Search results for: Qiang Tong

Authors: Qiang Ren

Abstract:

In the past 20 years, under the high standard of international investment protection, there have been numerous cases of investors ignoring the host country's measures to protect human health. Examples include investment disputes triggered by the Argentine government's measures related to human health, quality, and price of drinking water under the North American Free Trade Agreement. Examples also include Philip Morris v. Australia, in which case the Australian government announced the passing of the Plain Packing of Cigarettes Act to address the threat of smoking to public health in 2010. In order to take advantage of the investment treaty protection between Hong Kong and Australia, Philip Morris Asia acquired Philip Morris Australia in February 2011 and initiated investment arbitration under the treaty before the passage of the Act in July 2011. Philip Morris claimed the Act constitutes indirect expropriation and violation of fair and equitable treatment and claimed 4.16 billion US dollars compensation. Fortunately, the case ended at the admissibility decision stage and did not enter the substantive stage. Generally, even if the host country raises a human health defense, most arbitral tribunals will rule that the host country revoke the corresponding policy and make huge compensation in accordance with the clauses in the bilateral investment treaty to protect the rights of investors. The significant imbalance in the rights and obligations of host states and investors in international investment treaties undermines the ability of host states to act in pursuit of human health and social interests beyond economic interests. This squeeze on the nation's public policy space and disregard for the human health costs of investors' activities raises the need to include human health in investment rulemaking. The current international investment law system that emphasizes investor protection fails to fully reflect the requirements of the host country for the healthy development of human beings and even often brings negative impacts to human health. At a critical moment in the reform of the international investment law system, in order to achieve mutual enhancement of investment returns and human health development, human health should play a greater role in influencing and shaping international investment rules. International investment agreements should not be limited to investment protection tools but should also be part of national development strategies to serve sustainable development and human health. In order to meet the requirements of the new sustainable development goals of the United Nations, human health should be emphasized in the formulation of international investment rules, and efforts should be made to shape a new generation of international investment rules that meet the requirements of human health and sustainable development.

Keywords: human health, international investment law, Philip Morris v. Australia, investor protection

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Authors: Jodie Lee, Lorena Chan, Esther Tong

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Hong Kong students face a unique learning environment. Starting from the 2010/2011 school year, The Education Bureau (EDB) of the Government of the Hong Kong Special Administrative Region implemented the fine-tuned Medium of Instruction (MOI) arrangements for secondary schools. Since then, secondary schools in Hong Kong have been given the flexibility to decide the most appropriate MOI arrangements for their schools and under the new academic structure for senior secondary education, particularly on the compulsory part of the mathematics curriculum. In 2019, Hong Kong Diploma of Secondary Education Examination (HKDSE), over 40% of school day candidates attempted the Mathematics Compulsory Part examination in the Chinese version while the rest took the English version. Moreover, only 14.38% of candidates sat for one of the extended Mathematics modules. This results in a serious of intricate issues to students’ learning in post-secondary education programmes. It is worth to note that when students further pursue to an higher education in Hong Kong or even oversea, they may facing substantial difficulties in transiting learning from learning mathematics in their mother tongue in Chinese-medium instruction (CMI) secondary schools to an English-medium learning environment. Some students understood the mathematics concepts were found to fail to fulfill the course requirements at college or university due to their learning experience in secondary study at CMI. They are particularly weak in comprehending the mathematics questions when they are doing their assessment or attempting the test/examination. A government funded project was conducted with the aims of providing integrated learning context and language support to students with a lower level of numeracy and/or with CMI learning experience. By introducing this “integrated concept + language mapping approach”, students can cope with the learning challenges in the compulsory English-medium mathematics and statistics subjects in their tertiary education. Ultimately, in the hope that students can enhance their mathematical ability, analytical skills, and numerical sense for their lifelong learning. The “Concept + Language Mapping “(CLM) approach was adopted and tried out in the bridging courses for students with a lower level of numeracy and/or with CMI learning experiences. At the beginning of each class, a pre-test was conducted, and class time was then devoted to introducing the concepts by CLM approach. For each concept, the key thematic items and their different semantic relations are presented using graphics and animations via the CLM approach. At the end of each class, a post-test was conducted. Quantitative data analysis was performed to study the effect on students’ learning via the CLM approach. Stakeholders' feedbacks were collected to estimate the effectiveness of the CLM approach in facilitating both content and language learning. The results based on both students’ and lecturers’ feedback indicated positive outcomes on adopting the CLM approach to enhance the mathematical ability and analytical skills of CMI students.

