KAIST

Researchers observed a wide window of chiroptical activity from nanomaterials A research team transferred chirality from the molecular scale to a microscale to extend material platforms and applications. The optical activity from this novel chiral material encompasses to short-wave infrared region.  This platform could serve as a powerful strategy for hierarchical chirality transfer through self-assembly, generating broad optical activity and providing immense applications including bio, telecommunication, and imaging technique. This is the first observation of such a wide window of chiroptical activity from nanomaterials. “We synthesized chiral copper sulfides using cysteine, as the stabilizer, and transferring the chirality from molecular to the microscale through self-assembly,” explained Professor Jihyeon Yeom from the Department of Materials Science and Engineering, who led the research. The result was reported in ACS Nano on September 14. Chiral nanomaterials provide a rich platform for versatile applications. Tuning the wavelength of polarization rotation maxima in the broad range is a promising candidate for infrared neural stimulation, imaging, and nanothermometry. However, the majority of previously developed chiral nanomaterials revealed the optical activity in a relatively shorter wavelength range, not in short-wave infrared. To achieve chiroptical activity in the short-wave infrared region, materials should be in sub-micrometer dimensions, which are compatible with the wavelength of short-wave infrared region light for strong light-matter interaction. They also should have the optical property of short-wave infrared region absorption while forming a structure with chirality.  Professor Yeom’s team induced self-assembly of the chiral nanoparticles by controlling the attraction and repulsion forces between the building block nanoparticles. During this process, molecular chirality of cysteine was transferred to the nanoscale chirality of nanoparticles, and then transferred to the micrometer scale chirality of nanoflowers with 1.5-2 2 μm dimensions formed by the self-assembly. “We will work to expand the wavelength range of chiroptical activity to the short-wave infrared region, thus reshaping our daily lives in the form of a bio-barcode that can store vast amount of information under the skin,” said Professor Yeom.  This study was funded by the Ministry of Science and ICT, the Ministry of Health and Welfare, the Ministry of Food and Drug Safety, the National Research Foundation of Korea,the KAIST URP Program, the KAIST Creative Challenging Research Program, Samsung and POSCO Science Fellowship. -PublicationKi Hyun Park, Junyoung Kwon, Uichang Jeong, Ji-Young Kim, Nicholas A.Kotov, Jihyeon Yeom, “Broad Chrioptical Activity from Ultraviolet to Short-Wave Infrared by Chirality Transfer from Molecular to Micrometer Scale," September 14, 2021 ACS Nano (https://doi.org/10.1021/acsnano.1c05888) -ProfileProfessor Jihyeon YeomNovel Nanomaterials for New Platforms LaboratoryDepartment of Materials Science and EngineeringKAIST  

Clinical trial of flexible sensor-integrated radiofrequency ablation (RFA) needle tip monitors physical changes and steam pop Researchers have designed a thin polymeric sensor platform on a radiofrequency ablation needle to monitor temperature and pressure in real time. The sensors integrated onto 1.5 mm diameter needle tip have proven their efficacy during clinical tests and expect to provide a new opportunity for safer and more effective medical practices. The research was reported in Advanced Science as the frontispiece on August 5. Radiofrequency ablation (RFA) is a minimally invasive surgery technique for removing tumors and treating cardiovascular disease. During a procedure, an unintended audible explosion called ‘steam pop’ can occur due to the increased internal steam pressure in the ablation region. This phenomenon has been cited as a cause of various negative thermal and mechanical effects on neighboring tissue. Even more, the relationship between steam pop and cancer recurrence is still being investigated.  Professor Inkyu Park said that his team’s integrated sensors reliably detected the occurrence of steam pop. The sensors also monitor rapidly spreading hot steam in tissue. It is expected that the diverse properties of tissue undergoing RFA could be checked by utilizing the physical sensors integrated on the needle.  “We believe that the integrated sensors can provide useful information about a variety of medical procedures and accompanying environmental changes in the human body, and help develop more effective and safer surgical procedures,” said Professor Park. Professor Park’s team built a thin film type pressure and temperature sensor stack with a thickness of less than 10 μm using a microfabrication process. For the pressure sensor, the team used contact resistance changes between metal electrodes and a carbon nanotube coated polymeric membrane. The entire sensor array was thoroughly insulated with medical tubes to minimize any exposure of the sensor materials to external tissue and maximize its biocompatibility. During the clinical trial, the research team found that the accumulated hot steam is suddenly released during steam pops and this hot air spreads to neighboring tissue, which accelerates the ablation process. Furthermore, using in-situ ultrasound imaging and computational simulations, the research team could confirm the non-uniform temperature distribution around the RFA needle can be one of the primary reasons for the steam popping. Professor Park explained that various physical and chemical sensors for different targets can be added to create other medical devices and industrial tools. “This result will expand the usability and applicability of current flexible sensor technologies. We are also trying to integrate this sensor onto a 0.3mm diameter needle for in-vivo diagnosis applications and expect that this approach can be applied to other medical treatments as well as the industrial field,” added Professor Park. This study was supported by the National Research Foundation of Korea. -PublicationJaeho Park, Jinwoo Lee, Hyo Keun Lim, Inkyu Park et al. “Real-Time Internal Steam Pop Detection during Radiofrequency Ablation with a Radiofrequency Ablation Needle Integrated with a Temperature and Pressure Sensor: Preclinical and clinical pilot tests," Advanced Science (https://doi/org/10.1002/advs.202100725) on August 5, 2021 -ProfileProfessor Inkyu ParkMicro & Nano Tranducers Laboratory http://mintlab1.kaist.ac.kr/ Department of Mechanical EngineeringCollege of EngineeringKAIST  

Researchers propose a deep neural network-based forward design space exploration using active transfer learning and data augmentation A new study proposed a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods. Professor Seungwha Ryu believes that this study will help address a variety of optimization problems that have an astronomical number of possible design configurations. For the grid composite optimization problem, the proposed framework was able to provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5% of the initial training data-set size. This study was reported in npj Computational Materials last month. “We wanted to mitigate the limitation of the neural network, weak predictive power beyond the training set domain for the material or structure design,” said Professor Ryu from the Department of Mechanical Engineering.  Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome this limitation also suffer from weak predictive power for the unseen domain. Professor Ryu’s team, in collaboration with researchers from Professor Grace Gu’s group at UC Berkeley, devised a design method that simultaneously expands the domain using the strong predictive power of a deep neural network and searches for the optimal design by repetitively performing three key steps.  First, it searches for few candidates with improved properties located close to the training set via genetic algorithms, by mixing superior designs within the training set. Then, it checks to see if the candidates really have improved properties, and expands the training set by duplicating the validated designs via a data augmentation method. Finally, they can expand the reliable prediction domain by updating the neural network with the new superior designs via transfer learning. Because the expansion proceeds along relatively narrow but correct routes toward the optimal design (depicted in the schematic of Fig. 1), the framework enables an efficient search. As a data-hungry method, a deep neural network model tends to have reliable predictive power only within and near the domain of the training set. When the optimal configuration of materials and structures lies far beyond the initial training set, which frequently is the case, neural network-based design methods suffer from weak predictive power and become inefficient.  Researchers expect that the framework will be applicable for a wide range of optimization problems in other science and engineering disciplines with astronomically large design space, because it provides an efficient way of gradually expanding the reliable prediction domain toward the target design while avoiding the risk of being stuck in local minima. Especially, being a less-data-hungry method, design problems in which data generation is time-consuming and expensive will benefit most from this new framework. The research team is currently applying the optimization framework for the design task of metamaterial structures, segmented thermoelectric generators, and optimal sensor distributions. “From these sets of on-going studies, we expect to better recognize the pros and cons, and the potential of the suggested algorithm. Ultimately, we want to devise more efficient machine learning-based design approaches,” explained Professor Ryu.This study was funded by the National Research Foundation of Korea and the KAIST Global Singularity Research Project. -Publication Yongtae Kim, Youngsoo, Charles Yang, Kundo Park, Grace X. Gu, and Seunghwa Ryu, “Deep learning framework for material design space exploration using active transfer learning and data augmentation,” npj Computational Materials (https://doi.org/10.1038/s41524-021-00609-2) -Profile Professor Seunghwa Ryu Mechanics & Materials Modeling Lab Department of Mechanical Engineering KAIST

