Miracle-dz/UniFAR-FTUs huggingface dataset:
Participatory certifications for the sustainability transition of food systems in Costa Rica: barriers and opportunities for scaling out

ABSTRACT Participatory certifications for organic production are proliferating around the world, especially in India and Latin America. Costa Rica is one of the Latin American countries that recognizes Participatory Guarantee Systems (PGS) by law although, so far, only few such types of certifications exist in the country. PGS schemes for promoting organic agriculture can be especially important for the vegetable sector of Costa Rica given the historically large use of agrochemicals in conventional (fruits and vegetables) production and its environmental and public health consequences. In this article, we use transition theory and the Multi-Level Perspective to analyze PGS for organic vegetables as a niche with a potential for but facing barriers to scaling out. Our literature review, supported by field surveys and observations, revealed different social and institutional barriers characterizing the niche–regime interactions which foster, or at times hamper, the scalability of PGS. Examples from other countries like Brazil and Mexico helped shed light on potential future paths for PGS in Costa Rica. Our results suggest that current institutional recognition of PGS might support scaling out if a more flexible legislative framework is implemented and political and technical support provided to participatory certification initiatives is increased.
Processive Enzymes Kept on a Leash: How Cellulase Activity in Multienzyme Complexes Directs Nanoscale Deconstruction of Cellulose

Biological deconstruction of polymer materials gains efficiency from the spatiotemporally coordinated action of enzymes with synergetic function in polymer chain depolymerization. To perpetuate enzyme synergy on a solid substrate undergoing deconstruction, the overall attack must alternate between focusing the individual enzymes locally and dissipating them again to other surface sites. Natural cellulases working as multienzyme complexes assembled on a scaffold protein (the cellulosome) maximize the effect of local concentration yet restrain the dispersion of individual enzymes. Here, with evidence from real-time atomic force microscopy to track nanoscale deconstruction of single cellulose fibers, we show that the cellulosome forces the fiber degradation into the transversal direction, to produce smaller fragments from multiple local attacks (“cuts”). Noncomplexed enzymes, as in fungal cellulases or obtained by dissociating the cellulosome, release the confining force so that fiber degradation proceeds laterally, observed as directed ablation of surface fibrils and leading to whole fiber “thinning”. Processive cellulases that are enabled to freely disperse evoke the lateral degradation and determine its efficiency. Our results suggest that among natural cellulases, the dispersed enzymes are more generally and globally effective in depolymerization, while the cellulosome represents a specialized, fiber-fragmenting machinery.
Delay-Optimal Temporal-Spatial Computation Offloading Schemes for Vehicular Edge Computing Systems

Vehicular edge computing (VEC) is a potential solution to meet delay-sensitive and computation-intensive vehicular applications demands. However, the nature of mobility brings a significant challenge, i.e., when and where to offload task can capture the optimal delay performance for VEC. To address this challenge, firstly, we characterizes the temporal-spatial correlation for VEC system. With the aid of this, we formulate the energy-constrainted delay minimization problem as a mixed integer nonlinear programming (MINLP). Due to the fact that it is hard to tackle the features about non-convex and coupling of designed MINLP problem, this paper transforms the original problem into task placement sub-problem and delayed offloading sub-problem. Specifically, the former is solved by two-stage decision tree algorithm and the latter is obtained by dynamic programming technique. On this basis, a temporal-spatial computation offloading scheme is proposed. Simulations demonstrate that our proposed scheme achieves superior delay performance and provides useful guides for choosing suitable schemes in different conditions.
An uncertainty inversion technique using two-way neural network for parameter identification of robot arms