Keywords: mathematics, Concept+Language Mapping, level of numeracy, medium of instruction

Procedia PDF Downloads 81

Authors: D. Courault, L. Hossard, V. Demarez, E. Ndikumana, D. Ho Tong Minh, N. Baghdadi, F. Ruget

Abstract:

In the global change context, efficient management of the available resources has become one of the most important topics, particularly for sustainable crop development. Timely assessment with high precision is crucial for water resource and pest management. Rice cultivated in Southern France in the Camargue region must face a challenge, reduction of the soil salinity by flooding and at the same time reduce the number of herbicides impacting negatively the environment. This context has lead farmers to diversify crop rotation and their agricultural practices. The objective of this study was to evaluate this crop diversity both in crop systems and in agricultural practices applied to rice paddy in order to quantify the impact on the environment and on the crop production. The proposed method is based on the combined use of crop models and multispectral data acquired from the recent Sentinel 2 satellite sensors launched by the European Space Agency (ESA) within the homework of the Copernicus program. More than 40 images at fine spatial resolution (10m in the optical range) were processed for 2016 and 2017 (with a revisit time of 5 days) to map crop types using random forest method and to estimate biophysical variables (LAI) retrieved by inversion of the PROSAIL canopy radiative transfer model. Thanks to the high revisit time of Sentinel 2 data, it was possible to monitor the soil labor before flooding and the second sowing made by some farmers to better control weeds. The temporal trajectories of remote sensing data were analyzed for various rice cultivars for defining the main parameters describing the phenological stages useful to calibrate two crop models (STICS and SAFY). Results were compared to surveys conducted with 10 farms. A large variability of LAI has been observed at farm scale (up to 2-3m²/m²) which induced a significant variability in the yields simulated (up to 2 ton/ha). Observations on more than 300 fields have also been collected on land use. Various maps were elaborated, land use, LAI, flooding and sowing, and harvest dates. All these maps allow proposing a new typology to classify these paddy crop systems. Key phenological dates can be estimated from inverse procedures and were validated against ground surveys. The proposed approach allowed to compare the years and to detect anomalies. The methods proposed here can be applied at different crops in various contexts and confirm the potential of remote sensing acquired at fine resolution such as the Sentinel2 system for agriculture applications and environment monitoring. This study was supported by the French national center of spatial studies (CNES, funded by the TOSCA).

Keywords: agricultural practices, remote sensing, rice, yield

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Authors: Wee Tong Liaw, Elaine Wong Yee Sing

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Since 2001, many companies in Singapore have voluntarily participated in the bi-annual Singapore HEALTH Award initiated by the Health Promotion Board of Singapore (HPB). The Singapore HEALTH Award (SHA), is an industry wide award and assessment process. SHA assesses and recognizes employers in Singapore for implementing a comprehensive and sustainable health promotion programme at their workplaces. The rationale for implementing a sustained health promoting workplace and participating in SHA is obvious when company management is convinced that healthier employees, business productivity, and profitability are positively correlated. However, performing research or empirical studies on the impact of a sustained health promoting workplace on stock returns are not likely to yield any interests in the absence of a systematic and independent assessment on the comprehensiveness and sustainability of a health promoting workplace in most developed economies. The principles of diversification and mean-variance efficient portfolio in Modern Portfolio Theory developed by Markowitz (1952) laid the foundation for the works of many financial economists and researchers, and among others, the development of the Capital Asset Pricing Model from the work of Sharpe (1964), Lintner (1965) and Mossin (1966), and the Fama-French Three-Factor Model of Fama and French (1992). This research seeks to support the rationale by studying whether there is a significant relationship or impact of a sustained health promoting workplace on the performance of companies listed on the SGX. The research shall form and test hypotheses pertaining to the impact of a sustained health promoting workplace on company’s performances, including stock returns, of companies that participated in the SHA and companies that did not participate in the SHA. In doing so, the research would be able to determine whether corporate and fund manager should consider the significance of a sustained health promoting workplace as a risk factor to explain the stock returns of companies listed on the SGX. With respect to Singapore’s stock market, this research will test the significance and relevance of a health promoting workplace using the Singapore Health Award as a proxy for non-diversifiable risk factor to explain stock returns. This study will examine the significance of a health promoting workplace on a company’s performance and study its impact on stock price performance and beta and examine if it has higher explanatory power than the traditional single factor asset pricing model CAPM (Capital Asset Pricing Model). To study the significance there are three key questions pertinent to the research study. I) Given a choice, would an investor be better off investing in a listed company with a sustained health promoting workplace i.e. a Singapore Health Award’s recipient? II) The Singapore Health Award has four levels of award starting from Bronze, Silver, Gold to Platinum. Would an investor be indifferent to the level of award when investing in a listed company who is a Singapore Health Award’s recipient? III) Would an asset pricing model combining FAMA-French Three Factor Model and ‘Singapore Health Award’ factor be more accurate than single factor Capital Asset Pricing Model and the Three Factor Model itself?

