Miracle-dz/UniFAR-FTUs huggingface dataset:
Laser‐Driven Shape Memory Effect for Transfer Printing Combining Parallelism with Individual Object Control

Transfer printing, a method to transfer microobjects using polymeric stamps, has been demonstrated either as a parallel process with high throughput, or as a low throughput process allowing individual manipulation of microobjects. This work presents a unique transfer printing approach which enables arbitrary pattern transfer from an array of microobjects via localized control of adhesion. This approach relies on thermally induced shape change of shape memory polymer (SMP) stamp arrays with carbon black‐composite (CBSMP) microstructuring. Heat is delivered first globally by a resistive heater, facilitating parallel microobject pickup, then locally by laser illumination absorbed within the CBSMP during printing, enabling precise and selective microobject release with packing density only limited by the spot size of the accompanying laser system. The thermal response of the CBSMP system is investigated computationally using experimentally measured laser power absorption within the CBSMP system and compared with high speed photography. Several transfer printing demonstrations are provided to indicate the robust microassembly capabilities of the approach. This work provides transfer printing‐based material integration with a path toward high process scalability and flexibility.
Study the Headway Distance and Physiological Responses of Driver Distraction – An experiment on Networked Multi-Drivers Simulator

This study aimed to find the interactive behaviors between drivers and the effect of distractions on driving. In this project, 14 subjects conducted a two trials car-following driving tasks at with/without distraction conditions. The auditory distraction was a recorded sound, and subjects were asked to repeat certain numbers at different time of driving. This experiment was conducted on a new simulation framework – a Networked Multi-Drivers Simulator- by replacing the lead car driver by pre-trained driver instead of driver model to enhance the fidelity feeling of subject. The Headway distance (HD) and physiological parameters including heart rate (HR) and skin conductance (SC) were measured. ANOVA and back wise regression were conducted to infer the level of inattentiveness for several factors such as driver’s gender, stress and distraction levels and their cause-effect relationships. The experimental validation showed that the contact physiological features were not significant factors for inferring the distraction state of a driver.
Compact dual polarised V slit, stub and slot embedded circular patch antenna for UMTS/WiMAX/WLAN applications

A single probe-fed novel V slit, stub and slot embedded circular microstrip antenna with resonances in the three bands of UMTS, worldwide interoperability for microwave access (WiMAX) and WLAN is presented. The polarisation is circular in the UMTS band and linear in the other two bands. Asymmetrical V slits in the patch boundary along with an embedded circular slot in the patch centre are employed to introduce the perturbation necessary for circular polarisation. Impedance bandwidths (BWs) of 4.2, 2 and 6.2%, respectively, in the three bands centred at 2, 3.5 and 5.6 GHz are obtained. The axial ratio BW is 2.3% in the UMTS band. Moderate gains of 4.4, 3.5 and 2.8 dBi are obtained in the three bands. There is a patch size reduction of about 48% when compared with the conventional circular patch antenna at a fixed operating frequency. The radiation pattern is directional in the WiMAX band and nearly omnidirectional in the WLAN band.
A 220 GHz subharmonic mixer based on schottky diodes with an accurate terahertz diode model

This article introduces the design process of a 220 GHz subharmonic mixer with low conversion loss, which is comprised of a pair of anti‐parallel Schottky diodes (SBD). For terahertz circuits, the wavelength of the electromagnetic (EM) wave is very close to the size of devices, which results in complicated parasitic effects. This will leads to a very limited optimization space for circuits design when matching the impedance. To solve this problem, we developed the precise 3D EM planar SBD model and the field‐circuit co‐simulation method to design this terahertz mixer. The diodes are mounted on the quartz‐based suspended microstrip line. The measured results show that the single side band conversion loss is less than 12 dB during radio frequency (RF) range from 211 to 226 GHz and the minimum loss is about 5.9 dB. With the intermediate frequency fixed at 1 GHz, the conversion loss varies from 8 dB to 11.2 dB over the RF bandwidth of 211 to 221 GHz. The mixer can be applied in heterodyne receivers of terahertz systems. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:2311–2316, 2016
Ultralow-Power and Robust Implantable Neural Interfaces: An Algorithm-Architecture-Circuit Co-Design Approach