Due to structural complexity of robot arms, constraints of experiments, especially uncertainty of design parameters, numerical models for dynamics analysis of robot arms can produce erroneous results, which can seriously affect the performance of the designed robot arms. Reliable parameter uncertainty identification for robot arms becomes important. The current methods for uncertainty analysis have double-layered processes, in which the inner layer is for uncertainty propagation and the outer layer is an iterative optimization process. Such a nested double-layered approach limits computational efficiency. This work proposes a novel inverse method for parameter uncertainty identification using a two-way neural network. First, an element (FE) model of a robot arm is established and validated experimentally. Sensitivity analysis is then conducted using the FE model to determine a set of major parameters to be identified. A two-way neural network is next established, and the explicit formulae of direct weight inversion (DWI) use to inverse these parameters of the robot arm. Finally, the inverse result is validated by experiments. Our study show that the present inverse method can greatly improve the computational efficiency. It provides a new avenue to tackle complex inverse problems in engineering and sciences.
Translation and Validation of the Boston Technical Performance Score in a Developing Country

Introduction The Technical Performance Score (TPS) was developed and subsequently refined at the Boston Children's Hospital. Our objective was to translate and validate its application in a developing country. Methods The score was translated into the Portuguese language and approved by the TPS authors. Subsequently, we studied 1,030 surgeries from June 2018 to October 2020. TPS could not be assigned in 58 surgeries, and these were excluded. Surgical risk score was evaluated using Risk Adjustment in Congenital Heart Surgery (or RACHS-1). The impact of TPS on outcomes was studied using multivariable linear and logistic regression adjusting for important perioperative covariates. Results Median age and weight were 2.2 (interquartile range [IQR] = 0.5-13) years and 10.8 (IQR = 5.6-40) kilograms, respectively. In-hospital mortality was 6.58% (n=64), and postoperative complications occurred in 19.7% (n=192) of the cases. TPS was categorized as 1 in 359 cases (37%), 2 in 464 (47.7%), and 3 in 149 (15.3%). Multivariable analysis identified TPS class 3 as a predictor of longer hospital stay (coefficient: 6.6; standard error: 2.2; P=0.003), higher number of complications (odds ratio [OR]: 1.84; 95% confidence interval [CI]: 1.1-3; P=0.01), and higher mortality (OR: 3.2; 95% CI: 1.4-7; P=0.004). Conclusion TPS translated into the Portuguese language was validated and showed to be able to predict higher mortality, complication rate, and prolonged postoperative hospital stay in a high-volume Latin-American congenital heart surgery program. TPS is generalizable and can be used as an outcome assessment tool in resource diverse settings.
A Review of methods for Textureless Object Recognition

Textureless object recognition has become a significant task in Computer Vision with the advent of Robotics and its applications in manufacturing sector. It has been very challenging to get good performance because of its lack of discriminative features and reflectance properties. Hence, the approaches used for textured objects cannot be applied for textureless objects. A lot of work has been done in the last 20 years, especially in the recent 5 years after the TLess and other textureless dataset were introduced. In our research, we plan to combine image processing techniques (for feature enhancement) along with deep learning techniques (for object recognition). Here we present an overview of the various existing work in the field of textureless object recognition, which can be broadly classified into View-based, Feature-based and Shape-based. We have also added a review of few of the research papers submitted at the International Conference on Smart Multimedia, 2018. Index terms: Computer Vision, Textureless object detection, Textureless object recognition, Feature-based, Edge detection, Deep Learning
Augmented Perception with Cooperative Roadside Vision Systems for Autonomous Driving in Complex Scenarios

Performing autonomous driving in urban environments is a challenging task, especially when there is a reduced visibility of traffic participants in complex driving scenarios. For this reason, we investigate the advantages of cooperative perception systems to enhance on-board perception capabilities. In this paper, we present a cooperative roadside vision system for augmenting the embedded perception of an autonomous vehicle navigating in a complex urban scenario. In particular, we use an HD map to implement a map-aided tracking system that merges the information from both onboard and remote sensors. The road users detected by the on-board LiDAR are represented as bounding polygons that include the localization uncertainty whereas, for the camera, the detected bounding boxes are projected in the map frame using a geometric constrained optimization. We report experimental results using two experimental vehicles and a roadside camera in a real traffic scenario in a roundabout. These results quantify how the cooperative data fusion extends the field of view and how the accuracy of the pose estimation of perceived objects is improved.
A Hybrid High Performance Intelligent Computing Approach of CACNN and RNN for Skin Cancer Image Grading