Keywords: asset pricing model, company's performance, stock prices, sustained health promoting workplace

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Authors: Ruijia Hu, Susanna T.Y. Tong

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Light detection and ranging (LiDAR) and four-channel red, green, blue, and near-infrared (RGBI) remote sensed imageries allow an accurate quantification and contiguous measurement of vegetation characteristics and forest structures. This information facilitates the generation of habitat structure variables for forest species distribution modelling. However, applications of remote sensing data, especially the combination of structural and spectral information, to support evidence-based decisions in forest managements and conservation practices at local scale are not widely adopted. In this study, we examined the habitat requirements of pileated woodpecker (Dryocopus pileatus) (PW) in Hamilton County, Ohio, using ecologically relevant forest structural and vegetation characteristics derived from LiDAR and RGBI data. We hypothesized that the habitat of PW is shaped by vegetation characteristics that are directly associated with the availability of food, hiding and nesting resources, the spatial arrangement of habitat patches within home range, as well as proximity to water sources. We used 186 PW presence or absence locations to model their presence and absence in generalized additive model (GAM) at two scales, representing foraging and home range size, respectively. The results confirm PW’s preference for tall and large mature stands with structural complexity, typical of late-successional or old-growth forests. Besides, the crown size of dead trees shows a positive relationship with PW occurrence, therefore indicating the importance of declining living trees or early-stage dead trees within PW home range. These locations are preferred by PW for nest cavity excavation as it attempts to balance the ease of excavation and tree security. In addition, we found that PW can adjust its travel distance to the nearest water resource, suggesting that habitat fragmentation can have certain impacts on PW. Based on our findings, we recommend that forest managers should use different priorities to manage nesting, roosting, and feeding habitats. Particularly, when devising forest management and hazard tree removal plans, one needs to consider retaining enough cavity trees within high-quality PW habitat. By mapping PW habitat suitability for the study area, we highlight the importance of riparian corridor in facilitating PW to adjust to the fragmented urban landscape. Indeed, habitat improvement for PW in the study area could be achieved by conserving riparian corridors and promoting riparian forest succession along major rivers in Hamilton County.

Keywords: deadwood detection, generalized additive model, individual tree crown delineation, LiDAR, pileated woodpecker, RGBI aerial imagery, species distribution models

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Authors: Xingyu Gao, Qiang Wu, Yuanyuan Liu, Yue Yang

