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This study aims to highlight the growth and development of Indo-German collaborative research over the past three decades. Moreover, this study encompasses an in-depth examination of funding acknowledgements to gain valuable insights into the financial support that underpins these collaborative endeavours. Together with this paper, the authors provide an openly accessible data set of Indo-German research papers for further and reproducible research activities (the “Indo-German Literature Dataset”).

The data were retrieved from the Web of Science (WoS) database from the year 1990 till the 30th of November 2022. A total of 36,999 records were retrieved against the used query. Acknowledged entities were extracted using a named entity recognition (NER) model specifically trained for this task. Interrelations between the extracted entities and scientific domains, lengths of acknowledgement texts, number of authors and affiliations, number of citations and gender of the first author, as well as collaboration patterns between Indian and German funders were examined.

The study reveals a consistent and increasing growth in the publication trend over the years. The study brings to light that Physics, Chemistry, Materials Science, Astronomy and Astrophysics and Engineering prominently dominate the Indo-German collaborative research. The USA, followed by England and France, are the most active collaborators in Indian and German research. Largely, research was funded by major German and Indian funding agencies, international corporations and German and American universities. Associations between the first author’s gender and acknowledged entity were observed. Additionally, relations between entity, entity type and scientific domain were discovered.

The study paves the way for enhanced collaboration, optimized resource utilization and societal advantages by offering a profound comprehension of the intricacies inherent in research partnerships between India and Germany. Implementation of the insights gleaned from this study holds the promise of cultivating a more resilient and influential collaborative research ecosystem between the two nations.

The study highlights a deeper understanding of the composition of the Indo-German collaborative research landscape of the past 30 years and its significance in advancing scientific knowledge and fostering international partnerships. Furthermore, the authors provide an open version of the original WoS data set. The Indo-German Literature Data set consists of 22,844 papers from OpenAlex and is available for related studies like literature studies and scientometrics.

The purpose of this article’s research was threefold. Firstly, it aimed to investigate how social services professionals coped during the pandemic period by comparing their involvement in five different national contexts. Secondly, it analysed how these professionals have performed their duties according to the street-level bureaucracy theory. Finally, the third question examines social workers' strategies to guide their professional role when they may have had more discretion in their actions. The research also examined whether discretion has increased during this phase and, if so, how it has been exercised.

This article is based on the findings of the project called “Theory and Practice of Social Work in the World in Times of Pandemic”, which was funded by the International Association of Schools of Social Work (IASSW). The project involved 11 universities across five countries in Europe and Latin America, including Argentina, Brazil, Italy, Spain and Sweden. The research was conducted over 24 months to analyse the practices and measures taken to protect vulnerable people by adapting social services during the extraordinary period of the pandemic. In addition, the research explored how the awareness of emergency arose among social workers and how it influenced the services delivered from the perspective of the street-level bureaucracy theory. In different countries, the level of resourcefulness of services has varied based on their recognition of the severity of the pandemic and the impact of the government’s narratives. In some cases, these narratives have been conspiratorial or even anti-scientific. Additionally, there has been a reduction in the distance between professionals and clients, increased inequalities in access to services and a positive reassessment of the potential of new professional tools, such as digital social work, in establishing a trusting relationship.

As revealed in the interviews, the elements shaping this high degree of discretion among social workers can be classified into three levels: macro- and micro- (Saruis, 2015) plus a meso-level. Each level has four significant aspects. At the macro-level, these are legislation, public information, large associationism and integration of the social services-health system. At the meso-level, these are interpretations of role performance by managers technical equipment, self-organization and community action. The micro-level pertains to personal and family situations, emotions, ethics and social workers' direct relationships with those in charge.

This study investigated how social workers responded to the Covid-19 pandemic. The data gathered sheds light on their understanding of the situation, as well as the differences in experiences across the five countries studied. However, it is essential to note that the findings may not apply to all situations or countries. Nevertheless, this research serves as a stepping stone for future studies to delve deeper into the results and explore them in greater detail .

