Analytical Framework Research Articles

U.S. beef producer perspectives on “sustainable beef” and implications for sustainability transitions

Beef production systems are at the center of ongoing discussion and debate on food systems sustainability. There is a growing interest among beef producers, consumers, and other beef supply chain stakeholders in achieving greater sustainability within the industry, but the relationship of this interest to general sustainability issues such as climate change, biodiversity loss, food security, livelihood risks, and animal welfare concerns is unclear. Specifically, there is very little research documenting how beef producers define and view the concept of sustainability and how to achieve it. Producer perspectives are critical to identifying constraints to sustainability transitions or to help build agreement with other producers about the shared values such transitions may support. Through a secondary analysis of survey data of U.S. beef producers (n = 911) conducted in 2021 by the Trust in Food division of Farm Journal, a corporation that provides content, data, and business insights to the agricultural community (e.g., producers, processors/distributors, and retailers), we investigated what “sustainable beef” means to U.S. beef producers, highlighting the key components and constraints they perceive to achieving desirable sustainability outcomes. Leveraging the three-pillar model of sustainability as a framework for analysis, we identified key themes producers use to define “sustainable beef.” We found that producers collectively viewed sustainability as: (1) multidimensional and interconnected; (2) semi-closed and regenerative; (3) long-lasting; and (4) producer-centered, although an integrated perspective uniting these aspects was rare. We discuss how these perspectives may be the basis for sustainability efforts supported by producers and raise future research considerations toward a shared understanding of what sustainability is and what is needed for enduring sustainability solutions in the U.S. beef industry.

The synergy effect of agricultural dual-scale management on farmers' income: evidence from rural China

PurposeThis study aims to estimate the synergy effect of agricultural dual-scale management (ADM) on farmers' total household income, its heterogeneous effects and its mechanisms.Design/methodology/approachThis study constructs a theoretical analysis framework based on the division of labor and synergy theory, empirically assesses the impact of ADM on farmers' income, and further discusses the heterogeneity and mechanisms using the propensity score matching (PSM) and quantile treatment effect (QTE) models. Data is collected from 1,076 households across 4 cities in Liaoning Province of China in 2021.FindingsADM can improve the total household income of farmers, and the impact force is greater than that of the single-scale management mode. ADM is more conducive to improving the income of farmers with low income and low labor endowment. Moreover, ADM can improve agriculture production efficiency, increase net grain production income. Nevertheless, it has no significant effect on farmers' off-farm employment income.Originality/valuePrevious studies have mainly focused on the income effect of land scale management or service scale management. To the best of our knowledge, this study is the first to identify the synergy effect of ADM on farmers' income in China. It provides new insights into the process of agricultural production and management mode transitions in rural China.

Patients’ experiences with tele-mental health services during COVID-19 in Pakistan

Background: Although the concept of telehealth is of great interest globally, its potential has not yet been realized in Pakistan. It is therefore essential to explore the perspectives of stakeholders on the technology, particularly for mental health, to be able to increase and improve its use. Aim: To assess the perceptions and experiences of patients receiving tele-mental health services, including telepsychiatry and tele-psychotherapy, in Pakistan. Methods: For this qualitative exploratory study, we conducted in-depth interviews with 49 individuals at a tertiary care hospital in Karachi, Pakistan. Using the Cresswell framework for content analysis, we identified 3 major themes that focused on the positive and negative aspects of tele-mental health services and made suggestions for enhancing them. Results: Twenty-six of the participants received telepsychiatry, while the remaining 23 received tele-psychotherapy services. Technical literacy, cost of consultation, privacy, and therapeutic alliance were the major challenges identified by the patients, while convenience and the absence of stigma were highlighted as key facilitators for tele-mental health. Tele-consultations reduced travel and waiting time, thus improving access to healthcare. Participants suggested that the processes for booking appointments and making payments should be streamlined and the cost of tele-consultation reduced. Conclusion: This study provides insightful findings on tele-mental health services from the perspectives of patients living in an Asian culture. The major benefits highlighted were destigmatization of mental health and elimination of commuting costs and travel time. There were concerns about privacy, therapeutic alliance and availability and affordability of the technology.