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In the era of the knowledge economy, the relationship between scientific knowledge and patents has garnered significant attention. Understanding the intricate interplay between the foundations of science and technological innovation has emerged as a pivotal challenge for both researchers and policymakers. This study establishes a coupled network of artificial intelligence patents based on co-cited scientific papers. Leveraging centrality metrics from network analysis offers a fresh perspective on understanding the influence of information flow and knowledge sharing within the network on patent impact. The study initially obtained patent numbers for 446,890 granted US AI patents from the United States Patent and Trademark Office’s artificial intelligence patent database for the years 2002-2020. Subsequently, specific information regarding these patents was acquired using the Lens patent retrieval platform. Additionally, a search and deduplication process was performed on scientific non-patent references (SNPRs) using the Web of Science database, resulting in the selection of 184,603 patents that cited 37,467 unique SNPRs. Finally, this study constructs a coupled network comprising 59,379 artificial intelligence patents by utilizing scientific papers co-cited in patent backward citations. In this network, nodes represent patents, and if patents reference the same scientific papers, connections are established between them, serving as edges within the network. Nodes and edges collectively constitute the patent coupling network. Structural characteristics such as node degree centrality, betweenness centrality, and closeness centrality are employed to assess the scientific connections between patents, while citation count is utilized as a quantitative metric for patent influence. Finally, a negative binomial model is employed to test the nonlinear relationship between these network structural features and patent influence. The research findings indicate that network structural features such as node degree centrality, betweenness centrality, and closeness centrality exhibit inverted U-shaped relationships with patent influence. Specifically, as these centrality metrics increase, patent influence initially shows an upward trend, but once these features reach a certain threshold, patent influence starts to decline. This discovery suggests that moderate network centrality is beneficial for enhancing patent influence, while excessively high centrality may have a detrimental effect on patent influence. This finding offers crucial insights for policymakers, emphasizing the importance of encouraging moderate knowledge flow and sharing to promote innovation when formulating technology policies. It suggests that in certain situations, data sharing and integration can contribute to innovation. Consequently, policymakers can take measures to promote data-sharing policies, such as open data initiatives, to facilitate the flow of knowledge and the generation of innovation. Additionally, governments and relevant agencies can achieve broader knowledge dissemination by supporting collaborative research projects, adjusting intellectual property policies to enhance flexibility, or nurturing technology entrepreneurship ecosystems.

Keywords: centrality, patent coupling network, patent influence, social network analysis

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Authors: Xingyu Gao, Qiang Wu

Abstract:

Patents play a crucial role in protecting innovation and intellectual property. Early prediction of the impact of artificial intelligence (AI) patents helps researchers and companies allocate resources and make better decisions. Understanding the key factors that influence patent impact can assist researchers in gaining a better understanding of the evolution of AI technology and innovation trends. Therefore, identifying highly impactful patents early and providing support for them holds immeasurable value in accelerating technological progress, reducing research and development costs, and mitigating market positioning risks. Despite the extensive research on AI patents, accurately predicting their early impact remains a challenge. Traditional methods often consider only single factors or simple combinations, failing to comprehensively and accurately reflect the actual impact of patents. This paper utilized the artificial intelligence patent database from the United States Patent and Trademark Office and the Len.org patent retrieval platform to obtain specific information on 35,708 AI patents. Using six machine learning models, namely Multiple Linear Regression, Random Forest Regression, XGBoost Regression, LightGBM Regression, Support Vector Machine Regression, and K-Nearest Neighbors Regression, and using early indicators of patents as features, the paper comprehensively predicted the impact of patents from three aspects: technical, social, and economic. These aspects include the technical leadership of patents, the number of citations they receive, and their shared value. The SHAP (Shapley Additive exPlanations) metric was used to explain the predictions of the best model, quantifying the contribution of each feature to the model's predictions. The experimental results on the AI patent dataset indicate that, for all three target variables, LightGBM regression shows the best predictive performance. Specifically, patent novelty has the greatest impact on predicting the technical impact of patents and has a positive effect. Additionally, the number of owners, the number of backward citations, and the number of independent claims are all crucial and have a positive influence on predicting technical impact. In predicting the social impact of patents, the number of applicants is considered the most critical input variable, but it has a negative impact on social impact. At the same time, the number of independent claims, the number of owners, and the number of backward citations are also important predictive factors, and they have a positive effect on social impact. For predicting the economic impact of patents, the number of independent claims is considered the most important factor and has a positive impact on economic impact. The number of owners, the number of sibling countries or regions, and the size of the extended patent family also have a positive influence on economic impact. The study primarily relies on data from the United States Patent and Trademark Office for artificial intelligence patents. Future research could consider more comprehensive data sources, including artificial intelligence patent data, from a global perspective. While the study takes into account various factors, there may still be other important features not considered. In the future, factors such as patent implementation and market applications may be considered as they could have an impact on the influence of patents.

