Technology Decisions Research Articles

Defining the growth paths of strategic scientists based on case studies

In this paper, 18 scientists from China and other countries were selected for consideration as case studies of ‘strategic scientists’. They were chosen according to their involvement in science and technology policy decision-making, development of key and advantageous fields, creation of new disciplines, construction of academic traditions, and intersections between science and industry. On this basis, we found that the growth paths of strategic scientists can be divided into three phases: as talented young scientists, as gatekeepers in related disciplines or research fields, and as consultants for national science and technology decision-making. Based on this trichotomy, we explored the characteristics of strategic scientists and the conditions required for their growth in each phase. Finally, policy suggestions are proposed for the cultivation and use of strategic scientists.

IMPACT ASSESSMENT OF INFORMATION TECHNOLOGY ON INVESTMENT CHOICE DECISIONS OF ACADEMIC PROFESSIONALS

The substantial impact of information technology on investment decision-making is explored in this study, and strong empirical support is provided through a significant regression model. Understanding the extent to which technology influences investment choices is of utmost relevance in a time of fast technological advancement. The research makes use of a varied dataset that includes a variety of economic and market characteristics, employing robust statistical analysis to evaluate the relationship between technology and investment decisions. Our findings show that information technology and investing choices are statistically significantly correlated. The regression model illustrates how technology is increasingly being used into investment strategies and how it influences investors' decisions. This study emphasizes the importance of incorporating technical expertise into investment plans, with a focus on data analytics, artificial intelligence, and long-term tech investments. Furthermore, the ease with which mutual funds can be invested in, with alternatives for both novice and experienced investors, has made them a practical and accessible option as the study points mutual funds as the most preferred option for investment. Their liquidity and transparency also resonate with investors who appreciate the ability to monitor their investments easily and access their funds when needed. The results of the study highlight how crucial it is to comprehend investor preferences for financial goods. The popularity of mutual funds not only demonstrates their benefits but also emphasizes the necessity for financial institutions and advisors to customise their services and products in order to meet investor needs. KEYWORDS: Information Technology, Investment Choice Decisions, Mutual Funds

Emerging challenges of SMEs for the post-COVID-19 era: implications for gresilience and sustainability

PurposeThe main aim of this paper is to investigate key challenges that hinder sustainable and resilient green supply chain (GSC) building in small and medium-sized enterprises (SMEs) with respect to how they interact after the COVID era.Design/methodology/approachUsing a questionnaire, policymakers and industry managers from SMEs identify the main obstacles that will affect their resilience. These issues are then discussed with 50 experienced professionals from the top management field and further divided into sustainability and gresilience challenges. The Grey-DEMATEL (Grey sets and Decision-Making Trial and Evaluation Laboratory) has been employed to assess the significance of challenges to SMEs' development of green resilience. Overall, the paper aims to build up a novel decision-making technology for the interconnectedness of green and resilience in the post-pandemic supply chain world.FindingsThe 12 sustainability and gresilience challenges highlighted by experts in the sector were analyzed. Insufficient government assistance and the handling of resources are the most significant challenges to developing a sustainable and gresilient value chain system, according to the study. The study highlights supply chain restructuring, waste reduction management, shortages of skilled manpower and insufficient knowledge of digitalization as key challenges for SMEs to prioritize.Practical implicationsThe outcomes of this research are crucial for SMEs building resilient supply networks through the green concept. Policymakers may utilize the data to pinpoint shortcomings in the supply chain for tactical as well as strategic development and improvement.Originality/valueWhen it comes to building green resilience and sustainability in an unpredictable business climate, the outcomes of this research will be significant for SMEs.

Profiling patent compounds in lipid nanoparticle formulations of siRNA

Lipid nanoparticles (LNPs) have emerged as a prominent delivery system for nucleic acid drugs, attracting significant attention especially through the successful development of several commercial products. As a key component in LNPs, cationic lipids along serve as key technical barrier to block competitors by building up complex patent thicket. However, there have been few studies as yet that have comprehensively analyzed the patented compounds in LNP formulations, despite a large number of technical reviews and original articles. In the context, this study focuses on analyzing the macroscopic landscapes and microscopic molecular characteristics of LNP patents, aiming to provide a valuable reference for researchers and developers in making informed technological and commercial decisions. By mining 2,994 patents, 265 formulas, 7,674 compounds and 28,789 fragments, this work sketched the empirical golden ratio of lipid materials in LNP formulation, disclosed the advanced technology in the formulation, characterized high-frequency fragments of heads, linkers and tails in both novel cationic lipids as well as targeting lipids, and established a virtual focus library of LNP materials.