Perspectives of people experiencing homelessness with recent non-fatal street drug overdose on the Pharmacist and Homeless Outreach Engagement and Non-medical Independent prescribing Rx (PHOENIx) intervention.

In Scotland, a third of all deaths of people experiencing homelessness (PExH) are street-drug-related, and less than half of their multiple physical- and mental health conditions are treated. New, holistic interventions are required to address these health inequalities. PHOENIx (Pharmacist Homeless Outreach Engagement and Non-medical Independent prescribing Rx) is delivered on outreach by National Health Service (NHS) pharmacist independent prescribers in partnership with third sector homelessness charity workers. We describe participant's perspectives of PHOENIx. This study aims to understand experiences of the PHOENIx intervention by participants recruited into the active arm of a pilot randomised controlled trial (RCT). Semi-structured in-person interviews explored participants' evaluation of the intervention. In this study, the four components (coherence, cognitive participation, collective action, reflexive monitoring) of the Normalisation Process Theory (NPT) framework underpinned data collection and analyses. We identified four themes that were interpreted within the NPT framework that describe participant evaluation of the PHOENIx intervention: differentiating the intervention from usual care (coherence), embedding connection and consistency in practice (cognitive participation), implementation of practical and emotional operational work (collective action), and lack of power and a commitment to long-term support (reflexive monitoring). Participants successfully engaged with the intervention. Facilitators for participant motivation included the relationship-based work created by the PHOENIx team. This included operational work to fulfil both the practical and emotional needs of participants. Barriers included concern regarding power imbalances within the sector, a lack of long-term support and the impact of the intervention concluding. Findings identify and describe participants' evaluations of the PHOENIx intervention. NPT is a theoretical framework facilitating understanding of experiences, highlighting both facilitators and barriers to sustained engagement and investment. Our findings inform future developments regarding a subsequent definitive RCT of PHOENIx, despite challenges brought about by challenging micro and macro-economic and political landscapes.

Everyone Matters: A Multimodal Learning Analysis Framework Based on Individual Time Series

Everyone Matters: A Multimodal Learning Analysis Framework Based on Individual Time Series

Me, us and IT

The adoption and pedagogical use of technologies such as virtual worlds to support teaching and learning, and research in higher education involves a complex interplay of technical, organisational and personal factors. In this paper, eighteen educators and researchers provide an overview of how they perceive a virtual world can be used in education from the perspective of themselves as individuals ‘me’, their educational organisations and as members of the Australian and New Zealand Virtual Worlds Working Group (VWWG) community of practice ‘us’, as well as the complex technology that underpins this learning environment ‘IT’. Drawing on Linstone’s (1981, 1984) Technical, Organisational and Personal (TOP) multiple perspective concept as the framework for analysis, the authors discuss their perspectives of how the personal, organisational and technical aspects of teaching through the use of virtual worlds have impacted on their teaching and research in higher education. The potential of employing the TOP framework to inform future research into the use of technologies such as virtual worlds in teaching and learning is explored.

Biomechanical changes in the proximal femur before and after removal of femoral neck system

BackgroundAs an innovative internal fixation system, FNS (femoral neck system) is increasingly being utilized by surgeons for the treatment of femoral neck fractures. At present, there have been numerous finite element analysis experiments studying the immediate stability of FNS and CSS in treating femoral neck fractures. However, there is scarce mechanical analysis available regarding the effects post internal fixation removal. This study aimed to investigate the alterations in mechanical parameters of the proximal femur before and after the removal of FNS (femoral neck system), and to assess potential distinctions in indicators following the extraction of CSS (Cannulated Screws).MethodsA proximal femur model was reconstructed using finite element numerical techniques. The models for CSS and FNS were formulated utilizing characteristics and parametric definitions. The internal fixation was combined with a normal proximal femur model to simulate the healing state after fracture surgery. Within the framework of static analysis, consistent stress burdens were applied across the entirety of the models. The total deformation and equivalent stress of the proximal femur were recorded before and after the removal of internal fixation.ResultsUnder the standing condition, the total deformation of the model before and after removing CSS was 0.99 mm and 1.10 mm, respectively, indicating an increase of 12%. The total deformation of the model before and after removing FNS was 0.65 mm and 0.76 mm, respectively, indicating an increase of 17%. The equivalent stress for CSS and FNS were 55.21 MPa and 250.67 MPa, respectively. The average equivalent stress on the cross-section of the femoral neck before and after removal of CSS was 7.76 MPa and 6.11 MPa, respectively. The average equivalent stress on the cross-section of the femoral neck before and after removal of FNS was 9.89 MPa and 8.79 MPa, respectively.ConclusionsThe retention of internal fixation may contribute to improved stability of the proximal femur. However, there still existed risks of stress concentration in internal fixation and stress shielding in the proximal femur. Compared to CSS, the removal of FNS results in larger bone tunnels and insufficient model stability. Further clinical interventions are recommended to address this issue.