Skin cancer is characterized as the uncontrollable growth of skin cells caused by unrepairable DNA damage. Melanoma is the deadliest form of skin cancers caused by melanocyte and early diagnosis supports therapists in curing it. Computational pathology offers a one-of-a-kind ability to spatially dissect certain interfaces on digitized histology images. A hybrid context-aware convolutional neural networks with recurrent neural network (CA-CNN-RNN) based on skin cancer histological images is proposed in this research. The proposed model encodes a histology image's local representation into higher-dimensional features first, then aggregated the feature by consider their spatial arrangement to enable the final predictions. In this research, H&E-stained sectioned images from the Cancer Genome Atlas are used as the dataset for assessment. From 58 images, 37 images were used for training and 21 images are used for testing. The process on histology images of melanoma skin cancer was analyzed and validated with various classifiers such as VGG-19, Inception, ResNet50, and DarkNet-53 using the hybrid CA-CNN-RNN model. The dataset is used to generate the results, which are then analyzed based on criteria such as accuracy, recall, precision, and F-score. The performance analysis shows that the proposed CA-CNN-RNN with different classifiers has performed better and among the classifiers the DarkNet-53 model has the better performance in all the parameters.
INVESTIGATION OF OXYGEN-RELATED DEFECTS IN ZnO: GROWING TIME AND Mn CONCENTRATION EFFECTS

The optical and photoluminescence properties of ZnO nanocrystals synthesized via hydrothermal method are determined in this study. The effect of growing time ( 1 h, 6 h, 12 h, 24 h and 36 h ) and Mn concentration (5x10 -4 mol, 10x10 -4 , 25x10 -4 mol, 75x10 -4 mol, 100x10 -4 mol, 250x0 -4 mol) on these properties are investigated and presented in detail. Optical properties of the nanocrystals are investigated by using UV-VIS spectrophotometer. The ultraviolet–visible (UV-Vis) and photoluminescence (PL) spectroscopy techniques are used for optical and photoluminescence properties characterization. Room temperature PL spectra of the ZnO nanopowders show a near band-edge emission (peak at 385 nm) and a red light emission (peak at 650 nm) for both ZnO synthesized for different growing time and different Mn concentration. The ZnO prepared with 1 h and 12 h includes the lowest oxygen related defects. The ZnO doped with 5x10 -4 mol shows the highest oxygen related defects whereas that of 100x10 -4 mol shows the lowest defects.
Application of generalised equivalent extreme-value event in structural system reliability analysis

Abstract To alleviate the difficulty of combination explosion, in this work, a generalised equivalent extreme-value event is presented to the problem of multi-component simultaneous failure in system reliability analysis, where m components in total l components are failed simultaneously. Firstly, the generalised equivalent extreme-value event is formulated with rigorous mathematical derivation for single boundary problem. Secondly, based on a mathematical transformation, the generalised equivalent extreme-value event is extended to double boundaries problem. Thirdly, by introducing the point estimation method (PEM) and the principle of maximum entropy to calculate the first four-order statistical moments and the failure probability of the equivalent performance function, an easy-to-implement method is proposed, which is capable of solving the multi-component simultaneous failure problem of static and dynamic systems. Finally, the feasibility of the proposed method is verified through a mathematical example and two engineering examples. The comparison to the Monte Carlo simulation shows that the proposed method is of high precision and high efficiency.
Accounting for Temporal Dynamics in Document Streams

