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Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.

The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.

Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.

Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.

This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.

Interorganizational collaboration has been a major source of exploratory innovation. Despite much research, the authors’ understanding about how partner cultural distance is harnessed for exploratory innovation is limited. The authors’ conceptual framework aims to address this gap by explaining the social-psychological processes between perceived partner cultural distance and exploratory innovation.

Drawing on research in organizational learning and culture mixing, the authors propose a multilevel model with two parallel processes – cultural brokering and cultural defense. If managers are engaged in the former and are protected from the latter, then the partnership will produce more exploratory innovation. Cultural brokering is encouraged by prompting a learning mindset, while cultural defense is preempted by dampening social categorization across organizational boundaries.

Cultural brokering can be encouraged by building operational-level managers' (OLMs') collaborative strength through developing a learning orientation, allowing them delivery for exploration, cultivating mutual trust with partners. Cultural defense can be preempted by protecting OLMs from intergroup anxieties through providing organizational support to the OLMs, bridging social categorization faultlines and setting shared collaborative goals. Whether an alliance can unleash its potential depends on not just how cultural brokering is enabled but also how cultural defense is curtailed.

This paper takes a microfoundational approach and considers micro-level processes in a partnership. Furthermore, the model takes the operational managers' perspective and defines culture at the organizational level. All these differences allow us to provide a nuanced picture of how diverse partnerships can be harnessed for exploratory innovation through a few easily-implementable measures.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

The aim of this paper is to analyze the interrelationship between film-induced tourism and visits to theme parks by tourists and examine the most viewed movies worldwide (2010–2022) which are related with theme parks and their attractions.

The methodology used in this study is a set of qualitative and quantitative indicators for measuring the most visited theme parks worldwide and the most viewed movies that have allowed to answer the research questions.

This research provides a new contribution to tourism and entertainment industries. Results have shown that film-induced tourism can be the perfect marketing tool to attract more tourists to theme parks through movies and their real or fictional characters represented in films such as Marvel, Minions, Avatar, Harry Potter and Disney, amongst many others. These famous characters are part of theme park’s rides to attract tourists to visit film theme parks These rides allow users to travel in a fantasy experience and interact with film characters of cartoons or fictions. There is a high correlation between film-induced tourism and theme parks’ attractions. Film-induced tourism provides great benefits to the society, theme parks, cinemas and tourist destinations. Indeed, movies help to increase the number of visitors to theme parks. In 2019, 215 million people visited the top 20 theme parks worldwide analyzed in this study, this figure is quite lucrative to entertainment and tourism sectors, local and regional economies where these theme parks are localized.

This study presents the importance of theme parks at tourist destinations because theme parks are an extension of movies, thus is, the end of the physical perfect experience at theme parks which began in a movie seat or on your TV/videogame/PC screens.

本文的主要目的是分析电影引起的旅游和游客参观主题公园之间的相互关系, 并研究与主题公园及其景点有关的全球最受欢迎的电影（2010-2022年）。

本研究使用的方法是一套定性和定量的指标来衡量全球访问量最大的主题公园和观看次数最多的电影, 使我们能够回答本研究中提出的问题。

这项研究为旅游和娱乐业提供了新的贡献, 结果表明, 电影引起的旅游可以成为完美的营销工具, 通过电影及其影片吸引更多的游客到主题公园。通过电影和他们在电影中所代表的真实或虚构的人物, 如漫威、小人、阿凡达、哈利波特、迪斯尼等, 主题公园。这些著名人物是主题公园景点的一部分, 以吸引游客参观与电影有关的景点。这些景点让游客有一个幻想的体验, 并与卡通人物互动。电影引起的旅游和主题公园景点之间存在着高度的相关性。我们必须认识到, 电影引发的旅游为社会、主题公园、电影院和旅游目的地提供了巨大的利益。事实上, 电影有助于增加主题公园的游客数量。2019年, 有2.15亿人参观了本研究中分析的世界前20大主题公园, 这个数字对于娱乐和旅游部门、这些主题公园所在的地方和区域经济来说是相当有利可图的。

本研究揭示了主题公园在旅游目的地的重要性, 因为这些旅游景点是电影的延伸, 也就是说, 主题公园及其基于电影和动画的景点是游客在主题公园生活体验的最后一部分, 而这种体验是从电影院的座位上、电视、视频游戏或电脑屏幕前开始的。

El principal desafío de este artículo es analizar la interrelación entre el turismo inducido por las películas y las visitas a los parques temáticos por parte de los visitantes, y examinar las películas más vistas en todo el mundo (2010-2022) relacionadas con los parques temáticos y sus atracciones.

La metodología utilizada en este estudio es un conjunto de indicadores cualitativos y cuantitativos para medir los parques temáticos más visitados a nivel mundial y las películas más vistas que nos han permitido responder a las preguntas planteadas en esta investigación.