Property, Materiality, Proximity: The Analytic Frame and In-Person Work.

In-person meeting offers psychologically usable material-signifiers that serve as day's residue-that cannot be duplicated or substituted for in remote ways of working. Questions of materiality, the history and specificity of location, and bodily proximity all are key aspects of the psychoanalytic frame, as Bleger's classic formulations attest. The COVID-19 pandemic has changed the choreography of engagement between analyst and patient: the ghostly dust in the frame enters the room. As Bleger says, with ghosts so rustled, nonprocess has a chance to become process. Two clinical examples highlight these points about materiality and in-person working. The final section of the paper extends Bleger's description to tackle the perplexing situation of patients who hesitate to return to the office. Issues of "ghosting," vanishing, disappearing are discussed, and linked to the constitutive absence that grounds any meaningfully structured presence. This constitutive absence is evoked by the prospect of the return to in-person analytic work. A final clinical example is used to illustrate this disturbing and irreducible fact about human interaction when two bodies are together in a room to discuss, over time, the life of one of the participants.

The coordination network toolkit: a framework for detecting and analysing coordinated behaviour on social media

AbstractThis paper introduces and evaluates the Coordination Network Toolkit, an open-source software package and methodological framework designed to detect and analyse coordinated behaviour on social media platforms. As the dynamics of online communication continue to evolve, coordination analysis has emerged as an important field of study with significant implications for understanding online influence, digital astroturfing, and online activism. Recognising the absence of a comprehensive, open-source tool for constructing coordination networks, our approach fills this gap, catering to multiple behaviors across diverse social media platforms. Our approach synthesises and significantly enhances various methods to provide a methodological framework for ‘multi-behaviour’ coordination detection, utilising weighted, directed multigraphs to capture intricate coordination dynamics. We evaluate our approach by revisiting a case study of the 2020 #ReopenAmerica Covid protest movement on Twitter. The paper concludes with a set of recommendations for future work, emphasising the need for a tailored statistical framework for coordination analysis and a deeper exploration into the motives behind online coordination.

Multi-Input data ASsembly for joint Analysis (MIASA): A framework for the joint analysis of disjoint sets of variables.

The joint analysis of two datasets [Formula: see text] and [Formula: see text] that describe the same phenomena (e.g. the cellular state), but measure disjoint sets of variables (e.g. mRNA vs. protein levels) is currently challenging. Traditional methods typically analyze single interaction patterns such as variance or covariance. However, problem-tailored external knowledge may contain multiple different information about the interaction between the measured variables. We introduce MIASA, a holistic framework for the joint analysis of multiple different variables. It consists of assembling multiple different information such as similarity vs. association, expressed in terms of interaction-scores or distances, for subsequent clustering/classification. In addition, our framework includes a novel qualitative Euclidean embedding method (qEE-Transition) which enables using Euclidean-distance/vector-based clustering/classification methods on datasets that have a non-Euclidean-based interaction structure. As an alternative to conventional optimization-based multidimensional scaling methods which are prone to uncertainties, our qEE-Transition generates a new vector representation for each element of the dataset union [Formula: see text] in a common Euclidean space while strictly preserving the original ordering of the assembled interaction-distances. To demonstrate our work, we applied the framework to three types of simulated datasets: samples from families of distributions, samples from correlated random variables, and time-courses of statistical moments for three different types of stochastic two-gene interaction models. We then compared different clustering methods with vs. without the qEE-Transition. For all examples, we found that the qEE-Transition followed by Ward clustering had superior performance compared to non-agglomerative clustering methods but had a varied performance against ultrametric-based agglomerative methods. We also tested the qEE-Transition followed by supervised and unsupervised machine learning methods and found promising results, however, more work is needed for optimal parametrization of these methods. As a future perspective, our framework points to the importance of more developments and validation of distance-distribution models aiming to capture multiple-complex interactions between different variables.