Textual information, such as news articles, social media, and online forum discussions, often comes in a form of sequential text streams. Events happening in the real world trigger a set of articles talking about them or related events over a period of time. In the meanwhile, even one event is fading out, another related event could raise public attention. Hence, it is important to leverage the information about how topics influence each other over time to obtain a better understanding and modeling of document streams. In this paper, we explicitly model mutual influence among topics over time, with the purpose to better understand how events emerge, fade and inherit. We propose a temporal point process model, referred to as Correlated Temporal Topic Model (CoTT), to capture the temporal dynamics in a latent topic space. Our model allows for efficient online inference, scaling to continuous time document streams. Extensive experiments on real-world data reveal the effectiveness of our model in recovering meaningful temporal dependency structure among topics and documents.
An Extensive Survey on Applications of Advanced Deep Learning Algorithms on Detection of Neurodegenerative Diseases and in Tackling the Security Threats in their Treatment Protocol

Introduction: A degenerative disorder results in deterioration of the structure or functions of the “nervous system”, either by killing or rupturing the nerve cells, which is known as a neurodegenerative disease. The most frequently occurring nerve disorder is “Parkinson’s Disease”. Objectives: The main objective is to focus on neurodegenerative Parkinson’s disease its analysis using deep neural networks. Materials and Methods: This survey briefly reviews some literature on advanced Deep learning algorithms in the diagnosis of neurodegenerative diseases and their treatment protocol. Results: This paper presented few techniques followed by various researchers; accuracy, sensitivity, recall achieved by the methodologies have been presented for Parkinson’s disease. Conclusion: In conclusion, this study has reviewed some significant datasets related to Parkinson’s disease, applications of advanced deep learning algorithms in their detection, and to ensure advanced neurodegenerative disorder treatments that make use of Brain Computing Interface.
Integration of Anaerobic Digestion by UASB into a Hybrid Treatment Process of Waste Streams from Paper Recycling and its Potential for Bioconversion of Carbon Dioxide to Methane

Waste management and renewable energies are both major environmental concerns related to the largest global issue of today, climate change. Anaerobic digestion (AD) systems are able to reduce greenhouse gases (GHGs) that lead to climate change by producing biomethane gas to use as renewable energy, while treating organic waste. AD systems can also serve as carbon sinks because they are able to biologically convert excess carbon dioxide dissolved in the culture, into additional methane gas. Continuous experiments were conducted with five-litre UASB reactors that focused on simulating full-scale operating parameters at a Cascades’ paper recycling plant, and employed recycling waste streams as substrates. Experiments compared conditions of single and 2 phase anaerobic digestion (2-PAD), liquid and granular sludge beds, influent wastewater and deinking sludge substrates, temperatures between 25-45 °C, hydraulic retention times between 1 to 5 days and organic loading rates between 0.5-5.0 kg COD/m3-d. Methane yields across all conditions averaged 0.15 m3 CH4/kg CODrmv for total COD and 0.24 m3 CH4/kg CODrmv for soluble COD. Removal efficiencies consistently averaged 90% for all conditions. After feeding 2-PAD systems CO2 infused wastewater, successful bioconversion for soluble COD was observed in all organic loading rate conditions, showing between 5-21% higher methane yields for CO2 conditions, compared to control. Heavy metals from deinking sludge were monitored though no inhibition was observed. Cost analysis showed that adopting AD as a pretreatment could result in up to 1,733,100 CAD/year in additional revenue. Results of this research provide a solid guideline to pursue developing a pilot scale system.
CaTch: A Confidence Range Tolerant Misbehavior Detection Approach

Misbehavior detection is a challenging problem that needs to be addressed in vehicular communications. Misbehavior detection consists of monitoring the semantic of the exchanged messages to identify potential misbehaving entities. This is achieved by performing plausibility and consistency checks on exchanged beacon and warning messages. However, existing misbehavior detection solutions ignore the mandatory information on data inaccuracy, being gathered by the vehicular sensors. In this paper, we propose CaTch, an embedded misbehavior detection solution that integrates data inaccuracy when performing plausibility and consistency checks. Through extensive simulations, we show that CaTch is able to attribute an accurate uncertainty factor to misbehaving nodes and that it performs better than the state-of-the-art solutions.
On the Convergence of Local Descent Methods in Federated Learning