The study highlights the crucial role of social workers as street-level workers in managing, negotiating and creating meaning in the interaction between professionals and the people they serve. This is especially relevant in Latin America. Additionally, the study emphasizes the significance of social workers as policy actors and the political nature of social work practice. The findings also underscore the importance of effective communication and collaboration between social workers, their teams and the organizations they work for. The sources cited in the study are Barberis and Boccagni (2014) and Cuadra and Staaf (2014). The research has also underscored the potential of social service workers to build networks and cooperate. Such networking can play a vital role in implementing their acquired knowledge. The study has, therefore, emphasized the importance of social workers being an integral part of the societies they serve. They need to continuously enhance their communication skills, using all the necessary tools to gain a comprehensive and updated understanding of the evolving needs of their clients. Integrating digital social work as a mode of service provision has emerged as a crucial aspect, especially in the three European countries observed. This approach has demonstrated its potential and is expected to continue being a part of services to some extent, even after the return to normalcy. However, it is essential to ensure that the accessibility and proximity of services are not compromised in any way.

An unexpected result was observed during the research: the pandemic circumstances have led to valuable reflections. These reflections can help in rethinking and recreating social services. Social workers have been given a unique opportunity to return to the essence of their profession and develop less bureaucratic and more humane ways of working. This experience has also enabled them to recover a closer relationship with the people they serve. To sum up, this study emphasizes that social workers, when given more leeway in their work, rely on cultivating and upholding relationships with other professionals, organizations and stakeholders to stay connected with the community they serve. This is crucial for ensuring the delivery of effective and sustainable social services.

The research employed a thematic analysis approach (Bazeley, 2007) to identify themes related to the concept of consciousness as derived from the field experiences of social workers. Additionally, an in-case and cross-case analysis method (Fereday and Muir-Cochrane, 2006) was used to connect themes related to individual experiences with those gathered from the overall experiences.

The study's objective was to address the factors impacting the employability attributes of fresh graduates in the Kingdom of Bahrain while considering the various challenges. This study used a quantitative approach which employed the questionnaire tool, and data were collected by using a convenience sampling method. The study sample comprised n = 385 respondents from different industries, including manufacturing, banking and finance, hospitality, healthcare, oil and gas, and real estate sectors of Bahrain. Data gathered from questionnaire were analyzed using Statistical Package for the Social Sciences (SPSS), where descriptive and inferential statistics was used to analyze the data. The results of the study showed for the major Hypothesis 1 that the demographic variables have no significant statistical impact on employment attributes of the new fresh graduates. Moreover, findings suggest that null hypothesis for major Hypothesis 2 has been rejected as applied academic skills and critical thinking skills have no significant impact on employability attribute of fresh graduates in the Kingdom of Bahrain. Null hypothesis for major Hypothesis 3 has been accepted as findings suggest that technology use skills (β ₁ = 0.080), system thinking skills (β ₂ = 0.210), communication skills (β ₃ = 0.402), and information skills (β ₄ = −0.100) which are an antecedent of workplace skills, have significant statistical impact on employability attribute of fresh graduates in Kingdom of Bahrain. Lastly, null hypothesis for major Hypothesis 4 has been accepted as findings suggest that interpersonal skills (β ₅ = 0.229) which are an antecedent of effective relationship have significant statistical impact on employability attribute of fresh graduates in the Kingdom of Bahrain.

This paper addresses a significant research gap in the study of Rayleigh surface wave propagation within a piezoelectric medium characterized by piezoelectric properties, thermal effects and voids. Previous research has often overlooked the crucial aspects related to voids. This study aims to provide analytical solutions for Rayleigh waves propagating through a medium consisting of a nonlocal piezo-thermo-elastic material with voids under the Moore–Gibson–Thompson thermo-elasticity theory with memory dependencies.

The analytical solutions are derived using a wave-mode method, and roots are computed from the characteristic equation using the Durand–Kerner method. These roots are then filtered based on the decay condition of surface waves. The analysis pertains to a medium subjected to stress-free and isothermal boundary conditions.

