Data is increasingly at the core of any business or organisation and is a critical raw material for intelligent analytics and the driving force behind digital transformation. Most organisations are dealing with massive amounts of data in various formats, types and across numerous systems. Their challenge is to turn that data into insights that are useful for complex decision making.
Graph technology is being seen as foundational to data management and analytics, empowering user collaboration and fostering data democratisation. With a vast amount of data, organisations need to bring a deeper layer to give them the competitive edge, insights, and knowledge. In the new normal, exploiting data and using it confidently for complex, intelligent decision-making is vital. Neo4j knowledge graphs can help as an insight for available data, enriched with semantics and revealing its complex interconnectedness.
OpenGov Asia had the opportunity to speak with Dr Maya Natarajan, Senior Director, Knowledge Graphs, Neo4j to gain her insights on how organisations should utilise knowledge graphs for complex decision-making.
Maya is responsible for the go-to-market strategy for knowledge graphs at Neo4j. She is passionate about bringing different technologies together to solve complex problems and is championing the use of knowledge graphs to bring context to various systems.
She has positioned technologies from Blockchain to Predictive & User-Based Analytics to Machine Learning to Deep Learning to Search to BPM and beyond in a myriad of industries including Life Sciences, Financial Services, Supply Chain, Manufacturing, etc at various small and large companies. Maya started her career in the biotechnology area where she was in R&D focusing on cardiovascular drugs, and she has five patents to her name.
Why Knowledge Graphs are Better than Traditional Data Tools
The obvious place to start would be why should organisations move from traditional data representation and tools to knowledge graphs for complex decision-making.
Data volumes are consistently increasing – from about 40-50 zettabytes in 2019 to around 60 zettabytes in 2020 and approximately 75 zettabytes in 2021. Maya explained that as data volumes grow, organisations need to find new ways to use the massive amount of information to drive business value. Traditional analytics are no longer suitable for complex business operations and analysis.
Traditional tools based on relational databases have existed for over 40 years and relational databases are one of the most popular query tools across businesses. Traditional analytics are suitable for transactional and straightforward data that fit easily into a relational database’s format of tables and columns.
On the other hand, graph technology focuses on the relationships between data and considers the relationship between data to be just as significant as the data itself. In OpenGov Asia’s article with Nik Vora, Vice President, Asia-Pacific, Neo4j explains that graph technology is important because it can extract the inherent value in the data itself. The purpose of the technology is to store information without restricting it to a pre-defined model.
Graph technology is the ‘most obvious approach’ to look at connections as the value of relationships itself is the underlying drive for this technology. Maya emphasises that relationships among data can be harnessed to find known and unknown patterns in data that are not identified or analysed through traditional means. What relationships bring to the table is they add dynamic context to data.
It is important, Maya says when talking about a knowledge graph, that it is defined first. A knowledge graph is an insight layer of interconnected data enriched with semantics. A knowledge graph gets richer as new data is added. Through a combination of data, graphs and semantics (meaning), organisations get a knowledge graph with deep and dynamic context.
Maya gave an example of the pharmaceutical industry to illustrate how knowledge graphs work. A pharmaceutical company will know how to get drugs in a particular therapeutic area to market – the domain knowledge that the particular pharmaceutical company has in this area is very specific and proprietary.
Knowledge graphs have three components: data, graph and semantics. Relationships are stored along with the data in a graph database, and they are important as they provide the first level of context to data. In a knowledge graph, the pharmaceutical company’s domain knowledge can be viewed as its semantics and is key as it adds a second layer of context to data. Deep dynamic context makes knowledge graphs the top choice of use for cases that require complex decision-making as context is the prerequisite to complex decisions.
Industries from supply chain to financial services to life sciences and beyond currently require complex decision-making. Hence, knowledge graphs have become the most popular choice for diverse cases.
Unique Benefits of Knowledge Graphs
Maya believes that knowledge graphs are immensely useful for organisations to solve their business challenges. Specifically, organisations should enhance their toolkit and adopt a Neo4j knowledge graph as it has two distinct benefits that other tools do not possess.
First, Maya reiterates, that semantics is one of the key components and advantages of knowledge graphs. Semantics are encoded alongside the data in the graph itself. This is how knowledge graphs drive intelligence into data and significantly enhance its value. Essentially, knowledge graphs increase the value of data through semantics by adding more context.
