Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing feeding history and health status through analysis of fatty acids and fat content in golden mullet Liza aurata

D. Gonzalez-Silvera1*, L. Martinez-Rubio2, M. E. Abad Mateo3, R. Rabadan-Ros1, J. A. López Jiménez1, and F. J. Martínez López1 Department of Physiology, Faculty of Biology, Regional Campus of International Excellence “Campus Mare Nostrum”, University of Murcia, Murcia 30100, Spain Institute of Aquaculture, University of Stirling, Stirling, Scotland, FK9 4LA, UK Department of Cell Biology and Histology, Faculty of Biology, Regional Campus of International Excellence “Campus Mare Nostrum”, University of Murcia, Murcia 30100, Spain
Design and Development of a Flexible, Plug-and-Play, Cost-Effective Tool for on-Field Evaluation of Gas Sensors

Atmospheric pollution is one of the biggest concerns for public health. Air quality monitoring is currently performed by expensive and cumbersome monitoring stations. For this reason, they are sparse, and therefore, inadequate to provide enough accurate information on the personal exposure to pollutant gases. The current worldwide trend to address this issue consists in the use of low-cost small gas sensors, already available on the market, with a wide range of costs and performances. However, the performance of these sensors is heavily affected by the environmental conditions of the specific location used for their deployment. For this reason, it is of fundamental importance to test them in real-world scenarios. Field evaluation of sensor performance could be a challenging task because, on the one hand, they have heterogeneous output signals, and on the other hand, there is no widely shared evaluation protocol. The SentinAir system has been designed and developed to facilitate this task. It can carry out performance evaluations for any type of sensor thanks to its configurable and adaptable sensing capability, multiple wireless sensor network compatibility, flexibility, and usability. In order to evaluate SentinAir capabilities and functionalities, the performances of CO2, NO2, and O3 sensors were tested in real-world scenarios against reference instruments. To the best of our knowledge, there is no previous study providing information about the performance of SP-61 (O3 sensor), IRC-A1 (CO2 sensor), and TDS5008 (CO2 sensor) achieved during on-field tests. On the contrary, results obtained by OXB431 (O3 sensor) and NO2B43F (NO2 sensor) are consistent with the ones shown in previous studies carried out in similar conditions. During validation tests, we have found for the best performing NO2 sensor, and for the best O3 sensor. Concerning the indoor experiment, the best CO2 sensor performance showed an excellent . In conclusion, the effectiveness of this tool in evaluating the performance of heterogeneous gas sensors in different real-world scenarios has been demonstrated. Therefore, we anticipate that the use of SentinAir will facilitate researchers to carry out these challenging tasks.
Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics

On-board sensory systems in autonomous vehicles make it possible to acquire information about the vehicle itself and about its relevant surroundings. With this information the vehicle actuators are able to follow the corresponding control commands and behave accordingly. Localization is thus a critical feature in autonomous driving to define trajectories to follow and enable maneuvers. Localization approaches using sensor data are mainly based on Bayes filters. Whitebox models that are used to this end use kinematics and vehicle parameters, such as wheel radii, to interfere the vehicle’s movement. As a consequence, faulty vehicle parameters lead to poor localization results. On the other hand, blackbox models use motion data to model vehicle behavior without relying on vehicle parameters. Due to their high non-linearity, blackbox approaches outperform whitebox models but faulty behaviour such as overfitting is hardly identifiable without intensive experiments. In this paper, we extend blackbox models using kinematics, by inferring vehicle parameters and then transforming blackbox models into whitebox models. The probabilistic perspective of vehicle movement is extended using random variables representing vehicle parameters. We validated our approach, acquiring and analyzing simulated noisy movement data from mobile robots and vehicles. Results show that it is possible to estimate vehicle parameters with few kinematic assumptions.
Transparency in Plea Bargaining