Computational simulations are performed to determine the attenuation coefficient and phase velocity of Rayleigh waves. This investigation goes beyond mere calculations and examines particle motion to gain deeper insights into Rayleigh wave propagation. Furthermore, this investigates how kernel function and nonlocal parameters influence these wave phenomena.

The results of this study reveal several unique cases that significantly contribute to the understanding of Rayleigh wave propagation within this intricate material system, particularly in the presence of voids.

This investigation provides valuable insights into the synergistic dynamics among piezoelectric constituents, void structures and Rayleigh wave propagation, enabling advancements in sensor technology, augmented energy harvesting methodologies and pioneering seismic monitoring approaches.

This study formulates a novel governing equation for a nonlocal piezo-thermo-elastic medium with voids, highlighting the significance of Rayleigh waves and investigating the impact of memory.

To measure the dynamic features of compassion as an emotional and behavioral construct, the present research used a univariate latent growth modeling (LGM) approach within the structural equation modeling (SEM) framework. The aim was to trace the dynamic development of compassion longitudinally in accounting and business students during a three-credit English course at university.

The suggested method ensures the measurement invariance over time, deals with the first order latent variable, traces its growth and takes into account the measurement errors. This longitudinal analytical method was used to explore the initial state and the growth of compassion in four points of time during a language course. The data were collected from 60 adult accounting and business students in four time phases using Sprecher and Fehr's Compassionate Love Scale and were analyzed in Mplus 8.4 with univariate LGM.

The model fit was accepted and the invariance of the latent factor was confirmed over time. The negative covariance between intercept and slope (second-order latent variables) suggested that lower initial scores in L2 learners' compassion show a faster increase in compassion over time as the mean of slope is larger than that of the intercept. L2 learners who started off at a higher level of compassion showed a slower change in compassion over time. This can be at least partly explained by the teacher's motivating role or learners' compassion but needs to be further explored in complementary qualitative phases for deeper insights.

In the present research, awareness was raised of the developmental nature of compassion as an emotional and behavioral construct essential to the accounting and business profession. The great strength of this research lies in the dynamic approach to the compassion construct and the LGM used to capture the temporal growth of compassion and how it evolved through the L2 course.

This study aims to use a qualitative approach to explore and clarify the mechanism by which heuristic-driven biases influence the decisions and performance of individual investors actively trading on the Pakistan Stock Exchange (PSX). It also aims to identify how to overcome the negative effect of heuristic-driven biases, so that finance practitioners can avoid the expensive errors which they cause.

This study adopts an interpretative approach. Qualitative data was collected in semistructured interviews, in which the target population was asked open-ended questions. The sample consists of five brokers and/or investment strategists/advisors who maintain investors’ accounts or provide investment advice to investors on the PSX, who were selected on a convenient basis. The researchers analyzed the interview data thematically.

The results confirm that investors often use heuristics, causing several heuristic-driven biases when trading on the stock market, specifically, reliance on recognition-based heuristics, namely, alphabetical ordering of firm names, name memorability and name fluency, as well as cognitive heuristics, such as herding behavior, disposition effect, anchoring and adjustment, repetitiveness, overconfidence and availability biases. These lead investors to make suboptimal decisions relating to their investment management activities. Due to these heuristic-driven biases, investors trade excessively in the stock market, and their investment performance is adversely affected.

This study provides a practical framework to explore and clarify the mechanism by which heuristic-driven biases influence investment management activities. To the best of authors’ knowledge, the current study is the first to focus on links between heuristic-driven biases, investment decisions and performance using a qualitative approach. Furthermore, with the help of a qualitative approach, the investigators also highlight some factors causing an increased use of heuristic variables by investors and discuss practical approaches to overcoming the negative effects of heuristics factors, so that finance practitioners can avoid repeating the expensive errors which they cause, which also differentiates this study from others.

Open educational resources (OERs) are internet-based digital content that is used for academic purposes by instructors, students and researchers in the era of the information economy. Hence, this study aims to systematically review the literature, focusing on OERs’ benefits and challenges in the academic world.