The second benefit is of knowledge graphs can make incumbent technologies better by providing better data management, better predictions and better innovations. Partly because knowledge graphs fuel machine learning and they can be adopted well to a variety of use cases.
How Neo4j Tailors Specific Solutions to Different Business Challenges
Knowledge graphs ease the complex process because they add or imbue intelligence to every stage of the data. However, each organisation has different business challenges and context – including its digital strategies, clients and outcomes. This begs the question: how does Neo4j tailor its solution to generate value for each organisation’s unique circumstances?
Maya explains that every organisation is identified by its domain knowledge. Knowledge graphs explicitly take domain information into account in the form of semantics. By utilising knowledge graphs, Neo4j tailors the solution for each organisation according to its domain knowledge.
She illustrates this point by sharing the example of a large global pharmaceutical company – one of Neo4j’s clients – who use knowledge graphs for analysing patient journeys. A patient journey is described as a patient experience throughout an entire episode of care, starting from the admission of the patients to their discharge.
The large global pharmaceutical company recognises that no two patient journeys are exactly the same, but they want to find places where they could improve the outcomes of patients. Complex diseases develop over years, so the company would like to intervene faster and earlier during the patients’ journey to improve outcomes. They feel they could do this by finding similarities between patients.
Using a combination of a Neo4j knowledge graph, graph algorithms and machine learning, this large pharmaceutical company identified journey archetypes and journey patterns and used those as influential touchpoints to intervene at the earliest moment in a patient journey to make the most impact. In this case, it is a knowledge graph that allowed them to customise this solution.
Below is the visualisation of a single patient and journey through their disease progression.
Every blue dot represents a medical claim, every red dot represents a diagnosis, and every green dot represents a prescription. When laying out this data from left to right, it became real; this data became humanised and patterns emerged. In this example, the green (prescription) dot is followed by another condition or diagnosis, after which the physician pivoted to a new prescription in response to the diagnosis that happened after the first prescription.
These kinds of patterns were exactly what this large pharmaceutical company was trying to understand within the patients, how physicians treated patients and whether their products would help these patients. In many cases, it would yield a better patient outcome. This individual visualisation became an anchoring point; it became a very different way to analyse data. The Neo4j knowledge graph helped facilitate these analyses rather rapidly.
Combining Knowledge Graphs and Artificial Intelligence
Maya agrees that the combination of knowledge graphs and Artificial Intelligence (AI) is a platform on steroids. Companies are increasingly using AI applications for decision-making. Due to a lack of contextual information, AI systems have not been able to achieve their full potential as reliable solutions for complex problems.
This is where knowledge graphs come in. They offer a logical way to capture data relationships and convey their meaning. Knowledge graphs embed intelligence into the data itself and offer AI the tools to make sense of it all – to be more explainable, accurate and repeatable. THE FUTURE OF AI: Machine Learning and Knowledge Graphs is suitable for forward-thinking organisations that are keenly aware of the power their data represents and who understand that its proper use empowers intelligent decision-making.
Recently, both knowledge graphs and AI have joined forces. The powerful combination of the two has spurred the interest in using both technologies. AI/machine learning benefits from knowledge graphs as knowledge graphs provide context in two different ways: First, knowledge graphs give data context by the addition of semantics. Second, relationships between data provide another level of context.
With knowledge graphs, data scientists get to more data in the form of relationships – by double-dipping on the data they already have and taking advantage of relationship data that they previously tossed out because it was too hard to process. Because it is built on graph technology, a knowledge graph captures relationships for analysis, so not only do data scientists have more data, but they also have more data variety.
“In Machine learning, the more data you have, the higher data quality is. The more data variety, the higher the accuracy,” Maya emphasises.
Versatile Use Case applications of Neo4j Knowledge Graphs
NASA uses Neo4j knowledge graphs to solve issues in future missions to space. While working on a mission to send Orion, a space shuttle, into space, they found that its uprighting system was not working correctly. Knowing that Apollo used a similar uprighting system to Orion, they were confident they could use the knowledge from the Apollo mission to correct this issue before Orion’s launch.
NASA deployed a knowledge graph to comb through millions of documents, reports, project data, lessons learned, scientific research, medical analysis, geospatial data and much more across departments. By using a Neo4j knowledge graph, they found a way to correct the uprighting system in Orion. Without the knowledge graph, the team would have spent years testing different designs. They saved two years of work and one million dollars of taxpayers’ money.
