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Metallic molybdenum obtained by atomic layer deposition from Mo(CO)<sub>6</sub>
Get PDFA feasibility study was conducted into the atomic layer deposition (ALD) of metallic molybdenum from the molybdenum hexacarbonyl [Mo(CO)6] precursor. Without the use of a coreactant, Mo(CO)6 decarbonylates in a nonself-limiting fashion to form molybdenum oxycarbide (MoCxOy) films in the low-temperature regime between 100 and 250 °C. Introducing (atomic) hydrogen as a coreactant, in an attempt to drive self-limiting growth and provide metallic molybdenum films, caused hardly a difference in both film composition and growth kinetics. With ozone as a coreactant, an ALD process was developed to grow molybdenum oxide (MoO3) films without carbon contamination. The MoO3 ALD cycle times were optimized and the existence of an ALD window was investigated. The MoO3 films were subsequently reduced by atomic hydrogen to form metallic molybdenum at temperatures between 150 and 450 °C. The degree of reduction was shown to increase with the reduction temperature, with the limitation that the film exhibited multiple cracks after reduction at 450 °C. Spectroscopic ellipsometry, x-ray photoelectron spectroscopy, and scanning and transmission electron microscopy were employed for thin-film characterization.</p
Embedding (Semi-) automatic cadastral boundary extraction into fit-for-purpose land administration in peri-urban Ethiopia
Get PDFThe creation and upkeep of cadastral data is essential for maintaining land and resources and advancing sustainable development. However, developing nations face land use challenges in peri-urban areas due to the fast-paced increase in population and rapid urbanization. Moreover, conventional cadastral surveying methods are neither time nor cost-effective. Automatic feature extraction (AFE) is an emerging alternative to conventional field surveying methods, which can help land sector professionals adhere to the principles of fit-for-purpose land administration (FFPLA). The study aims to test and subsequently affirm the potential of the AFE approach using open-source tools for cadastral mapping in peri-urban areas. It adopts a generalized pre-exiting AFE workflow, utilizing free and open-source tools for the complete solution, including image segmentation, boundary classification, interactive delineation, and validation. High-resolution satellite images and a reference cadastral dataset are used for the pilot. The case location is a peri-urban area in Dukem, with source material obtained from the United States Agency for International Development (USAID) Land Governance Activity (LGA) Ethiopia branch. The LGA experts actively participated in the pilot testing through demonstrations, hands-on practice, focus group discussions, and questionnaire data collection. The pilot testing demonstrate that interactively delineated cadastral parcel boundaries delivered 66 % correctness for buffer widths of 0.5 and 0.4 m for the reference and interactively extracted boundary lines, respectively. The implemented AFE approach was further evaluated against the FFPLA elements and found to meet the affordability, attainability, flexibility, and upgradeability requirements. The strengths, weaknesses, opportunities, and threats (SWOT) analysis indicated favorable strengths and opportunities with manageable weaknesses and threats. The approach is supposed to be applicable for cadastral mapping and updating in peri-urban areas and newly emerging towns across the country due to rapid urbanization. Nonetheless, comparisons against conventional non-AFE methods, such as GNSS or total station surveys, in terms of time and cost implications are still needed. Moreover, further enhancement and testing with different land administration settings are recommended to apply the approach to real-world scenarios such as the LGA cadastral mapping project
Improving hybrid brainstorming outcomes with computer-supported scaffolds: Prompts and cognitive group awareness
Get PDFGuided by the dual pathways to creativity model (DPCM), this study explores how two computer-supported scaffolds—prompts and cognitive group awareness—can enhance the quality of ideas generated in hybrid brainstorming sessions that combine individual and group brainstorming. While prior research has employed these scaffolds to improve group work focusing on convergent thinking in CSCL settings, their application to stimulate divergent thinking in brainstorming sessions remains unexplored. To address this gap, 94 higher education students were randomly assigned into triads and tasked with generating business ideas addressing sustainability issues under three different conditions. In control condition, students generated ideas in a hybrid brainstorming session following an individual-group-individual sequence without any additional support. In experimental 1 condition (prompts), students followed the same sequence