The relevant literature systematically reviewed following the preferred reporting items for systematic literature reviews and meta-analyses (PRISMA) guidelines. The pertinent literature was obtained from four main scholarly databases, and finally, 21 papers that met the inclusion criteria were included in this study.

The findings revealed that the key benefits of OERs include expanded access to knowledge, supporting lifelong learning, pedagogical benefits and enhancing students’ learning outcomes. However, the key challenges include lack of time to find appropriate resources, lack of awareness about the usage and copyrights, quality assurance and technological limitations and lack of organizational support.

The practical and policy implications highlight the joint venture of academia and library professionals to help the students evaluating OERs, quality assurance, copyright issues and lifelong learning.

Earlier studies missed few significant insights of OERs, such as they did not address the quality assurance of OERs; the issue of understanding of copy right (creative common license) challenges related with OERs; and lack of time for finding suitable resources. Hence, this study identified significance insights related with OERs.

The purpose of this paper is to compile a comprehensive status report on pipes/piping networks across different industrial sectors, along with specifications of materials and sizes, and showcase welding avenues. It further extends to highlight the promising friction stir welding as a single solid-state pipe welding procedure. This paper will enable all piping, welding and friction stir welding stakeholders to identify scope for their engagement in a single window.

The paper is a review paper, and it is mainly structured around sections on materials, sizes and standards for pipes in different sectors and the current welding practice for joining pipe and pipe connections; on the process and principle of friction stir welding (FSW) for pipes; identification of main welding process parameters for the FSW of pipes; effects of process parameters; and a well-carved-out concluding summary.

A well-carved-out concluding summary of extracts from thoroughly studied research is presented in a structured way in which the avenues for the engagement of FSW are identified.

The implications of the research are far-reaching. The FSW is currently expanding very fast in the welding of flat surfaces and has evolved into a vast number of variants because of its advantages and versatility. The application of FSW is coming up late but catching up fast, and as a late starter, the outcomes of such a review paper may support stake holders to expand the application of this process from pipe welding to pipe manufacturing, cladding and other high-end applications. Because the process is inherently inclined towards automation, its throughput rate is high and it does not need any consumables, the ultimate benefit can be passed on to the industry in terms of financial gains.

To the best of the authors’ knowledge, this is the only review exclusively for the friction stir welding of pipes with a well-organized piping specification detailed about industrial sectors. The current pipe welding practice in each sector has been presented, and the avenues for engaging FSW have been highlighted. The FSW pipe process parameters are characteristically distinguished from the conventional FSW, and the effects of the process parameters have been presented. The summary is concise yet comprehensive and organized in a structured manner.

Surface acoustic wave (SAW) sensors have attracted great attention worldwide for a variety of applications in measuring physical, chemical and biological parameters. However, stability has been one of the key issues which have limited their effective commercial applications. To fully understand this challenge of operation stability, this paper aims to systematically review mechanisms, stability issues and future challenges of SAW sensors for various applications.

This review paper starts with different types of SAWs, advantages and disadvantages of different types of SAW sensors and then the stability issues of SAW sensors. Subsequently, recent efforts made by researchers for improving working stability of SAW sensors are reviewed. Finally, it discusses the existing challenges and future prospects of SAW sensors in the rapidly growing Internet of Things-enabled application market.

A large number of scientific articles related to SAW technologies were found, and a number of opportunities for future researchers were identified. Over the past 20 years, SAW-related research has gained a growing interest of researchers. SAW sensors have attracted more and more researchers worldwide over the years, but the research topics of SAW sensor stability only own an extremely poor percentage in the total researc topics of SAWs or SAW sensors.

Although SAW sensors have been attracting researchers worldwide for decades, researchers mainly focused on the new materials and design strategies for SAW sensors to achieve good sensitivity and selectivity, and little work can be found on the stability issues of SAW sensors, which are so important for SAW sensor industries and one of the key factors to be mature products. Therefore, this paper systematically reviewed the SAW sensors from their fundamental mechanisms to stability issues and indicated their future challenges for various applications.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.