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This paper aims to examine the Dufour and Soret combined effects on the study of two-dimensional squeezed flow of copper water nanofluid between parallel plates along with applied (external) magnetic field. Impact of higher order chemical reaction is also considered.

The nonlinear partial differential equations (PDEs) are changed into system of ordinary differential equations (ODEs) by employing suitable similarity transformations. These transformed ODEs are then solved by means of a semianalytical method called differential transform method (DTM). Effects of several changing physical parameters on fluid flow, temperature and concentration have been deliberated through graphs.

It is observed that Dufour and Soret numbers are directly related to temperature profile and a reverse trend was observed in the concentration profile. Temperature enhancement is perceived for the enhanced Dufour number. Enhancement in Dufour number shows a direct association with Sh and Nu for all values of squeezing parameter.

The combined Dufour and Soret effects are used in separation of isotopes in mixture of gases, oil reservoirs and binary alloys solidification. The squeeze nanoliquid flow can be used in the field of composite material joining, rheological testing and welding engineering.

This study is mainly useful for geosciences and chemical engineering.

The uniqueness in this research is the study of the impact of cross diffusion on chemically reacting squeezed nanoliquid flow with the chemical reaction order more than one in the presence of applied magnetic force using a semianalytical procedure, named DTM.

The current study seeks to investigate and determine the principal barriers to the adoption of work–life balance (WLB) practices amongst construction companies operating in the developing countries using Lagos, Nigeria as a case study.

This study adopts a survey research technique. Snowball sampling technique was adopted to identify the target respondents for the administration of questionnaire. A total of 156 questionnaires were distributed and a response rate of 66% (103 questionnaires were completely filled and returned) was attained. Data elicited were analysed using descriptive and inferential statistics.

The results from the exploratory factors analysis show that the principal barriers to the adoption of WLB practices amongst construction companies in Lagos, Nigeria are categorised into four components: nature of construction job and limited evidence on impact of WLB, support, awareness and culture, lack of involvement of management staff and organisational factors.

This study contributed to more effective WLB studies by highlighting the barriers to the adoption of WLB practices in the construction sector. An understanding of these barriers can aid policy makers and management personnel in construction organisations as well as facilitate development of strategies required to reduce the barriers to WLB practices in the construction sector.

This article presents a numerical study of the heat transfer properties of a nanofluid created using engine oil as the common fluid and Fe3O4 nanoparticles within a square cavity embedded with porous media using the LTNE model in the presence of a Cattaneo–Christov heat flux. To obtain the governing boundary layer equations, the Boussinesq approximation and Darcy model are employed.

By applying the Finite Element method, the modeling equations for dimensionless vorticity, stream function and temperature contours with conforming boundary and initial conditions are scrutinized.

One important finding is that streamlines create a core vortex that is oriented centrally and has longer thermal relaxation times. In contrast, solid state isotherms are hardly affected by growth in thermal relaxation parameter values when compared to fluid state isotherms.

The research work carried out in this work is original and no part is copied from others.

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.

Herein, this study aims to use our recently tailored and fully characterized poly acrylonitrile (AN)-starch nanoparticle graft copolymer having 60.1 graft yield percentage as a starting substrate for copper ions removal from wastewater effluent after chemical modification with hydroxyl amine via oximation reaction as a calorimetric sensor.

The calorimetric sensor batch technique was used to determine the resin's adsorption capacity, while atomic adsorption spectrometry was used to determine the residual copper ions concentration in the filtrate before and after adsorption. This was done to convert the copolymer's abundant nitrile groups into amidoxime groups, and the resulting poly (amidoxime) resin was used as a copper ion adsorbent. To validate the existence of amidoxime groups, the resin was qualitatively characterized using a rapid vanadium ion test and instrumentally using Fourier transform infrared spectroscopy spectra and scanning electron microscopy morphological analysis.

At pH 7, 400 ppm copper ions concentration and 0.25 g adsorbent at room temperature, the overall adsorption potential of poly (amidoxime) resin was found to be 115.2 mg/g. The process's adsorption, kinetics and isothermal analysis were examined using various variables such as pH, contact time, copper ion concentration and adsorbent dose. To pretend the adsorption kinetics, various kinetics models, including pseudo-first-order and pseudo-second-order, were applied to the experimental results. The kinetic analysis indicated that the pseudo-second-order rate equation promoted the development of the chemisorption phase better than the pseudo-first-order rate equation. In the case of isothermal investigations, a study of observed correlation coefficient (R²) values indicated that the Langmuir model outperformed the Freundlich model in terms of matching experimental data.

To the best of the author's information, there is no comprehensive study for copper ions removal from waste water effluent using the recently tailored and fully characterized poly (AN)-starch nanoparticle graft copolymer having 60.1 graft yield percentage as a starting substrate after chemical modification with hydroxyl amine via oximation reaction as a calorimetric sensor.

Effective and flexible organizational models have become an avenue for driving smallholder competitiveness in the agricultural sector. However, little is understood about the processes by which resource-constrained actors deploy their organizational networks to generate and retain value in rapidly changing agrifood environments. This study examines the moderating effects of business contingencies on the interplay between organizational relationships and the resource-based performance of small-scale farmers in a developing country.

The authors propose a novel conceptual framework grounded in the relational view, netchain and contingency theories. Cross-sectional data obtained from 330 maize farmers in rural Zambia were analyzed using variance-based structural equation modeling, which involves mediation-moderation analysis.