A Scene Unmixing Deep Learning Network for Local Climate Zone Mapping and Analysis Using Very High Resolution Remote Sensing Imagery

Abstract. With the acceleration of urbanization, the environment and climate necessary for our survival have gradually deteriorated, leading to the increasing prominence of the Urban Heat Island (UHI) effect. Local Climate Zone (LCZ) classification, as a standard of urban morphology, has become an essential tool for monitoring the UHI effect and conducting temperature studies. Deep Learning (DL) models have the ability to represent high-level semantic features. Therefore, this paper proposes amixed scene unmixing DL framework for LCZ mapping and analysis using Very High Resolution (VHR) remote sensing images. This framework consists of a two-stream deep network, including a pure scene classification network (PS-Net) and a mixed scene unmixing network (MSU-Net). We conducted random sampling tests in Wuhan, China in the experiment A. The results show that this model achieved a satisfactory accuracy with the Overall Accuracies (OAs) is 96.78% and a mixed scene unmixing Mean Absolute Error (MAE) of 0.0495. Furthermore, we applied the proposed model to generate LCZ map for five districts in Wuhan in the experiment B. The test accuracy between two experiments differs very slightly. These results demonstrate the applicability and potential of our model for LCZ mapping and urban climate analysis.

Research on China's End-of-Life Care Policy from the Perspective of Aging Population: A Textual Analysis of 30 Policies from 2017 to 2022

As the proportion of elderly population increases and the level of social development continues to rise, the issue of end-of-life care for the elderly has gradually gained societal attention. End-of-life care, as an emerging field, relies crucially on optimizing top-level policy design for its healthy development. This study focuses on 30 national-level end-of-life care policy texts from 2017 to 2022. Utilizing analysis tools such as ROST CM 6 software, NLPIR Chinese word segmentation system, and SPSS, the research employs a three-dimensional framework of keyword frequency analysis, semantic network analysis, and "policy tools-policy time-policy intensity" to comprehensively explore the current state of China's end-of-life care policy. The study reveals existing issues in the policy's practical operability, uneven application of policy tools, neglected funding and information technology support, and weak regulatory aspects. It emphasizes the need to prioritize a people-centered approach, enhance the overall balance in policy tools, promote the standardization of end-of-life care, and improve a diversified service system. These findings broaden the research perspective in the field of end-of-life care, providing insights for optimizing policy design and advancing nationwide end-of-life care efforts.

African continental free trade area and greenfield investment: Likely effect and transmission channels

AbstractWe examine the likely effect of the African Continental Free Trade Area (AfCFTA) on African investment using data on announced greenfield investment. Departing from existing work on this subject, we add value by providing estimates based on a general equilibrium counterfactual analysis in a structural gravity framework. Results suggest that the stock of announced intra‐African greenfield investment in 2018 would have increased by 0.7 percent from a successful implementation of the AfCFTA in that year relative to the baseline scenario of no agreement, with considerable heterogeneity across source and destination countries. Moreover, the positive effect is found to increase with the depth of the negotiated agreement. Exploring possible transmission channels we find the trade elasticity of announced greenfield investment to be positive, especially for trade in intermediates in African host countries with strong governance indicators. This suggests that, by improving institutional quality and galvanising regional value chains, integration within Africa could also incentivise investment.