Researchers develop a versatile and powerful tool for studying the spatiotemporal dynamics of secretory proteins, a valuable class of biomarkers and therapeutic targets Researchers have presented a method for profiling tissue-specific secretory proteins in live mice. This method is expected to be applicable to various tissues or disease models for investigating biomarkers or therapeutic targets involved in disease progression. This research was reported in Nature Communications on September 1. Secretory proteins released into the blood play essential roles in physiological systems. They are core mediators of interorgan communication, while serving as biomarkers and therapeutic targets. Previous studies have analyzed conditioned media from culture models to identify cell type-specific secretory proteins, but these models often fail to fully recapitulate the intricacies of multi-organ systems and thus do not sufficiently reflect biological realities. These limitations provided compelling motivation for the research team led by Jae Myoung Suh and his collaborators to develop techniques that could identify and resolve characteristics of tissue-specific secretory proteins along time and space dimensions. For addressing this gap in the current methodology, the research team utilized proximity-labeling enzymes such as TurboID to label secretory proteins in endoplasmic reticulum lumen using biotin. Thereafter, the biotin-labeled secretory proteins were readily enriched through streptavidin affinity purification and could be identified through mass spectrometry. To demonstrate its functionality in live mice, research team delivered TurboID to mouse livers via an adenovirus. After administering the biotin, only liver-derived secretory proteins were successfully detected in the plasma of the mice. Interestingly, the pattern of biotin-labeled proteins secreted from the liver was clearly distinctive from those of hepatocyte cell lines. First author Kwang-eun Kim from the Graduate School of Medical Science and Engineering explained, “The proteins secreted by the liver were significantly different from the results of cell culture models. This data shows the limitations of cell culture models for secretory protein study, and this technique can overcome those limitations. It can be further used to discover biomarkers and therapeutic targets that can more fully reflect the physiological state.” This work research was supported by the National Research Foundation of Korea, the KAIST Key Research Institutes Project (Interdisciplinary Research Group), and the Institute for Basic Science in Korea. -PublicationKwang-eun Kim, Isaac Park et al., “Dynamic tracking and identification of tissue-specific secretory proteins in the circulation of live mice,” Nature Communications on Sept.1, 2021(https://doi.org/10.1038/s41467-021-25546-y) -ProfileProfessor Jae Myoung Suh Integrated Lab of Metabolism, Obesity and Diabetes Researchhttps://imodkaist.wixsite.com/home Graduate School of Medical Science and Engineering College of Life Science and BioengineeringKAIST

A study on social network data of EDM DJs finds the relationship between social standing and identity building If you would like to succeed in your career, carve out your own distinctiveness, then break your boundaries along with collaborators. This sounds very common. However, a study on social networks has proven that is the recipe for success. A recent research on electric dance music DJs’ music identity and their reputation found that music DJs with a distinct genre identity as well as network positions combining brokerage and cohesion tend to place higher in terms of their social standing.  What do Calvin Harris, the star of Electro house, Diplo, the icon of Moombahton & Trap, Sebastian Ingrosso, the master of Progressive House, and Armin Van Buuren, the leader of Trance have in common? One commonality of these star DJs in the electronic music market is that they are the leaders who build their genres with solid musical identities and are artists who constantly try experimental and innovative connections with other genres. Professor Wonjae Lee and Dr. Hyeongseok Wi from the Graduate School of Culture and Technology analyzed the playlist data performed by electronic dance music (EDM) DJs at several EDM festivals that were popular around the world before COVID-19 and the track data that they released during that period.  “This study investigates how social standing is attained within a professional group of artists whose members play a key role in evaluating their artistic products in the EDM market,” said Professor Lee. Particularly, the team considered DJs' social standing as an effective means of ensuring the quality of their artwork in emerging music markets such as EDM and identified two important factors, the musical identity and the social position within the professional DJ’s group.  They analyzed the data from 3,164 playlists of 815 DJs who performed at nine festivals held from 2013 to 2016 as a sort of citation network among DJs, and transformed it into network data to measure social positions among the DJs. They considered the DJs who received a lot of citations from other DJs as having a high social standing. In addition, the genre, beats per minute (BPM), and key scale data of the songs released during the period were quantified to analyze the association with the musical identity. First, the results of analysis of the released track data demonstrated that focused distinct musical identity is correlated with social standing among EDM DJs. The EDM market is an emerging specialist market that is constantly developing and differentiating new styles and genres. It includes artists who establish value criteria and demarcate categorical space into separate identity positions reflecting the artistic forms of a similar type. Second, this study focuses on the two advantages of two types of social positioning, brokerage and cohesive, which can effectively reduce uncertainty in the market. The results show that DJs with a hybrid position, combining elements of both brokerage and cohesion, have higher social standing. This hybrid position is the most advantageous position for controlling new opportunities and inflows of resources and for utilizing them. Unlike existing studies that divide the merits of the two positions into a dichotomy, this study follows the practice of recent studies that show that the two positions can generate synergy in a complementary manner.  The remix culture prominent in EDM provides a convincing explanation for this phenomenon. Because constructing playlist sets represents a DJ’s main specialty, the ability to creatively combine a variety of tracks using one’s own artistic style is crucial. To showcase their remix skills, DJs skillfully select tracks to maximize the displays of their talent. Recognized DJs prefer to select tracks from other genres, borrowing from existing contexts and creating new reinterpretations while drawing upon their own musical backgrounds.  “Acquiring social acknowledgement within a professional group is an effective way to ensure the quality of products they produce and a strong reputation,” explained Professor Lee. The research team also pointed out the unique case of Techno DJs, who are showing Galápagos syndrome by avoiding crossover between genres and sticking to their own musical identity, unlike most genres in EDM. This research was reported in PLos ONE on Aug. 25 and funded by KAIST and the BK21 Plus Postgraduate Organization for Content Science. -ProfileProfessor Wonjae LeeGraduate School of Culture TechnologyKAIST -PublicationHyeongseok Wi, Wonjae Lee “Stars inside have reached outside: The effects of electronic dance music DJ’s social standing and musical identity on track success,” Aug.25, 2021 PLosONE (https://doi.org/10.1371/journal.pone.0254618)

Rainbow Robotics stock used to endow the development fund  Emeritus Professor Jun-Ho Oh, who developed the 2015 DARPA Challenge winning humanoid robot DRC-Hubo, donated 5 billion KRW on October 25 during a ceremony held at the KAIST campus in Daejeon. Professor Oh donated his 20% share (400 shares) of his startup Rainbow Robotics, which was established in 2011. Rainbow Robotics was listed on the KOSDAQ this February. The 400 shares were converted to 200,000 shares with a value of approximately 5 billion KRW when listed this year. KAIST sold the stocks and endowed the Jun-Ho Oh Fund, which will be used for the development of the university. He was the 39th faculty member who launched a startup with technology from his lab and became the biggest faculty entrepreneur donor. “I have received huge support and funding for my research. Fortunately, the research had a good result and led to the startup. Now I am very delighted to pay back the university. I feel that I have played a part in building the school’s startup ecosystem and creating a virtuous circle,” said Professor Oh during the ceremony. KAIST President Kwang Hyung Lee declared, “Professor Oh has been a very impressive exemplary model for our aspiring faculty and student tech startups. We will spare no effort to support startups at KAIST.” Professor Oh, who retired from the Department of Mechanical Engineering last year, now serves as the CTO at Rainbow Robotics. The company is developing humanoid bipedal robots and collaborative robots, and advancing robot technology including parts for astronomical observations. Professor Hae-Won Park and Professor Je Min Hwangbo, who are now responsible for the Hubo Lab, also joined the ceremony along with employees of Rainbow Robotics.

