Miracle-dz/UniFAR-FTUs huggingface dataset:
Fatty acids composition and lipolysis of Parmigiano Reggiano PDO cheese: effect of the milk cooling temperature at the farm

Objective The aim was to study the influence of cooling milk at 9°C at the farm versus keeping it at 20°C on Parmigiano Reggiano cheese lipolysis. Methods A total of six cheesemaking trials (3 in winter and 3 in summer) were performed. In each trial, milk was divided continuously into two identical aliquots, one of which was kept at 9°C (MC9) and the other at 20°C (MC20). For each trial and milk temperature, vat milk (V-milk) and the resulting 21 month ripened cheese were analysed. Results Fat and dry matter and fat/casein ratio were lower in MC9 V-milk (p≤0.05) than in MC20. Total bacteria, mesophilic lactic acid and psychrotrophic and lipolytic bacteria showed significant differences (p≤0.05) between the two V-milks. Regarding cheese, fat content resulted lower and crude protein higher (p≤0.05) both in outer (OZ) and in inner zone (IZ) of the MC9 cheese wheels. Concerning total fatty acids, the MC9 OZ had a lower concentration of butyric, capric (p≤0.05) and medium chain fatty acids (p≤0.05), while the MC9 IZ had lower content of butyric (p≤0.05), caproic (p≤0.01) and short chain fatty acids (p≤0.05). The levels of short chain and medium chain free fatty acids (p≤0.05) were lower and that of long chain fatty acids (p≤0.05) was higher in MC9 OZ cheese. The principal component analysis of total and free fatty acids resulted in a clear separation among samples by seasons, whereas slight differences were observed between the two different milk temperatures. Conclusion Storing milk at 9°C at the herd affects the chemical composition of Parmigiano Reggiano, with repercussion on lipolysis. However, the changes are not very relevant, and since the cheese can present a high variability among the different cheese factories, such changes should be considered within the “normal variations” of Parmigiano Reggiano chemical characteristics.
A review on artificial pancreas and regenerative medicine used in the management of Type 1 diabetes mellitus

Abstract Diabetes mellitus is one of the fastest-growing lifestyle disorders in the world. While numerous regimes have been developed to manage diabetes, there continue to be high numbers of diabetes-related deaths worldwide. The review gives a brief introduction to the pathology and aetiology of the disorder, different solutions developed over time with their advantages and disadvantages, and highlights the technological components and challenges of the latest technologies: artificial pancreas and regenerative medicine. The study is restricted to a set of high-quality publications from the last decade.
Proceedings of the Tenth International IFIP WG5.2/WG5.3 Conference on Globalization of Manufacturing in the Digital Communications Era of the 21st Century: Innovation, Agility, and the Virtual Enterprise

The International PROLAMAT Conference is an internationally well known event for demonstrating and evaluating activities and progress in the field of discrete manufacturing. Sponsored by the International Federation for Information Processing (IFIP), the PROLAMAT is traditionally held every three years and it includes the whole area of advanced software technology for Design and Manufacturing in Discrete Manufacturing. Past editions of the International PROLAMA T Conference have explored: -Manufacturing Technology, -Advances in CAD/CAM, -Software for Discrete Manufacturing, -Software for Manufacturing. The Eight International PROLAMAT held in 1992 (Tokyo), focused on the theme of Man in CIM. The 1995 PROLAMAT (Berlin), featured the theme of Life Cycle Modelling for Innovative Products and Processes. This past emphasis on human aspects and innovation provides a strong foundation for the next PROLAMAT. Under the title: The globalization of manufacturing in the digital communications era of the 21th century: innovation, agility and the virtual enterprise, the 1998 conference expands the PROLAMAT scope to include teams and virtual enterprises which come together across space and time to develop new products and bring them to global markets. Manufacturing issues and information models have long been part of concurrent engineering; they are increasingly important in new product innovation and in the development of manufacturing plans and processes which span multiple companies along with multiple time zones.
Analytical Solution of Time-Fractional Advection Dispersion Equation

In this paper, we get exact solution of the time-fractional advection-dispersion equation with reaction term, where the Caputo fractional derivative is considered of order   2 , 0   . The solution is achieved by using a function transform, Fourier and Laplace transforms to get the formulas of the fundamental solution, which are expressed explicitly in terms of Fox’s Hfunction by making use of the relationship between Fourier and Mellin transforms. As special cases the exact solutions of time-fractional diffusion and wave equations are also obtained, and the solutions of the integer order equations are mentioned.
Intraday optical variability of BL Lacertae

