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This paper aims to determine new-normal uncertainty considerations stemming from the COVID-19 pandemic to consider within transaction-cost analysis for pharmaceuticals. It also aims to propose new-normal market entry strategies to address the uncertainty as a result of COVID-19's implications and provide for lack of knowledge and information in an uncertain business environment by way of Internet of Things (IoT) ecosystem for pharmaceutical market entry.

In this paper, we focus on the uncertainty facet within transaction-cost analysis consideration and utilise a descriptive three-case study approach taking in Johnson and Johnson (J&J), GlaxoSmithKline (GSK) and Novartis to present an ADO (Antecedent-Decisions-Outcomes) understanding of their usual market entry approach, the approach undertaken during the pandemic and the outcomes thereafter facilitating new-normal uncertainty considerations to factor in. Further with this insight, we develop a conceptual framework addressing the transaction-cost analysis implications of uncertainties toward lack of knowledge and information for a new-normal market entry approach and operating strategy for pharmaceuticals applicable due to IoT (Internet of Things).

Uncertainty (external and internal) is different now in the new-normal business environment for pharmaceuticals and boils down to acute shortage of knowledge and information impact to make an appropriately informed decision. Therefore, considering the changed factors to consider, pharmaceuticals need to be able to undertake market entry with vaccines and medicines by way of IoT thereby enabling, the filling of the gap via real-time data access and sharing, including enhancing predictive analysis for sustenance.

The paper's findings have many theoretical implications highlighted in the manuscript.

The paper's findings have many practical implications highlighted in the manuscript.

This is the first study to our knowledge that throws light on transaction-cost analysis theory's uncertainty facet for pharmaceuticals. It is also the first study that provides a new-normal market entry strategy for pharmaceutical companies built on interoperability of real-time IoT.

The purpose of this study is to model the dependences of the output voltage, temperature, current and electrical power dissipation of a voltage limiter based on a two-layer varistor–posistor structure on time and analysis the influence of operating modes and design parameters of such a limiter on these characteristics.

The behavior of the limiting voltage, temperature and other parameters of the voltage limiter when an input constant overvoltage is applied is studied by the simulation method. The voltage limiter was a two-layer construction. One layer was a zinc oxide ceramic varistor. The second layer was a posistor polymer composite with a nanocarbon filler of PolySwitch technology.

The output voltage across the varistor layer decreases and reaches some fixed value related to its breakdown voltage after applying a constant overvoltage to the structure over time. The temperature of the structure increases to some steady state value, while the current decreases significantly. The amplitude of the transient current pulse increases, its duration and energy of the transient process decrease with increasing overvoltage. An increase in the internal resistance of the overvoltage source can cause a decrease in the amplitude and an increase in the duration of transient currents.

The ranges of values for the activation energy of conduction of the varistor layer in weak electric fields, the intensity of heat exchange between the structure under study and the environment are determined to ensure the stable operation of this structure as a voltage limiter. The results obtained make it possible to select the necessary parameters of the indicated structures to ensure the required operating modes of the voltage limiter for various applications.

The primary objective of this study is to tackle the enduring challenge of preserving feature integrity during the manipulation of geometric data in computer graphics. Our work aims to introduce and validate a variational sparse diffusion model that enhances the capability to maintain the definition of sharp features within meshes throughout complex processing tasks such as segmentation and repair.

We developed a variational sparse diffusion model that integrates a high-order L₁ regularization framework with Dirichlet boundary constraints, specifically designed to preserve edge definition. This model employs an innovative vertex updating strategy that optimizes the quality of mesh repairs. We leverage the augmented Lagrangian method to address the computational challenges inherent in this approach, enabling effective management of the trade-off between diffusion strength and feature preservation. Our methodology involves a detailed analysis of segmentation and repair processes, focusing on maintaining the acuity of features on triangulated surfaces.

Our findings indicate that the proposed variational sparse diffusion model significantly outperforms traditional smooth diffusion methods in preserving sharp features during mesh processing. The model ensures the delineation of clear boundaries in mesh segmentation and achieves high-fidelity restoration of deteriorated meshes in repair tasks. The innovative vertex updating strategy within the model contributes to enhanced mesh quality post-repair. Empirical evaluations demonstrate that our approach maintains the integrity of original, sharp features more effectively, especially in complex geometries with intricate detail.