PhD candidates Soo Ye Kim and Sanghyun Woo from the KAIST School of Electrical Engineering and Hae Beom Lee from the Kim Jaechul Graduate School of AI were selected as the 2021 Google PhD Fellows. The Google PhD Fellowship is a scholarship program that supports graduate school students from around the world that have produced excellent achievements from promising computer science-related fields. The 75 selected fellows will receive ten thousand dollars of funding with the opportunity to discuss research and receive one-on-one feedback from experts in related fields at Google. Kim and Woo were named fellows in the field of "Machine Perception, Speech Technology and Computer Vision" with research of deep learning based super-resolution and  computer vision respectively.  Lee was named a fellow in the field of "Machine Learning" for his research in meta-learning. Kim's research  includes the formulation of novel methods for super-resolution and HDR video restoration and deep joint frame interpolation and super-resolution methods. Many of her works have been presented in leading conferences in computer vision and AI such as CVPR, ICCV, and AAAI. In addition, she has been collaborating as a research intern with the Vision Group Team at Adobe Research to study depth map refinement techniques.  (Kim's research on deep learning based joint super-resolution and inverse tone-mapping framework for HDR videos) Woo’s research includes an effective deep learning model design based on the attention mechanism and learning methods based on self-learning and simulators. His works have been also presented in leading conferences such as CVPR, ECCV, and NeurIPS. In particular, his work on the Convolutional Block Attention Module (CBAM) which was presented at ECCV in 2018 has surpassed over 2700 citations on Google Scholar after being referenced in many computer vision applications. He was also a recipient of Microsoft Research PhD Fellowship in 2020. (Woo's research on attention mechanism based deep learning models) Lee’s research focuses effectively overcoming various limitations of the existing meta-learning framework. Specifically, he proposed to deal with a realistic task distribution with imbalances, improved the practicality of meta-knowledge, and made meta-learning possible even in large-scale task scenarios. These various studies have been accepted to numerous top-tier machine learning conferences such as NeurIPS, ICML, and ICLR. In particular, one of his papers has been selected as an oral presentation at ICLR 2020 and another as a spotlight presentation at NeurIPS 2020. (Lee's research on learning to balance and continual trajectory shifting) Due to the COVID-19 pandemic, the award ceremony was held virtually at the Google PhD Fellowship Summit from August 31st to September 1st. The list of fellowship recipients is displayed on the Google webpage.

Visiting Distinguished Professor Jo will enrich KAIST’s scholarship and inspire futuristic art and technology research Soprano Sumi Jo will join the KAIST faculty from the spring 2022 semester. Named as a visiting distinguished professor in the Graduate School of Culture Technology, she will give special leadership lectures. Her tenure will be through September 2024. Jo joined the appointment ceremony held online at KAIST on October 14 from Portugal and expressed her high expectations for teaching KAIST students from next year. “I am very grateful for this opportunity to meet students at KAIST, the birthplace of advanced science and technology in Korea,” she said. KAIST President Kwang Hyung Lee, who has stressed the importance of humanities and the arts in convergence studies of science and technology, lauded her joining the faculty as a big asset who will enrich KAIST’s scholarship. “Soprano Sumi Jo rose to stardom on the global music stage with her unrivaled talent and effort. I truly believe her experience and passion will inspire our students to expand their horizon of thought and knowledge,” said President Lee. Distinguished Professor Jo will also participate in convergence research at the Graduate School of Culture Technology with KAIST professors and many other experts. The Sumi Jo Performing Arts Research Center at the Graduate School of Culture Technology will conduct research on the converging of imaging and audio processing technologies that will enhance virtual artists’ performances. Distinguished Professor Jo explained, “The world is changing so fast. I look forward to working on culture technology research at KAIST that will raise our life quality.” Professor Juhan Nam from the Graduate School of Culture Technology said, “We look forward to working closely with her and her team to develop research themes that envision futuristic art combined with technology such as the metaverse and non-fungible tokens (NFTs). Coloratura soprano Jo was born in Seoul and educated at Seoul National University and the Conservatorio Santa Cecilia in Italy. Among her teachers were Carolo Bergonzi and Giasnnelas Borelli. Following her graduation from the Conservatorio Santa Cecilia in 1985, she swept major international competitions in Seoul and Europe. In 1986, she was unanimously awarded the first prize in the Carlo Alberto Cappelli International Competition in Verona which is open only to the first-prize winners of major competitions. Since her debut in the role of Gilda in Verdi’s Rigolleto in Italy in 1986, she has performed on the world's biggest stages along with noted maestros such as Herbert von Karajan, Georg Solti, Zubin Mehta, and James Levine. Distinguished Professor Jo, one of the most sought-after sopranos in the world,  released more than 40 albums.