Plea bargaining is the dominant method by which our criminal justice system resolves cases. More than 95% of state and federal convictions today are the product of guilty pleas. Yet the practice continues to draw widespread criticism. Critics charge that it is too coercive and leads innocent defendants to plead guilty, that it obscures the true facts in criminal cases and produces overly lenient sentences, and that it enables disparate treatment of similarly situated defendants. Another feature of plea bargaining — its lack of transparency — has received less attention, but is also concerning. In contrast to the trials it replaces, plea bargaining occurs privately and off-the-record. Victims and the public are excluded, and the defendant is typically absent. While the Sixth and First Amendments rights of public access extend to a range of pretrial criminal proceedings, they do not apply to plea negotiations. For the most part, rules and statutes also fail to require transparency in the process. As a result, plea bargaining is largely shielded from outside scrutiny, and critical plea-related data are missing. There are some valid reasons for protecting aspects of plea negotiations from public scrutiny. Confidentiality fosters candor in the discussions and may encourage prosecutors to use their discretion more leniently. It can help protect cooperating defendants from retaliation. And it may expedite cases and conserve resources. Yet the secrecy of the process also raises concerns. It prevents adequate oversight of coercive plea bargains, untruthful guilty pleas, and unequal treatment of defendants. It can hinder defense attorneys from providing fully informed advice to their clients. It can also potentially impair victims’ rights and interests. Finally, the absence of transparency leaves judges with few guideposts by which to evaluate plea bargains and inhibits informed public debate about criminal justice reform. This Article reviews plea bargaining laws and practices across the United States and argues that we can do more to enhance the documentation and transparency of plea bargaining. It then proposes concrete areas in which transparency can be improved without significant costs to the criminal justice system.
Fuzzy evidence theory and Bayesian networks for process systems risk analysis

Abstract Quantitative risk assessment (QRA) approaches systematically evaluate the likelihood, impacts, and risk of adverse events. QRA using fault tree analysis (FTA) is based on the assumptions that failure events have crisp probabilities and they are statistically independent. The crisp probabilities of the events are often absent, which leads to data uncertainty. However, the independence assumption leads to model uncertainty. Experts’ knowledge can be utilized to obtain unknown failure data; however, this process itself is subject to different issues such as imprecision, incompleteness, and lack of consensus. For this reason, to minimize the overall uncertainty in QRA, in addition to addressing the uncertainties in the knowledge, it is equally important to combine the opinions of multiple experts and update prior beliefs based on new evidence. In this article, a novel methodology is proposed for QRA by combining fuzzy set theory and evidence theory with Bayesian networks to describe the uncertainties, aggregate experts’ opinions, and update prior probabilities when new evidences become available. Additionally, sensitivity analysis is performed to identify the most critical events in the FTA. The effectiveness of the proposed approach has been demonstrated via application to a practical system.
Semi-Supervised Enhancement and Suppression of Self-Produced Speech Using Correspondence between Air- and Body-Conducted Signals

We propose a semi-supervised method for enhancing and suppressing self-produced speech recorded with wearable air- and body-conductive microphones. Body-conducted signals are robust against external noise and predominantly contain self-produced speech. As a result, these signals provide informative acoustical clues when estimating a linear filter to separate a mixed signal into self-produced speech and background noise. In a previous study, we proposed a blind source separation method for handling air- and body-conducted signals as a multi-channel signal. While our previously proposed method demonstrated the superior performance that can be achieved by using air- and body-conducted signals in comparison to using only air-conducted signals, the enhanced and suppressed air-conducted signals tended to be contaminated with the acoustical characteristics of the body-conducted signals due to the nonlinear relationship between these signals. To address this issue, in this paper, we introduce a new source model which takes into consideration the correspondence between these signals and incorporates them within a semi-supervised framework. Our experimental results reveal that this new method alleviates the negative effects of using the acoustical characteristics of the body-conducted signals, outperforming our previously proposed method, as well as conventional methods, under a semi-supervised condition.
Minimizing Communication Conflicts in Network-On-Chip Based Processing-In-Memory Architecture

Deep Neural Networks (DNNs) have made significant breakthroughs in various fields. However, their enormous computations and parameters seriously hinder their applications. Emerging Processing-In-Memory (PIM) architectures provide extremely high energy efficiency to accelerate DNN computing. Moreover, Network-on-Chip (NoC) based PIM architectures significantly improve the scalability of PIM architectures. However, the contradiction between high communication and limited NoC bandwidth introduces severe communication conflicts. Existing work neglects the impact of communication conflicts. On the one hand, neglecting communication conflicts leads to the lack of precise performance estimations in the mapping process, making it hard to find optimal results. On the other hand, communication conflicts cause low NoC bandwidth utilization in the schedule process. And there is over 70% latency gap in existing work caused by communication conflicts. This paper proposes communication conflict optimized mapping and schedule strategies for NoC-based PIM architectures. The proposed mapping strategy constructs communication conflict graphs to model communication conflicts. Based on this constructed graph, we adopt a Graph Neural Network (GNN) as a precise performance estimator. Our schedule strategy predefines the communication priority and NoC communication behavior tables for target DNN workloads. In this way, it can improve the NoC bandwidth utilization effectively. Compared with existing work, for typical classification DNNs on the CIFAR and ImageNet datasets, the proposed strategies reduce 78% latency and improve the throughput by 3.33× on average with negligible deployment and hardware overhead. Experimental results also show that our strategies decrease the average gap to ideal cases without communication conflicts from 80.7% and 70% to 12.3% and 1.26% for latency and throughput, respectively.
Preadmission use of inhaled corticosteroids and risk of fatal or severe COVID-19: a meta-analysis

