Structural Modeling Research Articles

Sexual Well-Being as a Factor of Social and Psychological Rehabilitation of Persons with Disabilities

<p dir="ltr"><span>The article introduces the genetic connection between the synthetic multiple-factor activity-based personality-oriented neosocial model of rehabilitation of a person with disabilities by A. Shevtsov and the structural functional model of human sexuality by V. Hupalovska. In the multiple-factor model of rehabilitation, the socio-psychological component is important, which corresponds to the dispositional model of Allport and the provisions on the “fully functioning person” according to Rogers. An attempt to consider the self-actualisation of a person with a disability and his/her subjective well-being through the lens of sexual well-being as a factor in the social construction by the person with disability of his/her life at the internal level is made. The study investigates the role of sexual well-being as a factor contributing to the social and psychological rehabilitation of people with disabilities (PwD) who acquired lifelong severe somatic diseases. Based on a comparative empirical analysis involving 125 PwD and 121 conditionally healthy participants, it was found that sexual attitudes and subjective well-being in PwD are comparable to those of their healthy peers. Notably, PwD report higher levels of positive relationships and affect balance but encounter challenges in goal-setting due to their health limitations. Predictors of subjective well-being include the adoption of romantic, patriarchal, and traditional-parental sexual scenarios, while self-realisation, market-oriented, and passionate sexual scripts negatively influence subjective well-being. The study highlights that transcendental and hedonistic-communicative scripts enhance subjective satisfaction with life, while intimate and market scripts demonstrate an inverse relationship. These findings emphasise the importance of addressing sexual well-being in rehabilitation programs. Sexuality, functioning across physical, emotional, and spiritual dimensions, plays a crucial role in enabling PwD to construct meaningful lives and achieve subjective psychological balance. The research supports the integration of personal, social, and psychological factors into rehabilitation strategies, aligning with the neosocial model of rehabilitation.</span></p><div><span><br /></span></div>

Interspecies signaling modulates the biosynthesis of antimicrobial secondary metabolites related to biological control activities of Pseudomonas fluorescens 2P24.

Rhizosphere microorganisms release vital signals that shape microbial communities, with antibiotics at low concentrations acting as intra- and interspecies signals. However, the mechanisms of these signals in coordinating gene expression are unclear. In non-pyoluteorin-producing Pseudomonas fluorescens 2P24, pyoluteorin was identified as an interspecies signal that regulates the phl biosynthesis gene cluster for 2,4-DAPG production. TetR family repressors PhlH and PhlF were found to positively regulate 2,4-DAPG hydrolysis and negatively regulate its synthesis in response to pyoluteorin. Structural modeling and docking analyses revealed the interactions between pyoluteorin and both PhlH and PhlF, modulating gene expression. Phylogenetic analyses showed a wide distribution of PhlH and PhlF across Pseudomonas spp. with conserved ligand-binding domains. These findings deepen our understanding of interspecies signaling mechanisms and highlight the potential for designing co-inhabiting Pseudomonas spp. as effective biocontrol agents.

Exact and Approximate Analysis of Structural Models with Linear Viscous or Viscoelastic Dampers and Singular Mass Matrices

This research is concerned with model reduction methods in analysis of structural models with viscous or viscoelastic damping and singular mass matrices. These types of models are commonly used in the preliminary design of building structures containing supplemental damping devices to define locations and parameters of the energy dissipaters. In this paper we develop an exact reduced-order technique for the modal analysis of models with singular mass matrices and viscous or viscoelastic supplemental dampers. Using static condensation of generalized coordinates without mass and no damper connection, and using a transformation using the eigenvectors of the damping matrix, an exact state-space formulation with non-singular mass matrix is developed. The above-mentioned alternative model-reduction strategies are revisited and the accuracy of these methods in the estimation of poles (natural frequencies and damping ratios) and frequency response functions is compared with the exact model. To compare the accuracy of the alternative models developed a simple benchmark structural model with singular mass matrix is used.