Extreme, Outrageous, and (Un)reliable: Navigating Uncertainty in the Development of Sound-Based Fog Signals in Scotland, 1860-1900.

This case study demonstrates how an analysis grounded in sound studies, rather than visual studies, reveals a different technological outcome in the development of coastal navigation systems. In the late nineteenth century, engineers at Scotland's Northern Lighthouse Board developed and managed a growing network of sound-based fog signals, primarily using steam-powered sirens. Despite the prevailing perception that sound signaling was more unreliable and risky than lighthouses for coastal navigation, engineers focused on maximizing the loudness of these devices to address the uncertainty of sound transmission. By prioritizing the siren and narrowing the system's goals to warning sailors about imminent danger, the engineers created a system with fundamentally different aims, implementation methods, and design priorities than those of visually-based lighthouse technology. This sound-based analysis uncovers how sensory hierarchies shaped technological decisions, leading to a unique and distinct approach to coastal navigation.

Application of artificial intelligence in the new generation of underwater humanoid welding robots: a review

Underwater welding robots play a crucial role in addressing challenges such as low efficiency, suboptimal performance, and high risks associated with underwater welding operations. These robots face a dual challenge encompassing both hardware deployment and software algorithms. Recent years have seen significant interest in humanoid robots and artificial intelligence (AI) technologies, which hold promise as breakthrough solutions for advancing underwater welding capabilities. Firstly, this review delves into the hardware platforms envisioned for future underwater humanoid welding robots (UHWR), encompassing both underwater apparatus and terrestrial support equipment. Secondly, it provides an extensive overview of AI applications in underwater welding scenarios, particularly focusing on their implementation in UHWR. This includes detailed discussions on multi-sensor calibration, vision-based three-dimensional (3D) reconstruction, extraction of weld features, decision-making for weld repairs, robot trajectory planning, and motion planning for dual-arm robots. Through comparative analysis within the text, it becomes evident that AI significantly enhances capabilities such as underwater multi-sensor calibration, vision-based 3D reconstruction, and weld feature extraction. Moreover, AI shows substantial potential in tasks like underwater image enhancement, decision-making processes, robot trajectory planning, and dual-arm robot motion planning. Looking ahead, the development trajectory for AI in UHWR emphasizes multifunctional models, edge computing in compact models, and advanced decision-making technologies in expansive models.

Challenges to Public Administration Related to the Implementation of the Scientific and Technological Development Strategy of the Russian Federation

The author considers the requirements of the Decree of the President of Russia “On the Scientific and Technological Development Strategy of the Russian Federation” to the characteristics and principles of functioning of the public administration system in Russia. It is shown that the list of these characteristics and requirements is incomplete and insufficient for the implementation of the Decree. The main gaps in the requirements formed by the Decree relate to limiting the reconfiguration of the management system to only one subsystem, management in the field of scientific and technological development. The article argues that the complex nature of grand scientific and technological challenges implies profound changes in the public administration system as a whole, the transition from a structural and functional management system (a combination of meritocratic and client-oriented models) to a project management system (indicative and public-state models). The changes should affect the nomenclature and the scheme of interaction between structural divisions of state bodies, increase the level of project intra- and interdepartmental coordination. In addition, significant changes must be made in the public service system. We should organize training of specialists capable of using data analytics tools and data-based decision-making technologies as well as of carrying out an indicative assessment of project implementation indicators. The structure of the civil service should be associated with project offices and temporary performance of duties in these offices, and not only with permanent functions, taking into account the specifics of project service activities and project teams within government agencies. Civil servants should actively interact with independent expert structures and with civil society, which is the ultimate beneficiary of science and technology projects.