Abstract Objective: With emerging of observational evidence, we aimed to perform a meta-analysis to summarize the overall effect of the chronic use of inhaled corticosteroids on the clinical outcomes in patients with coronavirus disease 2019 (COVID-19). Methods:Systematic literature search in electronic databases was performed to identify observational studies that investigated the preadmission use of inhaled corticosteroids on the risk of a fatal or severe course of illness in patients with COVID-19 and reported adjusted measures of association. Adjusted odds ratios or relative risks and the corresponding 95% confidence intervals from each study were pooled to produce pooled odds ratio and 95% confidence interval. Results: The meta-analysis revealed no significant difference in the risk for the development of a fatal course of COVID-19 with preadmission use of inhaled corticosteroids in patients with COVID-19 relative to non-use of inhaled corticosteroids (pooled odds ratio=1.28; 95% confidence interval 0.73–2.26). Similarly, the meta-analysis observed no significant difference in the risk for the development of a severe course of COVID-19 with preadmission use of inhaled corticosteroids in patients with COVID-19 relative to non-use of inhaled corticosteroids (pooled odds ratio=1.45; 95% confidence interval 0.96–2.20). Conclusions: Our findings assured the safety of continued use of inhaled corticosteroids during the COVID-19 pandemic.
Simultaneous estimation of canagliflozin and metformin hydrochloride in tablet dosage form by UV spectrophotometry

The combination of Canagliflozin and Metformin was available as fixed dose tablets for the treatment of type 2 diabetes. The present method aims to develop a simple, precise and accurate spectrophotometric method for simultaneous determination of Canagliflozin and Metformin in commercial formulation. The method utilizes Vierordt’s equation based on the measurement at two wavelengths 290nm (? max of Canagliflozin) and 236nm (? max of Metformin). The method exhibited linear range of 2.5 to 15µg/ml and 5 to 17. 5µg/ml for Canagliflozin and Metformin, respectively, with a correlation coefficient of 0.999. The LOD and LOQ for Canagliflozin were found to be 0.43 and 1.31 respectively. For Metformin the LOD and LOQ were found to be 0.49 and 1.49 respectively. The recovery of Canagliflozin and Metformin were found to be 99.43 and 98.82 respectively. The results were validated statistically as per ICH guidelines and were found to be satisfactory. To conclude, the developed UV spectrophotometric method is more economical for analysis of Canagliflozin and Metformin in both bulk and pharmaceutical dosage form for routine analysis. Keywords: Canagliflozin, Metformin, Vierordt’s equation, UV-Spectrophotometry, ICH guidelines
Unsupervised 3D Human Mesh Recovery from Noisy Point Clouds

This paper presents a novel unsupervised approach to reconstruct human shape and pose from noisy point cloud. Traditional approaches search for correspondences and conduct model ﬁtting iteratively where a good initialization is critical. Relying on large amount of dataset with ground-truth annotations, recent learning-based approaches predict correspondences for every vertice on the point cloud; Chamfer distance is usually used to minimize the distance between a deformed template model and the input point cloud. However, Chamfer distance is quite sensitive to noise and outliers, thus could be unreliable to assign correspondences. To address these issues, we model the probability distribution of the input point cloud as generated from a parametric human model under a Gaussian Mixture Model. Instead of explicitly aligning correspondences, we treat the process of correspondence search as an implicit probabilistic association by updating the posterior probability of the template model given the input. A novel unsupervised loss is further derived that penalizes the discrepancy between the deformed template and the input point cloud conditioned on the posterior probability. Our approach is very ﬂexible, which works with both complete point cloud and incomplete ones including even a single depth image as input. Our network is trained from scratch with no need to warm-up the network with supervised data. Compared to previous unsupervised methods, our method shows the capability to deal with substantial noise and outliers. Extensive experiments conducted on various public synthetic datasets as well as a very noisy real dataset (i.e. CMU Panoptic) demonstrate the superior performance of our approach over the state-of-the-art methods. Code can be found https://github.com/wangsen1312/unsupervised3dhuman.git
A Comprehensive Review of Lithium-ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems

Highly nonlinear characteristics of lithium-ion batteries (LIBs) are significantly influenced by the external and internal temperature of the LIB cell. Moreover, cell temperature beyond the manufacturer’s specified safe operating limit could lead to thermal runaway and even fire hazards and safety concerns to operating personnel. Therefore, accurate information of cell internal and surface temperature of LIB is highly crucial for effective thermal management and proper operation of a battery management system (BMS). Accurate temperature information is also essential to BMS for the accurate estimation of various important states of LIB such as state of charge, state of health and so on. High capacity LIB pack, used in electric vehicles and grid-tied stationary energy storage system essentially consists of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell especially at the cell core is not practically feasible from the solution cost, space and weight point of view. A solution is to develop a suitable estimation strategy which led scholars to propose different temperature estimation schemes aiming to establish a balance among accuracy, adaptability, modelling complexity and computational cost. This article presented an exhaustive review of these estimation strategies covering recent developments, current issues, major challenges, and future research recommendations. The prime intention is to provide a detailed guideline to the researchers and industries towards developing a highly accurate, intelligent, adaptive, easy to implement and compute efficient online temperature estimation strategy applicable to health-conscious fast charging and smart onboard BMS.
3GPP V2X on Unlicensed Spectrum: Performance Analysis and Optimum Channel Access Strategies

Featuring unprecedentedly high data rates and high connection densities, 3GPP New Radio (NR) vehicle-to-everything (V2X) communications have been regarded as the most promising remedy to support the next generation driving use cases. For this purpose, although dedicated bands on 5.9 GHz spectrum have been allocated for Intelligent Transportation Systems (ITS), the limited bandwidth may not afford the desired service requirements. This engineering concern thus drives the utilization of unlicensed bands on 5 GHz spectrum adjacent to the ITS bands. However, subject to the fairness constraint in sharing unlicensed spectrum, various communication regulations have been imposed, and four different types of multi-band channel access procedures are consequently developed both in 3GPP LTE-A Licensed-Assisted Access (LAA) and NR on unlicensed spectrum (NR-U), to lead to a phenomenon that occupying more unlicensed bands may not suggest a proportional growth in the throughput. To empower high-throughout and high-density V2X communications, in this paper, we consequently propose the optimum channel access strategies for each V2X transmitter to decide the optimum number of unlicensed bands to access. To this end, analytic models for four types of multi-band channel access procedures in 3GPP NR-U are provided, which capture both inter-system and intra-system interference from coexisting WiFi transmitters and V2X transmitters. Subsequently, game-theoretically optimum strategies for each type of multi-band channel access procedures are derived. Performance evaluation results fully demonstrate the optimality of the proposed strategies, to serve the urgent demands in supporting the next generation driving use cases on unlicensed spectrum.
Astabilizing system for butter pastes based on the dry concentrates of milk protein

The composition of the stabilizing system for butter pastes based on dry concentrates of milk and whey proteins has been substantiated; that would help reduce the deficiency of protein in the diet of modern human and would make it possible to further improve the balance of the composition of the butter paste. Considering their functional-technological characteristics, conditions for gelation and synergy, the polysaccharides carrageenan and guar gum were introduced to the composition of the stabilizing system. The dynamics in the gradient of the limiting stress of protein and protein-polysaccharide systems have been studied. We established that gels based on the dry concentrate of milk protein are the plastic systems, they have sufficient strength and possess thixotropic properties. In order to reduce the quantitative content of the stabilizing system in the production of butter paste with a structural frame similar to that of butter, we introduced carrageenan to the system. However, an increase in its concentration led to the formation of strong cross-linked gels unsuitable for the production of butter pastes. Increasing the stability of the system against the "freeze-defrost" cycles could be achieved by the introduction of guar gum. Based on the indicator of the limiting stress at a variable deformation rate of the model samples, a rational ratio of the components in the stabilizing system was established. Its composition includes: milk protein concentrate: whey protein concentrate: guar gum: carrageenan: 10:3.0:0.3:0.05. The rational concentration of the stabilizing component based on skimmed milk was determined, which was 13.35 %. The water activity indicator is determined for the model samples of the selected stabilizing substances and mixtures in certain ratios. Stabilizing substances have been shown to exhibit the pronounced moisture-retaining properties, which increase at their combination. The effectiveness of the developed system is proven based on indicators for the water activity and enthalpy of the system. The indicator of water activity for the butter paste with a 40 % mass fraction of fat was 0.981, which is close to the respective indicator for the butter with a mass fraction of fat of 72.5 % (control) ‒ 0.979. The enthalpy index of the butter paste was 61.35 J/g; for control, it was 61.13 J/g. This is due to the additional bonding of moisture by the functional groups of components in the protein-polysaccharide complex, indicating the thermodynamic stability of the butter paste. The efficiency of application of the developed system in the technology of butter pastes has been determined: indicator of heat resistance of the butter paste with a mass fraction of fat of 40 % was 0.87 (control, 091), the size of droplets in the aqueous phase at the cut did not exceed 0.2 mm