Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Remnant Tissue Preservation in Anatomic Double-Bundle ACL Reconstruction on Knee Stability and Graft Maturation

Abstract Several investigators have developed anterior cruciate ligament reconstructions (ACLR) with remnant tissue preservation (RTP) and have reported better clinical outcomes. However, the effects of RTP remain controversial. To date, no reports have compared both clinical and radiological outcomes of anatomic double-bundle ACLR using the hamstring tendon and outside-in technique with/without RTP. This article evaluates the effectiveness of RTP in ACLR on knee stability and graft maturation. In total, 75 patients with unilateral ACL injury who had undergone anatomic double-bundle ACLR using autografted hamstring tendon either with RTP (Group P, n = 43) or without (Group N, n = 32) were enrolled. Clinical scores, pre- and postoperative side-to-side differences (SSDs) obtained using Telos, radiological evaluations of the grafted tendon using the signal/noise quotient (SNQ) measured using magnetic resonance imaging, and arthroscopic evaluations of the grafted tendon were retrospectively compared between the groups. Postoperative SSDs were smaller in the Group P (0.78 ± 1.90 mm) than in the Group N (1.29 ± 2.18 mm); however, this difference was not significant. Comparing two subgroups of the Group P, the SSD was significantly smaller in those with sufficient remnant coverage (−0.56 ± 1.38 mm) than in those without (1.48 ± 1.77 mm) (p = 0.019), as well as in the Group N patients (p = 0.019). The degree of synovial coverage of the anteromedial (p = 0.0064) and posterolateral (p = 0.032) bundle grafted tendon at the time of second-look arthroscopy was significantly better in the Group P than in the Group N. SNQ values of ACL grafted tendon at proximal (p = 0.049), middle, and distal (p = 0.039) one-third in Group P were better than those in Group N. RTP may enhance synovial coverage and maturation of the grafted tendon. Sufficient remnant tissue coverage may contribute to better knee stability. This is a Level III, retrospective comparative study.
Uncertainty of Consumption-Based Carbon Accounts

Consumption-based carbon accounts (CBCAs) track how final demand in a region causes carbon emissions elsewhere due to supply chains in the global economic network, taking into account international trade. Despite the importance of CBCAs as an approach for understanding and quantifying responsibilities in climate mitigation efforts, very little is known of their uncertainties. Here we use five global multiregional input-output (MRIO) databases to empirically calibrate a stochastic multivariate model of the global economy and its GHG emissions in order to identify the main drivers of uncertainty in global CBCAs. We find that the uncertainty of country CBCAs varies between 2 and 16% and that the uncertainty of emissions does not decrease significantly with their size. We find that the bias of ignoring correlations in the data (that is, independent sampling) is significant, with uncertainties being systematically underestimated. We find that both CBCAs and source MRIO tables exhibit strong correlations between the sector-level data of different countries. Finally, we find that the largest contributors to global CBCA uncertainty are the electricity sector data globally and Chinese national data in particular. We anticipate that this work will provide practitioners an approach to understand CBCA uncertainties and researchers compiling MRIOs a guide to prioritize uncertainty reduction efforts.
A New Decomposition-Based NSGA-II for Many-Objective Optimization

Multiobjective evolutionary algorithms (MOEAs) have proven their effectiveness and efficiency in solving problems with two or three objectives. However, recent studies show that MOEAs face many difficulties when tackling problems involving a larger number of objectives as their behavior becomes similar to a random walk in the search space since most individuals are nondominated with respect to each other. Motivated by the interesting results of decomposition-based approaches and preference-based ones, we propose in this paper a new decomposition-based dominance relation to deal with many-objective optimization problems and a new diversity factor based on the penalty-based boundary intersection method. Our reference point-based dominance (RP-dominance), has the ability to create a strict partial order on the set of nondominated solutions using a set of well-distributed reference points. The RP-dominance is subsequently used to substitute the Pareto dominance in nondominated sorting genetic algorithm-II (NSGA-II). The augmented MOEA, labeled as RP-dominance-based NSGA-II, has been statistically demonstrated to provide competitive and oftentimes better results when compared against four recently proposed decomposition-based MOEAs on commonly-used benchmark problems involving up to 20 objectives. In addition, the efficacy of the algorithm on a realistic water management problem is showcased.
Fluorescence in situ hybridization (FISH) in food pathogen detection