The results show that all relational networks – vertical, horizontal and lateral – positively mediate the effects farm resources and social capital have on farmers' performance. However, these effects change depending on the predominant agency situations. Specifically, asymmetric power from customers and reputable competitors weakens the positive effect of closer horizontal relationships on business performance, while the positive effect of tighter informal vertical relationships on farmers' performance weakens under conditions of high affective trust. Moreover, the gender-based multigroup analyses highlight variations in the contingent relational view of men- and women-headed households.

The study relies on cross-sectional data from one agribusiness sector in Zambia, thus generalizations should be cautious.

The uniqueness of this study lies in the proposed theoretical framework and new empirical insights, which extend the scope of the relational view to small-scale farming households in developing countries.

Cooperatives are used as one of the strategic tools to reach smallholder farmers and reduce household poverty through augmenting access to inputs, technologies, farm productivity and markets. Our study aims to investigate the impacts of the Meki Batu Cooperative Union on reducing household poverty.

This study employed a cross-sectional survey of households in the Dugda district of the East Shewa zone in the Oromia region. A two-stage sampling design was applied, where four rural Kebeles were first randomly picked, followed by stratified random samples of 217 producers comprising 100 members and 117 non-members of cooperatives. The standard probit model was estimated with a set of observable factors. Propensity score matching (PSM), doubly robust inverse probability weighting and treatment effect estimation were performed along with matching techniques.

The results reveal that education, livestock assets, access to irrigation and extension contact positively determine participation in cooperatives. As the income-based poverty attests, the poverty gap was reduced by 5.9–6.3% and the severity of poverty by 3.7–3.8% due to the cooperative membership.

The investigation suggests the need for continued and comprehensive social services to address development challenges through the facilitation of producers’ engagement in collective actions and agribusinesses.

Existing research evidence is inconclusive with the view of impacts of collective actions on housed welfare in Ethiopia. This study empirically tested the impacts in connection to the production and marketing of high-value crops.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2024-0231

This paper aims to find the effective 3D printing process parameters based on mechanical characteristics such as tensile strength and hardness of poly lactic acid (PLA)/carbon fibre composites (CF-PLA) by implementing intelligent frameworks.

The experiment trials are conducted based on design of experiments (DoE) using Taguchi L9 orthogonal array with three factors (speed, infill % and pattern type) and three levels. The factors have been optimized by solving the regression equation which is obtained from analysis of variance (ANOVA). The contour plots are generated by response surface methodology (RSM). The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness.

The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness. The results obtained from RSM are also confirmed by implementing the machine learning classifiers, such as logistic regression, ridge classifier, random forest, K nearest neighbour and support vector classifier (SVC). The results show that the SVC can predict the optimized process parameters with an accuracy of 95.65%.

3D printing parameters which are considered in this work such as pattern types for PLA/CF-PLA composites based on intelligent frameworks has not been attempted previously.

This research aims to evaluate students’ perception of using a chatbot to develop their understanding of the various parts of a research article, and their ability to apply what they have learned to write a new research plan.

This research is qualitative in approach. The sample contained 10 students on a research skills course as part of the master of educational technologies program. The research instruments consisted of (1) semi-structured interviews to gather the students’ perceptions of the effectiveness of using a chatbot, and (2) observation notes to triangulate the interview results, relating to the students’ application of what they had learned from the teacher and ChatGPT, in new contexts.

This study found that the use of ChatGPT contributed to developing students’ knowledge of writing a research plan. The students were satisfied with their personalized learning experience, and believed that ChatGPT had improved their autonomy, competence and relatedness. These three variables had played a role in developing their application skill. However, there were concerns about using ChatGPT in learning, like limiting the students’ creativity, human interaction and critical thinking skills. There were also concerns over accuracy, ethics and plagiarism.

The originality of this study lies in its examination of how ChatGPT can help satisfy students' need for autonomy (having more knowledge to communicate with the app and control their choices), competence (feeling more capable of using artificial intelligence [AI] and possessing the necessary knowledge and skills to succeed) and relatedness (being less disappointed when failing to obtain satisfactory responses from the AI application), while improving their skills in application. According to self-determination theory (SDT), these factors affect students’ skills in applying what they have learned to new tasks. Therefore, this study extends the examination of SDT and its effect to include application skill, using qualitative methods to study the relationships between variables in depth.

This study aims to develop a predictive model using machine learning techniques to forecast construction dispute outcomes, thereby minimizing economic and social losses and promoting amicable settlements between parties.

This study develops a novel conceptual model incorporating project characteristics, root causes, and underlying causes to predict construction dispute outcomes. Utilizing a dataset of arbitration cases in Türkiye, the model was tested using five machine learning algorithms namely Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors, and Random Forest in a Python environment. The performance of each algorithm was evaluated to identify the most accurate predictive model.

The analysis revealed that the Support Vector Machine algorithm achieved the highest prediction accuracy at 71.65%. Twelve significant variables were identified for the best model namely, work type, root causes, delays from a contractor, extension of time, different site conditions, poorly written contracts, unit price determination, penalties, price adjustment, acceptances, delay of schedule, and extra payment claims. The study’s results surpass some existing models in the literature, highlighting the model’s robustness and practical applicability in forecasting construction dispute outcomes.

This study is unique in its consideration of various contract, dispute, and project attributes to predict construction dispute outcomes using machine learning techniques. It uses a fact-based dataset of arbitration cases from Türkiye, providing a robust and practical predictive model applicable across different regions and project types. It advances the literature by comparing multiple machine learning algorithms to achieve the highest prediction accuracy and offering a comprehensive tool for proactive dispute management.