Keywords: patent influence, interpretable machine learning, predictive models, SHAP

Procedia PDF Downloads 50

Authors: Akeel A. Shah, Tong Zhang

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Computational approaches to learning the properties of materials are commonplace, motivated by the need to screen or design materials for a given application, e.g., semiconductors and energy storage. Experimental approaches can be both time consuming and costly. Unfortunately, computational approaches such as ab-initio electronic structure calculations and classical or ab-initio molecular dynamics are themselves can be too slow for the rapid evaluation of materials, often involving thousands to hundreds of thousands of candidates. Machine learning assisted approaches have been developed to overcome the time limitations of purely physics-based approaches. These approaches, on the other hand, require large volumes of data for training (hundreds of thousands on many standard data sets such as QM7b). This means that they are limited by how quickly such a large data set of physics-based simulations can be established. At high fidelity, such as configuration interaction, composite methods such as G4, and coupled cluster theory, gathering such a large data set can become infeasible, which can compromise the accuracy of the predictions - many applications require high accuracy, for example band structures and energy levels in semiconductor materials and the energetics of charge transfer in energy storage materials. In order to circumvent this problem, multi-fidelity approaches can be adopted, for example the Δ-ML method, which learns a high-fidelity output from a low-fidelity result such as Hartree-Fock or density functional theory (DFT). The general strategy is to learn a map between the low and high fidelity outputs, so that the high-fidelity output is obtained a simple sum of the physics-based low-fidelity and correction, Although this requires a low-fidelity calculation, it typically requires far fewer high-fidelity results to learn the correction map, and furthermore, the low-fidelity result, such as Hartree-Fock or semi-empirical ZINDO, is typically quick to obtain, For high-fidelity outputs the result can be an order of magnitude or more in speed up. In this work, a new multi-fidelity approach is developed, based on a graph convolutional network (GCN) combined with semi-supervised learning. The GCN allows for the material or molecule to be represented as a graph, which is known to improve accuracy, for example SchNet and MEGNET. The graph incorporates information regarding the numbers of, types and properties of atoms; the types of bonds; and bond angles. They key to the accuracy in multi-fidelity methods, however, is the incorporation of low-fidelity output to learn the high-fidelity equivalent, in this case by learning their difference. Semi-supervised learning is employed to allow for different numbers of low and high-fidelity training points, by using an additional GCN-based low-fidelity map to predict high fidelity outputs. It is shown on 4 different data sets that a significant (at least one order of magnitude) increase in accuracy is obtained, using one to two orders of magnitude fewer low and high fidelity training points. One of the data sets is developed in this work, pertaining to 1000 simulations of quinone molecules (up to 24 atoms) at 5 different levels of fidelity, furnishing the energy, dipole moment and HOMO/LUMO.

Keywords: .materials screening, computational materials, machine learning, multi-fidelity, graph convolutional network, semi-supervised learning

Procedia PDF Downloads 40

Authors: Chung Yan Natalia Yeung, Man Kit Indy Ho, Kin Yu Stan Chan, Ho Pui Kipper Lam, Man Wah Genie Tong, Tze Chung Jim Luk