Food borne diseases (FBDs) are of foremost public health concern in the world. Those are accountable for millions of deaths worldwide and placing a tremendous burden on the socioeconomic status of developing countries. Currently, reliable statistical estimates for the global influence of FBDs are not available. However, diarrhoeal diseases alone, which form a generous percentage of FBDs, kill 1.9 million children globally every year.1 The majority of FBDs are generated by the consumption of foods contaminated with pathogenic bacteria, viruses or parasites. Listeria monocytogenes, Escherichia coli O157:H7, Staphylococcus aureus, Salmonella enterica, Bacillus cereus, Vibrio spp. , Campylobacter jejuni, Clostridium perfringens, Shiga toxin-producing Escherichia coli and norovirus are some common examples of such microorganisms.2 Mass scale food processors are generally implementing food quality management systems (e.g. ISO 9001) and food safety management systems (e.g. ISO 22000) throughout their entire food chain which restricting the access of food borne pathogens (FBPs) into particular food products. However, increasing demand for street foods and minimally processed foods yet has the risk. Therefore, detection, identification, characterization, and quantification of FBPs in foodstuffs with greater accuracy, sensitivity and rapidity are an utmost important proactive approach to eliminate FBDs.
Escaping Flatlands - Interdisciplinary Collaborative Prototyping Solutions to Current Architectural Topics

The paper describes the interdisciplinary course, Escaping Flatlands, focusing on improving communication between students, who were either from the field of architecture or media informatics and human-computer interaction. There were two underlying themes. The first, the integration and augmentation of digital media and haptic models, escaping the flatland of classic architectural media such as paper or screens. The second theme, expert-laymen communication in public participation, was addressed in the contextual theme and content of the course task, the communication between students of different fields, and the presentation of robust working prototypes at an architectural exhibition. Students, in groups of four, developed three interactive architectural models enhanced with digital content. The course resulted in a number of benefits to students, the chairs, and implications for research. It also led to further collabourations between the two universities involved, including cross-over Bachelor and Master Thesis.
Multicell migration tracking within angiogenic networks by deep learning-based segmentation and augmented Bayesian filtering

Abstract. Cell migration is a key feature for living organisms. Image analysis tools are useful in studying cell migration in three-dimensional (3-D) in vitro environments. We consider angiogenic vessels formed in 3-D microfluidic devices (MFDs) and develop an image analysis system to extract cell behaviors from experimental phase-contrast microscopy image sequences. The proposed system initializes tracks with the end-point confocal nuclei coordinates. We apply convolutional neural networks to detect cell candidates and combine backward Kalman filtering with multiple hypothesis tracking to link the cell candidates at each time step. These hypotheses incorporate prior knowledge on vessel formation and cell proliferation rates. The association accuracy reaches 86.4% for the proposed algorithm, indicating that the proposed system is able to associate cells more accurately than existing approaches. Cell culture experiments in 3-D MFDs have shown considerable promise for improving biology research. The proposed system is expected to be a useful quantitative tool for potential microscopy problems of MFDs.
Dynamic resource allocation and rate coverage analysis of two-tier HetNet with cognitive femtocell