ANALYSIS OF ACCIDENT DATA AT PT X FOR THE 2018-2022 PERIOD USING THE HFACS-MINING INDUSTRY FRAMEWORK METHOD

The mining industry is a high-risk industrial activity. Human factors have been identified as the most common cause of major accidents in the mining industry. The Incident Management System of PT X, which is a company in exploration, mining, processing, and marketing sector of copper, gold and silver concentrate, part of the state-owned mining holding enterprise Mining Industry Indonesia, documented 322 cases of accidents within the 2018-2022 period. This research aims to analyze accident data at PT X using the Human Factor Analysis and Classification System-Mining Industry (HFACS-MI) framework. Methods. This research collected qualitative data for 322 accident cases at PT X in 2018-2022 from the incident management system database categorized as recordable injuries. Factors causing the accident were coded using the HFACS-MI framework. Accident data analysis used descriptive statistics. Results. The study findings revealed that 84% of all accidents involved contractor workers and 16% involved permanent workers at PT X. The results of the analysis using the HFACS-MI framework showed that each layer or level contributes to accidents, namely external factors by 44%, organizational influence by 68%, unsafe leadership by 90%, preconditions for unsafe acts by 99%, and unsafe acts by 99.7%. Conclusion. These findings emphasize the need to reduce the number of human errors during mining operations to reduce the current accident trend. The HFACS-MI framework has proven to be a valuable tool for robust accident analysis of human factors in mining.

Hidden Variable Models in Text Classification and Sentiment Analysis

In this paper, we are proposing extensions to the multinomial principal component analysis (MPCA) framework, which is a Dirichlet (Dir)-based model widely used in text document analysis. The MPCA is a discrete analogue to the standard PCA (it operates on continuous data using Gaussian distributions). With the extensive use of count data in modeling nowadays, the current limitations of the Dir prior (independent assumption within its components and very restricted covariance structure) tend to prevent efficient processing. As a result, we are proposing some alternatives with flexible priors such as generalized Dirichlet (GD) and Beta-Liouville (BL), leading to GDMPCA and BLMPCA models, respectively. Besides using these priors as they generalize the Dir, importantly, we also implement a deterministic method that uses variational Bayesian inference for the fast convergence of the proposed algorithms. Additionally, we use collapsed Gibbs sampling to estimate the model parameters, providing a computationally efficient method for inference. These two variational models offer higher flexibility while assigning each observation to a distinct cluster. We create several multitopic models and evaluate their strengths and weaknesses using real-world applications such as text classification and sentiment analysis.

Neural networks-based spring element for second-order analysis of pile-supported structures with nonlinear soil- structure interaction

This paper proposes a novel structural analysis approach, the neural networks-based spring element (NNSE) method, to synergize machine learning (ML) techniques with the line finite element method (LFEM) for the second-order analysis method of pile-supported structures. Traditional LFEM, widely used in upper structure design, showcases limitations in efficiently modeling complex Soil-Structure Interaction (SSI) along piles since it requires dense element mesh for accuracy. Conversely, ML offers a mesh-free alternative for analyzing single piles but struggles in simulating pile-supported structures as the training sample collection for large-scale problems might be unbearable. This paper addresses these challenges by proposing a new analysis framework to utilize the neural network (NN) model, which only describes structural responses of single piles, for the simulation of entire pile-supported structures. In the proposed method, the NN model is not directly used for structural analysis but employed to formulate a new spring element named the NNSE to model single piles in pile-supported structures. This NNSE can be seamlessly implemented within the existing LFEM framework to analyze pile-supported structures, eliminating the dense mesh requirement for single piles and thereby significantly improving the computational efficiency. Extensive examples are provided to verify the effectiveness of the proposed method, indicating its potential in promoting the second-order analysis method to the design of pile-supported structures.

CASi: A framework for cross-timepoint analysis of single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) technology has been widely used to study the differences in gene expression at the single cell level, providing insights into the research of cell development, differentiation, and functional heterogeneity. Various pipelines and workflows of scRNA-seq analysis have been developed but few considered multi-timepoint data specifically. In this study, we develop CASi, a comprehensive framework for analyzing multiple timepoints’ scRNA-seq data, which provides users with: (1) cross-timepoint cell annotation, (2) detection of potentially novel cell types emerged over time, (3) visualization of cell population evolution, and (4) identification of temporal differentially expressed genes (tDEGs). Through comprehensive simulation studies and applications to a real multi-timepoint single cell dataset, we demonstrate the robust and favorable performance of the proposal versus existing methods serving similar purposes.

