Adaptation Model Research Articles

Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving

We present the Process Engineering Operations Assistant (PEOA), an AI-driven framework designed to solve complex problems in the chemical and process industries. The framework employs a modular architecture orchestrated by a meta-agent, which serves as the central coordinator, managing an action generator and instruction-tuned small-scale language models (expert models). The action generator decomposes complex problems into sub-tasks and identifies suitable expert models to execute each, delivering precise solutions for multi-step problem-solving. Key techniques include advanced knowledge modeling using property graphs for improved information retrieval, facilitating more accurate and contextually relevant solutions. Additionally, the framework utilizes a teacher-student transfer-learning approach with GPT-4 (Omni) to fine-tune the action generator and expert models for domain adaptation, alongside an iterative problem-solving mechanism with sophisticated error handling. Custom datasets were developed to evaluate the framework against leading proprietary language models on various engineering tasks. The results demonstrate the framework’s effectiveness in automating calculations, accelerating prototyping, and providing AI-augmented decision support for industrial processes, marking a significant advancement in process engineering capabilities.

A Double ABC-X Model of Family Stress and Adaptation of Asian Parents

This study examined a moderated mediation model examining parent stress from the pile-up of pandemic-related stressors informed by the Double ABCX theoretical model of family stress and adaptation. Participants included a subset (n = 25346) of nine Asian countries’ (China, N = 5387; India, N = 4892; Indonesia, N = 3651; Malaysia, N = 3768; Philippines, N = 4401; Mongolia, N = 2658; Pakistan, N = 4836; Singapore, N = 3209; Bangladesh, N = 4625) of the Covid Family Life Study. The results revealed that family and relationship satisfaction were positively associated with stress at low, moderate, and high levels of family resilience beliefs, with the strongest association for individuals with low family resilience beliefs. However, both family and relationship satisfaction did not mediate the association between stressor pile-up and stress across all levels of family resilience beliefs. Implications of these results for interventions aiming to foster parental well-being in disaster contexts were discussed.

Adaptation and Mitigation Model for Flood Disaster Resilience in West Malangke District, North Luwu Regency, Indonesia

Adaptation and Mitigation Model for Flood Disaster Resilience in West Malangke District, North Luwu Regency, Indonesia

Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model

Predicting the remaining useful life (RUL) of mechanical bearings is crucial in the industry. Estimating the RUL enables the assessment of health bearing, maintenance planning, and significant cost reduction, thereby fostering industrial development. Existing models rely on traditional feature engineering with feature changes because operating conditions pose a major challenge to the generalization of RUL prediction models. This study focuses on neural network-based feature engineering and the downstream prediction of the RUL, eliminating the need for specific prior knowledge and simplifying the development and maintenance of models. Initially, a convolutional neural network (CNN) model is employed for feature engineering. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) model is used to capture the time-series degradation characteristics of the engineered features and predict the RUL through regression. Finally, the study examines the influence of operating conditions in the model and integrates domain adaptation to minimize differences in feature distribution, thereby enhancing the model’s generalizability for the RUL prediction.

Telehealth Postpandemic: A Model for Michigan and Beyond.

During the COVID-19 pandemic, telemedicine and telehealth saw a groundswell of growth, only to be shackled in the aftermath of the Public Health Emergency. A Think Tank, funded by Agency for Healthcare Research and Quality, was held at Michigan State University in August 2024. This paper serves as an introduction to a series of articles focusing on the evolution and future of telehealth in a postpandemic world. It highlights key themes including patient equity, technology, clinical opportunities, research, and education, using Michigan as a model for national adaptation. The paper aims to ignite further discussion and innovation within the telehealth community.