Prioritising elements of digitalisation for lean and green SME operations: an ISM-MICMAC study in the Indian context

PurposeThe purpose of this paper is to identify the key elements of digitalization for lean and green operations and develop a conceptual framework for their implementation. The paper focuses on small and medium-sized enterprises (SMEs) and aims to explore the role of digitalisation in enhancing their operational efficiency and sustainability. By identifying key factors and metrics related to digitalisation, the paper seeks to provide insights for strategic management to improve lean and green practices in SMEs.Design/methodology/approachInterpretive structural modeling (ISM) and Matrix of Cross-Impact Multiplication Applied to a Classification (MICMAC) approaches were deployed to classify the major dimensions of digitalisation. These methods were used to analyse the direct and indirect relationships among the identified elements of digitalisation. A comprehensive literature review and expert consultations were conducted to identify 13 key elements relevant to lean and green operations. The experts also assisted in determining the contextual relationships between the variables for the ISM model.FindingsThe analysis classified the 13 identified elements of digitalisation into different levels according to their driving power and dependence. The results from the ISM model indicated three levels of classifications. At level-1, Internet of things (IoT) and smart sensors (IoT & SS), automation and robotics directly influence lean and green operations. At level-2, real-time monitoring and control system and at level-3 fundamental elements of digitalisation such as big data analytics, predictive maintenance, cloud computing, energy management systems (EMSs), additive manufacturing, blockchain, digital workflow automation and digital collaboration platform.Originality/valueAll elements are interrelated and essential for making strategic decisions. This study emphasis the significance of prioritising these attributes to attain long-term excellence through digitalisation. For the industries that seek the reward of lean and green operations for their growth, this paper has great practical utility. Identifying the key factors of digitalisation would help strategic managers in handling lean and green environment of SMEs through these aspects.

Decoding sustainable entrepreneurship current research and future direction through application of machine learning-based structured topic modeling on intellectual corpus

Abstract This study leverages structured topic modeling (STM) to decode the expansive intellectual corpus on sustainable entrepreneurship, utilizing a dataset of 363 peer-reviewed articles from Scopus over a decade. Focused on “sustainable entrepreneurship” and related terms, the STM method integrated document-specific metadata to enhance the analysis of thematic developments. The findings revealed ten distinct topics, such as innovation in firm performance, sustainability in business models, and the role of education in sustainable intentions, highlighting the interplay between these themes and their evolution. This research identifies key thematic areas and examines the influence of source titles and publication years on topic prevalence, indicating shifts in academic focus and identifying emerging trends. The study’s implications suggest integrating sustainability into core business and educational strategies, enhancing the understanding of sustainable entrepreneurship’s dynamic nature, and providing a foundation for future scholarly and practical efforts.

Prediction of Shear Strength in Anisotropic Structural Planes Considering Size Effects

It is essential to elucidate the shear mechanical behavior of structural planes to assess the risk to rock masses and protect them from shear failure. Current research on shear mechanical behavior is focused on isotropic structural planes with the same lithology on both sides. However, anisotropic structural planes, commonly found in nature, may exhibit unique mechanical behavior that differs from isotropic structural planes. Therefore, it is necessary to study the factors affecting the shear strength of the anisotropic structural planes. In this paper, the direct shear numerical tests on anisotropic structural planes were carried out using the three-dimensional distinct element code (3DEC) based on the laboratory test. The numerical test results illustrate that the error between the peak shear strength of the numerical test and the laboratory test is basically within 10%. The shear stress-displacement curves of the numerical and laboratory tests are similar, which verifies the accuracy of the numerical test. According to the Barton standard sections, anisotropic structural plane models with different roughness and size were established, and the direct shear numerical tests with different normal stresses were carried out. To predict the peak shear strength of the anisotropic structural planes, one hundred and eighty-one sets of direct shear numerical test data were selected. Normal stress, roughness, compressive strength of soft and hard rock masses, basic friction angle of soft and hard rock masses, and structural plane size were used as input parameters to establish a back propagation (BP) neural network model. The research results show that, under identical conditions, the shear strength of the anisotropic structural planes decreases as the structural plane size increases. On the contrary, the shear strength increases with the increasing structural plane roughness and normal stress. For the BP neural network prediction model, the root mean square error (RMSE) and coefficient of determination (R2) of the training set are 0.441 and 0.957. For the test set, the RMSE is 0.489, and R2 is 0.947, which indicates that the predicted values are in good agreement with the actual values.