The tremendous growth of mobile multimedia applications leads to increased traffic as well as energy consumption in cellular mobile communications. Hence, further improvements in resource allocation are essential. Wireless communication systems are aimed at providing higher capacity, improved, and continuous connectivity and minimizing energy consumption to support data-intensive multimedia applications. The main contribution of this paper is toward the maximization of the spectrum and power efficiencies of such networks through intelligent Q-learning-based control algorithms in the Downlink. The model in this paper considers a two-tier HetNet with Cognitive Open Access femtocell, which is designed using stochastic geometry tool. Furthermore, we analyzed and evaluated the Rate Coverage performance for macrocell and femtocell users over the Rayleigh Fading environment in the Downlink. The MonteCarlo simulations results that have been obtained and validated showed that the proposed method achieved an increased overall capacity while guaranteeing the primary user service level. Moreover, Rate Coverage signifies the user population meeting their target threshold. These factors cumulatively enhanced the overall network capacity.
Fluorometric Sensor for Mercury Ion Detection in a Fluidic MEMS Device

Mercury (Hg<sup>2+</sup>) ion is one of the heavy metal ions present in water and highly poisonous to human consumption. Thus, developing an efficient sensor for detection of mercury ions at different concentration levels in the water down to nM is essential. Here, we report the development of fluorometric sensor using the fluidic MEMS device (i.e., microfluidic device) with high sensitivity and selective detection of Hg<sup>2+</sup> in water using tiny quantity of sensing fluids (<inline-formula> <tex-math notation="LaTeX">$\sim 2.8~\mu \text{L}$ </tex-math></inline-formula>). L-Arginine-capped gold nanoparticles functionalized with Rhodamine 6G allowed the on-chip fluorescence detection of Hg<sup>2+</sup> ions concentration regime from 0 to 16 nM in water samples. The developed microfluidic device senses the presence of Hg<sup>2+</sup> ions through the change in intensity of fluid fluorescence. The sensor response is found to be linear with respect to the Hg<sup>2+</sup> ions concentration ranging from 2 to 12 nM.
Power allocation for multiceli massive MIMO systems under Rician fading with statistical CSI

In this paper, we consider the power allocation problem for downlink multiceli massive multiple-input multiple-output (MIMO) communications over Rician fading channels. Each link between a user equipment (UE) and a base station (BS) forms a jointly correlated Rician channel, on which some properties of the channel covariance matrices in the massive MIMO scenario are presented. Based on these properties, assuming perfect channel state information (CSI) at UEs and statistical CSI, i.e. the channel coupling matrix (CCM) and the line-of-sight (LOS) component, at BSs, we design a near-optimal power allocation algorithm in terms of maximizing the deterministic equivalent of the ergodic sum-rate in the beam domain. The closed-form objective function of the power allocation problem turns out to be a difference of concave functions, thus we search for the solutions iteratively. Numerical results show that the proposed method performs well in terms of achievable sum-rate.
Adaptive real-time dual-mode filter design for seamless pedestrian navigation

Seamless navigation requires that the mobile device is capable of offering a position solution both indoors and outdoors. Novel seamless navigation system design was implemented and tested to achieve this aim. The design consists of general navigation system framework blocks and of the necessary interface agreements between the blocks. This approach enables plug-and-play style design of modules. The implementation used four preselected key technologies. Microstrain 3DM-GX4-45 foot-mounted inertial measurement unit sensor data was fused together with the u-blox GNSS receiver positions outdoors. Context sensitive inference engine enabled the fusion of position updates indoors from the De-cawave TREK1000 Ultra WideBand ranging kit and from the 6 Kontakt.io/Raspberry Pi anchor-based Bluetooth low energy fingeprinting system. Novel dual-mode filter design uses a particle filter and the pentagon buffer enhanced Kalman filter in the position solution derivation. Depending on the map and the walls in the environment and on the quality of position updates, the implemented control logic employs the most fit filter for the current context. Computational power is now focussed, when particle filter is needed. The novel pentagon buffer enhanced Kalman filter is 10 times faster, allowing power saving when situation is not too critical. Moreover, the buffer provides position updates by interacting with the map and helps to correct the position solution. The navigation system is seamless according to the tests conducted around and within the Nottingham Geospatial building. No user input is needed for smooth transition from outdoors to indoors and vice versa. The system achieves an accuracy of 2.35 m outdoors and 1.4 m indoors (95% of error). Inertial system availability was continuous, while GNSS was available outdoors and BLE and UWB indoors.
A Basal Ganglia Network Centric Reinforcement Learning Model and Its Application in Unmanned Aerial Vehicle

