Technical Roadmap Research Articles

QEYSSat 2.0 - White Paper on Satellite-based Quantum Communication Missions in Canada

We present the white paper developed during the QEYSSat 2.0 study, which was undertaken between June 2021 and March 2022. The study objective was to establish a technology road-map for a Canada-wide quantum network enabled by satellites. We survey the state-of-art in quantum communication technologies, identify the main applications and architectures, review the technical readiness levels and technology bottlenecks and identify a future mission scenario. We report the findings of a dedicated one-day workshop that included Canadian stakeholders from government, industry and academia to gather inputs and insights for the applications and technical road-map. We also provide an overview of the Quantum EncrYption and Science Satellite (QEYSSat) mission expected to launch in 2024-2025 and its anticipated outcomes. One of the main outcomes of this study is that developing the main elements for a Canada-wide quantum internet will have the highest level of impact, which includes Canada-wide entanglement distribution and teleportation. We present and analyze a possible future mission ('QEYSSat 2.0') that would enable a long range quantum teleportation across Canada as an important step towards this vision.

Enhancing Air Traffic Control Communication Systems with Integrated Automatic Speech Recognition: Models, Applications and Performance Evaluation.

In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and the pilot. As a result, the integration of automatic speech recognition (ASR) systems holds immense potential for reducing controllers' workload and plays a crucial role in various ATC scenarios, which is particularly significant for ATC research. This article provides a comprehensive review of ASR technology's applications in the ATC communication system. Firstly, it offers a comprehensive overview of current research, including ATC corpora, ASR models, evaluation measures and application scenarios. A more comprehensive and accurate evaluation methodology tailored for ATC is proposed, considering advancements in communication sensing systems and deep learning techniques. This methodology helps researchers in enhancing ASR systems and improving the overall performance of ATC systems. Finally, future research recommendations are identified based on the primary challenges and issues. The authors sincerely hope this work will serve as a clear technical roadmap for ASR endeavors within the ATC domain and make a valuable contribution to the research community.

Feasibility study of high-power electron linac for clinical X-ray ROAD-FLASH therapy system

Introduction: This study examines how a practical source of X-ray radiation, capable of delivering unprecedented X-ray of 100 Gy/s at 1 m for X-ray FLASH radiotherapy can be designed.Methods: We proposed the design of a linac, capable of accelerating 18 MeV 8 mA electron beam with further conversion to bremsstrahlung X-rays. The design is based on L-band traveling wave accelerating structures with high power efficiency, operating in a short-burst/long-pulse regime that allows operating power supply in a regime, beyond its specifications.Results: This study demonstrates the feasibility of a high-power linac for a clinical X-ray FLASH therapy system, using detailed analysis and simulations. Despite ∼500x higher output than a standard clinical linac, the design utilizes available accelerator components for maximal practicality.Discussion: Recent studies have demonstrated that the FLASH effect that allows to effectively kill tumor cells while sparing normal tissue occurs when large dose rates (≥40 Gy/s) are delivered in less than 1 s. Photons are very attractive since modest energies of several MeV are needed, which can be achieved with compact and cost-efficient accelerators. However, since the efficiency of electron-to-photon conversion is only a few percent, the required beam intensity must be an order of magnitude higher than that state-of-the-art accelerators can provide. The proposed ROAD-FLASH accelerator layout allows achieving both the FLASH dose rate and superior dose conformity, comparing to the similar projects. The current paper focuses on providing a technical roadmap for building an economical and practical linear accelerator for ROAD X-ray FLASH delivery.