PUBLIC ADMINISTRATION UNDER MILITARY THREATS: UKRAINIAN EXPERIENCE OF ADAPTING TO CRISIS SITUATIONS

The article examines the Ukrainian experience of public administration under conditions of military threat as of 2022-2024. The key challenges faced by Ukraine during the full-scale war are analyzed, including the destruction of critical infrastructure and the need to ensure uninterrupted provision of public services. Special attention is paid to the digitization of public administration, which played a decisive role in maintaining the stability of state institutions. The role of the «Action» platform and other innovative solutions that enabled interaction between citizens and the government in crisis conditions is described. The impact of decentralization on the ability of local authorities to respond to the challenges of war has been studied. The interaction with international partners within the framework of the SIGMA program, which is aimed at providing support in the reform of public administration with a course towards European integration, is characterized. A comparative analysis of the Ukrainian experience with other European countries that faced large-scale crises was carried out, and unique aspects of the Ukrainian model of adaptation were revealed. The article analyzes strategies for the restoration and modernization of the state administration of Ukraine in crisis conditions. Attention is focused on the interaction between the state and corporate sectors to maintain economic activity even in conditions of active hostilities. The role of business relocation to safer regions and the influence of state support on the stabilization of the labor market and the economy are outlined. Programs aimed at restoring infrastructure and vital services in cooperation with international partners are mentioned. The experience of implementing temporary solutions to ensure the basic needs of the population is described. It examines the impact of international aid and financial support through IMF and World Bank programs that helped maintain budget stability and avoid economic collapse. The perspectives of public administration during Ukraine's integration into European structures and further strengthening of public administration are highlighted. Special attention is paid to the prospects for the development of public administration in the post-war period, the solution of socio-economic problems, the restoration of infrastructure and the attraction of investments. Strategic directions for reforming public administration for the future are proposed, taking into account the experience of the war and cooperation with international institutions.

Exploring user adaptation behaviors toward mobile technology: a higher education perspective

PurposeGrounded in coping model of user adaptation (CMUA), this research aims to provide insights into understanding the social mechanism influencing faculty’s adoption and adaptation of mobile technology (MT). An extensive review of the literature highlights a significant gap in empirical research regarding the adoption of MT and faculty adaptation when viewed through the lens of group dynamics.Design/methodology/approachThis study is based on an exploratory study conducted at two engineering universities in France. A qualitative method enabled a comprehensive examination of faculty members using multiple field-based observations and semi-directive interviews at both University A and University B.FindingsWhile extant research tends to prioritize individual-level approaches, the integration of MT within higher education inherently involves social dynamics. Our results reveal that faculty’s perceived control and their initial perception over adoption of MT play an important role in shaping their adaptation behavior. The findings suggest that the adoption of MT among faculty members is influenced by various organizational factors. Specifically, the organizational logic of adoption affects their primary appraisal of MT, while group norms and social influence shape their adaptation acts. Furthermore, the organization’s continuous commitment to supporting faculty members also impacts their coping activities, ultimately influencing their overall adoption and utilization of MT.Originality/valueThis study builds upon the limited yet growing body of literature on a theme highly relevant for practitioners, scholars as well as MT users in a higher education environment. The paper extends the CMUA by exploring the relationship between MT adoption and continuous user adaptation at both group and organizational levels. Our proposed framework assists universities in articulating their MT adoption and implementation strategy in harmony with a clear vision of their users’ adaptation activities before, during and after the implementation.

A test of the career construction model of adaptation in a sample of Turkish undergraduate students: a two-wave longitudinal mediation study

A test of the career construction model of adaptation in a sample of Turkish undergraduate students: a two-wave longitudinal mediation study

Stuart Stress Adaptation and Nola Pender's Model on Mental Nursing Care of Patients Schizophrenia: Case Study

To describe the application of the Stuart Stress Adaptation and Nola Pender Models in mental nursing care for a patient with schizophrenia and risk of violent behavior. This research method used case studies. The sample is one patient with schizophrenia. Data collection is done through direct observation, interviews, and patient medical records. The results showed that the client showed a major nursing problem in the form of risk of violent behavior. Predisposing factors included passive childhood experiences and socio-cultural issues, while precipitation included feeling disrespected by the husband's family. Nursing interventions provided to the patient included logotherapy, forgiveness therapy, and psychoeducation to the family. Family psychoeducation forms a supportive system in the client's recovery process. The results of this study indicate that there is a reduction in symptoms of violent behavior in schizophrenia patients. The importance of integrating the Stuart Stress Adaptation and Nola Pender Model in the psychiatric nursing care of patients with paranoid schizophrenia.

Impact of psychological resilience and social support on psycho-social adjustment in postoperative patients with primary hepatocellular carcinoma: mediating effects of fear of progression.