Architecture decisions in quantum software systems: An empirical study on Stack Exchange and GitHub

Context:Quantum computing provides a new dimension in computation, utilizing the principles of quantum mechanics to potentially solve complex problems that are currently intractable for classical computers. However, little research has been conducted about the architecture decisions made in quantum software development, which have a significant influence on the functionality, performance, scalability, and reliability of these systems. Objective:The study aims to empirically investigate and analyze architecture decisions made during the development of quantum software systems, identifying prevalent challenges and limitations by using the posts and issues from Stack Exchange and GitHub. Methods:We used a qualitative approach to analyze the obtained data from Stack Exchange Sites and GitHub projects — two prominent platforms in the software development community. Specifically, we collected data from 385 issues (from 87 GitHub projects) and 70 posts (from 3 Stack Exchange sites) related to architecture decisions in quantum software development. Results:The results show that in quantum software development (1) architecture decisions are articulated in six linguistic patterns, the most common of which are Solution Proposal and Information Giving, (2) the two major categories of architectural decisions are Implementation Decision and Technology Decision, (3) Software Development Tools are the most common application domain among the twenty application domains identified, (4) Maintainability is the most frequently considered quality attribute, and (5) Design Issues and High Error Rates are the major limitations and challenges that practitioners face when making architecture decisions in quantum software development. Conclusions:Our results show that the limitations and challenges encountered in architecture decision-making during the development of quantum software systems are strongly linked to the particular features (e.g., quantum entanglement, superposition, and decoherence) of those systems. These issues mostly pertain to technical aspects and need appropriate measures to address them effectively.

An Automatic Vehicle Navigation System Based on Filters Integrating Inertial Navigation and Global Positioning Systems

The key technologies for advanced autonomous vehicles include those relating to perception, decision making, and execution. Path-tracking control in autonomous vehicles is heavily dependent on their positioning system. Therefore, the development of low-cost and reliable positioning systems is crucial to improving perception and decision-making technologies for autonomous vehicles. Although the accuracy of the global positioning system (GPS) is extremely high, it is vulnerable to interference. Further, despite the low positioning accuracy of inertial navigation systems (INSs), their robustness is notably high. Therefore, an integrated navigation information method based on the Adaptive Particle Filter and the Iterative Kalman Filter (APF-IKF) was developed in this study. Firstly, an integrated navigation system model was established. Then, the IKF was adopted to estimate the speed, latitude and longitude errors of the INS. Thirdly, the newest estimated error results were introduced into the APF to optimize the distribution function, and the particle quality was improved. In this process, the APF can filter non-Gaussian noise, preliminarily estimate the error, optimize the result with the IKF and correct the output information of the INS with the final estimated error. Finally, by using differential GPS positioning as the benchmark, we built a real-vehicle test platform with a low-cost and low-precision GPS and inertial units and carried out a series of real-vehicle tests. The experimental results show that compared with the traditional KF method, APF-IKF can significantly improve the positioning accuracy and robustness of the system.

Expert systems: an effective tool for risk management – the case of Naftal Skikda

This study aims to investigate the impact of expert systems on improving risk management at Naftal Skikda, a key player in the oil and gas sector in Algeria. It seeks to determine the effectiveness of these advanced technologies in risk detection, real-time decision-making, and overall risk management efficiency. The hypotheses tested aim to assess whether the integration of expert systems can accelerate risk detection and contribute to risk reduction, while enhancing the speed and accuracy of decision-making. This research aims to provide strategic recommendations to strengthen practices for managing risks within the petroleum and natural gas sectors, highlighting the potential of expert systems as a transformative tool.

Price and carbon emission reduction technology competition in the electricity supply chain based on power structure

This study considers the research on electricity pricing and carbon reduction technology decisions of electricity supply chain enterprises under different power structures, and constructs a two-level electricity supply chain game model composed of two electricity producers and a single electricity retailer. By comparing the equilibrium solution results, the main research results are as follows: (1) In the market structure where electricity producers and electricity retailer make decisions at the same time, electricity producers are more willing to invest in low-carbon technologies and increase carbon emission reduction. The electricity retailer also set the lowest electricity prices at this time, and electricity demand is the highest. (2) In the case of the Stackelberg game for electricity producers, the wholesale electricity price is the highest, and the electricity producers obtain the most profits. In the case of the Stackelberg game for electricity retailer, the electricity price is the highest, and the profit of the electricity retailer is the highest. (3) In the market structure dominated by electricity retailer, the electricity price is the highest, and the electricity producers reduce the investment in carbon emission reduction technology after observation; the carbon emission reduction intensity is the least, and the electricity demand is also the least. (4) With the increase of price competition coefficient, the carbon emission reduction intensity, electricity demand, electricity producers and electricity retailer profit increase. However, as the competition coefficient of carbon reduction technology increases, these equilibrium solutions decline. We verify the above conclusions using multi-agent simulation.