Standard Chartered Bank in Singapore utilises Neo4j knowledge graphs for risk management to proactively identify cybersecurity risks to protect the bank from cyber threats. As cyberattacks are on the rise, this is an important use case for the bank. Other financial services customers are also utilising knowledge graphs for the same reason.
These are very different projects that utilise Neo4j knowledge graphs. The beauty of knowledge graphs is that they lend themselves well to a range of areas across the data spectrum, from data management to data analytics. Hence, any organisation from various industries can adopt Neo4j knowledge graphs to derive actionable insights for complex-decision making.
A research team from the LKS Faculty of Medicine at The University of Hong Kong (HKUMed) has developed more efficient CRISPR-Cas9 variants that could be useful for gene therapy applications. By establishing a new pipeline methodology that implements machine learning on high-throughput screening to accurately predict the activity of protein variants, the team has expanded the capacity to analyse up to 20 times more variants at once without needing to acquire additional experimental data, which vastly accelerates the speed in protein engineering.
The pipeline has been successfully applied in several Cas9 optimisations and engineered new Staphylococcus aureus Cas9 (SaCas9) variants with enhanced gene editing efficiency. The findings are now published in Nature Communications and a patent application has been filed based on this work.
Staphylococcus aureus Cas9 (SaCas9) is an ideal candidate for in vivo gene therapy owing to its small size that allows packaging into adeno-associated viral vectors to be delivered into human cells for therapeutic applications. However, its gene-editing activity could be insufficient for some specific disease loci.
Before it can be used as a reliable tool for the treatment of human diseases, further optimisations of SaCas9 are vital within precision medicine. These optimisations must comprise the boosting of its efficiency and precision by altering the Cas9 protein.
The standard protocol for modifying the protein involves saturation mutagenesis, where the number of possible modifications that could be introduced to the protein far exceeds the experimental screening capacity of even the state-of-art high-throughput platforms by order of magnitude.
In their work, the team explored whether combining machine learning with structure-guided mutagenesis library screening could enable the virtual screening of many more modifications to accurately identify the rare and better-performing variants for further in-depth validations.
The machine learning framework was tested on several previously published mutagenesis screens on Cas9 variants and the team was able to show that machine learning could robustly identify the best performing variants by using merely 5-20% of the experimentally determined data.
The Cas9 protein contains several parts, including protospacer adjacent motif (PAM)-interacting (PI) and Wedge (WED) domains to facilitate its interaction with the target DNA duplex. The research team married the machine learning and high-throughput screening platforms to design activity-enhanced SaCas9 protein by combining mutations in its PI and WED domains surrounding the DNA duplex bearing a (PAM). PAM is crucial for Cas9 to edit the target DNA and the aim was to reduce the PAM constraint for wider genome targeting whilst securing the protein structure by reinforcing the interaction with the PAM-containing DNA duplex via the WED domain.
In the screen and subsequent validations, the researchers identified new variants, including one named KKH-SaCas9-plus, with enhanced activity by up to 33% at specific genomic loci. The subsequent protein modelling analysis revealed the new interactions created between the WED and PI domains at multiple locations within the PAM-containing DNA duplex, attributing to KKH-SaCas9-plus’s enhanced efficiency.
Until recently, structure-guided design has dominated the field of Cas9 engineering. However, it only explores a small number of sites, amino-acid residues, and combinations. In this study, the research team was able to illustrate that screening with a larger scale and less experimental efforts, time and cost can be conducted using the machine learning-coupled multi-domain combinatorial mutagenesis screening approach, which led them to identify a new high-efficiency variant KKH-SaCas9-plus.
The Assistant Professor of the School of Biomedical Sciences, HKUMed stated that this approach will greatly accelerate the optimisation of Cas9 proteins, which could allow genome editing to be applied in treating genetic diseases more efficiently.
To preserve and propagate the species in the typhoon-affected Cagayan Valley and to investigate bamboo’s potential for use in the pharmaceutical and industrial industries, phytochemical screening and DNA barcoding of economically significant bamboos will be conducted in the Philippines.