In federated distributed learning, the goal is to optimize a global training objective defined over distributed devices, where the data shard at each device is sampled from a possibly different distribution (a.k.a., heterogeneous or non i.i.d. data samples). In this paper, we generalize the local stochastic and full gradient descent with periodic averaging-- originally designed for homogeneous distributed optimization, to solve nonconvex optimization problems in federated learning. Although scant research is available on the effectiveness of local SGD in reducing the number of communication rounds in homogeneous setting, its convergence and communication complexity in heterogeneous setting is mostly demonstrated empirically and lacks through theoretical understating. To bridge this gap, we demonstrate that by properly analyzing the effect of unbiased gradients and sampling schema in federated setting, under mild assumptions, the implicit variance reduction feature of local distributed methods generalize to heterogeneous data shards and exhibits the best known convergence rates of homogeneous setting both in general nonconvex and under {\pl}~ condition (generalization of strong-convexity). Our theoretical results complement the recent empirical studies that demonstrate the applicability of local GD/SGD to federated learning. We also specialize the proposed local method for networked distributed optimization. To the best of our knowledge, the obtained convergence rates are the sharpest known to date on the convergence of local decant methods with periodic averaging for solving nonconvex federated optimization in both centralized and networked distributed optimization.
Attitude of Asians to Calcium and Vitamin D Rich Foods and Supplements: A Systematic Review

The prevalence of osteoporosis is increasing in Asian countries. Optimum calcium intake and vitamin D level are important to prevent osteoporosis. The attitude of the society towards calcium food sources and supplements, as well as sun exposure, influence their bone health practices. This review aimed to summarize the current literature on the attitude of Asians on calcium and vitamin D rich foods and supplements to understand the barriers among the Asians in achieving adequate calcium intake and vitamin D level. Studies showed that most Asians have a negative attitude towards calcium-rich foods, mainly dairy products due to lactose intolerance, aversion to the taste of dairy products or the absence of motivation. Although they took supplements to fill the gap in their diet, they were not aware of the daily recommended intake of calcium. Besides, the majority of them have a negative attitude towards sun exposure due to the fear of getting skin cancer or tanning. As a conclusion, most of Asians are still not aware of the importance of calcium and vitamin D for their health and their negative attitude must be corrected to prevent the rise of osteoporosis.
Secure Interface Architecture for Charge Trap Transistor (CTT) Based EEPROM

Cryptographic attacks and memory observability among DRAM or PROM have drawn researcher’s attention. C. Kothandaraman et al. [1] have proposed a secure memory element using charge-trap based transistor for a potential memory application. Our work presents an on-chip interface architecture between CPU and secure EEPROM for control and data communication. This architecture allows the secure EEPROM to be embedded with the processing unit preventing interface eavesdropping so that encryption keys can be accessed locally and securely. It is also designed to overcome the cold-boot attacks and side channel attacks employing on-chip implementation and parallel data communication. The design is implemented in VHDL and validated with secure EEPROM model. The synthesis and simulation results of the design are presented.
High Throughput Resource Unit Assignment Scheme for OFDMA-based WLAN

Orthogonal Frequency Division Multiple Access (OFDMA) introduced in IEEE 802.11ax amendment promises to improve spectral efficiency by grouping subcarriers into resource units (RUs). It is important to design RU assignment to improve throughput for the OFDMA-based WLAN. In this paper, we present the High Throughput RU Assignment Scheme (HiTRAS) and formulate the optimization problem maximizing network throughput for the HiTRAS. The solution of the optimization problem leads to the optimal RU assignment that allocates RUs to multiple mobile stations (STAs) so that the STAs can transmit their uplink traffic simultaneously and thus high throughput is gained. The simulation results show that the HiTRAS outperforms the existing schemes in terms of throughput.