A Process-Based Standardization of an Intraoperative Escalation Protocol in Anesthesiology.

Prompt recognition and management of critical events is pivotal for the provision of safe anesthetic care. This requires a well-functioning team that focuses on effective communication, timely decision-making, and escalation of potential complications. We believe that variation in bedside care leads to "near-misses," adverse outcomes, and serious safety events (SSEs). The principles of an escalation culture have been used successfully in other highly reliable industries such as aviation, military, and manufacturing. We discuss here the introduction of a unique and compelling thought-process for developing an intraoperative escalation protocol that is specifically tailored for our institution. Inspired by a critical intraoperative event, this departmental protocol was developed based on an analysis of multispecialty literature and expert opinion to decrease the incidence of SSEs. It includes a stepwise approach and incorporates patient-specific information to guide team members who encounter dynamic clinical situations. The implementation of the protocol has facilitated continuous quality improvement through iterative education, improving communication, and enhancing decision-making. Concurrently, we have plans to incorporate technology and electronic decision support tools to enhance real-time communication, monitor performance, and foster a culture of safety.

Urban Flood Resilience Evaluation Based on Heterogeneous Data and Group Decision-Making.

In recent years, urban floods have occurred frequently in China. Therefore, there is an urgent need to strengthen urban flood resilience. This paper proposed a hybrid multi-criteria group decision-making method to assess urban flood resilience based on heterogeneous data, group decision-making methodologies, the pressure-state-response model, and social-economic-natural complex ecosystem theory (PSR-SENCE model). A qualitative and quantitative indicator system is formulated using the PSR-SENCE model. Additionally, a new weighting method for indicators, called the synthesis weighting-group analytic hierarchy process (SW-GAHP), is proposed by considering both intrapersonal consistency and interpersonal consistency of decision-makers. Furthermore, an extensional group decision-making technology (EGDMT) based on heterogeneous data is proposed to evaluate qualitative indicators. The flexible parameterized mapping function (FPMF) is introduced for the evaluation of quantitative indicators. The normal cloud model is employed to handle various uncertainties associated with heterogeneous data. The evaluations for Beijing from 2017 to 2021 reveal a consistent annual improvement in urban flood resilience, with a 14.1% increase. Subsequently, optimization recommendations are presented not only for favorable indicators such as regional economic status, drainability, and public transportation service capacity but also for unfavorable indicators like flood risk and population density. This provides a theoretical foundation and a guide for making decisions about the improvement of urban flood resilience. Finally, our proposed method shows superiority and robustness through comparative and sensitivity analyses.

Bring Out Your Dead: A Review of the Cost Minimisation Approach in Health Technology Assessment Submissions to the Australian Pharmaceutical Benefits Advisory Committee.

Published literature has levied criticism against the cost-minimisation analysis (CMA) approach to economic evaluation over the past two decades, with multiple papers declaring its 'death'. However, since introducing the requirements for economic evaluations as part of health technology (HTA) decision-making in 1992, the cost-minimisation analysis (CMA) approach has been widely used to inform recommendations about the public subsidy of medicines in Australia. This research aimed to highlight the breadth of use of CMA in Australia and assess the influence of preconditions for the approach on subsidy recommendations METHODS: Relevant information was extracted from Public Summary Documents of Pharmaceutical Benefits Advisory Committee (PBAC) meetings in Australia considering submissions for the subsidy of medicines that included a CMA and were assessed between July 2005 and December 2022. A generalised linear model was used to explore the relationship between whether medicines were recommended and variables that reflected the primary preconditions for using CMA set out in the published PBAC Methodology Guidelines. Other control variables were selected through the Bolasso Method. Subgroup analysis was undertaken which replicated this modelling process. While the potential for inferior safety or efficacy reduced the likelihood of recommendation (p<0.01), the effect sizes suggest that the requirements for CMA were not requisite for recommendation. The Australian practice of CMA does not strictly align with the PBAC Methodology Guidelines and the theoretically appropriate application of CMA. However, within the confines of a deliberative HTA decision-making process that balances values and judgement with available evidence, this may be considered acceptable, particularly if stakeholders consider the current approach delivers sufficient clarity of process and enables patients to access medicines at an affordable cost.