There are several benefits of using bamboo in the food, medicinal, phytochemical, medical, and industrial sectors, according to Alvin Jose L. Reyes and Eddie B. Abugan Jr from the Project Management Division (PMD) of the Department of Environment and Natural Resources (DENR)-Foreign Assisted and Special Projects.
They explained that seeds or living cells containing genetic resources beneficial for plant conservation and breeding are called germplasms. The DENR-PMD staff clarified that the classification of bamboo germplasm is an essential correlation between the preservation of diversity and utilisation of germplasm.
A study dubbed the Bamboo Characterisation Project of the Cagayan State University (CSU)-Gonzaga was recently presented to the DENR Protected Area Management Board (PAMB) in the province of Sta. Ana, Cagayan through its project leader Jeff M. Opeña. It has to do with its request for a free permit to carry out the bamboo characterisation and sample collecting tasks on the protected landscape and seascape of Palaui Island.
The CSU-Gonzaga research lab will also be renovated as part of the project. In the province of Cagayan, it will collect and classify various species in various environments. Furthermore, a contemporary and inventive method of classifying bamboo species will be DNA barcoding. It will speed up the process of experts identifying the species they want to utilise based on characteristics like quick reproduction or medicinal properties.
Bamboo has traditionally been classified according to how frequently or abundantly it flowers -annually, sporadically, or regularly, and gregariously. However, the demand for a long period of time, which might occur over years or even decades, made floral morphology description a limitation and a challenge.
On the other hand, professionals in pharmaceuticals and medicine can find plant secondary metabolites in bamboo that have application potential in the business through biochemical characterisation by phytochemical (plant chemistry) screening.
While secondary plant metabolites such as anthocyanins, alkaloids, flavonoids, saponins, phenols, steroids, tannins, and terpenoids are explored for medical plant herbal reasons, among other prospective commercial uses, primary metabolites comprise tiny molecules like amino acids and carbohydrates.
Additionally, Executive Order 879 required that 25% of the Department of Education’s annual supply of school desks be constructed of bamboo. Philippine Bamboo Industry Development Council (PBIDC) is created by Executive Order 879.
According to a direction sent to the DENR’s Forest Management Bureau, Laguna Lake Development Authority, and Mines and Geosciences Bureau, bamboo should be planted in the agency’s own reforestation zones.
In addition to reducing typhoon flooding, DENR wants to employ bamboo as a strategy for reducing climate change. Per hectare of a plantation, bamboo is known to absorb five metric tonnes of carbon dioxide. Bamboo is being planted in the Bicol and Marikina rivers, which are typically inundated during typhoons. Using engineered bamboo, DENR is also advocating its usage as a lumber replacement.
The first bamboo species studies to consider the various habitats where bamboo grows in the province of Cagayan are the phytochemical and morphological studies of bamboo species. The Smith Volcano, also known as Mount Babuyan, which is politically located in Calayan Island, and Mount Cagua in Gonzaga are the two volcanoes that the study of bamboo species growth will focus on.
Coastal locations, residential areas, grasslands, agroecosystems, next to water bodies, caverns, close the volcano, rainforests, islands, protected regions, and other habitats will be researched for the bamboo species using DNA barcoding.
The State Government is putting forward AU$ 1.2 million over four years to establish the State’s first Creative Technology Innovation Hub in Bunbury. This is aimed at boosting the regional creative enterprises of Western Australia.
The WA Creative Technology Innovation Hub (WACTIH) was announced by the region’s Innovation and ICT Minister in Bunbury and will operate in collaboration with the State Government, Edith Cowan University, the City of Bunbury and industry to stimulate and grow Western Australia’s emerging creative and immersive technology industry. The WACTIH aims to aid businesses and creative enterprises grow by linking research, entrepreneurship and education in the use of digital and immersive technologies.
As the South-West is home to over 320 creative and digital businesses, the WACTIH will help support hundreds more businesses across the State with specialised advice and services. The hub is being established to aid the growth of a future-ready workforce, entrepreneurs, start-ups and innovators in WA and its regions. The focus of the hub will be on creative digital industries including gaming, experiential and immersive technology, software development, product design, advertising, film and media.
Funded through the McGowan Government’s AU$ 16.7 million New Industries Fund, the WACTIH will become part of the State’s established innovation hubs in life sciences, data science and cyber security to build capability and capacity to diversify the economy, leverage new commercial opportunities and create jobs.
