversity, economic structures, and geographic disparities. The integration of the SDGs into Morocco's development agenda presents both challenges and opportunities. By addressing issues like adaptation, inter-sectoral coordination, data availability, climate resilience, and policy integration, Morocco seeks to overcome these challenges. Understanding these obstacles and their solutions is crucial for assessing the relevance and effectiveness of SDGs' implementation, ultimately contributing to Morocco's sustainable development journey by 2030.

Tumor detection in breast cancer pathology patches using a Multi-scale Multi-head Self-attention Ensemble Network on Whole Slide Images

Breast cancer (BC) is the most common type of cancer among women globally and is one of the leading causes of cancer-related deaths among women. In the diagnosis of BC, histopathological assessment is the gold standard, where automated tumor detection technologies play a pivotal role. Utilizing Convolutional Neural Networks (CNNs) for automated analysis of image patches from Whole Slide Images (WSIs) enhances detection accuracy and alleviates the workload of pathologists. However, CNNs often face limitations in handling pathological patches due to a lack of sufficient contextual information and limited feature generation capabilities. To address this, we propose a novel Multi-scale Multi-head Self-attention Ensemble Network (MMSEN), which integrates a multi-scale feature generation module, a convolutional self-attention module, and an adaptive feature integration with an output module, effectively optimizing the performance of classical CNNs. The design of MMSEN optimizes the capture of key information and the comprehensive integration of features in WSIs pathological patches, significantly enhancing the precision of tumor detection. Validation results from a five-fold cross-validation experiment on the PatchCamelyon (PCam) dataset demonstrate that MMSEN achieves a ROC-AUC of 99.01% ± 0.02%, an F1-score of 98.00% ± 0.08%, a Balanced Accuracy (B-Acc) of 98.00% ± 0.08%, and a Matthews Correlation Coefficient (MCC) of 96.00% ± 0.16% (p<0.05). These results demonstrate the effectiveness and potential of MMSEN in detecting tumors from pathological patches in WSIs for BC.