Reinforcement learning brings flexibility and generality for machine learning, while most of them are mathematical optimization driven approaches, and lack of cognitive and neural evidence. In order to provide a more cognitive and neural mechanisms driven foundation and validate its applicability in complex task, we develop a basal ganglia (BG) network centric reinforcement learning model. Compared to existing work on modeling BG, this paper is unique from the following perspectives: 1) the orbitofrontal cortex (OFC) is taken into consideration. OFC is critical in decision making because of its responsibility for reward representation and is critical in controlling the learning process, while most of the BG centric models do not include OFC; 2) to compensate the inaccurate memory of numeric values, precise encoding is proposed to enable working memory system remember important values during the learning process. The method combines vector convolution and the idea of storage by digit bit and is efficient for accurate value storage; and 3) for information coding, the Hodgkin–Huxley model is used to obtain a more biological plausible description of action potential with plenty of ionic activities. To validate the effectiveness of the proposed model, we apply the model to the unmanned aerial vehicle (UAV) autonomous learning process in a 3-D environment. Experimental results show that our model is able to give the UAV the ability of free exploration in the environment and has comparable learning speed as the ${Q}$ learning algorithm, while the major advances for our model is that it is with solid cognitive and neural basis.
Race-Condition-Aware and Hardware-Oriented Task Partitioning and Scheduling Using Entropy Maximization

In a multithreaded execution environment, race condition leads to computational errors and system hazards. Up to date, a series of task scheduling strategies have been presented in the literature to reduce the risk of race condition. Because of the increasing complexity of this problem in multi-core systems, existing task scheduling approaches are not very efficient. To deal with this challenge, in this work, we develop a race-condition-aware and hardware-oriented task partitioning and scheduling algorithm using entropy maximization model. We model uncertainty as a probabilistic occurrence within a time interval, and hence the characteristics of event ordering are analyzed through an uncertainty matrix. Next, a metric is developed to measure the uncertainty of task execution in various execution environments. Finally, a maximum entropy model is generated to ensure the lowest probability of race condition during task execution. The smallest one among maximum entropy values is chosen and used in our proposed task scheduling algorithm. Experimental results show that the proposed task scheduling strategy based on our maximum entropy model outperforms existing state-of-the-art approaches. For example, in a 128-core computing system, the task execution time, CPU utilization ratio, and throughput of our proposed task scheduling is improved by <inline-formula> <tex-math notation="LaTeX">$15.3\sim 36.4$</tex-math><alternatives><inline-graphic xlink:href="li-ieq1-2784829.gif"/> </alternatives></inline-formula> percent, <inline-formula><tex-math notation="LaTeX">$8.2\sim 17.6$</tex-math> <alternatives><inline-graphic xlink:href="li-ieq2-2784829.gif"/></alternatives></inline-formula> percent, and <inline-formula><tex-math notation="LaTeX">$20.7\sim 41.4$</tex-math><alternatives> <inline-graphic xlink:href="li-ieq3-2784829.gif"/></alternatives></inline-formula> percent, respectively. Moreover, our proposed scheduling algorithm exhibits low computational complexity and good adaptivity to diverse execution environments.
NOD is a plus end–directed motor that binds EB1 via a new microtubule tip localization sequence

