Multi-dimensional forces and niche dynamics in the socio-technical transitions: Future alternative fuels in the shipping industry.

World’s population is increasing, resulting in significant issues such as climate change and global warming. These issues damage the world's environment, which is evident from the increase in the earth’s temperature in the last few decades. Pollution is one of the primary reasons for these issues, and emissions significantly contribute to pollution. System change is needed because system innovations can improve environmental performance to address these significant issues. This study used the multi-level perspective (MLP) as a framework to study these system transitions in the context of the shipping industry because it contributes 3% to the world’s emissions. There are three levels in the MLP model: macro, meso, and niches. Niches are the layer for innovations and new technologies, and meso level is the current regime, and the macro level is the external environment known as the socio-technical landscape. Multi-dimensional forces are acting as drivers for innovation and new technologies, and, similarly, these forces pressure regimes. Furthermore, different niche dynamics are also studied. PESTEL analysis and Del-phi study is used for data collection. It was found out that environmental and political forces with the mediation of society and economy result in regulations. These regulations are driving technological development. Moreover, future alternative fuels for the shipping industry for the next 10 and 30 years are also discussed in the study

Leadership competencies and blockchain implementation in public sector organizations: a sensemaking approach

Purpose This study aims to explore the required leadership competencies for successful blockchain technology (BCT) implementation in public sector organizations from a sensemaking perspective. Design/methodology/approach The study uses a multiple case study design. Primary data are collected by conducting semi-structured interviews with several representatives of Finnish public sector organizations. Written material from the selected organizations complements the primary data. NVivo14 is used to generate codes and analyze data. Findings The analysis shows that through sensemaking, leaders identify cues for digitally transforming their organizations through blockchain by leveraging their curious and rational vision. After identifying the cues, leaders then interpret these cues through technological understanding and exploring different technological solutions. Once the cues are interpreted for blockchain implementation, the third step is enactment after interpreting the cues. Leaders can facilitate the enactment of blockchain by connecting the outcomes of blockchain with organizational processes and goals. Furthermore, a dark side of BCT is identified, consisting of overly optimistic expectations and creating technological dependencies in the public sector. Research limitations/implications The study was conducted in 11 public organizations in Finland, which limits the generalizability of the findings. Leadership competencies that are required for blockchain implementation within organizations can be studied further by considering more use cases. The potential dark side of blockchain implementation can be explored further. Originality/value The presented research model of leadership competencies for blockchain implementation is derived from sensemaking research and contributes to the literature on leadership competencies by applying sensemaking to the study of BCT.© Syed Hammad Ul Haq, Sorin Dan and Khuram Shahzad. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and noncommercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcodefi=vertaisarvioitu|en=peerReviewed

GHG emission reduction measures and alternative fuels in different shipping segments and time horizons – A Delphi study

The bodies governing the global maritime industry have set short- and long-term targets for reducing GHG emissions from shipping. Various emission abatement measures exist, but their applicability in different contexts widely varies. The situation is unclear, especially for the so-called alternative fuels. These fuels hold the biggest emission reduction potential. Conversely, they are expensive, and the feasibility of investments in those technologies has high uncertainty. Despite a growing body of knowledge on the characteristics and potential of alternative fuels, no consensus exists as to which fuels would be best for each segment of the maritime industry – in the near future and the long run. We contribute with a Delphi study to fill this gap. Our results pinpoint the differences between the shipping segments and the short- and long-term choices for alternative fuels.© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

A study to determine the predictors of game addiction based on loneliness, motivation, and interpersonal competence

The objective of this study is to determine the predictor that will lead to Online Game Addiction. The dependent variable of this study is Online Game addiction and the independent variables are Loneliness, Inter-personal Competence, and Regulatory Focus (Promotion and Prevention Motivation). The data was collected through a convenience sampling technique from 500 respondents who are playing an online game on a daily basis in Karachi. SPSS software was used to test the hypothesis. The finding of the research is Regulatory Focus, Loneliness, and Inter-Personal Competence have a significant impact on Online Game Addiction. The recommendation for future researchers is to increase their sample size and variable in order to study this topic more deeply