We monitored BL Lacertae simultaneously in the optical B, V, R and I bands for 13 nights during the period 2012-2016. The variations were well correlated in all bands and the source showed significant intraday variability (IDV). We also studied its optical flux and colour behaviour, and searched for inter-band time lags. A strong bluer-when-brighter chromatism was found on the intra-night time-scale. The spectral changes are not sensitive to the host galaxy contribution. Cross-correlation analysis revealed possible time delay of about 10 min between variations in the V and R bands. We interpreted the observed flares in terms of the model consisting of individual synchrotron pulses.
Survival of rehabilitated yearling American black bears

Abstract: Scant information regarding the fate of rehabilitated American black bears (Ursus americanus) postrelease exists in the literature. We estimated survival of 28 rehabilitated bears that were released between 2015 and 2018 in North Carolina, USA, and investigated what biological, landscape, and behavioral factors influenced their survival in their first year postrelease. Sixty-one percent of the bears died within 1 year and 65% of the mortalities were from legal hunter harvest. Bear survival declined rapidly with increasing road density. Few mortalities were attributed to vehicular incidents; therefore, road density likely acts as proxy to hunting accessibility, especially in rural areas. Home ranges of bears in our study included 0–11% development and we estimated our bears had a 90% survival rate within their first year postrelease if their home range included 6–11% of developed land. In urban areas, ordinances and small parcels tend to restrict the opportunity for hunting, which may create a refugia for some black bears. Our results also indicate that female rehabilitated bears may be more susceptible to hunting pressures than are wild females. Release weight had a positive effect on survival; heavier bears were likely less attracted to anthropogenic food sources (e.g., crops, bird feeders) that would have made them susceptible to hunters compared with more reliable, natural food sources. Individuals that moved slightly more tortuous paths than average in our study had a lower survival probability, likely from remaining in the same area. We recommend that managers select release sites for bears that limit harvest via accessible roads. We also recommend ensuring 1.25–1.5-year-old bears are ≥71 kg at release to enhance survival probability. Our results add to the growing body of bear rehabilitation research and can inform programs in which rehabilitation is critical to species restoration efforts.
Simulación numérica del impacto provocado por la alteración del uso del suelo en clima termal del Valle de México

Jsing numérica! simulations oftfie thermal field variations due to land usechanges in theValley of México, it is shown that the warming ofthe valley during tfie past century, could be attribute not only to the increase of the urban area, but also to the drastic decrease of the lacustrine system that used to exist.
Noninvasive optoacoustic imaging of the human brain validated with magnetic resonance angiography

Imaging modalities capable of visualizing the human brain have led to major advances in neuroscience. Functional magnetic resonance imaging has enabled a better understanding of brain function and connectivity abnormalities in brain diseases and has become a workhorse in neuroimaging. Nuclear imaging technologies such as positron emission tomography and single-photon emission computed tomography assisted with radiolabelled tracers have further enabled molecular diagnosis and monitoring of brain diseases. Limitations of these established technologies, such as the high cost or the use of ionizing radiation as well as accessibility, have fostered the development of new imaging approaches that can complement or enhance their performance. Optoacoustic tomography (OAT) provides unique capabilities to study cerebral function by mapping changes in several hemodynamic parameters within the brain. It further provides molecular imaging capabilities that can facilitate disease diagnosis and treatment monitoring. However, OAT imaging of the human brain is severely hampered by acoustic attenuation and aberrations of ultrasound waves propagating through the skull. Herein, we performed transcranial OAT imaging through the temporal bone of an adult healthy volunteer based on a previously reported spherical ultrasound array. We validated the OAT results by using head-to-head time-of-flight (TOF) magnetic resonance angiography (MRA) and T1-weighted structural MRI. The superior middle cerebral vein in the temporal cortex was identified in the OAT images matching the observed location in TOF-MRA. This basic demonstration anticipates the development of new hardware and reconstruction algorithms, ultimately enabling accurate OAT imaging of the human brain cortex.
Bi-level optimization for optimizing charging path and charging time in wireless rechargeable sensor networks