KAIST will launch the KAIST Entrepreneurial Partnership (KEP) program, which connects faculty members who own technology with those who want to launch startup. The program encourages open innovation startups using strategies tailored to market-client demand requirements. This is also one of efforts to help realize ‘one startup per lab,’ initiated by President Kwang Hyung Lee’s new innovation strategy. KEP also aims to introduce the best technologies developing at KAIST to startups and to raise the success rate of technology commercialization.  The program will match KAIST faculty and student entrepreneur candidates with parties enrolled in the new Entrepreneur in Residence and Entrepreneurial Partner programs. Each team will be given a six-month test period with funding support. KAIST will invite entrepreneurial experts from both technology and management fields to support the program participants. Around 30 experts with experience in developing new businesses, startups, and investments in large corporations or venture companies will be recruited as entrepreneurial partners. They will offer support for research and business development (R&BD), technology marketing, attracting venture investment from corporations, mergers and acquisitions, and business openings. A survey showed that KAIST members who are interested in starting a business are experiencing difficulties finding an entrepreneurial expert (72.2%), with the complicated startup approval procedures (33.3%), and their lack of knowledge on entrepreneurship and funding (27.8%). The KEP program hopes to encourage KAIST faculty members and students who have well-developed business ideas and the appropriate technology but lack the capabilities to realize and develop them into a business. Associate Vice President of Startups Young-Tae Kim said, “We will develop KEP into KAIST’s distinct entrepreneurial support system and produce exemplary outcomes of faculty and student startups. We will spread the startup DNA and lead the building of a virtuous cycle between entrepreneurship and the venture ecosystem.”

The ILP’s one-stop solutions target all industrial sectors including conglomerates, small and medium-sized enterprises, venture companies, venture capital (VC) firms, and government-affiliated organizations. The Industrial Liaison Center at KAIST launched the Industrial Liaison Program (ILP) on September 28, an industry-academic cooperation project to provide comprehensive solutions to industry partners. The Industrial Liaison Center will recruit member companies for this service every year, targeting all industrial sectors including conglomerates, small and medium-sized enterprises, venture companies, venture capital (VC) firms, and government-affiliated organizations. The program plans to build a one-stop support system that can systematically share and use excellent resource information from KAIST’s research teams, R&D achievements, and infrastructure to provide member companies with much-needed services.  More than 40 KAIST professors with abundant academic-industrial collaboration experience will participate in the program. Experts from various fields with different points of view and experiences will jointly provide solutions to ILP member companies. To actively participate in academic-industrial liaisons and joint consultations, KAIST assigned 10 professors from related fields as program directors.  The program directors will come from four different fields including AI/robots (Professor Alice Oh, School from the School of Computing, Professor Young Jae Jang from the Department of Industrial & Systems Engineering, and Professor Yong-Hwa Park from Department of Mechanical Engineering), bio/medicine (Professor Daesoo Kim from Department of Biological Sciences and Professor YongKeun Park from Department of Physics), materials/electronics (Professor Sang Ouk Kim from the Department of Materials Science and Engineering and Professors Jun-Bo Yoon and Seonghwan Cho from the School of Electrical Engineering), and environment/energy (Professor Hee-Tak Kim from the Department of Biological Sciences and Professor Hoon Sohn from the Department of Civil and Environmental Engineering).  The transdisciplinary board of consulting professors that will lead technology innovation is composed of 30 professors including Professor Min-Soo Kim (School of Computing, AI), Professor Chan Hyuk Kim (Department of Biological Sciences, medicine), Professor Hae-Won Park (Department of Mechanical Engineering, robots), Professor Changho Suh (School of Electrical Engineering, electronics), Professor Haeshin Lee (Department of Chemistry, bio), Professor Il-Doo Kim (Department of Materials Science and Engineering, materials), Professor HyeJin Kim (School of Business Technology and Management), and Professor Byoung Pil Kim (School of Business Technology and Management, technology law) The Head of the Industrial Liaison Center who is also in charge of the program, Professor Keon Jae Lee, said, “In a science and technology-oriented generation where technological supremacy determines national power, it is indispensable to build a new platform upon which innovative academic-industrial cooperation can be pushed forward in the fields of joint consultation, the development of academic-industrial projects, and the foundation of new industries. He added, “KAIST professors carry out world-class research in many different fields and faculty members can come together through the ILP to communicate with representatives from industry to improve their corporations’ global competitiveness and further contribute to our nation’s interests by cultivating strong small enterprises