The Innovation and ICT Minister of WA stated the Creative Technology Innovation Hub establishment announcement will not only boost creative tech enterprises across the State but is a vote of confidence in regional innovators. The hub is expected to push economic value in the regions through business and skills transformation for increased, long-term advantage.
The Government of Western Australia’s New Industries Fund supports and accelerates WA’s innovative start-ups, emerging businesses and small and medium enterprises to diversify local and regional economies and create jobs and industries. Through the new hub, the McGowan Government aims to expand the State’s presence in the global digital supply chain of services, content and code.
The Bunbury MLA stated that Bunbury is the gateway to the State’s pristine South-West region and is now proudly home to hundreds of digital and creative businesses and innovators. The MLA added that he looks forward to the establishment of the WA Creative Technology Innovation Hub and the significant opportunities and benefits it will offer to the Bunbury community.
About the New Industries Fund
The New Industries Fund is a AU$ 16.7 million initiative to support and accelerate new and emerging businesses to create local jobs over 4 years. The Fund is a key component of the Western Australian Government’s Plan for Jobs and its approach to diversifying the economy as well as creating jobs. The WA innovation hubs bring a critical mass of people together, with access to expertise and facilities, making better use of talent and technology, and creating local jobs.
While innovation takes place across Western Australia, a designated hub provides focus and acts as a beacon to attract start-ups and small and medium-sized enterprises (SMEs). They also foster community pride by helping to promote academic excellence and industry strength.
Researchers at the Massachusetts Institute of Technology (MIT) demonstrated a robotic arm that searches for things hidden by radio frequency (RF) marks, which reflect signals from an antenna, using both optical information and RF waves.
They have now developed a new system that can efficiently retrieve any object buried in a pile based on their previous work. If some of the items in the pile have RFID tags, the system does not need to tag the target item to recover it.
The system’s algorithms, known as FuseBot, reason about the likely location and orientation of objects beneath the pile. FuseBot then determines the most efficient method for removing obstructing objects and extracting the target item. FuseBot was able to find more hidden items than a cutting-edge robotics system in half the time.
This speed could be particularly beneficial in an e-commerce warehouse. According to senior author Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science and director of the Signal Kinetics group in the Media Lab, a robot tasked with processing returns could find items in an unsorted pile more efficiently using the FuseBot system.
More than 90% of U.S. stores already utilise RFID tags, according to recent market analysis, but since the technology is not widespread, only some items inside piles may be tagged. This issue served as the group’s research’s impetus.
With FuseBot, a robotic arm retrieves an untagged target object from a jumbled pile using an attached video camera and RF antenna. To produce a 3D model of the surroundings, the system scans the pile with its camera.
It simultaneously transmits signals to find RFID tags from its antenna. Since most solid objects can be penetrated by these radio waves, the robot can “see” deep inside the pile. FuseBot is aware that the target object cannot be found in the exact same location as an RFID tag because it is not marked.
Since the robot is aware of the target item’s size and shape, algorithms combine this data to update the 3D model of the surroundings and suggest suitable spots for it. The system then uses the pile of things and the positions of the RFID tags to decide which item should be removed to locate the target item quickly.
The robot doesn’t know how the components are arranged underneath the pile or how a flimsy object can be distorted by rubbing against bigger others. By leveraging its knowledge of an object’s size, shape, and the location of its RFID tag to create a model of the 3D space that object is most likely to occupy, it overcomes this difficulty via probabilistic reasoning.
It utilises logic to determine which thing would be “better” to eliminate next as it removes objects. After removing one item, the robot scans the pile once more and adjusts its plan considering the new knowledge.
Compared to the other robotic system’s 84 per cent success rate, FuseBot removed the target object successfully 95 per cent of the time. It was able to identify and collect the targeted goods more than twice as quickly and did it with 40% fewer moves.
The software that does the complicated reasoning for FuseBot may be built on any computer; it only needs to interact with a robotic arm that has a camera and an antenna.
FuseBot will soon feature more intricate models that the researchers hope will improve its performance with deformable things.
Advances in LED technology as a light source have made it possible to design lights based on how they look to the human eye. What was once just a theory can be proven true when a solid-state light source is present.
“To test lamps and luminaires, two tools called integrating-sphere and type C goniophotometer are used,” says Dr Revantino, ST, MT, Sub Coordinator of Electricity and Battery, Ministry of Industry of the Republic of Indonesia, and an alumnus of Bandung Institute of Technology (ITB).