Modelo teórico de superación profesional en misiones diplomáticas orientada a la resolución de conflictos

The objective is to design a model for professional development in diplomatic missions oriented to conflict resolution. The methodology was based on systemic structural functional modeling, and collective workshops of critical and constructive analysis for the validation of the proposed model. The result was a model composed of the prescriptive subsystems of the context of professional development in diplomatic missions oriented to conflict resolution, the theoretical and methodological projection of its actions, and its professionalized transformation. From the relationships of the subsystems emerged the professional performance of the actors of the diplomatic mission for conflict resolution, as a quality of superior order of the modeled system, understood as the concretion of a whole process of accumulation of knowledge from the collective improvement of the diplomatic mission

Multidimensional Scaling of the Wechsler Intelligence Scales for Children in a Clinical Sample Assessed for Fetal Alcohol Spectrum Disorder (FASD)

Fetal Alcohol Spectrum Disorder (FASD) is a significant public health concern arising from prenatal alcohol exposure. This study examines the clinical utility of Wechsler intelligence tests in assessing cognition in 108 children with confirmed prenatal alcohol exposure. Data were analysed using multidimensional scaling and Guttman’s Structural Model of Intelligence, with a view to assessing the application of the Wechsler Intelligence Scale for Children 4th Edition (WISC-IV) and 5th Edition (WISC-V) in characterising cognitive ability for this clinical population. WISC-IV and WISC-V subtests exhibited distinct clustering patterns within the sample compared to normative populations. Subtests appeared to cluster based on response modality, aligning with Guttman’s Structural Model of Intelligence. The findings demonstrate an alternative interpretation approach for intelligence tests in children with prenatal alcohol exposure, which may complement existing FASD diagnostic frameworks. The clustering patterns underscore the importance of considering response modality in understanding cognitive abilities.

Psychometric Properties of the Flourish Index and the Secure Flourish Index in Healthcare Settings.

Research on the construct of flourishing spans many fields of study. This study extends previous work by VanderWeele by investigating the measurement of flourishing, focusing on the structure and convergent validity of the Flourish Index (FI) and the Secure Flourish Index (SFI) within a national, multi-site sample of resident physicians. Through exploratory and confirmatory factor analyses (EFAs and CFAs), we assessed whether the FI and the SFI aligned with the theoretical flourishing models that VanderWeele suggested. We examined the convergent validity of both indices by testing whether they exhibited expected correlations with six different scales. The results of factor analyses and scale validation showed that data collected by the FI and the SFI fit the structural model of flourishing proposed by VanderWeele. Although prior studies reliably indicate that CFA results align with VanderWeele's model, this is a rare study where the EFA results also demonstrated a structure that aligns with his framework. Both scales exhibited strong convergent validity, producing data correlated with all six measures in the predicted directions. Although convergent validity has been previously shown, this study replicated and expanded evidence of the construct validity of data provided by the FI and the SFI.

Organizational Climate as a Key to Positive Mental Health and Academic Engagement in University Students: A Structural Equation Modeling Approach

This study aimed to examine the relationships between mental health, organizational climate, and engagement through a structural equation model, for which a quantitative methodology was developed. A total of 1971 students from a public university in Lima (between 16 and 56 years of age, with a mean age of 21.09 years and standard deviation of 3.2) participated in the study. Three instruments with high internal consistency were used: the Positive Mental Health Scale, the Utrecht Work Engagement Scale, and the FOCUS Organizational Climate Questionnaire. The results show that organizational climate is positively related to mental health and engagement, with life satisfaction being the most significant dimension in mental health (0.768), the vigor the strongest in engagement (0.814), and the innovation climate stood out in organizational climate (0.819). At the same time, mental health directly impacts engagement. The structural model revealed that organizational climate directly influences mental health (0.64) and engagement (0.49), while mental health has a direct impact on engagement (0.43). In addition, this structural model presents an adequate fit. The findings highlight the need to design interventions prioritizing students’ psychosocial well-being and managing a positive organizational climate as a premise. Implications of the study are discussed.