An Attempt to Understand Islamic Arts with Deconstruction

Deconstruction is not about destroying a whole, but about understanding how it is constructed. It is first taking it apart and then trying to understand the whole. It can also be considered as a kind of self-criticism towards the work of art, which is deconstructed to create paradoxical situations and reconstruct it. It is the breaking down of fundamentalism, of stereotypes, of traditional thought patterns, and the search for new ways and different motivational methodologies in the inspiration jargon of Islamic art. This study is not a technical roadmap on how to apply deconstruction in Islamic arts or the methods by which deconstruction can be realized in Islamic arts. On the contrary, it is based on the hypothesis that some intellectual gaps, contradictions and information pollution can be found by re-evaluating and deconstructing the ornamental and decorative elements of Islamic arts such as architecture, metal, wood, stone, brick, tile, ceramic, calligraphy, miniature, illumination, illumination, binding, marbling, etc. from the perspective of the 21st century consciousness thinking, perception, power to interpret the world, etc. Cult is the reversal of canonical clichés, structures, compositions. Indeed, in an example where cultural and artistic interaction is deconstructed, it is demonstrated that new answers can be found with different questions. Deconstruction in Islamic art is a movement that shows that art is not only an aesthetic experience, but also carries historical, cultural and political meanings, since it is the acknowledgment of the influences of different cultures and traditions rather than a single universal art form.

Promoting Li2S Nucleation/Dissolution Kinetics via Multiple Active Sites over TiVCrMoC3Tx Interface.

Lithium-sulfur batteries (LSBs) are still limited by some issues such as polysulfides shuttle and lithium dendrites. Recently, the concept "high-entropy" has been considered as the research hotspot and international frontier. Herein, a high entropy MXene (TiVCrMoC3Tx, HE-MXene) doped graphene is designed as the modified coating on commercial separators for LSBs. The HE-MXene affords multiple metal active sites, fast Li+ diffusion rate, and efficient adsorption toward polysulfide intermediates. Furthermore, strong lithophilic property is favorable for uniform Li+ deposition. The combination of in situ characterizations confirms TiVCrMoC3Tx effectively promotes the Li2S nucleation/dissolution kinetics, reduces the Li+ diffusion barrier, and exhibits favorable lithium uniform deposition behavior. This TiVCrMoC3Tx/G@PP provides a high-capacity retention rate after 1000 cycles at 1 C and 2 C, with a capacity decay rate of merely 0.021% and 0.022% per cycle. Surprisingly, the cell operates at a low potential of 48mV while maintaining at 5mA cm-2/5 mAh cm-2 for 4000h. Furthermore, it still maintains a high-capacity retention rate under a high sulfur loading of 4.8/6.4mg cm-2 and a low E/S ratio of 8.6/7.5µgmL-1. This work reveals a technical roadmap for simultaneously addressing the cathode and anode challenge, thus achieving potential commercially viable LSBs.

25‐1: Invited Paper: Technical Roadmap to Realize Reflective Full‐Color Video Displays for Street Furniture

Outdoor displays become rapidly digital in our digital world. These digital displays are typically emissive, implying a very high‐power consumption to be seen in the daylight, and light pollution in nighttime. Last year Etulipa announced and demonstrated a full colour reflective display based on Electro Wetting Display technology with 10mm pixel pitch. The vertically stacked Cyan Magenta Yellow architecture yields very bright colours. The high switching speed of electrowetting cells enable displaying of video content. The power consumption is 7 W/m 2 implying that the displays can run on solar systems without any need for charging from the grid. The market feedback consists of several exciting applications which are reviewed. Street furniture is expected to be a very early application. Because the typical viewing distance for street furniture applications will be in the order of meters, the pixel pitch should be in the order of millimeters. This requirement defines a technical roadmap for development of the existing electrowetting architecture.

MOSS: An Open Conversational Large Language Model

Conversational large language models (LLMs) such as ChatGPT and GPT-4 have recently exhibited remarkable capabilities across various domains, capturing widespread attention from the public. To facilitate this line of research, in this paper, we report the development of MOSS, an open-sourced conversational LLM that contains 16 B parameters and can perform a variety of instructions in multi-turn interactions with humans. The base model of MOSS is pre-trained on large-scale unlabeled English, Chinese, and code data. To optimize the model for dialogue, we generate 1.1 M synthetic conversations based on user prompts collected through our earlier versions of the model API. We then perform preference-aware training on preference data annotated from AI feedback. Evaluation results on real-world use cases and academic benchmarks demonstrate the effectiveness of the proposed approaches. In addition, we present an effective practice to augment MOSS with several external tools. Through the development of MOSS, we have established a complete technical roadmap for large language models from pre-training, supervised fine-tuning to alignment, verifying the feasibility of chatGPT under resource-limited conditions and providing a reference for both the academic and industrial communities. Model weights and code are publicly available at https://github.com/OpenMOSS/MOSS.

Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon

AbstractWhile deep learning (DL) models exhibit superior simulation accuracy over traditional distributed hydrological models (DHMs), their main limitations lie in opacity and the absence of underlying physical mechanisms. The pursuit of synergies between DL and DHMs is an engaging research domain, yet a definitive roadmap remains elusive. In this study, a novel framework that seamlessly integrates a process‐based hydrological model encoded as a neural network (NN), an additional NN for mapping spatially distributed and physically meaningful parameters from watershed attributes, and NN‐based replacement models representing inadequately understood processes is developed. Multi‐source observations are used as training data, and the framework is fully differentiable, enabling fast parameter tuning by backpropagation. A hybrid DL model of the Amazon Basin (∼6 × 106 km2) was established based on the framework, and HydroPy, a global‐scale DHM, was encoded as its physical backbone. Trained simultaneously with streamflow observations and Gravity Recovery and Climate Experiment satellite data, the hybrid model yielded median Nash‐Sutcliffe efficiencies of 0.83 and 0.77 for dynamic and distributed simulations of streamflow and total water storage, respectively, 41% and 35% higher than those of the original HydroPy model. Replacing the original Penman‒Monteith formulation in HydroPy with a replacement NN produces more plausible potential evapotranspiration (PET) estimates, and unravels the spatial pattern of PET in this giant basin. The NN used for parameterization was interpreted to identify the factors controlling the spatial variability in key parameters. Overall, this study lays out a feasible technical roadmap for distributed hydrological modeling in the big data era.

Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach

Invariant representation learning (IRL) encourages the prediction from invariant causal features to labels deconfounded from the environments, advancing the technical roadmap of out-of-distribution (OOD) generalization. Despite spotlights around, recent theoretical result verified that some causal features recovered by IRLs merely pretend domain-invariantly in the training environments but fail in unseen domains. The fake invariance severely endangers OOD generalization since the trustful objective can not be diagnosed and existing causal remedies are invalid to rectify. In this paper, we review a IRL family (InvRat) under the Partially and Fully Informative Invariant Feature Structural Causal Models (PIIF SCM /FIIF SCM) respectively, to certify their weaknesses in representing fake invariant features, then, unify their causal diagrams to propose ReStructured SCM (RS-SCM). RS-SCM can ideally rebuild the spurious and the fake invariant features simultaneously. Given this, we further develop an approach based on conditional mutual information with respect to RS-SCM, then rigorously rectify the spurious and fake invariant effects. It can be easily implemented by a small feature selection subnet introduced in the IRL family, which is alternatively optimized to achieve our goal. Experiments verified the superiority of our approach to fight against the fake invariant issue across a variety of OOD generalization benchmarks.

All-Dielectric Integrated Meta-Antenna Operating in 6G Terahertz Communication Window.

Efficient transceivers and antennas at terahertz frequencies are leading the development of 6G terahertz communication systems. The antenna design for high-resolution terahertz spatial sensing and communication remains challenging, while emergent metallic metasurface antennas can address this issue but often suffer from low efficiency and complex manufacturing. Here, an all-dielectric integrated meta-antenna operating in 6G terahertz communication window for high-efficiency beam focusing in the sub-wavelength scale is reported. With the antenna surface functionalized by metagrating arrays with asymmetric scattering patterns, the design and optimization methods are demonstrated with a physical size constraint. The highest manipulation and diffraction efficiencies achieve 84.1% and 48.1%. The commercially accessible fabrication method with low cost and easy to implement has been demonstrated for the meta-antenna by photocuring 3D printing. A filamentous focal spot is measured as 0.86λ with a long depth of focus of 25.3λ. Its application for integrated imaging and communication has been demonstrated. The proposed technical roadmap provides a general pathway for creating high-efficiency integrated meta-antennas with great potential in high-resolution 6G terahertz spatial sensing and communication applications.