Changing the strength of the individual colour chips in an LED light, which is called “tuning,” can make the spectrum of the light change. This gives users the freedom to choose the colour of the light based on their tastes, but it also changes how the colour of the object looks.
The Physical Engineering Vocational Body of the Indonesian Engineers Association (BKTF-PII) held a sharing session on the topic “Spectral-Based Lighting” to talk about the progress of LED technology.
If the spectral reflection properties of an object are known, the spectral interaction method can be used to simulate how the colour of an object will look under a certain LED light spectrum. This is useful in lighting situations where the colour of the object needs to be brought out so that it looks right. This is a step toward the Lighting 4.0 era, which is all about putting people at the centre of lighting.
Solid-state lighting is one type of lighting that is based on the colour of light. Solid-state lighting is a kind of lamp that gets its light from light diodes, organic light diodes, or polymer light diodes. This type of lamp is called a “solid lamp” because it doesn’t have gas or electric filaments like most lamps. This sturdy lamp has a lot going for it. From an economic point of view, flexibility can open the door to theoretical validation, and in the last ten years, it has grown very quickly.
To help with Industry 4.0, technology is also improving in the lighting industry. This is shown by the presence of spectral-based lighting, which is part of the Lighting 4.0 innovation – as an enabler, flexibility in light control, and human-centred lighting.
Data Engineering in Industry 4.0
“Data is the new oil” has become a popular saying in the world of technology. Data must be used to make decisions and make predictions about the future of a company or even an industry. In the world of technology, many sciences and jobs that deal with data are also growing quickly. From a Data Scientist to a Data Analyst to a Data Engineer.
Data Engineering is a subfield of software engineering that focuses on building data systems and infrastructure. Data engineering focuses on how data scientists can quickly and correctly get to the data they need, on the other hand, data engineers are usually in charge of building systems or infrastructure that deal with large amounts of data.
Data engineers have a role in the industrial world. They help solve problems that come up in the industrial world. In the process of digitalising an industry, there are steps that must be taken. Starting with system integration, visibility of operations, data analysis, and operational optimisation.
Several real-world solutions, from different stages of flow to digital parts that can shape the digitalisation of industry, have been created. Starting with operational intelligence, operator assistance, digital maintenance, energy management, condition monitoring, a training portal, quality management, and inventory management. These different solutions that have already been put in place can help users learn how to use Industry 4.0 empowerment technology effectively in Indonesia.
With the rapid advancement of global technology development, the Hong Kong Applied Science and Technology Research Institute (ASTRI) is engaging more enterprises in the cooperation and common development of “industry, academia and research”, ASTRI has, thus, launched the “IPs and Service Offerings for Technology Start-ups and SMEs”, selected 20 innovative technological companies from varying categories of entry services, including 8 hardware, 6 software and 6 consulting service companies, with the entry price of HK$50,000 to HK$150,000.
ASTRI focuses on transferring technology to the industry, transforming it into commodities, developing high-quality and affordable patents, information and communication technologies, and creating important and far-reaching influence. In cooperation with research institutions, enterprises and academia, ASTRI researches important technologies that the industry pays attention to, and assists enterprises to enhance their competitiveness.
The relevant scientific research projects selected have a wide range of content, mainly to solve company evaluation, technology and network security issues, writing, electronic technology and electricity issues and more. Private institutions in Hong Kong can contact relevant professionals and engineers at ASTRI for assistance and enquiries.
Hong Kong’s scientific research has undergone many years of development. However, many start-ups, and even small and medium-sized enterprises that have been rooted in Hong Kong for many years striving to improve the field of technology, have been paying high fees for the solutions to technical problems.
Until now, no platform provided cost-effective solutions for them, and their business needs were not understood. Thus, the support provided via the “IPs and Service Offerings for Technology Start-ups and SMEs” caters to the needs of enterprises and is expected to help the industry to solve their difficulties.
Since its establishment 22 years ago, ASTRI has provided different innovative technology software, hardware or technical support to various government departments, public organizations and many private enterprises in Hong Kong, contributing to the smooth enhancement or assistance in their development processes. With the industrialisation of technology and the intellectualization of industries, a new era of competition in Hong Kong will emerge.
