Ag-alloyed Cu(In,Ga)Se₂ (ACIGS) is a promising material for the bottom subcell in tandem solar cell structures, offering a tunable bandgap that spans the optimal range for maximum efficiency;however, the bandgap widen...
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This study aims to investigate the adoption of robo-advisors in the Indonesian insurance sector, focusing on key factors influencing their acceptance, such as trust, anxiety, performance expectancy, preference for hum...
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ISBN:
(数字)9798331517649
ISBN:
(纸本)9798331517656
This study aims to investigate the adoption of robo-advisors in the Indonesian insurance sector, focusing on key factors influencing their acceptance, such as trust, anxiety, performance expectancy, preference for human advisors, attitude, and perceived privacy. The research employed a quantitative approach using a structural equation modeling (SEM) technique through Partial Least Squares (PLS). Data were collected via a Google Forms survey distributed to 442 valid respondents using purposive sampling, with the study conducted in Jakarta, Indonesia. The findings revealed that performance expectancy, anxiety, preference for human advisors, and perceived privacy significantly impact the intention to use robo-advisors, whereas trust and attitude showed limited influence. The study concludes that while robo-advisors hold potential in enhancing efficiency and personalization, concerns such as user privacy and the need for human interaction remain critical. Insurance companies should prioritize robust data privacy measures and consider hybrid models combining robo-advisors with human advisors to enhance user trust and satisfaction. Implications for practice include fostering user trust through transparent data handling policies and providing educational resources to mitigate technology-related anxiety. Limitations of this research include its focus on respondents in a single geographic region and its reliance on self-reported data, which may introduce biases. Future research should explore the application of robo-advisors in broader insurance markets and examine their long-term impact on user behavior and financial decision-making.
AlphaFold 3 (AF3), an artificial intelligence (AI)-based software for protein complex structure prediction, represents a significant advancement in structural biology. Its flexibility and enhanced scalability have unl...
AlphaFold 3 (AF3), an artificial intelligence (AI)-based software for protein complex structure prediction, represents a significant advancement in structural biology. Its flexibility and enhanced scalability have unlocked new applications in various fields, specifically in plant science, including improving crop resilience and predicting the structures of plant-specific proteins involved in stress responses, signalling pathways, and immune responses. Comparisons with existing tools, such as ClusPro and AlphaPulldown, highlight AF3’s unique strengths in sequence-based interaction predictions and its greater adaptability to various biomolecular structures. However, limitations persist, including challenges in modelling large complexes, protein dynamics, and structures from underrepresented plant proteins with limited evolutionary data. Additionally, AF3 encounters difficulties in predicting mutation effects on protein interactions and DNA binding, which can be improved with molecular dynamics and experimental validation. This review presents an overview of AF3’s advancements, using examples in plant and fungal research, and comparisons with existing tools. It also discusses current limitations and offers perspectives on integrating molecular dynamics and experimental validation to enhance its capabilities.
The security closure of integrated circuits (ICs) is an emerging area of research within the very large scale integration (VLSI) community. Malicious third parties, referred to as "attackers", can employ var...
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Hybrid solar simulators (SS) using halogen lamps and light-emitting diodes (LEDs), classified as A for spectral match (SM) and temporal instability (TI), are primarily reported for characterizing small solar cells (1...
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Hybrid solar simulators (SS) using halogen lamps and light-emitting diodes (LEDs), classified as A for spectral match (SM) and temporal instability (TI), are primarily reported for characterizing small solar cells (160cm2). However, spatial nonuniformity (SNU) reports are rare. This article presents the characterization of a large-area hybrid SS capable of simulating sunlight intensity and spectrum using a combination of ten white LEDs, six halogen lamps, and 12 power LEDs being three blue (450nm), three magenta (750nm), and three infrared (850nm). This setup ensures homogeneous light distribution over a600cm2surface. The proposed SS was characterized according to the International Electrotechnical Commission (IEC 60904-9) standard, analyzing SM, SNU, and TI. The obtained values (SM = 1.29, SNU = 1.5%, and TI = 0.17%) classify the simulator as BAA. Electrical characterization of a 5W solar panel (300cm2) was performed via current–voltage (I–V) and power–voltage (P–V) curves under indoor and outdoor conditions. A relative error of less than 4% was found compared to outdoor measurements, making it suitable for photovoltaic device testing. The results demonstrate the feasibility of developing a low-cost SS using incandescent and semiconductor light sources, with the potential for characterizing commercial photovoltaic devices.
DC faults in High Voltage Direct Current (HVDC) systems can significantly impact AC grid stability, especially in Multiterminal DC (MTDC) grids. Effective DC Fault Ride-Through (FRT) analysis is crucial for maintainin...
ISBN:
(数字)9781837242634
DC faults in High Voltage Direct Current (HVDC) systems can significantly impact AC grid stability, especially in Multiterminal DC (MTDC) grids. Effective DC Fault Ride-Through (FRT) analysis is crucial for maintaining AC system stability. Initially, a DC fault may manifest as a sudden loss of power input, prompting primary control to restore balance using reserves like FCR and inertia. However, HVDC protection often blocks converters during current peaks, failing to address power imbalances on the AC side. This necessitates coordinated AC-DC grid actions to sustain stability. Grid-Forming (GFM) control in Modular Multilevel Converters (MMCs) enhances system operation by providing inertia, voltage support, and reactive power control. However, the role of GFM in DC FRT is underexplored. This paper evaluates DC faults in MTDC systems with GFM converters, considering AC grid characteristics alongside DC-side impacts like submodule energy control and DC voltage regulation. An outer energy control loop and a shift from GFM to Grid-Following (GFL) control are proposed solutions in this paper to improve DC FRT capability and to assure stable operation against DC faults. The findings provide key recommendations for enhancing DC FRT in MTDC grids with GFM converters, emphasizing control and protection integration.
The mARCIIis a30kWarc-jet facility at NASA Ames Research Center developed to pro duce high-enthalpy flows for low-cost technology development purposes. In this work, we intro ducenewmeasurementcapabilitiesfollowing th...
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Aquatic plant invasions endanger lake biodiversity and ecosystem services, resulting in significant economic losses for local communities. Therefore, it is crucial to accurately delineate the extent and frequency of d...
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Due to the changing global business environment, the operations of engineering companies in the U.S. are moving from self sufficient engineering operations toward the integration of various engineering operations, inc...
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In this study, a combined approach using the Software Usability Measurement Inventory (SUMI) and the Technology Acceptance Model (TAM) is employed to evaluate an e-commerce application's usability and user accepta...
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ISBN:
(数字)9798331541545
ISBN:
(纸本)9798331541552
In this study, a combined approach using the Software Usability Measurement Inventory (SUMI) and the Technology Acceptance Model (TAM) is employed to evaluate an e-commerce application's usability and user acceptance. Usability plays a significant role in shaping consumer satisfaction, while user acceptance is crucial for the application's success in a competitive market. This research explores how users interact with the platform, aiming to identify usability issues and factors influencing user acceptance. Key usability criteria, including visual appeal, information clarity, and ease of navigation, are analyzed alongside TAM constructs such as perceived usefulness and ease of use. The study's primary objective is to uncover the platform's strengths and weaknesses, providing insights into functionality, design, and user acceptance. By addressing identified usability concerns and understanding the factors driving user adoption, the research aims to enhance customer satisfaction and engagement. Ultimately, the findings are expected to guide the development of a more user-friendly interface, improving the e-commerce platform's overall effectiveness, user experience, and market competitiveness.
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