but received prompts during the first individual phase, encouraging the use of SCAMPER principles to enhance cognitive persistence. In experimental 2 condition (prompts + cognitive group awareness), students received the same prompts during the individual phases and additional support during the group phase, aimed at enhancing cognitive group awareness through the sharing of individually generated ideas to increase cognitive flexibility. To evaluate the impact of providing prompts, the outcomes of the first individual phase across all three conditions were compared, revealing that students in both experimental conditions generated ideas with significantly higher originality compared to those in control condition. To assess the influence of fostering cognitive group awareness, the outcomes of experimental 1 and 2 conditions were compared. Students in experimental 2 condition showed superior idea quality in both the group and final individual phases, as evidenced by higher originality, outperforming experimental 1 condition. Furthermore, the findings revealed that flexibility mediated the relationship between cognitive group awareness and idea originality, while also suggesting that originality can emerge through alternative pathways beyond those proposed by the DPCM
Crop productivity under heat stress:a structural analysis of light use efficiency models
Get PDFThe increasing frequency and intensity of extreme heat events necessitate reliable global estimates of crop productivity under heat stress. Light use efficiency (LUE) models are commonly used for macroscale crop productivity estimation but exhibit uncertainties under high-temperature extremes related to the representation of model components and their interactions. They also struggle to isolate heat stress effects from other factors. This study reduced LUE model uncertainty for crop productivity estimation under heat stress by systematicallyassessing the representations of three essential components: the fraction of photosynthetically active radiation absorbed by the canopy (FPAR), the temperature constraint (FT), and the moisture constraint (FM), and thesynergy among them under heat-stressed and normal conditions. Model optimizations used data from 75 heat periods (HP) across 18 cropland flux sites worldwide for gross primary production (GPP) estimation, where cropswere solely stressed by high temperatures, independent of low soil moisture and unfavorable light. By testing 200 LUE configurations in HP conditions, combing five FPAR and FT representations, and four FM representations, weidentified the best-performing model, which combined the Enhanced Vegetation Index (EVI)-based FPAR, the evaporative fraction (EF)-based FM, and an inverse double exponential FT. This model notably improved GPP estimation under heat stress, comparable to three existing models under normal conditions, further enhancing aboveground biomass estimation across general conditions. Additionally, this study highlighted the limitations of five air temperature-based FTs, while emphasizing the critical contributions of EVI-based FPAR and EF-based FM under heat stress. These findings emphasize the importance of considering interactions among model components, such as the evapotranspiration effect on FT and FM, to reduce LUE model uncertainty under extreme conditions. Our findings offer valuable insights for improving crop productivity estimation under heat stress and developing adaptation strategies to mitigate heat stress impacts, thereby ensuring food security in the warming futur
Variable Stiffness Mechanism using a Cam Profile
Get PDFWe present a design optimisation method for the routing of tendons in a tendon-driven mechanism with the objective of maximising force transmission efficiency (FTE). We formulate a friction model for the different routing elements, accounting for routing point radii and slipping/rolling contacts. We then construct a numerical design optimisation problem to optimise the design parameters, routing point locations, for a given tendon routing topology. We apply the method to the design of an existing tendon-driven gripper. The results show that frictional losses can be reduced by approximately half compared to the baseline design, and that taking into account the routing point radii is indeed of significant influence
Are Entrepreneurial Employees More Inclined to Accept Artificial Intelligence? An Extension of the UTAUT2