Postoperative patients with primary hepatocellular carcinoma (HCC) confront not only physiological challenges but also psychological and social adaptation issues. It is imperative to enhance psycho-social adjustment (PSA) levels and further improve the quality of life among this population. However, research on PSA levels in postoperative HCC patients is lacking, and investigations into its associations with psychological resilience, social support, and fear of progression (FoP) remain unexplored currently. This study aims to: (1) investigate the current status of PSA and analyze its influencing factors among postoperative HCC patients; (2) explore the interrelationships among psychological resilience, social support, FoP and PSA based on the Chronic Illness Adaptation Model employing a structural equation model. Convenience sampling methods were employed to recruit participants from the Department of Hepatobiliary Surgery at a tertiary hospital in Xi'an, Shaanxi, China, and a total of 399 patients completed the surveys. The survey instruments included a general information questionnaire, Connor-Davidson Resilience Scale (CD-RISC), Social Support Rating Scale (SSRS), Fear of Progression Questionnaire-Short Form (FoP-Q-SF), and Psychosocial Adjustment to Illness Scale Self-report (PAIS-SR). Data entry was conducted using Epidata 3.1 with dual verification, followed by statistical analyses performed using SPSS 27.0 and Amos 28.0. The structural equation model revealed two paths. In Path 1 (psychological resilience → FoP → PSA), the direct effect was -0.383 (95% CI [-0.589, -0.112]), with an indirect effect of -0.075 (95% CI [-0.170, -0.018]). In Path 2 (social support → FoP → PSA), the direct effect was -0.297 (95% CI [-0.587, -0.063]), with an indirect effect of -0.069 (95% CI [-0.156, -0.019]). Postoperative patients with primary HCC exhibit lower levels of PSA. Higher levels of psychological resilience and social support correspond to elevated PSA levels. Conversely, advanced age, greater financial burden, and increased FoP are associated with lower PSA levels. FoP serves as a partial mediator between psychological resilience and PSA, as well as between social support and PSA. Future research would benefit from longitudinal designs to elucidate the developmental trajectories and causal links among these variables.

MODELLING OF SOCIO-PSYCHOLOGICAL ADAPTATION OF AN INDIVIDUAL TO DIGITAL RISKS IN EDUCATION AND PROFESSIONAL SELF-DETERMINATION

Automated semantic analysis and topic modelling methods were first applied to a large corpus of public online communities and discussions to analyse strategies for responding to digital risks in modern society. A large array of public data on profiles of the social network “Vkontakte” and their subscriptions to online communities was used to train a predictive model of individual adaptation to risks in the field of education and professional self-determination. Based on the results of thematic modelling, three topics of subscriptions to online communities were identified as signs of psychological adaptation: preparation for exams, self-development and “popular” psychology, education and science. The developed version of the predictive model of psychological adaptation showed high accuracy and the importance of such factors as age, gender and place of residence for individual adaptation to digital risks in the field of education and professional self-determination.

A stimulus-based model of the team adaptation process: An integrated conceptual review.

As organizations face constant pressures to respond to changing situations and emergent demands, team members are frequently called upon to change their processes and routines and adapt to new ways of working together. In examining adaptation, most researchers have taken a behavior-driven approach where they collapse across the many types of adaptive demands teams face and rely on traditional input-process-outcome frameworks (e.g., Hackman, 1987; McGrath, 1984) to isolate specific behavioral responses. However, this perspective has resulted in several critical limitations. There are key differences in the way teams must collectively respond to different types of adaptive stimuli to be successful, and current research cannot account for or differentiate adaptive demands by stimulus type and needed responses. In this integrated conceptual review, we address these limitations and develop a novel, stimulus-based phase model of team adaptation. We examine studies across our newly developed stimulus detection, urgency identification, and duration assessment phases, and through the team's adaptive response, adaptive performance, and learning from the experience. We integrate research within each phase of the adaptive process, highlighting factors that demonstrate what successful team adaptation "looks like," and describe future avenues of research to address key issues within each phase. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The genome of the Australian water dragon (Intellagama lesueurii), an agamid model for urban adaptation.