by SEETHARAM NARASIMHAN Implantable systems are used in various contexts for interfacing with the body and for providing real-time monitoring and control capability. In particular, implantable neural interfaces can be used to radically improve our understanding of the nervous system and to provide precise treatments for a variety of neurological problems. However, these systems require significant computing power to perform real-time in-situ analysis of neural signals to recognize behaviorally meaningful patterns which are used to trigger appropriate corrective actions. Due to the tight area and power constraints of neural implants, it is important to develop novel algorithm-architecture-circuit co-design approaches for efficient implementation of neural signal analysis. First, we develop an algorithmic framework which is suitable for ultralow-power hardware implementation while simultaneously satisfying emerging design requirements like reliability and security. The algorithm is based on building a dynamic hierarchical multi-level vocabulary of neural patterns in the wavelet domain. The vocabularybased analysis allows recognition of neural patterns at multiple levels (spike, burst, and pattern of bursts across multiple channels) and transmission of recorded data with large compression, thus, saving power and communication bandwidth of the integrated telemetry device. Hardware implementation of the proposed algorithm aims at reducing system power through choice of appropriate architecture and circuit-level design techniques. We show that a super-threshold design operating at a much higher frequency can achieve comparable energy dissipation as a sub-threshold low-frequency design through application of extensive power gating. It also provides significantly higher robustness of operation and yield under large process variations. We propose an architecture-level preferential design approach for further energy reduction at the
GROWTH AND QUALITY OF ‘PÉROLA’ PINEAPPLE AS A FUNCTION OF LEVELS OF GIBBERELLIC ACID

Gibberellin is a plant hormone capable of enhancing the productivity of plant’s productivity rants, due to its action in plant physiology, mainly in fruit formation. In this sense, this study aimed to evaluate gibberellic acid levels and application times on the growth and quality of ‘Pérola’ pineapple. The experiment was conducted under field conditions in the municipality of Pedras de Fogo, state of Paraíba, Brazil. The treatments consisted of five levels of gibberellic acid (0.0, 1.5, 3.0, 6.0, and 12.0 mg/plant), distributed in a randomized block experimental design, with four repetitions, in which three applications were made at 50, 80, and 110 days after flower induction. The soluble solids content, fruit height, fruit diameter, and weight were evaluated. The period of application and the levels of gibberellic acid altered the responses for the variables analyzed. The level of 7.5 mg/plant of gibberellic acid promoted an increase in the weight and diameter of the fruit. The application at 110 days after floral induction induced an increase in fruit weight and a greater concentration of sugar in the fruit. Further studies on the application of gibberellic acid and periods of application in different soil and climatic conditions should be carried out to determine more consistent results with this crop.
Perception of Urban Leftover Spaces: A Comparative Study of Built Environment and Non-Built Environment Participants

The built environment, urban processes, and experience all work together to create the spatial environment of a city. Many urban spaces, especially those that appear to be ‘leftover’, do not reflect a set concept but are constantly questioned and recreated. Recognizing leftover spaces in an urban environment is an important aspect of the urban redevelopment process. Researchers have highlighted the difficulties, circumstances, and relevance of making good use of leftover space. To accomplish environmental and social benefits, these places can be created, changed, and incorporated into the main urban fabric; however, there is a scarcity of knowledge on how to go about constructing such environments. This study explores the visual perception of two groups of people, those with knowledge of the built environment and those with other educational backgrounds regarding leftover spaces in Wellington City. The research, which employs a mixed approach, consists of three studies, beginning with a visual preference study to better understand human perceptions, which might lead to better design solutions. The second study looked at differences in design preferences across the built environment and non-built environment participants. Finally, individuals from the built and non-built environments participants were invited to a focus group discussion for study three. To summarize, the findings demonstrated that adding vegetation is a crucial design feature. The findings refute the hypotheses of non-built environment specialists have different design perceptions for a built environment.
Geometric real-time modeling and scanning using distributed depth sensors

Technological breakthroughs have in recent years heavily increased the availability of different types of sensors and the amount of computational power, allowing for much more advanced robotic applications. By mounting multiple depth sensors on the robot and designing a fast and robust system with state-of-the-art algorithms we have made the robot aware of its surroundings. First, the surrounding is scanned into a static geometrical model. Then that model is used to detect and extract deviations in new scannings, for further analysis, to make suitable decisions on how the robot should act. We have demonstrated the system in a safety-critical setup for industrial robots, slowing down and stopping the robot when a human is too close. The system is designed for modularity, allowing for many other completely different applications, such as complex object tracking and motion planning. (Less)
In the wake of the ironworks - entrepreneurship and the spatial connections to empowerment and emancipation

In this article, we explore connections between entrepreneurship, gender, empowerment and emancipation. Through the lens of entrepreneurship, we investigate the spatial aspects of these interlinked processes and illustrate emancipation through oppressive gender structures. This spatial lens allows us to see how emancipation changes in practice over time, with empowerment being one of these practices. Through ethnographic longitudinal fieldwork that studies an ironworks turned into a tourist garden, we highlight the collective reproduction of established oppressive structures. Our findings prioritise a spatial understanding of how entrepreneurship connects to empowerment and emancipation.
Data on the Impact of Epidemic on Nursing Staff's Mental Health in the Context of Wireless Network

