Robust Package Research Articles

Qluster: An easy-to-implement generic workflow for robust clustering of health data.

The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant variability in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this article proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity of applications (e.g. usable on small or big data, on continuous, categorical or mixed variables, on database of high-dimensionality or not), (2) ease of implementation (need for few packages, few algorithms, few parameters, ...), and (3) robustness (e.g. use of proven algorithms and robust packages, evaluation of the stability of clusters, management of noise and multicollinearity). This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. It can be useful both for data scientists with little experience in the field to make data clustering easier and more robust, and for more experienced data scientists who are looking for a straightforward and reliable solution to routinely perform preliminary data mining. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors.

A novel medical image enhancement technique based on hybrid method

Medical images are a specific type of image that can be used to diagnose disease in patients. Critical uses for medical images can be found in many different areas of medicine and healthcare technology. Generally, the medical images produced by these imaging methods have low contrast. As a result, such types of images need immediate and fast enhancement. This paper introduced a novel image enhancement methodology based on the Laplacian filter, contrast limited adaptive histogram equalization, and an adjustment algorithm. Two image datasets were used to test the proposed method: The DRIVE dataset, forty images from the COVID-19 Radiography Database, endometrioma-11, normal-brain-MRI-6, and simple-breast-cyst-2. In addition, we used the robust MATLAB package to evaluate our proposed algorithm’s efficacy. The results are compared quantitatively, and their efficacy is assessed using four metrics: Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Contrast to Noise Ratio (CNR), and Entropy (Ent). The experiments show that the proposed method yields improved images of higher quality than those obtained from state-of-the-art techniques regarding MSE, CNR, PSNR, and Ent metrics.

Community implementation of contingency management to address stimulant use

IntroductionContingency management (CM) is efficacious for reinforcing stimulant abstinence, and technical assistance (TA) is increasingly sought to aid its community-based implementation. In an interagency partnership involving a sponsoring single-state authority and statewide treatment agency in Oregon, an intermediary purveyor organization provided a robust TA package to support design, implementation, and evaluation of CM programming for an opioid treatment program (OTP) over the course of a 12-month implementation service project. MethodsIn addition to an online training offering OTP leaders and staff conceptual foundation for CM, the TA package included purveyor-led activities to: 1) engage leaders in collaborative design to customize CM programming; 2) assemble a local implementation team to logistically prepare OTP systems for CM delivery; 3) provide virtual coaching-to-criterion to assure readiness of counseling staff to deliver CM programming; 4) compile a tailored CM resource library of implementation support materials; and 5) avail ongoing consultation during implementation. Stimulant abstinence was targeted via a voucher-based protocol with escalating reinforcement, for which gift cards from local vendors served as reinforcers. Virtual coaching eventuated in individual role-play assessments, wherein staff delivery of CM programming with a standardized patient was scored via Likert scale (1 = Very Poor, 7 = Excellent) on six CM fidelity dimensions. This observational cohort design subsequently assessed clinical effectiveness during active implementation via OTP records review for CM-exposed and comparison client groups. ResultsIn role-play assessments, all counseling staff exceeded an a priori fidelity benchmark signifying implementation readiness (M = 31.33, SD = 3.72). Among 73 clients enrolled in the CM programming, rate of stimulant-free urine drug screens was 11 % greater than among 120 clients serving as historical controls (p < .01; Cohen's D = 0.40). The study also identified secondary therapeutic benefit in six-month treatment retention, with clients enrolled in CM retained at a 14 % greater rate than 162 CM-ineligible clients concurrently enrolled in OTP services (p < .05). ConclusionsFindings from this interagency partnership offer reason for optimism regarding community-based implementation. Beyond the demonstrated empirical support for this TA package and resulting clinical effectiveness of the CM programming, an eventual sustainment decision by OTP leadership strengthens the rationale for customizing CM to clinical settings' local needs and resources.