Corporate Governance Dynamics and Financial Performance: Analysis of Listed Commercial Banks in the Ghanaian Context"

This study aims to determine the effect of corporate governance on the financial performance of 14 listed commercial banks in Ghana. The research employs a panel data set from 2008 to 2020 to examine how board size, ownership structure, board independence, capital adequacy ratio, audit committee effectiveness, board gender diversity, and overall corporate governance score affect ROA and ROE for banks. These relationships are analyzed using Ordinary Least Squares (OLS) regression within the framework of panel data analysis. Results indicate that ROA is positively related to board size, board independence, audit committee, and Board gender diversity. Also, ROE is positively influenced by companies’ ownership structure, capital adequacy ratio, audit committee effectiveness, and overall corporate employment governance rating. However, there is a significant negative correlation between ROE and women’s representation or board gender diversity. Also, another key finding is that both Return on Assets (ROA) and Return on Equity (ROE) are significantly affected by debt and debt-to-equity ratios. This indicates that banks borrowing more have reduced profitability, underlining the need for a well-managed capital structure and no over-reliance on loans. This study contributes to the understanding of the relationship between corporate governance and performance in the banking sector of Ghana, emphasizing that high-quality governance systems can improve and enhance financial performance. The insight gained from this analysis shows that Banks should aim towards good corporate governance practices such as; maintaining proper board size, promoting greater board independence, ensuring efficient audit procedures, and encouraging more gender diversity to enhance profitability. The outcomes also emphasized a well-built base of capital and concentrated ownership, enabling better financial results.The outcomes of this research are useful to Ghanaian policymakers, regulators, and bank executives who view the need for strong corporate governance environments to buttress the stability and growth of the banking sector.

Analytical solution of forced vibration of rectangular plates with part through surface crack based on wave propagation method

An analytical wave propagation framework is developed in this study for steady state forced vibration of plates with part through surface cracks for the first time. Rectangular thin plates with partial-width and full-width cracks are considered. The analytical waves are obtained by solving the dual equations of the plate established considering the line–spring model (LSM) of crack. The dual equations formed here are characterized by simultaneously solving kinematic variables and their dual dynamic variables. Both conventional and refined LSMs are considered. Two ways are proposed to generate the system equation in wave space based on the relationships of wave propagation, wave reflection and wave scattering. Compared with the traditional methods, most of them can only give exact analytical solutions under the condition of opposite-side simply supported boundaries, the developed analysis framework can treat arbitrary combinations of simple boundary condition of the cracked plate. Comparisons of results of the proposed method with those from the literature and finite element method (FEM) validate the effectiveness of the proposed method. A series of parametric studies for investigating the effects of length, position and depth of crack on the forced response of cracked plate are also presented. The proposed framework are effective for predicting the forced responses and natural frequencies of thin plates with part-through surface cracks.

Numerical Prediction and Experimental Verification of Inherent Defect Influences on Frequencies of Adhesively Bonded Joint

The vibrational response of adhesively bonded joints with and without defects in the adhesive layer is predicted by simulation for the different overlapping lengths in this work. The eigenvalue extraction method (Block Lanczo’s) is employed within the finite element analysis (FEA) framework to predict the modal values of adhesively bonded joints. The numerical analysis has adopted a solid 20-node quadratic brick elements with reduced integration (C3D20R) to predict the realistic values. The predicted eigenvalues of adhesively bonded joints using an FEA solution are compared with published data and verified the accuracy of the FEA model through experimentation considering defective and non-defective joints. Additionally, simulation solutions are predicted for different overlap lengths (i.e., 25, 30, 35 and 40 mm) by changing the boundary conditions, adhesive bond thickness aspect ratio, adherend thickness ratio, defect position, and defect aspect ratio. The geometries of the adhesive and adherend significantly affect vibration responses through the dampening effect and structural weight, enhancing the stiffness. Defect position and geometry have negligible impact on the natural frequency values for all overlapping lengths except when located around the overlapping edges.