Squamate reptiles are a highly diverse and intriguing group of tetrapods, offering valuable insights into the evolution of amniotes. The Australian water dragon (Intellagama lesueurii) is a member of the Agamidae, and sister to the core mesic Australian endemic radiation (Amphibolurinae). The species is renowned for its urban adaptability and complex social systems. We report a 1.8 Gb chromosome-length genome assembly together with the annotation of 23,675 protein-coding genes. Comparative analysis with other squamate genomes highlights gene family expansions associated with immune function, energetic homeostasis, and wound healing. This reference genome will serve as a valuable resource for studies of evolution and environmental resilience in lizards.

Flammable futures—storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia

Abstract Wildfire events are driven by complex interactions of climate and anthropogenic interventions. Predictions of future wildfire events, their extremity, and their impact on the environment and economy must consider the interactions between these drivers. Economic policy and land use decisions influence the susceptibility of an area to climate extremes, the probability of burning, and future decision making. To better understand how climate-driven drought events and adaptation efforts affect burned area, agricultural production losses, and land use decisions, we developed a storyline approach centered on Indonesia’s 2015 fire events, which saw significant (>5%) production losses of palm oil. We explored analogous events under three warming conditions and two storylines (multi-model ensemble mean climate change and high impact). We employed a model chain consisting of CMIP6 climate modeling to quantify climate change impacts, a wildfire climate impacts and adaptation model (FLAM) to predict burned areas, and the Global Biosphere Management Model (GLOBIOM) to predict resultant production losses and socio-economic consequences in the oil palm sector in Indonesia and, by extension, the EU. FLAM is a mechanistic fire model used to reproduce and project wildfires based on various criteria and input variables; GLOBIOM is a global economic land use model, which assesses land use competition and provides economic impacts based on scenario data. We found that total burned area and production loss can increase by up to 25% and lead to local price increases up to 70%, with only minor differences beyond 2.5 degrees of warming. Our results highlight the importance of considering the interactions of future warming, drought conditions, and extreme weather events when predicting their impacts on oil palm losses and burned area. This study sets the stage for further exploration on the impacts of land management policies on local and international environments and economies in the context of global warming.

Domain adaptation between heterogeneous time series data: A case study on real-time rotary machinery fault diagnosis

Artificial intelligence (AI) in fault diagnosis has emerged as a significant tool for addressing anomalies in rotary machineries (RMs). However, the success of AI models in this context largely depends on situations where the distribution of samples in the training dataset closely aligns with that of the testing dataset. In addition, practically, it is very difficult, costly, or even impossible to collect sufficient time series samples with proper labeling for AI modeling. To address these issues, this paper presents a study developing a structured methodology called frequency-domain supervised domain adaptation (FDSDA) considering heterogeneous data from versatile sensor technologies. In the context of application, two high-dimensional datasets were generated, each following distinct distributions: one being the vibration signals and the other the acoustic signals. The modeling begins with transforming signals from the time domain to the frequency domain through frequency analysis. Subsequently, a domain adaptation model is implemented, adopting an ensemble learning as an estimator to address the covariate shift challenge. Two experiments are conducted with the proposed method for binary and multi-class fault diagnosis of RMs. The results demonstrate that, despite having limited training and target data, the proposed method can effectively detect RMs fault conditions by outperforming the benchmark method.

Deep Into the Domain Shift: Transfer Learning Through Dependence Regularization.

Classical domain adaptation methods acquire transferability by regularizing the overall distributional discrepancies between features in the source domain (labeled) and features in the target domain (unlabeled). They often do not differentiate whether the domain differences come from the marginals or the dependence structures. In many business and financial applications, the labeling function usually has different sensitivities to the changes in the marginals versus changes in the dependence structures. Measuring the overall distributional differences will not be discriminative enough in acquiring transferability. Without the needed structural resolution, the learned transfer is less optimal. This article proposes a new domain adaptation approach in which one can measure the differences in the internal dependence structure separately from those in the marginals. By optimizing the relative weights among them, the new regularization strategy greatly relaxes the rigidness of the existing approaches. It allows a learning machine to pay special attention to places where the differences matter the most. Experiments on three real-world datasets show that the improvements are quite notable and robust compared to various benchmark domain adaptation models.