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Excessive foot pronation is a well-known risk factor of knee and foot injuries such as patellofemoral pain, patellar and Achilles tendinopathy, and plantar fasciitis. Low-Dye taping (LDT) application is not uncommon for basketball players to control excessive foot pronation for pain control and injury prevention. The primary potential benefits of using LDT include providing additional supports to medial longitudinal arch and restricting the excessive midfoot and subtalar motion in weight-bearing activities such as running and landing. Meanwhile, restrictions provided by the rigid tape may also potentially limit functional joint movements and sports performance. Coaches and athletes need to weigh the potential benefits and harmful effects before making a decision if applying LDT technique is worthwhile or not. However, the influence of using LDT on basketball-related performance such as explosive and reactive strength is not well understood. Therefore, the purpose of this study was to investigate the change of drop jump (DJ) and countermovement jump (CMJ) performance before and after LDT application for collegiate male basketball players. In this within-subject crossover study, 12 healthy male basketball players (age: 21.7 ± 2.5 years) with at least 3-year regular basketball training experience were recruited. Navicular drop (ND) test was adopted as the screening and only those with excessive pronation (ND ≥ 10mm) were included. Participants with recent lower limb injury history were excluded. Recruited subjects were required to perform both ND, DJ (on a platform of 40cm height) and CMJ (without arms swing) tests in series during taped and non-taped conditions in the counterbalanced order. Reactive strength index (RSI) was calculated by using the flight time divided by the ground contact time measured. For DJ and CMJ tests, the best of three trials was used for analysis. The difference between taped and non-taped conditions for each test was further calculated through standardized effect ± 90% confidence intervals (CI) with clinical magnitude-based inference (MBI). Paired samples T-test showed significant decrease in ND (-4.68 ± 1.44mm; 95% CI: -3.77, -5.60; p < 0.05) while MBI demonstrated most likely beneficial and large effect (standardize effect: -1.59 ± 0.27) in LDT condition. For DJ test, significant increase in both flight time (25.25 ± 29.96ms; 95% CI: 6.22, 44.28; p < 0.05) and RSI (0.22 ± 0.22; 95% CI: 0.08, 0.36; p < 0.05) were observed. In taped condition, MBI showed very likely beneficial and moderate effect (standardized effect: 0.77 ± 0.49) in flight time, possibly beneficial and small effect (standardized effect: -0.26 ± 0.29) in ground contact time and very likely beneficial and moderate effect (standardized effect: 0.77 ± 0.42) in RSI. No significant difference in CMJ was observed (95% CI: -2.73, 2.08; p > 0.05). For basketball players with pes planus, applying LDT could substantially support the foot by elevating the navicular height and potentially provide acute beneficial effects in reactive strength performance. Meanwhile, no significant harmful effect on CMJ was observed. Basketball players may consider applying LDT before the game or training to enhance the reactive strength performance. However since the observed effects in this study could not generalize to other players without excessive foot pronation, further studies on players with normal foot arch or navicular height are recommended.

Keywords: flight time, pes planus, pronated foot, reactive strength index

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Authors: Do-Won Kim, Andy Eu Jin Lim, Raja Muthusamy Kumarasamy, Vishal Vinayak, Jacky Wang Yu-Shun, Jason Liow Tsung Yang, Patrick Lo Guo Qiang

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In this study, we demonstrate 4-channel coarse wavelength division multiplexing (CWDM) optical transceiver based on silicon photonics integrated circuits (PIC) of waveguide Ge-photodiode (Ge-PD) and Mach Zehnder (MZ)-modulator. 4-channel arrayed PICs of Ge-PD and MZ-modulator are verified to operate at 25 Gbps/ch achieving 4x25 Gbps of total data rate. 4 bare dies of single-channel commercial electronics ICs (EICs) of trans-impedance amplifier (TIA) for Ge-PD and driver IC for MZ-modulator are packaged with PIC on printed circuit board (PCB) in a chip-on-board (COB) manner. Each single-channel EIC is electrically connected to the one channel of 4-channel PICs by wire bonds to trace. The PICs have 4-channel multiplexer for MZ-modulator and 4-channel demultiplexer for Ge-PD. The 4-channel multiplexer/demultiplexer have echelle gratings for4 CWDM optic signals of which center wavelengths are 1511, 1531, 1553, and 1573 nm. Its insertion loss is around 4dB with over 15dB of extinction ratio.The dimension of 4-channel Ge-PD is 3.6x1.4x0.3mm, and its responsivity is 1A/W with dark current of less than 20 nA.Its measured 3dB bandwidth is around 20GHz. The dimension of the 4-channel MZ-modulator is 3.6x4.8x0.3mm, and its 3dB bandwidth is around 11Ghz at -2V of reverse biasing voltage. It has 2.4V•cmbyVπVL of 6V for π shift to 4 mm length modulator.5x5um of Inversed tapered mode size converter with less than 2dB of coupling loss is used for the coupling of the lensed fiber which has 5um of mode field diameter.The PCB for COB packaging and signal transmission is designed to have 6 layers in the hybrid layer structure. 0.25 mm-thick Rogers Duroid RT5880 is used as the first core dielectric layer for high-speed performance over 25 Gbps. It has 0.017 mm-thick of copper layers and its dielectric constant is 2.2and dissipation factor is 0.0009 at 10 GHz. The dimension of both single ended and differential microstrip transmission lines are calculated using full-wave electromagnetic (EM) field simulator HFSS which RF industry is using most. It showed 3dB bandwidth at around 15GHz in S-parameter measurement using network analyzer. The wire bond length for transmission line and ground connection from EIC is done to have less than 300 µm to minimize the parasitic effect to the system.Single layered capacitors (SLC) of 100pF and 1000pF are connected as close as possible to the EICs for stabilizing the DC biasing voltage by decoupling. Its signal transmission performance is under measurement at 25Gbps achieving 100Gbps by 4chx25Gbps. This work can be applied for the active optical cable (AOC) and quad small form-factor pluggable (QSFP) for high-speed optical interconnections. Its demands are quite large in data centers targeting 100 Gbps, 400 Gbps, and 1 Tbps. As the demands of high-speed AOC and QSFP for the application to intra/inter data centers increase, this silicon photonics based high-speed 4 channel CWDM scheme can have advantages not only in data throughput but also cost effectiveness since it reduces fiber cost dramatically through WDM.