The research was aimed to analyze the impact of epidemic pneumonia on nursing personnel's mental health under wireless network background and to improve the selection of random forest classification (RFC) algorithm parameters by the whale optimization algorithm (WOA). Besides, a total of 148 in-service nursing personnel were selected as the research objects, and 148 questionnaires were recycled effectively. The collected data were analyzed by the improved RFC algorithm. In addition, the research investigated the impacts of demographic factors on nursing personnel's mental health by the one-way variance method. The results demonstrated that the accuracy of the improved algorithm in training samples and test samples reached 83.3% and 81.6%, respectively, both of which were obviously higher than those of support vector machine (SVM) (80.1% and 79.3%, respectively) and back-propagation neural network (BPNN) (78.23% and 77.9%, respectively), and the differences showed statistical meanings (P < 0.05). The Patient Health Questionnaire-9 (PHQ-9) showed that the depression levels of 9.46% of the included personnel were above moderate. The Generalized Anxiety Disorder (GAD-7) demonstrated that the anxiety levels of 3.38% of the included personnel were above moderate. The insomnia severity index (ISI) indicated that the insomnia levels of 3.38% of the included personnel were above moderate. The average score of male personnel (3.65) was obviously lower than that of female personnel (3.71). Besides, the average scale score of married personnel (3.78) was significantly higher than that of unmarried personnel (3.65). The average scale scores of personnel with bachelor's (3.66) and master's degrees (3.62) were obviously lower than those of personnel with junior college (3.77) and technical secondary school (3.75) diplomas. The average scale score of personnel with over 5-year work experience (3.68) was significantly lower than that of personnel working for less than five years (3.72). The average scale score of personnel with experience in responding to public emergencies (3.65) was obviously lower than that of personnel without related experience (3.74). The differences all showed statistical meaning (P < 0.05). The results of this research revealed that the accuracy of the improved RFC algorithm was remarkably higher than that of the SVM and BPNN algorithms. Furthermore, many nursing personnel suffered from mental diseases at different levels with the impact of the epidemic. Gender, marital status, education level, and experience in responding to public emergencies were the main factors affecting nursing personnel's mental health.
The Perceptions of Fifth-Graders Following Ecology Service Learning–The Case of the “Lesser Kestrel Day”

An instrumental case study examined the experiences and emotions of fifth-graders, aged 10-11, following their service-learning, which was designed to help the Lesser Kestrel, an endangered species in Israel. The case study methodology used three research tools that included analyses of drawings and explanations, personal reflections, and focus groups. The research revealed four processes that students went through: emotional, social, cognitive and behavioral. These processes supported the creation of an educational setting that allowed students to develop as independent learners with responsibilities towards their peers and a high level of empathy while experiencing environmental citizenship. In addition, the successful experience generated a positive feeling together with pro-environmental behavior leading towards biodiversity conservation.
In-situ study of InAs quantum dots encapsulated in asymmetric (Al)GaAs confinement barriers

In this work the self-assembling of InAs quantum dots (QDs) within asymmetric barriers of (Al)GaAs is studied via reflection high energy electron diffraction (RHEED). A comparative study between the AlGaAs/InAs/GaAs interfaces and its mirror-like heterostructure GaAs/InAs/AlGaAs showed significant differences in the self-assembling and capping of the QDs. The critical thickness of InAs QDs results was proven to be larger when it is grown on AlGaAs alloys, compared with the deposition on GaAs layers. This change is explained by the reduced mobility of In atoms on the Al-containing surfaces, for which the QDs density is increased due to the strain relieve. Through the in-situ analysis of diffusion parameters, it is concluded that the mobility of In atoms decreases the mass transport of 2D and 3D precursors that conduces to the self-assembling of the QDs nanoislands, modifying the rate at which the QDs are formed. Further, during the first stages of QDs capping it is observed that the III-V materials intermixing plays a predominant role. The nanoislands are less affected when are covered by AlGaAs in comparison with the GaAs capping, preserving the QDs morphology and avoiding materials alloying. By following the RHEED intensity behavior during the QDs capping, a model was proposed to obtain quantitative parameters for the smoothing process. High resolution x ray diffraction (HRXRD) measurements show the composition of sharp interfaces for the AlGaAs/InAs/GaAs heterostructure. Lastly, numerical simulations were performed to evaluate the strain changes using the experimental information as input data.
Self-learning based ship onboard decision support system