Development of Next Generation EV Coolant

Research indicates that there are two approaches to Electric Vehicle (EV) liquid cooling: direct and indirect. Direct liquid cooling involves direct contact between the coolant and the battery pack. Indirect liquid cooling requires a medium in between the battery pack and the coolant, preventing direct contact. Many prominent EV manufacturers have adopted indirect liquid cooling for example Tesla, and GM in North America. Currently, shelf coolants used for Internal Combustion Engines (ICE) are being used in many EV vehicles for indirect cooling. One of the challenges is absence of ASTM test methods for evaluating EV coolants. Some operators have used copper wires for generating deposits by employing voltage to test low conductivity EV coolants. Dober long ago realized that copper and aluminum will be the main components of EV engines hence used copper wire under high temp and pressure to show differentiation among coolants. The present paper focuses on Dober-in-house test Parr reactor methodology for comparing electrical conductivities, pH and additive depletion of various coolants thereby showing meaningful trends with time and temperature. This ultimately helps in developing new generation EV coolants. It is concluded that low electrical conductivity along with robust corrosion inhibition package is needed for optimum performance of EV coolants.

A Reliable Anti-Interference Probe Based on Fluorescence Intensity Ratio Technique for Cryogenic Temperature Detection

In this paper, a reliable anti-interference probe based on fluorescence intensity ratio technique is proposed for cryogenic temperature detection in refrigeration process, the sensing unit of which is based on a self-made coreless tellurite optical fiber (TOF) co-doped with Er <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3+</sup> /Yb <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3+</sup> ions. The temperature information was demodulated by analyzing the fluorescence spectral changes. In order to meet the needs of practical applications, the dual-curing method was applied to realize the TOF splicing, and the thin-walled silica tube was used to provide robust packaging. For the first time, the anti-cross-sensitive properties of the fluorescent probe under vibration and liquid interference were quantified, and the repeated response deviation was on the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> . The actual working capacity of the probe was experimentally investigated by applying it to measuring the circulating exhale and cryogenic equipment, in which a fast time response of about 2.11 s and a small absolute error of 0.80 K were obtained. The probe has good stability and repeatability, and can work stably even in complex environments with vibrations and liquids, showing strong anti-interference ability. In addition, the robust package and easy method make the probe suitable for mass fabrication.

Study of robust package strength characterization of memory packages for handheld application

In mobile market, the demand of memory packaging increase year over year. While the form factor of memory packages moves toward smaller and thinner geometry with higher pin counts, the package overall mechanical robustness becomes one of a key criteria for mobile companies regarding to consumer usage condition. Package strength is an indicator for package robustness. Package with low strength may likely lead to die cracking or epoxy mold compound (EMC) cracking during assembly, testing, reliability testing or consumer field usage. In this study, comprehensive factors will be covered to discuss the impact on package strength performance. In addition, the failure analysis would be studied aiming to observe initial crack point. Last, the study will discuss about the current testing methodology and will later propose new testing methodologies which are more suitable to field application condition.

Impact of data processing and robust machine learning process on accurate estimation of specific heat capacity property in energy storage applications

The specific heat capacity propery is a critical thermal property because it is directly related to heat storage and transfer. The need for better and novel thermal energy storage materials have led to the discovery of high temperature nanofluids with molten salts (nanosalts). The utilization of novel machine learning tools in accurately estimating the specific heat capacity of nanofluids, would further pave the way for more innovative studies on nanosalt, hence the prediction of specific heat capacity of hybrid nanofluids constitute a significant research area. Despite the few researches conducted in the area of prediction of these fluids, priority with regards to the robust analysis of input variables for different machine learning algorithms have not been given due consideration. Therefore, this present study aims to develop and validate a robust package of machine learning algorithms (twenty machine learning algorithms) in estimating the specific heat capacity of hybrid nanofluids, across different feature spaces. The different algorithm hyperparameters were adjusted to give optimum prediction. A total of 600 data points were retrieved and split into training, testing, and validation data set, for which the validation dataset was not used in the training and testing process. The input variables used in the training of the machine learning models are temperature, volume fraction, the specific heat capacity of the base fluid, the specific heat capacity of individual nanoparticles, and the mixture ratio of individual nanoparticles. The Model A, which constituted the seven (7) input variables gave the best prediction accuracy with validation score of 0.999703, while the Model C, which contained the feature variables of temperature, volume fraction and specific heat capacity of the base fluid gave the least prediction accuracy. The optimal machine algorithms retrieved for the Model A, B, C and D was the Ridge, Gradient Boost regressor, Passive Aggressive regressor, and Kernel ridge regressor. The combination of ensemble tree regressors and online learning algorithms can be coded and used in future prediction analysis of SHC, as they have both shown accurate prediction quality.