Technical roadmap towards trustworthy large-scale models in medicine

Technical roadmap towards trustworthy large-scale models in medicine

DDoS family: A novel perspective for massive types of DDoS attacks

Distributed Denial of Service (DDoS) defense is a profound research problem. In recent years, adversaries tend to complicate their attack strategies by crafting vast DDoS variants. On the one hand, this trend exacerbates both extremes of classification granularity (i.e., binary and attack level) in existing machine learning methods. On the other hand, massive attack categories make the filter rule table bulky, as well as cause problems of slow reaction presented in the recent state-of-the-art DDoS mitigation system. Therefore, we propose the concept of a DDoS family to reconcile/cope with these issues. The specific technical roadmap includes traffic pattern characterization, attack fingerprint production, and cross-executed family partition by community detection. Through extensive evaluations, we demonstrate the benefits of the proposal in terms of portraying similarities, guiding model classification/unknown attack detection, optimizing defense strategies, and speeding filtering reactions. For instance, our results show that using only one rule can defend 15 types of attacks due to their homogeneous behavioral representation. Particularly, we find the interesting observation that counting the backward packet is more efficient and robust against some attacks (e.g., Tor's Hammer Attack), which is very different from previous solutions.

Wastewater treatment plants: The missing link in global One-Health surveillance and management of antibiotic resistance

IntroductionAs a global public health crisis, antibiotic resistance (AR) should be monitored and managed under the One-Health concept according to the World Health Organization (WHO), considering the interconnection between humans, animals, and the environment. But this approach often remains focused on human health and rarely on the environment and its compartments, especially wastewater as the main AR receptor. Wastewater treatment plants (WWTPs) not only are not designed for reliving AR but also provide appropriate conditions for enhancing AR through different mechanisms. MethodsBy reviewing the research-based statistics on the inclusion of WWTPs in the One-Health/AR program crisis, this paper highlights the importance of paying attention to these hotspots, at first. Also, the importance and technical roadmap for the application of WWTPs in both surveillance and management of AR were provided. The current position of these facilities was also evaluated using strengths, weaknesses, opportunities, and threats (SWOT) analysis. In the end, the concluding knowledge gaps and research needs for future investigations were presented. ResultsDespite the fact that wastewater matrices are the hotspot for AR dissemination, WWTPs appear under-represented in One-Health/AR literature. So, of the 414434 articles retrieved for One-Health only 1.5% (n = 6321) focused on AR and about 0.04% (n = 158) on WWTPs. The potential of WWTPs inclusion in AR surveillance has been confirmed by several studies, however, when it comes to its inclusion for management of AR, more evidence should be presented, which confirmed by SWOT results. DiscussionAs such, WWTPs simultaneously provide opportunities for AR surveillance as it is assumed that this medium can reflect the reality of the corresponding society, and for managing unexpected crises which could impact the public. Nonetheless, there are still numerous considerations to change WWTPs role from Achilles’ heel to Ajax’ shield, including strengthening the research-based knowledge and conducting both surveillance and management strategies of AR under One-Health concept (One-Health/AR) in a clear straightforward framework.

Fabrication Technique of Micropatterned Inverse Photonic Crystal Films

AbstractA simple and frugal method for fabricating inverse micropatterned photonic crystal (PhC) films aimed at miniaturization and multiplexing of opaline PhCs is described. First, highly uniform micropatterned synthetic opal structures in the form of periodic stripes are formed via stick‐slip motion of the meniscus during SiO2 colloid solution evaporation. The prepared opal structures are then used as templates to produce inverse stripe patterned films with highly uniform colors via photopolymerization of ethoxylate trimethylolpropane triacrylate (ETPTA) photocurable resin. Local reflectance spectra are recorded to prove the PhC properties of the inverse stripes, and peaks over 40% associated with the first photonic stop band are observed. The readily fabrication process suggested here is an important step toward the development of many applications that can pave the technical road map for the next wave of innovations and breakthrough in PhCs for sensing. In particular, the resulting structures can be used as PhC arrays containing a line of periodically repeating sensor strips capable to detect the content of alcohols in water.

Membrane electrode assembly: Another technical roadmap for lithium-mediated nitrogen reduction?

Membrane electrode assembly: Another technical roadmap for lithium-mediated nitrogen reduction?