In recent years, Wireless Rechargeable Sensor Networks (WRSNs) have adopted wireless energy transfer technology, which has emerged as a promising solution to the limited energy issues in traditional Wireless Sensor Networks (WSNs). Since the sensor’s lifetime is decided by the charging scheme of the Mobile Charger (MC), designing an effective charging algorithm is challenging. Although many efforts have been devoted to optimizing charging schemes in WRSNs, the studies still face several critical issues. Firstly, most previous studies assumed that the MC’s battery capacity is sufficient or unlimited, resulting in the MC can move and charges all sensors in a charging cycle. That may cause a long waiting time for energy-hurry sensors and significant exhaustion of the MC’s energy. Secondly, existing works often optimize the MC’s charging path, whereas the charging time has not been thoroughly considered. This work aims to solve the limitations above by optimizing both the charging path and charging time simultaneously under the MC ’s limited-energy constraint. Our objective is to minimize the energy depletion of sensor nodes. To this end, we leverage the advantage of the bi-level optimization approach and propose a charging algorithm with two levels: the charging path optimization at the upper level and the charging time optimization at the lower level. The proposed charging scheme combines Genetic Algorithm (GA) and Differential Evolutionary (DE) to identify the optimal charging path and time. We conducted extensive experiments to demonstrate the effectiveness of our charging scheme in comparison to the related studies.
Comprehensive top-K recommender system for command and control, using novel evaluation and optimization algorithms

End-users of Command and Control (C2) systems can struggle to effectively determine which Course of Action (CoA) should be chosen out of many complex options. A novel top-K recommender system has been developed to provide C2 end-users with comprehensive CoA recommendations through four critical measures named for explanation purposes only: Optimality, Diversity, Feedback, and Preference. The Optimality measure introduces a novel “Pareto-mesh” technique for quantitative CoA metrics, which offers robust and computationally efficient comparisons of Pareto-front percent coverages among ranking options. The Diversity measure also introduces a novel linchpin-based qualitative/quantitative technique that considers qualitative data’s Hamming distances, quantitative data’s Euclidean distances, and optimizes diversity of top-K CoA’s based a chosen top linchpin CoA using Maximin optimization. The Feedback measure is based on implicit user choices, peripheral simulation results, and real-world scenario results, with benchmarks of state-of-the-art collaborative filtering methods SVD, BiVAE, and NCF for this domain. The Preference measure is based on explicit user preference input of quantitative data, and ranks CoA’s based on a simple weighted sum against quantitative CoA metrics. Representative data related to C2 end-users, request attributes, and CoA’s is randomly generated to perform evaluations of each recommender system measure. Hyper-parameters used to calculate each measure are also globally tuned by means of a Genetic Algorithm multi-objective optimization technique, relying on a per-measure, inversion-minimizing optimization function. The designed recommender system’s hyper-parameter training time is on the order of hours and the system inference time is on the order of milliseconds.
A Machine Learning Based Fuel Consumption Saving Method with Time and Environment Dependency Aware Management

Since the key factors affecting the fuel consumption of the long-distance vehicle are varied hourly on the whole truck running routes, which are long distance and stretched to various environmental district, the amount of saving fuel consumption must be limited until the following two things would have become able to do. The first is a better fuel-efficient driving control in terms of the low/high gear, accel, and the idling by increasing the driver's skills based on the training and driving assist systems that makes it possible to find own problem. And the second is a better mechanical maintenance to keep a better fuel-efficiency. Thus, this paper proposes a machine learning based time and environment condition dependency aware fuel consumption tracking method, which enables to give the timely feedback to the drivers and truck maintenance facility. The feedback will be given by making clearer what are the key factors affecting fuel consumption every period based on their time and environment dependencies analysis results. Since the various data obtained by the embedded sensors (CANbus &GPS) are complicated and its number of variables is over 100 (much larger than the conventional cases of less than 50), we initially used the LightGBM but we faced the bias vs variance tradeoff issue. To address this issue, we newly proposed the stacking scheme, which optimizes the trade-off by combining the random forest, the SVM, and the LightGBM. Thanks to this scheme, the variance (RMSE) error was reduced from 6.04 to 5.03, while keeping the best accuracy (R2) of 97.6%. We proposed the feedback system to reduce the fuel consumption based on the analysis of the key influencing factors that caused by the drivers. To our best knowledge, it is the first time to demonstrate that the saving of overall fuel consumption by the proposed feedback scheme can be reduced by 8% if some key factors could be carefully reduced by 50%.
The C-MĀIKI Gateway: A Modern Science Platform for Analyzing Microbiome Data

In collaboration with the Center for Microbiome Analysis through Island Knowledge and Investigations (C-MĀIKI), the Hawaii EPSCoR Ike Wai project and the Hawaii Data Science Institute, a new science gateway, the C-MĀIKI gateway, was developed to support modern, interoperable and scalable microbiome data analysis. This gateway provides a web-based interface for accessing high-performance computing resources and storage to enable and support reproducible microbiome data analysis. The C-MĀIKI gateway is accelerating the analysis of microbiome data for Hawaii through ease of use and centralized infrastructure.
Design of a hybrid fiber optic coaxial transmission medium