Chromosome congression, the process of positioning chromosomes in the midspindle, promotes the stable transmission of the genome to daughter cells during cell division. Congression is typically facilitated by DNA-associated, microtubule (MT) plus end–directed motors called chromokinesins. The Drosophila melanogaster chromokinesin NOD contributes to congression, but the means by which it does so are unknown in large part because NOD has been classified as a nonmotile, orphan kinesin. It has been postulated that NOD promotes congression, not by conventional plus end–directed motility, but by harnessing polymerization forces by end-tracking on growing MT plus ends via a mechanism that is also uncertain. Here, for the first time, it is demonstrated that NOD possesses MT plus end–directed motility. Furthermore, NOD directly binds EB1 through unconventional EB1-interaction motifs that are similar to a newly characterized MT tip localization sequence. We propose NOD produces congression forces by MT plus end–directed motility and tip-tracking on polymerizing MT plus ends via association with EB1.
Integrated SoC UWB radar with built-in 15-bit M-sequence generator

This paper presents a novel ultra-wide-band (UWB) system integrated in one structure on a chip (SoC). This is the newest solution that includes transmitter with built-in generator of pseudo-random binary sequence, consist of 15-bit linear shift register, synchronization unit and wide-band receiver. The main task of this structure is to improve the support of the management unit response to the control signals, make the more precise measurements and integrates system to the small compact device. This work is dedicated to the more detailed description of mentioned pseudo-random binary sequence generator and its comparison with other used circuits. There are also discussed advantages, disadvantages and possibilities of utilization in various applications in the case of active UWB sensing.
OpenDaylight controller — Enhancements for a smoother software defined network

Software Defined Network (SDN) is a new networking paradigm which has emerged recently. Research in SDN is flourishing with new architectures, methods and solutions. However, minimal efforts have been made for creating a vendor independent open source SDN solution; which can be readily deployed in Cloud networks, Internet of Things (IoT) etc. For betterment of open source SDN, suitable improvisations with thorough validation in real networks is necessary. In this paper, we have conducted evaluation of open source Open Flow (OF) based SDN network using OpenDaylight (ODL) controller. Both Mininet simulations and Openstack cloud network deployments have been used. As per the observations, certain functional drawbacks persist in ODL which affects the SDN control plane management. A new module called “Network Provider Module” (NPM) has been developed in ODL controller. NPM addresses the functional drawbacks in ODL and brings out comprehensive SDN capabilities for cloud provisioning as well.
PPDCA: Privacy-preserving crowdsensing data collection and analysis with randomized response

Randomized response mechanisms for guaranteeing crowdsensing data privacy have attracted scholarly attention; aggregators can ensure privacy by collecting only randomized data and individuals have plausible deniability regarding their responses. The analysts employed by organizations can still make predictions and conduct analyses using the randomized data. Existing randomized response-based data collection solutions have severely restricted functionality and usability, resulting in impractical and inefficient systems. Hence, we propose a randomized response-based privacy-preserving crowdsensing data collection and analysis (PPDCA) method, in which a complementary randomized response (C-RR) approach is designed to guarantee data privacy and to preserve features for data analysis. Moreover, we transform encoded data into binary vectors and generate a learning network using a deep learning framework. Through C-RR and our learning model, PPDCA can perform exceptionally in terms of high-utility analysis for the collected client-side strings, compared with state-of-the-art methods.
Recent Advances in Applications of Ionic Liquids in Miniaturized Microextraction Techniques

Green sample preparation is one of the most challenging aspects in green analytical chemistry. In this framework, miniaturized microextraction techniques have been developed and are widely performed due to their numerous positive features such as simplicity, limited need for organic solvents, instrumentation of low cost and short time of extraction. Also, ionic liquids (ILs) have unequivocally a “green” character, which they owe to their unique properties including the re-usage, the high reaction efficiency and selectivity in room temperature, the ability to dissolve both organic and inorganic compounds, and thermal stability. In the present review, the recent advances in the application of ionic liquids in miniaturized liquid and solid phase extraction techniques as extractants, intermediate solvents, mediators and desorption solvents are discussed, quoting the advantages and drawbacks of each individual technique. Some of the most important sample preparation techniques covered include solid-phase microextraction (SPME), dispersive liquid-liquid microextraction (DLLME), single-drop microextraction (SDME), stir bar sorptive extraction (SBSE), and stir cake sorptive extraction (SCSE).