Classic Evans’s Drop Corrosion Experiment Investigated in Terms of a Tertiary Current and Potential Distribution

Background: Evans’s drop is a classic corrosion experiment that is nearly 100 years old, and it is analogous to other corrosion systems promoted by O2 gradients. The availability of more robust finite element software packages opens the possibility to reach a deeper understanding of these kind of corrosion systems. Methodology: In order to solve the problem, the model includes the governing mass transport diffusion and migration equation and the material balance in a nonsteady state by the finite element method. This is performed using COMSOL Multiphysics to predict the tertiary current and potential distribution considering the geometry, reaction kinetics, and mass transport for each ionic species. Significant Findings: A simulation of the tertiary current and potential distribution of the Evans’s drop corrosion experiment on an iron surface is presented. An oxygen concentration difference of 0.18 mol m−3 between the center and the drop periphery sets up a potential difference of 60 mV which acts as a corrosion driving force. Reaction kinetics are described by Tafel equations. Results include the evolution of concentration profiles for OH−, Fe2+, Fe3+, Fe(OH)2, and Fe(OH)3.

Herbicide Tolerance Options for Weed Control in Lanza® Tedera

Tedera is a drought-tolerant perennial forage legume introduced in Australia in 2006. In October 2018, T15-1218 Lanza®, the world’s first tedera variety, was released by the Department of Primary Industries and Regional Development and Meat &amp; Livestock Australia for commercial use. A key agronomic practise for the successful establishment and adoption of tedera is to have a robust herbicide package to control a range of grass and broadleaf weeds well tolerated by tedera. A total of 9 pre-emergent and 44 post-emergent herbicide treatments were evaluated in eight experiments from 2017 to 2021. To control grasses such as annual ryegrass (Lolium rigidum Gaud.), propyzamide and carbetamide can be recommended for pre- or post-emergent applications and butroxydim, clethodim, and haloxyfop for post-emergent applications. The broadleaf pre-emergent herbicides recommended are clopyralid to control emerged capeweed (Arctotheca calendula (L.) Levyns), fomesafen to control pre-emergent wild radish (Raphanus raphanistrum L.), and the double mix of fomesafen + diuron, flumetsulam + diuron, and the triple mix of fomesafen + diuron + flumetsulam to control pre-emergent capeweed, pre- and post-emergent wild radish, and other broadleaf weeds. The most consistently well tolerated post-emergent herbicides by tedera seedlings and adult plants were diflufenican, diuron, flumetsulam, fomesafen, and their two- or three-way mixes that will provide good control of capeweed and wild radish. Desiccants such as paraquat or diquat were also well tolerated by 1-year-old tedera plants that recovered after being desiccated.

GeomBD3: Brownian Dynamics Simulation Software for Biological and Engineered Systems.

GeomBD3 is a robust Brownian dynamics simulation package designed to easily handle natural or engineered systems in diverse environments and arrangements. The software package described herein allows users to design, execute, and analyze BD simulations. The simulations use all-atom, rigid molecular models that diffuse according to overdamped Langevin dynamics and interact through electrostatic, Lennard-Jones, and ligand desolvation potentials. The program automatically calculates molecular association rates, surface residence times, and association statistics for any number of user-defined association criteria. Users can also extract molecular association pathways, diffusion coefficients, intermolecular interaction energies, intermolecular contact probability maps, and more using the provided supplementary analysis scripts. We detail the use of the package from start to finish and apply it to a protein-ligand system and a large nucleic acid biosensor. GeomBD3 provides a versatile tool for researchers from various disciplines that can aid in rational design of engineered systems or play an explanatory role as a complement to experiments. GeomBD version 3 is available on our website at http://chemcha-gpu0.ucr.edu/geombd3/ and KBbox at https://kbbox.h-its.org/toolbox/methods/molecular-simulation/geombd/.

A wireless battery-less and moisture-resistant packaged glucose sensing system employing hydrogel-based inductive sensing technique and low-power ASIC for long-term glucose monitoring

A wireless and battery-less glucose sensing system has been demonstrated for future long-term implantable glucose monitoring. The proposed glucose sensing system employs an oscillator-based inductive sensor that detects a volumetric change of a glucose-sensitive hydrogel. A robust 3D-printed moisture-resistant package is used to encapsulate the sensing system for in-vitro characterization. An application-specific integrated circuit (ASIC) is designed to enable in-situ temperature characterization as well as system miniaturization. The overall system dissipates 600 μW and exhibits a dimension of 6 mm × 6 mm x 25 mm. A rotation-insensitive wireless powering scheme has been demonstrated to reliably deliver power to a tube-shaped implantable system. The prototype sensor operated at 190 MHz can be wirelessly powered by a 6.78 MHz RF power source over a distance of 2 cm. The sensor achieves a glucose sensing resolution of 0.025–0.055 mM within a typical physiological glucose concentration between 4 mM and 10 mM at a typical human body temperature of 37 ºC. The prototype sensor has demonstrated a reliable in-vitro operation for 60 days without noticeable performance degradation.