Esta investigación proporciona una nueva contribución a las industrias del turismo y el entretenimiento. Los resultados han demostrado que el turismo inducido por el cine puede ser la herramienta de marketing perfecta para atraer más visitantes a los parques temáticos a través de películas y sus personajes reales o ficticios representados en películas como Marvel, Minions, Avatar, Harry Potter, Disney, entre muchos otros. Estos personajes famosos son parte de las atracciones de los parques temáticos para atraer a los visitantes a visitar las atracciones relacionadas con las películas. Estas atracciones permiten a los visitantes tener una experiencia de fantasía e interactuar con personajes de películas de dibujos animados o ficción. Existe una alta correlación entre el turismo inducido por el cine y las atracciones de los parques temáticos. Debemos reconocer que el turismo inducido por el cine ofrece grandes beneficios a la sociedad, parques temáticos, cines y destinos turísticos. De hecho, las películas ayudan a aumentar el número de visitantes a los parques temáticos. En 2019, 215 millones de personas visitaron los 20 parques temáticos más importantes del mundo analizados en este estudio, esta cifra es bastante lucrativa para los sectores de entretenimiento y turismo, y la economía local y regional donde se encuentran estos parques temáticos.

Este estudio revela la importancia de los parques temáticos en los destinos turísticos, dado que estas atracciones turísticas son una extensión de las películas, es decir, los parques temáticos y sus atracciones basadas en películas y animaciones son la parte final de una experiencia vivida por los visitantes en dichos parques y que comenzó en un asiento de cine, frente a su TV, un videojuego, o pantalla de ordenador.

There has been an avalanche of global natural disasters in recent times. In recent years approximately 210 million people were affected, an estimated economic cost of US$153bn was incurred and 68,000 deaths were recorded. This was a work up call that made it imperative for humanitarian actors to impetuously adopt information and communication technologies (ICTs) to timeously assist affected populations in disaster prevention, mitigation response and recovery However, the use of ICTs in the humanitarian field is still at its infancy in most third world countries. The purpose of this study was, therefore, to evaluate the utilization of ICTs in humanitarian relief operations associated with Cyclone Idai in Zimbabwe.

Using a pragmatic approach, the study gathered data using semistructured questionnaires that were triangulated with interviews of humanitarian staff that were involved in Cyclone Idai relief efforts.

An observed suboptimal utilization of ICTs was further disadvantaged by the inequitable distribution of communication infrastructure. However, despite the suboptimal usage, there was a significant positive influence of ICT adoption on effectiveness, efficiency and flexibility in humanitarian relief operations.

Optimal use of ICTs has the potential to revolutionize humanitarian supply chain management. A smooth transition to new technologies is recommended in which personnel are given professional development opportunities on a regular basis.

Recent years have seen a meteoric rise in the study of narcissism in entrepreneurship, although little consolidation has occurred in this area. The purpose of this paper is the development of an integrative framework to harmonise the academic discussion and serve as a structured foundation for future research.

The authors conducted an artificial intelligence-aided, structured literature review focused on content analysis of concepts and contexts to map out current findings and research gaps in startup narcissism research.

According to the findings of this study, narcissistic tendencies have the potential to positively influence startup success early on in an entrepreneur's journey, but after a certain point in the process, the influence of narcissism on success becomes predominantly negative.

The research field is currently not very harmonised regarding research measures, research subjects and key research terms. Further research must use a standardised approach to add value to the research body.

Narcissism is a two-sided sword for founders. In the early stages of a company, many of the founder’s tasks can benefit from narcissistic tendencies. In the later stages of a company, that might shift to overwhelmingly negative effects of narcissism.

Methodically, this study is the first one to establish an artificial intelligence component to add value to the results of a review paper to the best of the authors’ knowledge. The results of this study provide a clear framework of entrepreneurial intention, entrepreneurial activity and entrepreneurial performance to give researchers the opportunity of a more differentiated way of organising work.

As research emerged in terms of how narcissism, a negative or dark trait, has been found to be constructive in enhancing entrepreneurial behaviour, there are mixed results regarding the significance of narcissism in the field of entrepreneurship. Additionally, this previous research has mostly been conducted on student or nascent entrepreneur samples within developed economies. Therefore, the purpose of this paper is to explore how narcissistic traits of established entrepreneurs in an emerging economy context infuence their entrepreneurial behaviour both positively and negatively.

Gioia methodology was applied in the qualitative study by means of in-depth interviews, which allowed for the unpacking of narcissistic traits among established entrepreneurs in South Africa. Four themes emerged from the data, and included insights related to entrepreneurial experience influencing behaviour; business growth linked to personal development; opportunity identification versus loss; and identity separation in relation to authentic identity versus an entrepreneurial identity.

The findings of the paper contribute to creating an understanding of how to hone individual narcissistic traits for positive influences that develop entrepreneurs while also contributing to their business development, opportunity realization and identity. In addition, the findings highlighted a separation between established entrepreneurs’ authentic personality and the inputs that end up resulting in the entrepreneurial personality.

This paper highlights the possibility of narcissism functioning as a business process involved in entrepreneurship rather than a necessary personality trait. An interesting dynamic contributed to what seems to be a constant battle between the authentic identity and the entrepreneur identity, gaining deeper insight surrounding established entrepreneurs’ experiences to survive and, more importantly, thrive as entrepreneurs.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.