Impact of dental school critical thinking demonstrations carryover to practice: Survey of 5-year graduates.

Little literature exists on graduates' application to practice for explicit critical thinking skills learned in dental school. Discern the (1) degree to which graduates apply explicit critical thinking skillsets in practice; (2) degree of adaptation of critical thinking skillsets to practice; (3) frequency of use for critical thinking skillsets in practice; and (4) perceptions to improve critical thinking learning guidance in dental school. Five critical thinking exercises/skillsets were selected that had been in place over 5 years with at least one paper: geriatrics, treatment planning, technology decision making, ethics, evidence-based dentistry; each followed concepts from an emulation model in critical thinking. Electronic survey administered in 2023/2024 to alumni graduated in the last 5 years. Of 98 (from 320 distributed) returned, 56 completed the entire survey. Dental school experiences positively influenced use of critical thinking skills in practice. On a five-point scale, mostly 4s and 5s were reported for "…benefit your thinking." Fifty-three percent reported "using ideas from the exercise and developed my own thought processes," 35% reported "using the thought process largely as offered in the college" and 5% reported "do not use the exercise." Sixty percent reported using the skillsets hourly or daily. With minor variations all skillsets were reported positively for use in practice. A positive influence of critical thinking skills was gained from the college experience with explicit positive impact for each of the five critical thinking experiences. The questions may be a model for future follow-up studies of explicit dental school critical thinking exercises.

Optimal strategies in electric vehicle battery closed-loop supply chain considering government subsidies and echelon utilization

This study examines an electric vehicle battery closed-loop supply chain including a battery manufacturer and a retailer, with a focus on echelon utilization and remanufacturing of waste electric vehicle battery. Optimal pricing strategies and technological effort decision of the supply chain members are derived based on a Stackelberg game model. Three government subsidy policies are considered to promote recycling and reuse of waste electric vehicle batteries, namely, no subsidies, production research and development (R&D) subsidies and recycling subsidies, respectively. Results show that both echelon utilization and recycling subsidies can notably contribute to recycling rate of waste electric vehicle batteries. While echelon utilization boosts the profit of retailer and the whole supply chain, it may have a negative impact on manufacturer's inclination to remanufacturing and its profit. Subsidies for production research and development are preferred by the supply chain and its members from the profit-oriented perspective. Further analyses are conducted under hybrid strategies: hybrid government subsidy policy and hybrid recycling mode. Results show that hybrid strategies could significantly increase the demand and recycling volume, thus positively affecting the profitability of the entire closed-loop supply chain. Our study comprehensively considers the game relationships among manufacture and retailer, enhancing the efficiency of recycling and profitability while also providing a theoretical basis for the government to formulate reasonable subsidy policies.

Using Artificial Intelligence Technologies in Administrative Decision-Making and Innovation Processes within Saudi Institutions

Using Artificial Intelligence Technologies in Administrative Decision-Making and Innovation Processes within Saudi Institutions

Unlocking synergies of drawdown operation: Multi-objective optimization of reservoir emergency storage capacity