The influence of employee participation, rewards and recognition, job security, and performance feedback on employee engagement

The fundamental goal of this research was to develop a model of employee engagement from the perspective of private sector employees in Melaka, Malaysia, which can be added to the body of knowledge. It was carried out to determine the relationships between employee participation, job security, performance feedback, as well as rewards and recognition on employee engagement. This study found that all tested variables are positively related to employee engagement. Based on the findings, it is recommended that the private sector should prioritise efforts that enhance employee participation, provide robust career package and constant feedback. Employees’ contributions also need to be adequately recognised at all levels to ensure organisations are able to enhance its performance.

AUGMENT: Navigation Apps Instruction for Older Adults With Cognitive Impairment

Augmenting User Geocordinates and Mobility by ENhanced Tutorials (AUGMENT) is a development project in the ENHANCE Rehabilitation Engineering Research Center aiming to promote community engagement for aging adults with cognitive impairment (CI) from stroke, traumatic brain injury, and mild cognitive impairment. AUGMENT aims include 1) providing proof of concept that a robust instructional package can support successful use of existing, complex navigation apps, Google maps and rideshare app Uber, by a diverse set of people with CI; and 2) providing proof of product by testing performance with and without instruction. We discuss the needs assessment phase and development of new tests to assess wayfinding abilities and reported difficulties with navigation, using a control sample of 384 community-dwelling older adults. We found that self-reported navigation difficulties are predicted (R-square = .28) by gender, a spatial orientation test, self-reported memory ability, and severity of memory difficulty.

Healthy volunteers in first-in-human oncology drug development for small molecules.

This review provides tools to consider the inclusion of healthy volunteers (HVs) in first-in-human (FIH) oncology clinical trials with small molecules, including targeted and immunomodulatory agents, a strategy that was not envisioned with classic chemotherapy. To enable an FIH oncology trial in HVs compared to cancer patients (CPs), a robust nonclinical package must be generated, which includes toxicokinetic and pharmacokinetic studies, as well as more extensive safety pharmacology, toxicology and genotoxicity studies. This strategy could provide an early clinical characterization of the pharmacokinetic parameters and clinical safety profile in the absence of comorbidities and concomitant medication. It also avoids the ethical issue of administrating subtherapeutic doses to CPs, and could potentially help to accelerate the timelines of clinical drug development for patient care. That being said, stakeholders involved in these studies need to proceed with caution, fully understand the regulatory guidance and thoroughly evaluate the benefits and risks. This paper serves to address the regulatory guidance and other considerations needed when using healthy volunteers in early oncology trials.

Photonic-lattice-like guiding microstructures in Nd:YVO4 waveguides: Fabrication, 3D splitting, and lasing

We report femtosecond laser direct writing (FsLDW) of three-dimensional (3D) photonic-lattice-like microstructures in Nd:YVO4 crystal. These structures are capable of simultaneously realizing optical waveguiding and beam splitting under both passive and active operations. Integrated further with the Ag nanoparticles (NPs) embedded in SiO2 crystal as saturable absorber (SA), these photonic-lattice-like microstructures have been used for generation of Q-switched mode-locked lasers with tailored optical modes. This work indicates the promising application of FsLDW in constructing complex 3D structures in functional dielectric crystals and their excellent performance as well as small footprints for on-chip integration in compact and robust packages.

A Systematic Review on Infliximab Biosimilar SB2: From Pre-Clinical Data to Real-World Evidence

ABSTRACT Introduction The infliximab biosimilar SB2 was approved in the EU (2016, Flixabi®) and the US (2017, Renflexis®) for the same indications as the reference product (Remicade®) based on a robust analytical and clinical data package. Areas covered This systematic literature review summarizes available analytical and clinical data on SB2, including randomized controlled clinical trials and real-world evidence studies. Overall, 184 articles and congress abstracts were identified (excluding duplicates), whereof 5 reports on analytical data, four reports on two randomized controlled trials and 13 reports of real-world evidence studies were included. Expert opinion The available analytical and clinical data support the equivalence of SB2 to the reference product across approved indications. This is further supported by emerging real-world evidence, particularly in extrapolated indications such as inflammatory bowel disease for both infliximab-naïve patients and patients already established on infliximab switching to SB2. Switching from originator or biosimilar infliximab to SB2 including both single and multiple switches was not associated with an increased risk of loss of treatment response or any safety or immunogenicity concerns. Overall, the approved infliximab biosimilar SB2 is safe and effective in clinical practice across licensed indications.