Focus on perovskite emitters in blue light-emitting diodes.

Blue perovskite light-emitting diodes (PeLEDs) are essential in pixels of perovskite displays, while their progress lags far behind their red and green counterparts. Here, we focus on recent advances of blue PeLEDs and systematically review the noteworthy strategies, which are categorized into compositional engineering, dimensional control, and size confinement, on optimizing microstructures, energy landscapes, and charge behaviors of wide-bandgap perovskite emitters (bandgap >2.5 eV). Moreover, the stability of perovskite blue emitters and related devices is discussed. In the end, we propose a technical roadmap for the fabrication of state-of-the-art blue PeLEDs to chase and achieve comparable performance with the other two primary-color devices.

Recent Progress on Hydrogen Production from Ammonia Decomposition: Technical Roadmap and Catalytic Mechanism

Ammonia decomposition has attracted significant attention in recent years due to its ability to produce hydrogen without emitting carbon dioxide and the ease of ammonia storage. This paper reviews the recent developments in ammonia decomposition technologies for hydrogen production, focusing on the latest advances in catalytic materials and catalyst design, as well as the research progress in the catalytic reaction mechanism. Additionally, the paper discusses the advantages and disadvantages of each method and the importance of finding non-precious metals to reduce costs and improve efficiency. Overall, this paper provides a valuable reference for further research on ammonia decomposition for hydrogen production.

Recent progress in the development of backplane thin film transistors for information displays

This review aims to provide a technical roadmap and an overview of recent progress in the development of backplane thin film transistors (TFTs) for organic light-emitting diodes flat panel displays and next-generation flexible displays. In the introduction, we provide a general overview of the research trends for backplane TFTs. The main part describes the current technical level and prospects for amorphous metal oxide semiconducting, metal halide perovskites, and 2D transition metal dichalcogenides TFTs. The summary and prospects are provided in the conclusion section.

Research on Rain Pattern Classification Based on Machine Learning: A Case Study in Pi River Basin

For the purpose of improving the scientific nature, reliability, and accuracy of flood forecasting, it is an effective and practical way to construct a flood forecasting scheme and carry out real-time forecasting with consideration of different rain patterns. The technique for rain pattern classification is of great significance in the above-mentioned technical roadmap. With the rapid development of artificial intelligence technologies such as machine learning, it is possible and necessary to apply these new methods to assist rain classification applications. In this research, multiple machine learning methods were adopted to study the time-history distribution characteristics and conduct rain pattern classification from observed rainfall time series data. Firstly, the hourly rainfall data between 2003 and 2021 of 37 rain gauge stations in the Pi River Basin were collected to classify rain patterns based on the universally acknowledged dynamic time warping (DTW) algorithm, and the classifications were treated as the benchmark result. After that, four other machine learning methods, including the Decision Tree (DT), Long- and Short-Term Memory (LSTM) neural network, Light Gradient Boosting Machine (LightGBM), and Support Vector Machine (SVM), were specifically selected to establish classification models and the model performances were compared. By adjusting the sampling size, the influence of different sizes on the classification was analyzed. Intercomparison results indicated that LightGBM achieved the highest accuracy and the fastest training speed, the accuracy and F1 score were 98.95% and 98.58%, respectively, and the loss function and accuracy converged quickly after only 20 iterations. LSTM and SVM have satisfactory accuracy but relatively low training efficiency, and DT has fast classification speed but relatively low accuracy. With the increase in the sampling size, classification results became stable and more accurate. Besides the higher accuracy, the training efficiency of the four methods was also improved.

Study on Medical Device E-classification in China from Perspective of Innovation and Development

With the overwhelmingly booming trend of innovation in medical devices, more and more innovative products are in urgent need of classification before marketing. Medical device classification is not only a basis of regulation but also a key factor affecting industry innovation and development. In view of the current situation of classification process in China has been time-consuming, this study discusses an E-classification framework including its classification basis, method, dimensions and technical roadmap, etc. based on medical devices regulations in China illustrated by the classification of radio therapeutic equipment, taking advantage of digitalization, networking and intelligence, and explores the improvement of classification efficiency to promote the innovation and development of medical devices.