Shipping contributes to about 3.3% of global carbon emissions equating to about 1,000 million tons of C02 being emitted into the atmosphere each year. Action therefore needs to be taken to reduce this amount considerably within the coming years. This can be achieved immediately, cost effectively and efficiently by increasing the energy efficiency through crew’s every day operations onboard. An Onboard Decision Support tool to aid crew in making the correct energy efficient decisions can significantly contribute towards reducing emissions. Voyage optimization (route, heading, speed, propeller trim, etc) and maintenance optimization of the main energy consuming systems onboard, are all factors that will be addressed by the proposed Decision Support System framework discussed within this paper. Automatic analytical methods, such as artificial intelligence, are developed for analysis of the historic and real time monitoring of data and ship performance data. Predictive methods are also adopted for forecasting future ship performance. The construction of a unique system framework with an Energy Efficiency Knowledge Bank, which will provide innovative experience sharing based on the analysed data, is presented by utilising a distributed database management system (DDBMS). A numerical optimization is required and the HCPSO and NSGAII optimisation methods are considered for application. The Decision Support takes its basis from an in-house integrated fuzzy decision support method. A few essential attributes and their corresponding importance weightings are used to perform the decision support after the optimization has been carried out; thus providing the crew members with clear and informative suggested best operational (voyage and maintenance) practices.
Underwater Image Enhancement With Reinforcement Learning

In this article, we develop an underwater image enhancement framework based on reinforcement learning. To do this, we model the underwater image enhancement as a Markov decision process (MDP), in which states are represented by image feature maps, actions are represented by image enhancement methods, and rewards are represented by image quality improvements. The MDP trained with reinforcement learning can characterize a sequence of enhanced results for an underwater image. At each step of the MDP, a state transitions from one to another according to an action of image enhancement selected by a deep Q network. The final enhanced image in the sequence is obtained with respect to the biggest overall image quality improvement. In this manner, our reinforcement learning framework effectively organizes a sequence of image enhancement methods in a principled manner. In contrast to the black box processing schemes of deep learning methods, our reinforcement learning framework gives a sequence of specific actions, which are transparent from the implementation perspective. Benefiting from the exploration and exploitation training fashion, our reinforcement learning framework possibly generates enhanced images that are of better quality than reference images. Experimental results validate the effectiveness of our reinforcement learning framework in underwater image enhancement. The code and detailed results are available at https://gitee.com/sunshixin_upc/underwater-image-enhancement-with-reinforcement-learning.
Challenges to Alternative Fuel Vehicle Usage in Trinidad

The Government of the Republic of Trinidad and Tobago has set itself some lofty targets in terms of reducing CO2 emissions from public transportation by 2030. Several initiatives have been launched since 2015 in order to fulfill these targets. One key strategy is the adoption of a higher percentage of alternative fuel vehicles (AFVs) into the overall fleet of vehicles. This article reviews the history and implementation of the various AFV oriented policies and examines the effectiveness of these initiatives. The authors describe the current state of the motorised vehicle fleet in terms of fuel type and highlight where adoption levels have fallen short of the target. The percentage of AVFs per public transit mode ranges from about 2 to 20 percent, where the higher percent represents the CNG buses belonging to the Public Transport Service Corporation (PTSC). In Trinidad, the natural gas supply is already well-established, refuelling stations are in operation and up to 20 original equipment manufacturer CNG vehicles are available on the market, supplemented by the option of vehicle conversion. The opposite is true for electric vehicles. The article further identifies the four key issues affecting the improved adoption of AFVs namely: (i) Fuel Prices (ii) Availability of Alternative Fuels, (iii) Consumer Perception and Resistance, and (iv) Lack of Institutional Support. In addition to presenting the proposals by others for improvements to the local transportation system, the authors recommend six key actions that should be taken to achieve the targets set for 2021 and beyond.
Design and Numerical Analysis of a Single-Polarization Filter Based on PCF with Plasmonic Layers of Gold and Indium Tin Oxide

This paper presents a single-polarization filter based on PCF with plasmonic layers of gold and indium tin oxide (ITO). The plasmonic materials are metallic gold and ITO coated on the inner walls of two extra-large vertically arranged air holes. The resonance effect is triggered by guided modes propagating through the silica core and coupling to the coating areas. The finite element method is used to analyze the properties of the filter for the two fundamental orthogonal polarizations. A filtering effect is achieved in the communication window by optimizing the structural factors as well as gold film and ITO deposition thicknesses. When the filter is 1 mm long, the obtained filtering effect is 1319.689 dB/cm for the y-polarization and 31.881 dB/cm for the x-polarization, thus efficiently attenuating the y-component at a communication window of 1.15 μm. With a filtering bandwidth of 602 nm, the proposed filter shows superior characteristics compared with previously reported results. Applications of the proposed plasmonic PCF-based filter can be found in polarization-maintaining and polarization-suppressing systems for optical sensing and broadband transmission.
Wave calibration in numerical wave tanks using AI methods

Analytical solutions for numerical wavetanks are limited presently to a simple bathymetry and third order accuracy. Furthermore, tanks are generally characterised using linear transfer functions to relate the wavemaker forcing amplitude to wave elevation at a probe located in the wavetank. This paper reports on a numerical wavetank implemented using the OpenFOAM software package. The aim of the research is to train neural networks to represent non-linear transfer functions mapping a desired wave surface-elevation time-trace at a probe to the wavemaker input required to create it