Abstract 294: In vitro efficacy studies to support engineered T cell therapies

Abstract Cell therapies, such as Chimeric Antigen Receptor (CAR) and T Cell Receptor (TCR) T cells, are some of the latest immune-therapeutic approaches, showing great momentum in research and now the clinic. Only recently, two anti-CD19-CAR-T products have been approved by the FDA and EMA for treatment of CD19+ lymphoid malignancies, including Acute Lymphoblastic Leukemia (B-ALL) and Diffuse Large B cell Lymphoma (DLBCL). Many more CAR-T cell products are being explored, targeting a wide variety of tumor antigens, directed towards both liquid and solid tumors as well other clinical indications such as autoimmune diseases or organ transplantations. In early stage development (pre-clinical research), the use of in vivo animal models has presented significant hurdles in translatability of cell therapies. As a result, the establishment of high-quality in vitro efficacy and safety studies to foster the development of such therapies has become critical. The purpose of this study is to show a number of in vitro efficacy experiments aimed at determining cell therapy activity, specificity and potency using an anti-HER2 CAR-T as a model system. To asses therapeutic activity, a proof of concept (PoC) study must be in place in which the therapy is tested for its ability to kill a reactive control cell line. Activity tests can be used to aid disease indications (including a panel of cell lines or primary material) and importantly, to aid lead optimization. Especially for cell therapies, activity experiments should be combined with “cellular fitness” tests, which are able to determine a therapy's ability to expand and persist following serial rounds of tumor challenges. Here we have developed an in vitro CAR-T activity and Repeated Antigen Stimulation (RAS) assay. Furthermore, specificity studies are aimed at quantifying the absence of killing of control cells that are negative for the target antigen. Here we used a three-way co-culture system in which effector cells are incubated with positive target cells and negative control cells in order to determine specificity under activating cellular conditions. Finally, to determine cell therapy potency we have used cytotoxicity co-culture experiments in which different effector-target ratios are tested at different time points to allow quantification of lower and upper limits of the cellular therapy to be tested. Our studies can generate a robust and essential efficacy data package that defines and describes a cell therapy's activity, specificity and potency, and is applicable to CAR-T cells and other engineered cell therapy approaches. One lesson learnt the hard way is that the most active and potent drug is not always the safest; for all cell therapies efficacy and safety will need to be balance and taken into considering during early development and lead optimization. Having developed a robust in vitro efficacy and safety platform, we keep strengthening our abilities to select the best therapies to further progress to clinic. Citation Format: Sanne L. Holt, Sophie Vermond, Monique Hazenoot, Rene McLaughlin, Marco Guadagnoli, Marijn Vlaming. In vitro efficacy studies to support engineered T cell therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 294.

Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

Temperature Sensor Based on Microbottle Resonator Immersed in Isopropanol

Temperature measurement of an isopropanol-immersed microbottle resonator is demonstrated. The theoretical analysis indicates that the resonance wavelength blue shifts as the increase of temperature, and the temperature sensitivity increases with the decrease of resonator dimension, which are also demonstrated by experimental results. The temperature sensitivity enhancement of up to 4.6 times relative to the sensor in the air is achieved, which could be benefit from the high thermal refraction coefficient of isopropanol. Furthermore, a robust package has also been proposed to make the present sensor more practically.

FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter

This letter presents a computationally efficient and robust LiDAR-inertial odometry framework. We fuse LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. To lower the computation load in the presence of a large number of measurements, we present a new formula to compute the Kalman gain. The new formula has computation load depending on the state dimension instead of the measurement dimension. The proposed method and its implementation are tested in various indoor and outdoor environments. In all tests, our method produces reliable navigation results in real-time: running on a quadrotor onboard computer, it fuses more than 1200 effective feature points in a scan and completes all iterations of an iEKF step within 25 ms. Our codes are open-sourced on Github. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>