A study to determine the predictors of game addiction based on loneliness, motivation, and interpersonal competence

The objective of this study is to determine the predictor that will lead to Online Game Addiction. The dependent variable of this study is Online Game addiction and the independent variables are Loneliness, Inter-personal Competence, and Regulatory Focus (Promotion and Prevention Motivation). The data was collected through a convenience sampling technique from 500 respondents who are playing an online game on a daily basis in Karachi. SPSS software was used to test the hypothesis. The finding of the research is Regulatory Focus, Loneliness, and Inter-Personal Competence have a significant impact on Online Game Addiction. The recommendation for future researchers is to increase their sample size and variable in order to study this topic more deeply

Comparison of dual task specific training and conventional physical therapy in ambulation of hemiplegic stroke patients: A randomized controlled trial

Objective: To compare the effectiveness of dual task specific training and conventional physical therapy in ambulation of patients with chronic stroke. Methods: The randomised controlled trial was conducted at the Habib Physiotherapy Complex, Peshawar, Pakistan, from January to August 2017, and comprised patients with chronic stroke. The patients were randomly assigned to two treatment groups. Group A received dual task training, while Group B received conventional physiotherapy. Dual task training included activities such as slowly walking backward, sideways, and forward on a smooth surface while holding a 100gm sandbag. The conventional physiotherapy included mat activities, stretching and strengthening exercises and gait training. Pre-test and post-test data was taken for both spatial and temporal variables for both groups using Time Up and Go Test and 10-meter walk test. Step length, stride length, cycle time and cadence were also calculated before and after treatment. SPSS 23 was used to analyse the data. Results: Of the 64 patients, there were 32(50%) in each of the two groups that both had 17(53%) males and 15(47%) females. Mean age in Group A was 58.28 ± 7.13 years, while in Group B it was 58.87 ± 6.13 years. Baseline parameters had no significant differences between the groups (p>0.05). Post-treatments scores revealed significant improvement of spatial and temporal variable of gait, 10-meter walk, cadence, step length, stride and cycle time in Group A compared to Group B (p<0.05 each). Continuous..

Use of Recurrent Neural Network with Long Short-Term Memory for Seepage Prediction at Tarbela Dam, KP, Pakistan

Estimating the quantity of seepage through the foundation and body of a dam using proper health and safety monitoring is critical to the effective management of disaster risk in a reservoir downstream of the dam. In this study, a deep learning model was constructed to predict the extent of seepage through Pakistan’s Tarbela dam, the world’s second largest clay and rock dam. The dataset included hydro-climatological, geophysical, and engineering characteristics for peak-to-peak water inflows into the dam from 2014 to 2020. In addition, the data are time series, recurring neural networks (RNN), and long short-term memory (LSTM) as time series algorithms. The RNN–LSTM model has an average mean square error of 0.12, and a model performance of 0.9451, with minimal losses and high accuracy, resulting in the best-predicted dam seepage result. Damage was projected using a deep learning system that addressed the limitations of the model, the difficulties of calculating human activity schedules, and the need for a different set of input data to make good predictions

Use of Recurrent Neural Network with Long Short-Term Memory for Seepage Prediction at Tarbela Dam, KP, Pakistan

Estimating the quantity of seepage through the foundation and body of a dam using proper health and safety monitoring is critical to the effective management of disaster risk in a reservoir downstream of the dam. In this study, a deep learning model was constructed to predict the extent of seepage through Pakistan&rsquo;s Tarbela dam, the world&rsquo;s second largest clay and rock dam. The dataset included hydro-climatological, geophysical, and engineering characteristics for peak-to-peak water inflows into the dam from 2014 to 2020. In addition, the data are time series, recurring neural networks (RNN), and long short-term memory (LSTM) as time series algorithms. The RNN&ndash;LSTM model has an average mean square error of 0.12, and a model performance of 0.9451, with minimal losses and high accuracy, resulting in the best-predicted dam seepage result. Damage was projected using a deep learning system that addressed the limitations of the model, the difficulties of calculating human activity schedules, and the need for a different set of input data to make good predictions