대학생의 진로탐색학점제 참여경험에 관한 질적 연구: 진로적응모형을 중심으로

대학생의 진로탐색학점제 참여경험에 관한 질적 연구: 진로적응모형을 중심으로

Cortical encoding of hierarchical linguistic information when syllabic rhythms are obscured by echoes

In speech perception, low-frequency cortical activity tracks hierarchical linguistic units (e.g., syllables, phrases, and sentences) on top of acoustic features (e.g., speech envelope). Since the fluctuation of speech envelope typically corresponds to the syllabic boundaries, one common interpretation is that the acoustic envelope underlies the extraction of discrete syllables from continuous speech for subsequent linguistic processing. However, it remains unclear whether and how cortical activity encodes linguistic information when the speech envelope does not provide acoustic correlates of syllables. To address the issue, we introduced a frequency-tagging speech stream where the syllabic rhythm was obscured by echoic envelopes and investigated neural encoding of hierarchical linguistic information using electroencephalography (EEG). When listeners attended to the echoic speech, cortical activity showed reliable tracking of syllable, phrase, and sentence levels, among which the higher-level linguistic units elicited more robust neural responses. When attention was diverted from the echoic speech, reliable neural tracking of the syllable level was also observed in contrast to deteriorated neural tracking of the phrase and sentence levels. Further analyses revealed that the envelope aligned with the syllabic rhythm could be recovered from the echoic speech through a neural adaptation model, and the reconstructed envelope yielded higher predictive power for the neural tracking responses than either the original echoic envelope or anechoic envelope. Taken together, these results suggest that neural adaptation and attentional modulation jointly contribute to neural encoding of linguistic information in distorted speech where the syllabic rhythm is obscured by echoes.

Original Research: Alarm Fatigue: Exploring the Adaptive and Maladaptive Coping Strategies of Nurses.

To improve patient safety, hospitals use alarm notification systems to increase nurses' real-time situational awareness of a patient's condition. Such alarms are critical to nurses' clinical decision-making and prioritization, thus helping to improve patient care and care efficiency. But the frequent and often simultaneous ringing of alarms, including many that are false, nonemergent, or nonactionable, has led to overwhelm, alarm distrust, and desensitization, resulting in alarm fatigue. This study aimed to explore oncology nurses' lived experiences with alarms and the adaptive and maladaptive strategies they use to cope with alarm fatigue. This qualitative, phenomenological study was guided by the theoretical framework of the Roy Adaptation Model. A purposive sample of nine nurses was recruited from two oncology units at a large midwestern Magnet hospital in the United States. Qualitative data were collected using a six-question, semistructured interview guide. Interviews were conducted either face-to-face in a private conference room on the unit or via the online videoconferencing platform Zoom. Data analysis yielded five themes, the most prominent being the high volume and frequency of alarms . Nurse participants reported adopting more maladaptive than adaptive coping strategies. Overall, they felt that the high frequency of false, nonemergent, and nonactionable alarms disrupted their workflow and contributed to a general desensitization to alarms. This study's findings offer valuable insight into the problem of alarm fatigue among nurses. Practical measures are urgently needed to reduce nurses' cognitive overload; shift nonnursing responsibilities to other staff; and implement efficiency-focused process changes, such as reengineering workflows to minimize interruptions. Every effort should be made to redesign protocols to reduce alarm fatigue, including by decreasing the number of false, nonemergent, and nonactionable calls and alarms.

Revisiting oxygen toxicity: evolution and adaptation to superoxide in a SOD-deficient bacterial pathogen.

Defenses against oxidants are crucial for the virulence of pathogens, with superoxide scavenging enzymes (SOSEs) playing a vital role for most aerobes. However, our knowledge of superoxide adaptation primarily stems from the study of SOSE-encoding bacteria. Here, we investigated the evolution of a naturally SOSE-deficient pathogen ( Leptospira spp.), along with the alternative mechanisms it recruits to combat superoxide stress. We demonstrate that emergence of pathogenic Leptospira correlated with SOD loss, but that a long-lasting adaptation to superoxide remains possible. We reveal that cysteine and leucine biosynthesis are the most induced pathways in response to superoxide and demonstrate the importance of sulfur metabolism in superoxide adaptation in this SOSE-deficient model. We also propose cysteine oxidation as a key mediator of superoxide toxicity in the absence of SOSEs. This study challenges our conventional understanding of the oxygen toxicity theory and proposes a new model of superoxide adaptation through metabolic rewiring in bacteria.