Optimizing reservoir drawdown operations holds significant implications for hydropower generation, water supply, and drought mitigation strategies. However, achieving multi-objective optimization in reservoir drawdown operations poses fundamental challenges, particularly considering emergency storage capacity and seasonal drought patterns. This study introduces a novel multi-objective optimization framework tailored for a mega reservoir, focusing on drawdown operations to enhance hydropower generation and water supply reliability. A drawdown operation model leveraging a multi-objective ant lion optimizer is developed to simultaneously maximize reservoir hydropower output and minimize water shortage rates. China's Three Gorges Reservoir (TGR), situated over the upper reaches of the Yangtze River, constitutes the case study, with the standard operation policy (SOP) serving as a benchmark. Results showcase the efficacy of the proposed method, with substantial improvements observed: a 10.6% increase in hydropower output, a 6.0% reduction in water shortage days, and a 9.5% decrease in minimal reservoir water release compared to SOP. This study provides robust technical and scientific bolster to optimize reservoir ESC and enhance the synergy between hydropower generation, water supply, and drought resilience. Additionally, it offers decision-makers actionable strategies that account for emergency water supply capacities. These strategies aim to support mega reservoir's resilience against extreme drought events facilitating the collaboration between modelers and policy-makers, by means of intelligent optimization and decision-making technologies.

Examining Shape Dependence on Small Mild Steel Specimens during Heating Processes.

With regard to the heating technology of small test specimens (D < 1 inch, i.e., 25.4 mm), only a limited amount of data and literature are available for making adequate technological decisions. Heating time of small geometric shapes is influenced by the technological parameters of the furnace, the temperature, the disposition technique in the furnace and the geometric characteristics of the workpiece. How to shorten heating time to achieve a suitable material structure is a vital question, while considerable energy is saved at the same time. Among the geometric characteristics, shape dependence is one of the important aspects that must be taken into account in terms of heating technology. Shape dependence is usually taken into account with empirically produced correction factors, which can result in significant oversizing of heating time, energy-wasting technology and material structure of insufficient fineness. In the course of our work, we investigated and compared the shape dependence of cylindrical and prismatic specimens with the same surface-to-volume ratios, which were combined with surface heat transfer analyses and geometric effect tests to formulate new approximate equations for determining heating time. As a result, we could mathematically derive a relationship between heating time, size and shape of the active surfaces, the correlation of which can shorten heating time by 20%. In addition, a shape factor (1.125) between cylinder and prismatic-shaped specimens was determined, which can be used with the new equation to calculate heating time for similar specimens. At last, a relationship is developed between the amount of heat that can be stored in the body during heat equalization and the complexity of the shape, which can be characterized through ratios depending on heating times and active surfaces in the function of total surface/volume ratio. Based on this relationship it can be determined more precisely when heat equalization occurs; therefore, shorter heating time can be achieved. In conclusion, with the help of this new method, optimal heating time for structural steel components, in the case of small cross-section and weight, can be determined.

Transforming higher education in Tanzania: unleashing the true potential of AI as a transformative learning tool

PurposeThis study aims to explore AI’s potential impact on the broader landscape of higher education in Tanzania. This study contributes to the ongoing discussion of AI’s potential to transform higher education and highlights the ethical considerations and challenges that must be addressed to ensure its successful implementation. This study informs future research and policy decisions in education and technology by providing a detailed understanding of AI’s perceived benefits and challenges in higher education.Design/methodology/approachA mixed-methods approach was used, which involves collecting and analyzing quantitative and qualitative data to understand the research problem comprehensively. This approach allowed data triangulation and led to a more robust and detailed understanding of this study.FindingsIn this study, it was discovered that enhanced assessment, time-saving, personalized learning, improved accessibility and detecting cheating are the perceived benefits of AI as a tool for enhancing learning in higher education, while cost and infrastructure, academic misconduct, data privacy and security, bias and ethical concerns and lack of human interaction are the perceived challenges of AI as a tool for enhancing learning in higher education. Further, it was revealed that students are more accepting of using AI tools in the classroom because they think they are more effective and engaging. On the other hand, faculty were more cautious and skeptical about employing AI tools in the classroom because they worried about how it would affect their teaching methods and job security.Research limitations/implicationsThe data collection was not conducted face-to-face. To fully capture respondents’ emotional responses, feelings, facial expressions, reactions, or body language was challenging. However, a sufficient number of individuals who participated were very cooperative, and their knowledge was very beneficial in understanding the topic.Originality/valueA unique view of this study is a clear understanding of the perceived benefits and challenges of using AI as a tool for enhancing learning in higher education, as well as the variations in these perceptions among students and faculty. By examining the perspectives of both groups, this study provides a comprehensive understanding of the complex role of AI in higher education. Understanding the broader implications of AI in higher education can inform policy decisions and ensure that AI is used responsibly and ethically.