Study on the Causes of Wellbore Leakage in Carbon Capture, Utilization, and Storage—Enhanced Oil Recovery

This study investigates wellbore leakage accidents associated with Carbon Capture, Utilization, and Storage Enhanced Oil Recovery (CCUS-EOR) to identify causal factors, clarify their degrees of influence, hierarchical structures, and substantive roles, while revealing the causal mechanisms behind these incidents to promote the safe development of CCUS-EOR. A distinctive aspect of this research is its integrated framework, which effectively combines the theory of integrated safety management with advanced methodologies such as the Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Models (ISM), and Cross-Impact Matrix Multiplication (MICMAC) to systematically analyze the interdependencies among risk factors. This comprehensive approach provides a nuanced understanding of the interactions among the 20 identified influencing factors across four domains, organized into a multilayered, three-stage structure. Furthermore, the study uncovers two critical causal pathways for wellbore leakage, namely F17 (lack of supervision and feedback) → F20 (inadequate safety investment) → F16 (lack of education and training) → F3 (weak safety awareness) → F9 (improper material selection) and F13 (high geological activity) → F11 (poor reservoir properties) → F6 (corrosion and aging failure), offering unique insights into risk dynamics that remain underexplored in the existing literature. This study could be enhanced in future research by taking into account a broader spectrum of causal factors, incorporating scenario simulations to facilitate a more comprehensive analysis, and involving a greater number of experts from diverse fields to enrich the insights derived.

The role of intolerance of uncertainty in health anxiety in cancer patients: Exploring demographic and cancer-related variations.

Health anxiety (HA) is a critical issue for cancer patients. This study investigates the role of intolerance of uncertainty (IU) in HA among a heterogeneous sample of cancer patients, comparing those with hematological malignancies and solid tumor cancers. It also explores the impact of demographic and cancer-related factors on HA. A total of 184 cancer patients participated in this study. Measurement model and structural model were assessed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the reliability, validity, direct and indirect effect, explanatory power, and predictive power of constructs in the model. Multigroup analyses and moderation effects of demographic and cancer-related variables were also examined. IU significantly predicted HA, mediated by negative problem orientation (NPO) and positive beliefs about worry (PBW). females scored higher in HA, NPO, and cognitive avoidance (CA), while males scored higher in PBW. Solid tumor patients exhibited a stronger positive relationship between IU and HA than hematological malignancy patients. Age and marital status also affected the relationships between IU, NPO, and PBW. Patients without a history of chronic illness or COVID displayed stronger associations between IU and NPO. IU is a key predictor of HA in cancer patients, with varying impacts across demographic and clinical groups. Tailored interventions addressing IU and related constructs help reduce HA, especially among patients with solid tumor cancers, female, married, older, lower educated, and newly diagnosed without former disease diagnosis. This study emphasizes the importance of considering patient heterogeneity in psychological interventions for HA.

Stressor fluctuations alter mechanisms underpinning seagrass responses to multiple stressors.

Multiple anthropogenic stressors degrade ecosystems globally. A key knowledge gap in multiple stressor research is how variability in stressor intensity (i.e., fluctuations) and synchronicity (i.e., timing of fluctuations) affect biological responses, and the mechanisms underpinning responses. We evaluated the mechanistic effects of reduced light and herbicide contamination on seagrass, and determined how variations in stressor intensity and synchronicity influence the underlying mechanisms of responses. We used structural causal modelling and structural equation modelling to elucidate direct and mediating effects. Out-of-phase introduction (i.e., asynchronous fluctuations) altered the mechanistic pathways of how stressors affect seagrass relative to static stressors, and resulted in the greatest biomass loss (under the most intense stressor combination, ∼50% reduction). Therefore, previous experiments that predominantly test only static stressor intensities might underestimate detrimental impacts in nature. Future experiments should explore mechanistic effects across realistic stressor intensities and synchronicities to improve our understanding and management of multiple stressors.

Advances in Protein Structure Prediction Highlight Unexpected Commonalities Between Gram-positive and Gram-negative Conjugative T4SSs.

Despite recent advances in our understanding of the structure and function of conjugative Type 4 Secretion Systems (T4SSs), there is still only very scarce data available for the ones from Gram-positive (G+) bacteria. This is a problem, as conjugative T4SSs are main drivers for the spread of antibiotic resistance genes and virulence factors. Here, we aim to increase our understanding of G+ systems, by using bioinformatic approaches to identify proteins that are conserved in all conjugative T4SS machineries and reviewing the current knowledge available for these components. We then combine this information with the most recent advances in structure prediction technologies to propose a structural model for a G+ T4SS from the model system encoded on pCF10. By doing so, we show that conjugative G+ T4SSs likely have more in common with their Gram-negative counterparts than previously expected, and we highlight the potential of predicted structural models to serve as a starting point for experimental design.