Get PDFAlbeit the increased use of artificial intelligence (AI) in organizations, the understanding of how employees perceive its introduction and which factors play a role in their usage intentions and behavior is still underdeveloped. This study draws on the UTAUT2 by Venkatesh et al. (2012) to investigate AI acceptance by employees in more detail. Structural equation modeling results of a survey with employees (N = 224) reveal that age, gender, experience, and attitude towards AI show partially moderating effects for the UTAUT2 measurements. We analyze how employees' entrepreneurial mindset influences their usage intention, as we expect entrepreneurially-minded individuals to perceive AI introduction differently. We find ambiguous results regarding entrepreneurial mindset as creativity, propensity to risk, and the perceived entrepreneurial benefits as well as attitude show moderating effects, both strengthening and weakening UTAUT2 relationships. This study contributes theoretical implications towards extending the UTAUT2 with moderators as well as practical implications for organizations.</p
Data preprocessing methods for selective sweep detection using convolutional neural networks
Get PDFThe identification of positive selection has been framed as a classification task, with Convolutional Neural Networks (CNNs) already outperforming summary statistics and likelihood-based approaches in accuracy. Despite the prevalence of CNN-based methods that manipulate the pixels of images representing raw genomic data as a preprocessing step to improve classification accuracy, the efficacy of these pixel-rearrangement techniques remains inadequately examined, particularly in the presence of confounding factors like population bottlenecks, migration and recombination hotspots. We introduce a set of pixel rearrangement algorithms aimed at enhancing CNN classification accuracy in detecting selective sweeps. These algorithms are employed to assess the performance of four CNN models for selective sweep detection. Our findings illustrate that the judicious application of rearrangement algorithms notably enhances the overall classification accuracy of a CNN across various datasets simulating confounding factors. We observed that sorting the columns of the genomic matrices has higher on CNN performance than rearranging the sequences. To some extent, these rearrangement algorithms are more robust to misspecified demographic models compared with the utilization of the default preprocessing algorithm as suggested by the respective authors of each CNN architecture. We provide the data rearrangement algorithms as a distinct package available for download at: https://github.com/Zhaohq96/Genetic-data-rearrangement.</p
A Programmable Filtering and Frequency Translation by Aliasing IF Receiver With Alias and Harmonic Rejection
Get PDFA programmable intermediate frequency (IF) receiver is proposed, employing a sampler, time-varying capacitor, and switched-capacitor integrator. It realizes high-order finite impulse response (FIR) low-pass or bandpass filtering (BPF) and frequency translation with harmonic rejection (HR). The receiver's center frequency and bandwidth (BW) can be independently programmed across the Nyquist zone. The primary focus is on balancing filter stopband rejection with harmonic, image, and alias rejection in a prototype 28 nm CMOS chip design. This work is also the first to address and quantify the adjacent and alternate adjacent channel aliasing in filtering by aliasing filters. The center frequency and BW of the receiver can be programmed to 0-1 GHz and 8-25 MHz, respectively. The receiver achieves a wideband impedance matching S 11 < - 9 dB across the first Nyquist zone. It employs 8-bit filter coefficients to achieve consistent > 50 dB stopband attenuation at 1 × BW offset, HR, image rejection (IR), and 50 dB in-band (IB) alias suppression. The I and Q receivers use only a single fixed 1 GHz clock frequency to achieve all the functionality and consume 73.6 mW.</p
Exploring smooth number-based MAC algorithms for secure communication in IoT devices:a systematic literature review
No full textThe landscape of Internet of Things (IoT) devices presents unique challenges and requirements for ensuring communication security. In this context, symmetric authentication mechanisms such as Message Authentication Codes (MACs) have gained prominence due to their efficiency and simplicity. However, traditional MAC schemes may face limitations in IoT environments with constrained computational resources and power supplies. This paper explores the feasibility of leveraging Smooth Number-based MAC (SNMAC) algorithms to address these challenges. Smooth numbers, a subset of integers with prime factors below a certain threshold, offer potential optimizations for cryptographic schemes due to their properties in fast integer factorization. Through a Systematic Literature Review (SLR) conducted between 2012 and 2023, we examine existing research to determine the viability of SNMAC algorithms for IoT devices. The SLR process identified 368 research papers initially, culminating in 37 relevant papers after filtering. Our findings suggest that while traditional MAC algorithms like HMAC are widely used, SNMAC algorithms remain largely unexplored in the IoT context. However, previous studies indicate the potential of smooth numbers in cryptographic applications, highlighting opportunities for further research in this area.</p