The present work shows the design and optimization of a means of hybrid coaxial transmission with optical fiber, perfected to reduce conductive losses at high frequencies. It is a work based on the RV 719 "Hybrid high-speed transmission medium of more coaxial fiber", the RV 1071 "N-Male connector for hybrid transmission medium with optical fiber" and the RV 1096 "N-Female connector for hybrid transmission medium with optical fiber", INDECOPI, 2020. It contains in its internal structure an optical fiber for the transmission of data in parallel of high speed by both means. The design and calculation of the parameters was carried out and correlated with their common market peers where the improvement in the design characteristics was demonstrated, the versatility of the proposed design allows the future to unify equipment and technologies.
ScriptManager: an interactive platform for reducing barriers to genomics analysis

ScriptManager was built to be a lightweight and easy to use genomics analysis tool for novice bioinformaticians. It includes both a graphical interface for easy navigation of inputs and options while also supporting a command line interface for automation and integration with workflow managers like Galaxy. We describe here how a user unfamiliar with the command line can leverage national supercomputing resources using a graphical desktop interface like Open OnDemand to perform their analyses and generate publication quality figures for their research. Widespread adoption of this tool in the genomics community would lower technical barriers to accessing supercomputing resources and allow biochemists to prototype their own workflows that can be integrated into large scale production pipelines. Source code and precompiled binaries available at https://github.com/CEGRcode/scriptmanager.
Guiding surrogate-assisted multi-objective optimisation with decision maker preferences

We present a new algorithm for efficiently solving expensive multi-and many-objective, black-box optimisation problems by interactively incorporating the preferences of an external decision maker. We define a novel acquisition function which combines the maximin distance to the summary attainment front with a set of aspirational objective-space target points chosen by the decision maker. This drives an exploitative multi-surrogate model to quickly converge to solutions favourable to the decision maker, without relying on surrogate posterior uncertainty estimates or arbitrary objective weighting. The performance of the algorithm is quantified through measurement of the hypervolume of interest to the decision maker which is dominated by the set of found solutions. This measure is used to compare the performance of our algorithm to one similar, which does not involve the decision maker. We demonstrate over a range of Walking Fish Group test problems, that incorporating preference in this way improves convergence in the vast majority of cases. Empirical evaluation further shows that preference inclusion is of increasing importance as the number of objectives increases, and that the greatest benefit is obtained when the targets dominate or are dominated by a small region of the Pareto front.
A classification-assisted level-based learning evolutionary algorithm for expensive multiobjective optimization problems

One essential issue in surrogate-assisted evolutionary algorithms (SAEAs) is how to evaluate solutions and find candidates for evolution, without resorting to the calculation of the computational-expensive real objective function. While most studies use regression models as surrogate models for SAEAs, some recent work propose to use classification models for single-objective expensive optimization, as classification models are more stable and much easier to train with limited available data. However, for multi-objective expensive optimization problems, it is more challenging to classify the solutions into different levels according to their quality, and thus the use of classification-based surrogate models for multi-objective SAEAs has not been explored in-depth. To this end, in this paper we propose a classification-assisted level-based learning swarm optimizer for expensive multi-objective optimization. First, a preference relationship among individuals taking both Pareto dominance and crowding distance into account is defined to divide the whole population into different quality levels. Then, a classification model is trained as the surrogate. In addition, a selection strategy is devised in decision space to acquire solutions on the sparse part on the Pareto front. Experimental results validate the superiority and efficiency of the proposed algorithm on benchmark test functions.
Unique Molecular Identifier-Based High-Resolution HLA Typing and Transcript Quantitation Using Long-Read Sequencing

HLA typing provides essential results for stem cell and solid organ transplants, as well as providing diagnostic benefits for various rheumatology, gastroenterology, neurology, and infectious diseases. It is becoming increasingly clear that understanding the expression of patient HLA transcripts can provide additional benefits for many of these same patient groups. Our study cohort was evaluated using a long-read RNA sequencing methodology to provide rapid HLA genotyping results and normalized HLA transcript expression. Our assay used NGSEngine to determine the HLA genotyping result and normalized mRNA transcript expression using Athlon2. The assay demonstrated an excellent concordance rate of 99.7%. Similar to previous studies, for the class I loci, patients demonstrated significantly lower expression of HLA-C than HLA-A and -B (Mann–Whitney U, p value = 0.0065 and p value = 0.0154, respectively). In general, the expression of class II transcripts was lower than that of class I transcripts. This study demonstrates a rapid high-resolution HLA typing assay using RNA-Seq that can provide accurate HLA genotyping and HLA allele-specific transcript expression in 7–8 h, a timeline short enough to perform the assay for deceased donors.