The originality of this research lies in the novel application of a high-order L₁ regularization framework to the field of mesh processing, a method not conventionally applied in this context. The value of our work is in providing a robust solution to the problem of feature degradation during the mesh manipulation process. Our model’s unique vertex updating strategy and the use of the augmented Lagrangian method for optimization are distinctive contributions that enhance the state-of-the-art in geometry processing. The empirical success of our model in preserving features during mesh segmentation and repair presents an advancement in computer graphics, offering practical benefits to both academic research and industry applications.

Defining and validating a map of related technologies is critical for managers, investors and inventors. Because of the increase in the applications of and demand for semiconductor lasers, analyzing the technological position of developers has become increasingly critical. Therefore, the purpose of this study is to adopt the technological position analysis to identify mainstream technologies and developments relevant to semiconductor lasers.

Correspondence analysis and k-means cluster analysis, which are data mining techniques, are used to reveal strategic groups of major competitors in the semiconductor laser market according to their Patent Cooperation Treaty (PCT) patent applications.

The results of this study reveal that PCT patent applications are generally obtained for masers, optical elements, semiconductor devices and methods for measuring and that technology developers have varying technological positions.

Through position analysis, this study identifies the technological focuses of different manufacturers to obtain information that can guide the allocation of research and development resources.

This paper aims to focus on solving the path optimization problem by modifying the probabilistic roadmap (PRM) technique as it suffers from the selection of the optimal number of nodes and deploy in free space for reliable trajectory planning.

Traditional PRM is modified by developing a decision-making strategy for the selection of optimal nodes w.r.t. the complexity of the environment and deploying the optimal number of nodes outside the closed segment. Subsequently, the generated trajectory is made smoother by implementing the modified Bezier curve technique, which selects an optimal number of control points near the sharp turns for the reliable convergence of the trajectory that reduces the sum of the robot’s turning angles.

The proposed technique is compared with state-of-the-art techniques that show the reduction of computational load by 12.46%, the number of sharp turns by 100%, the number of collisions by 100% and increase the velocity parameter by 19.91%.

The proposed adaptive technique provides a better solution for autonomous navigation of unmanned ground vehicles, transportation, warehouse applications, etc.

The purpose of this study is to evaluate the effects of COVID-19 on the Turkish manufacturing sector by using analytical hierarchy process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods on a sectoral basis. In this way, the sectors that had the highest negative effects of COVID-19 will be determined. Thanks to the findings to be obtained as a result of this study, it will be possible for decision-makers to develop strategies that may differ on a sectoral basis at the point of combating the pandemic.

The negative effects of COVID-19 were revealed by a large literature search. The importance levels of the factors determined within the scope of finance, supply, production and workforce were determined by the AHP method. Then, the TOPSIS method was used to determine which sectors were more affected by the COVID-19 pandemic, based on these factors.

The findings of this study obtained reveal that the most important negative effect of COVID-19 was on businesses in terms of reduced sales caused by domestic sales and export. Again, the findings show that the sector most affected by COVID-19 is automotive, which shows that the least affected sector is the pharmaceutical and medical equipment sector.

Primary data was used to determine the criteria weight. Therefore, there is a possibility that the answers are not objectively evaluated. The findings are limited to the criteria that was used, the views of the professionals and AHP-TOPSIS methods. Studies that use more criteria and different decision-making methods can have different results.

The manufacturing sector is at the forefront of the sectors affected by the global bottleneck caused by COVID-19. In this study, the effects of the COVID-19 pandemic on the Turkish manufacturing sector are discussed on a sectoral basis. Studies in the literature have revealed that the effects of pandemics such as COVID-19 vary between sectors. For this reason, it is important to evaluate the effects of COVID-19 from a sectoral perspective. Thanks to this study, it is thought that it will be possible to take measures that may differ between sectors to combat the negative effects of COVID-19.