Enhancing Offshore Wind Turbine Integrity Management: A Bibliometric Analysis of Structural Health Monitoring, Digital Twins, and Risk-Based Inspection

The grand challenge of sustainable development, increased demands for resilient critical infrastructure systems, and cost efficiency calls for thinking and acting “out of the box”. We must strive to search for, identify, and utilize new and emerging technologies and new combinations of existing technologies that have the potential to improve present best practices. In integrity management of, e.g., bridge, offshore, and marine structures, relatively new technologies have shown substantial potentials for improvements that not least concern structural health monitoring (SHM), digital twin (DT)-based structural and mechanical modeling, and risk-based inspection (RBI) and maintenance planning (RBI). The motivation for the present paper is to investigate and document to what extent such technologies in isolation or jointly might have the potential to improve best practices for integrity management of offshore wind turbine structures. In this pursuit, the present paper conducts a comprehensive bibliometric analysis to explore the current landscape of advanced technologies within the offshore wind turbine industry suitable for integrity management. It examines the integration of these technologies into future best practices, taking into account normative factors like risk, resilience, and sustainability. Through this analysis, the study sheds light on current research trends and the degree to which normative considerations influence the application of RBI, SHM, and DT, either individually or in combination. This paper outlines the methodology used in the bibliometric study, including database selection and search term criteria. The results are presented through graphical representations and summarized key findings, offering valuable insights to inform and enhance industry practices. These key findings are condensed into a road map for future research and development, aimed at improving current best practices by defining a series of projects to be undertaken.

Visualizing gaussian-chain like structural models of human α-synuclein in monomeric pre-fibrillar state: Solution SAXS data and modeling analysis.

Here, using small angle X-ray scattering (SAXS) data profile as reference, we attempted to visualize conformational ensemble accessible prefibrillar monomeric state of α-synuclein in solution. In agreement with previous reports, our analysis also confirmed that α-synuclein molecules adopted disordered shape profile under non-associating conditions. Chain-ensemble modeling protocol with dummy residues provided two weighted averaged clusters of semi-extended shapes. Further, Ensemble Optimization Method (EOM) computed mole fractions of semi-extended "twisted" conformations which might co-exist in solution. Since these were only Cα traces of the models, ALPHAFOLD2 server was used to search for all-atom models. Comparison with experimental data showed all predicted models disagreed equally, as individuals. Finally, we employed molecular dynamics simulations and normal mode analysis-based search coupled with SAXS data to seek better agreeing models. Overall, our analysis concludes that a shifting equilibrium of curved models with low α-helical content best-represents non-associating monomeric α-synuclein.

Overall survival with fruquintinib versus placebo after adjusting for subsequent anticancer therapy in patients with refractory metastatic colorectal cancer in the FRESCO-2 study.