Examples of selection options:
Cybersecurity awareness and benchmarking assessment
General cybersecurity awareness training for users of any skill level including general IT users and technical employees to management-level IT professionals aimed at reducing cyber risk at the human level. The training also includes general cybersecurity assessment and brief benchmarking covering web applications, mobile applications, networks, security architecture, cloud infrastructure to ensure SMEs have a comprehensive understanding of their cyber defence maturity.
ESG compliance analytics
Industry-specific (e.g., financial, energy) ESG benchmarking report that will list the average or distribution of listed companies in different ESG metrics as well as the top performers in each metric or category. It will be based on the SME’s ESG metrics; performance or status in the industry compared to peer companies will also be reported, along with improvement suggestions. Analytics will help SMEs generate reports automatically by filling in the minimum required information.
Mixed language speech recognition and audio indexing
Based on client-supplied audio records as training data, help train a preliminary mixed language model supporting Cantonese, English and Mandarin for applications in specific industry domains such as insurance, media, telecom, banking and/or KOL.
Other items include:
- Speech recognition & audio indexing
- Financial document analysis
- Smart OCR & document processing
- Behaviour and emotion analysis for driving safety
- Smart indoor and outdoor Geographic Information System
- IoT technologies and device communications
- Retired battery screening solutions
- Eco-friendly power system
- Safe energy storage solutions
- Analog IC design for medical devices
- 3D Integration power electronics modules
- ASTRI AR Glass
- Wearable technologies
- Gantry Free Electronic Road Pricing
- ESD protection design consultancy
- Digital document processing
- DC solutions for energy saving and protection
Researchers have developed a reusable, recyclable, washable, odourless, non-allergic, and anti-microbial N95 mask by using 3D printing technology. The multi-layer mask has a shelf life of more than 5 years, depending upon the use. The outer layer is made up of silicon.
Apart from its well-known uses to prevent infections like COVID-19, the mask can also be used in industries where workers are exposed to high volumes of dust like cement or cotton factories, brick kilns, and paint industries. It can be modified according to the requirement by changing the filter configuration. As per a government press release, the mask can help prevent severe lung diseases such as silicosis. A trademark and a patent have also been filed for the mask called Nano Breath.
The mask consists of a 4-layer filtration mechanism wherein the outer and first layer of the filter is coated with nanoparticles. The second layer is a high-efficiency particulate absorbing (HEPA) filter, the third layer is a 100 µm filter, and the fourth layer is a moisture absorbent filter.
A Zetasizer Nano ZS, a facility supported by the government’s Fund for Improvement of Science & Technology Infrastructure (FIST) project, was used to carry out the work. It enables high-temperature thermal analysis for ceramic materials and catalysis applications. It is a high-performance, versatile system for measuring particle size, zeta potential, molecular weight, particle mobility, and micro-rheology.
Technology has played a significant role in the fight against the COVID-19 pandemic over the past two years. Indian institutes have invested resources in developing tech-enabled solutions for the new normal. Earlier this year, researchers from the Indian Institute of Technology in Jodhpur (IIT-Jodhpur) created an artificial intelligence (AI) model that can detect COVID-19 by examining the chest X-ray of patients. The team proposed a deep learning-based algorithm called COMiT-Net, which learns the abnormalities present in the chest X-ray images to differentiate between an affected lung and a non-affected lung. It can also identify infected regions of the lungs.
In March, Bengaluru-based scientists from the Centre for Nano and Soft Matter Sciences (CeNS) and the Jawaharlal Nehru Centre for Advanced and Scientific Research (JNCASR) developed an affordable solution to develop low-cost touch-cum-proximity sensors, popularly called touchless touch sensors, through a printing technique. The scientists set up a semi-automated production plant to produce printing-aided patterned transparent electrodes (a resolution of around 300 micrometres). It has the potential to be utilised in advanced touchless screen technologies. It could be used for self-service kiosks, ATMs, and vending machines.
As OpenGov Asia reported, the team fabricated a touch sensor that can sense a proximal or hover touch even from a distance of 9 centimetres from the device. The team also announced they would make several more prototypes using their patterned electrodes to prove their feasibility for other smart electronic applications. Industry players and research institutions and labs can access the technology on a request basis and through collaborative projects. The patterned transparent electrodes could be used in advanced smart electronic devices like touchless screens and sensors.