Keywords: active optical cable(AOC), 4-channel coarse wavelength division multiplexing (CWDM), communication system, data center, ge-photodiode, Mach Zehnder (MZ) modulator, optical interconnections, optical transceiver, photonics integrated circuits (PIC), quad small form-factor pluggable (QSFP), silicon photonics

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Authors: I. M. K. Ho, S. K. Y. Chan, K. H. P. Lam, G. M. W. Tong, N. C. Y. Yeung, J. T. C. Luk

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Low-dye taping (LDT) is commonly used for treating foot problems, such as plantar fasciitis, and supporting foot arch for runners and non-athletes patients with pes planus. The potential negative impact of pronated feet leading to tibial and femoral internal rotation via the entire kinetic chain reaction was postulated and identified. The changed lower limb biomechanics potentially leading to poor activation of hip and knee stabilizers, such as gluteus maximus and medius, may associate with higher risk of knee injuries including patellofemoral pain syndrome and ligamentous sprain in many team sports players. It is therefore speculated that foot arch correction with LDT might enhance the use of gluteal muscles. The purpose of this study was to investigate the effect of applying LDT on surface electromyographic (sEMG) activity of superior gluteus maximus (SGMax), inferior gluteus maximus (IGMax), gluteus medius (GMed) and tibialis anterior (TA) during double-leg squat. 12 male collegiate basketball players (age: 21.72.5 years; body fat: 12.43.6%; navicular drop: 13.72.7mm) with at least three years regular basketball training experience participated in this study. Participants were excluded if they had recent history of lower limb injuries, over 16.6% body fat and lesser than 10mm drop in navicular drop (ND) test. Recruited subjects visited the laboratory once for the within-subject crossover study. Maximum voluntary isometric contraction (MVIC) tests on all selected muscles were performed in randomized order followed by sEMG test on double-leg squat during LDT and non-LDT conditions in counterbalanced order. SGMax, IGMax, GMed and TA activities during the entire 2-second concentric and 2-second eccentric phases were normalized and interpreted as %MVIC. The magnitude of the difference between taped and non-taped conditions of each muscle was further assessed via standardized effect90% confidence intervals (CI) with non-clinical magnitude-based inference. Paired samples T-test showed a significant decrease (4.71.4mm) in ND (95% CI: 3.8, 5.6; p < 0.05) while no significant difference was observed between taped and non-taped conditions in sEMG tests for all muscles and contractions (p > 0.05). On top of traditional significant testing, magnitude-based inference showed possibly increase in IGMax activity (small standardized effect: 0.270.44), likely increase in GMed activity (small standardized effect: 0.340.34) and possibly increase in TA activity (small standardized effect: 0.220.29) during eccentric phase. It is speculated that the decrease of navicular drop supported by LDT application could potentially enhance the use of inferior gluteus maximus and gluteus medius especially during eccentric phase in this study. As the eccentric phase of double-leg squat is an important component of landing activities in basketball, further studies on the onset and amount of gluteal activation during jumping and landing activities with LDT are recommended. Since both hip and knee kinematics were not measured in this study, the underlying cause of the observed increase in gluteal activation during squat after LDT is inconclusive. In this regard, the investigation of relationships between LDT application, ND, hip and knee kinematics, and gluteal muscle activity during sports specific jumping and landing tasks should be focused in the future.

Keywords: flat foot, gluteus maximus, gluteus medius, injury prevention

Procedia PDF Downloads 155