171 Background: Fruquintinib is a highly selective, oral inhibitor of all 3 vascular endothelial growth factor receptors (VEGFRs -1, -2, and -3) that is approved by the US FDA and in the EU, UK, and Japan for previously treated metastatic colorectal cancer (mCRC), regardless of biomarker status. The phase 3 FRESCO-2 study (NCT04322539) met its primary endpoint demonstrating significantly improved overall survival with fruquintinib + best supportive care (BSC) vs placebo + BSC. As patients may have received subsequent anticancer therapy after progression, understanding the impact on overall survival is important. This analysis examined overall survival data from FRESCO-2 after adjusting for subsequent anticancer therapy. Methods: In FRESCO-2, patients were randomized 2:1 to receive fruquintinib (5 mg) or matching placebo by mouth, once daily, 3 weeks on, 1 week off, + BSC. Eligible patients had received prior chemotherapy, anti-VEGF therapy, anti-EGFR therapies (if indicated), and TAS-102 and/or regorafenib. We assessed the impact of subsequent anticancer therapy on overall survival by excluding patients who received subsequent therapies, censoring these patients, and determining the causal hazard ratio using inverse probability of censoring weights (IPCW) and marginal structural models (MSM) to adjust for the potential bias introduced by subsequent treatments. Results: In FRESCO-2, a lower proportion of patients in the fruquintinib vs placebo arm received subsequent anticancer therapy (29.4% vs 34.3%). Among these patients, the most common subsequent anticancer therapies in the fruquintinib vs placebo arms were fluorouracil (7.7% vs 9.6%) and regorafenib (7.5% vs 7.8%). The overall survival benefit seen with fruquintinib vs placebo in the intent-to-treat population (hazard ratio [HR]: 0.66; 95% confidence intervals [CI]: 0.55–0.80) was improved when patients who had received subsequent anticancer therapy were excluded (HR: 0.45; 95% CI: 0.36–0.57) or censored (HR: 0.63; 95% CI: 0.50–0.79). In addition, the overall survival benefit seen with fruquintinib vs placebo was improved after adjusting for subsequent anticancer therapy using IPCW (HR: 0.43; 95% CI: 0.33–0.55) and MSM (HR: 0.48; 95% CI: 0.38–0.60). Patients receiving fruquintinib had similar rates of grade ≥3 adverse events, whether or not they received subsequent anticancer therapy (51.5% and 67.4%, respectively). Conclusions: Consistent with the primary analysis of the FRESCO-2 intent-to-treat population, fruquintinib improved overall survival vs placebo after adjusting for the impact of subsequent anticancer therapy. Furthermore, these analyses are robust with consistent results reported using IPCW and MSM. These findings support fruquintinib as a new treatment option for patients with previously treated mCRC. Clinical trial information: NCT04322539 .

Assessing the factors affecting users’ behavioural intention to use MOOCs: an integration of self-determination theory and the technology acceptance model

ABSTRACT Since the inception of massive open online courses (MOOCs), research has thus far documented MOOCs through surveys and case studies as having the most notable impact on teaching and learning. However, opening the black box of factors affecting users’ behavioural intention to continually use MOOCs has been overlooked by the bulk of scholars thus far. Therefore, this study was undertaken to fill this lacuna by integrating the technology acceptance model (TAM) and self-determination theory (SDT) within the context of MOOCs to determine the behavioural intention (BI) of users to enrol on MOOCs. The conceptual model engaged six parameters namely (1) autonomy, (2) relatedness, (3) competence, (4) perceived usefulness, (5) perceived ease of use, and (6) BI to use. A total of 316 participants provided valid responses which were utilised in testing the suggested structural model wherein the SmartPLS package was used to perform the test. The findings from the study confirmed the positive effect of intrinsic motivations, relatedness, and autonomy to enhance perceptions regarding the ease of use and usefulness of MOOCs and, hence, on the BI to use MOOCs. Finally, the study put forward a set of theoretical contributions and practical implications, while acknowledging the limitations of its scope.

GDFold2: A fast and parallelizable protein folding environment with freely defined objective functions.

An important step of mainstream protein structure prediction is to model the 3D protein structure based on the predicted 2D inter-residue geometric information. This folding step has been integrated into a unified neural network to allow end-to-end training in state-of-the-art methods like AlphaFold2, but is separately implemented using the Rosetta folding environment in some traditional methods like trRosetta. Despite the inferiority in prediction accuracy, the conventional approach allows for the sampling of various protein conformations compatible with the predicted geometric constraints, partially capturing the dynamic information. Here, we propose GDFold2, a novel protein folding environment, to address the limitations of Rosetta. On the one hand, GDFold2 is highly computationally efficient, capable of accomplishing multiple folding processes in parallel within the time scale of minutes for generic proteins. On the other hand, GDFold2 supports freely defined objective functions to fulfill diversified optimization requirements. Moreover, we propose a quality assessment (QA) model to provide reliable prediction on the quality of protein structures folded by GDFold2, thus substantially simplifying the selection of structural models. GDFold2 and the QA model could be combined to investigate the transition path between protein conformational states, and the online server is available at https://structpred.life.tsinghua.edu.cn/server_gdfold2.html.

Identifying maize architectural ideotypes through 3D structural model validated in the field: Assessing the impact of plant architecture and sowing pattern to improve canopy light regime

Identifying maize architectural ideotypes through 3D structural model validated in the field: Assessing the impact of plant architecture and sowing pattern to improve canopy light regime