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Business relatedness is important in international diversification because it enables a firm’s transfer of resources to business units operating in foreign markets. The purpose of this paper is to develop a conceptual model based on a review of the major contributions of studies regarding the relatedness of subsidiaries, joint ventures or any other foreign unit.

The paper examines theory bases, the relatedness construct, data issues and the key achievements of previous studies. Drawing on organizational learning, transaction costs economics and industrial organization, a conceptual model and propositions are developed that intend to close important research gaps.

The model includes competitive strategy as a mediator of the effects of relatedness on foreign unit performance, type of foreign unit – that is, a wholly owned unit or joint venture – as a moderator; and competition barriers as a moderator.

In future research, the propositions need to be transformed into testable hypotheses. It is recommended to treat relatedness as a multidimensional concept.

A firm is primarily advised to evaluate how its relatedness with foreign units enables knowledge transfer. A foreign cost leadership strategy benefits from product relatedness, while a differentiation strategy calls for resource relatedness.

The proposed model is unique as it includes an actionable component that mediates the effects of relatedness on international performance, i.e. competitive strategy, and concerns both wholly owned foreign units and international joint ventures.

This paper aims to design a magnetostrictive tactile sensor for surface depth detection. Unlike the human finger, although most tactile sensors have high sensitivity to pressure, they cannot detect millimeter-level depth information on the surface of objects precisely. To enhance the ability to detect surface depth information, a piezomagnetic sensor combining inverse magnetostrictive effect and bionic structure is developed in this paper.

A magnetostrictive tactile sensor based on Galfenol [(Fe₈₃Ga₁₇)_99.4B_0.6] is designed and studied for surface depth measurement. The optimal structure of the sensor is determined by experiment and theory. The test platforms for static and dynamic characteristics are set up. The static and the dynamic sensing performance of the sensor are studied experimentally.

The sensor can detect 0–2 mm depth change with a sensitivity of 91.5 mV/mm. A resolution of 50 µm can be achieved in the depth direction. In 50 cycles of loading and unloading tests, the maximum error of the sensor output voltage amplitude is only 2.23%.

The sensor can measure the depth information of object surface precisely with good repeatability through sliding motion and provide reference for object surface topography detection.

The aim of this study is to report the effect of different content of calcium oxide on the process of electromelting magnesia.

The process of molten magnesia was analyzed by finite element simulation and proved by scanning electron microscope.

The results show that with the increase of CaO content, the maximum temperature appreciation increases from 3,616°C To 3,729°C, showing an approximate nonlinear evolution. Low thermal conductivity and low specific heat of CaO result in higher temperature. With the increase of CaO content and temperature, the maximum flow velocity of MgO slag increases from 0.043 to 1.34 mm/s. Under different initial CaO contents, the distribution trend of CaO volume fraction is basically the same, and the CaO volume fraction is evenly distributed between 50 and 225 mm in the furnace.

The influence of different contents of impurity calcium oxide on the process of electromelting magnesia was analyzed and a theoretical system was established.

Shape memory polyurethanes (SMPUs) are typically synthesized using polyols of low molecular weight, Mw, and high hydroxyl number as it is believed that high density of cross-links in these polyols are essential for high performance shape memory polymers. In this study, polyethylene glycol (PEG-6000) with Mw ~ 6000 g/mol and low hydroxyl number (OH ~ 18 mg K OH/g) as the soft segment and diisocyanate as the hard segment were used to synthesize SMPUs. It revealed that although the PEG-6000 based SMPUs have lower maximum elongation at break (425%) and recovery stress than those of PCL-2000 polyol based SMPUs, they have much better shape recovery ratio (98%) and shape fixity (95%). Furthermore, these SMPUs showed a much shorter actuation time of <10sec for up to 85% shape recovery, much shorter than those low Mw SMPUs, clearly demonstrated their great potential for applications.

This study investigated four different economic level areas of China (Shanghai, Shandong, Shaanxi, and Guizhou) to analyze the eastern and western urban and rural media service status at different development stages. This set of data comes from the comparison of regional urban and rural areas and indicates the various aspects of differences in the survey area, including the media use habits, media resources, media consumer demand, evaluation of media services, the role of media in public life, public knowledge level, and so on. On analyzing data comprehensively, one thing can be found that there is a positive correlation between the public media contact degree and the public knowledge level. The media plays an extremely important role in public life and regional public knowledge gap between urban and rural areas exists. Furthermore, this gap is positively correlated to the media resources and media exposure. The trend of media using on mobile phone and computer in urban areas increases significantly greater than in rural areas. Then, how to narrow the urban–rural and regional public knowledge gap and reduce the negative impact of the digital divide will be an important urgent task.

Dams are constructed for many purposes such as for power generation, irrigation, water supply and flood control. However, dams can also impose risks to the public, and the situation could be disastrous if dam failure occurred. The study area, Bertam Valley, is located downstream of hydroelectric dam known as Sultan Abu Bakar Dam, Cameron Highlands. The key objectives of the study are to determine the potential risk area at downstream and to assess the flooding impact on damage to buildings and infrastructures due to dam break event. ArcGIS application and output from two-dimensional flood modelling have been used as an integrated approach to analyse the impact due to dam break flood, by creating flood severity grid analysis. The result obtained shows that the estimated inundated area is about 0.28 km², and almost 197 buildings are potentially affected. Results from this study show that in the event of dam break, the huge volume of impounding water will pound to the downstream areas, threatening the populations, and environment along its path. The finding is useful to assist the local authorities and emergency responders in formulating an emergency procedure to save the people during an emergency.

This research attempts to understand how individuals prevent themselves from exposure to COVID-19 when dining out at a restaurant and what situational factors shaping their COVID-19 preventive behavior (CPB) are. It collects 303 questionnaires responded by restaurant patrons in the United Arab Emirates. The resultant data reveals the ranks of the relative importance among the five CPBs proposed by this study that wearing a mask is considered the most critical CPB. In contrast, wearing gloves is the least desirable CPB. Concerning five health-risk factors under investigation, there is no difference in CPB between the vaccinated and non-vaccinated. People suffering from issues with their immune system show a significant inclination to stress social distancing compared to those without any immune issues. Those having an inflected family member are apt to wear gloves. Further, individuals bearing risk factors concerning chronic illness, an immune problem, and an infected family member are inclined to wash their hands and wear gloves. Lastly, this research finds six situational factors affecting an individual's CPB.

The determination of feasible self-stress modes and grouping of elements for tensegrities with predefined geometry and multiple self-stress modes is very important, though difficult, in the design of these structures. The purpose of this paper is to present a novel approach to the automated element grouping and self-stress identification of tensegrities.

A set of feasible solutions conforming to the unilateral behaviour of elements is obtained through an optimisation process, which is solved using a genetic algorithm. Each chromosome in the population having a negative fitness is a distinctive feasible solution with its own grouping characteristic, which is automatically determined throughout the evolution process.

The self-stress identification is formulated through an unconstrained minimisation problem. The objective function of this minimisation problem is defined in such a way that takes into account both the feasibility of a solution and grouping of elements. The method generates a set of feasible self-stress modes rather than a single one and automatically and simultaneously suggests a grouping of elements for every feasible self-stress mode. A self-stress mode with a minimal/subminimal grouping of elements is also obtained.

The method can efficiently generate sets of feasible solutions rather than a single one. The authors also address one of the challenging issues related to this identification, i.e., automated grouping of elements. These features makes the method very efficient since most of the state-of-the-art methods address the self-stress identification of tensegrities based on predefined groupings of elements whilst providing only a single corresponding solution.

To predict the price of battery-grade lithium carbonate accurately and provide proper guidance to investors, a method called MFTBGAM is proposed in this study. This method integrates textual and numerical information using TCN-BiGRU–Attention.

The Word2Vec model is initially employed to process the gathered textual data concerning battery-grade lithium carbonate. Subsequently, a dual-channel text-numerical extraction model, integrating TCN and BiGRU, is constructed to extract textual and numerical features separately. Following this, the attention mechanism is applied to extract fusion features from the textual and numerical data. Finally, the market price prediction results for battery-grade lithium carbonate are calculated and outputted using the fully connected layer.

Experiments in this study are carried out using datasets consisting of news and investor commentary. The findings reveal that the MFTBGAM model exhibits superior performance compared to alternative models, showing its efficacy in precisely forecasting the future market price of battery-grade lithium carbonate.

The dataset analyzed in this study spans from 2020 to 2023, and thus, the forecast results are specifically relevant to this timeframe. Altering the sample data would necessitate repetition of the experimental process, resulting in different outcomes. Furthermore, recognizing that raw data might include noise and irrelevant information, future endeavors will explore efficient data preprocessing techniques to mitigate such issues, thereby enhancing the model’s predictive capabilities in long-term forecasting tasks.

The price prediction model serves as a valuable tool for investors in the battery-grade lithium carbonate industry, facilitating informed investment decisions. By using the results of price prediction, investors can discern opportune moments for investment. Moreover, this study utilizes two distinct types of text information – news and investor comments – as independent sources of textual data input. This approach provides investors with a more precise and comprehensive understanding of market dynamics.

We propose a novel price prediction method based on TCN-BiGRU Attention for “text-numerical” information fusion. We separately use two types of textual information, news and investor comments, for prediction to enhance the model's effectiveness and generalization ability. Additionally, we utilize news datasets including both titles and content to improve the accuracy of battery-grade lithium carbonate market price predictions.

This information will be useful in the selection of materials and technology for the detection and removal of mercury ions at a low cost and with high sensitivity and selectivity. The purpose of this study is to provide the useful information for selection of materials and technology to detect and remove the mercury ions from water with high sensitivity and selectivity. The purpose of this study is to provide the useful information for selection of materials and technology to detect and remove the mercury ions from water with high sensitivity and selectivity.

Different nano- and bio-materials allowed for the development of a variety of biosensors – colorimetric, chemiluminescent, electrochemical, whole-cell and aptasensors – are described. The materials used for their development also make it possible to use them in removing heavy metals, which are toxic contaminants, from environmental water samples.

This review focuses on different technologies, tools and materials for mercury (heavy metals) detection and remediation to environmental samples.

This review gives up-to-date and systemic information on modern nanotechnology methods for heavy metal detection. Different recognition molecules and nanomaterials have been discussed for remediation to water samples. The present review may provide valuable information to researchers regarding novel mercury ions detection sensors and encourage them for further research/development.