Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Bacterial Polyhydroalkanoate (PHA) in a Sustainable Future: A Review on the Biological Diversity

Environmental challenges related to the mismanagement of plastic waste became even more evident during the COVID-19 pandemic. The need for new solutions regarding the use of plastics came to the forefront again. Polyhydroxyalkanoates (PHA) have demonstrated their ability to replace conventional plastics, especially in packaging. Its biodegradability and biocompatibility makes this material a sustainable solution. The cost of PHA production and some weak physical properties compared to synthetic polymers remain as the main barriers to its implementation in the industry. The scientific community has been trying to solve these disadvantages associated with PHA. This review seeks to frame the role of PHA and bioplastics as substitutes for conventional plastics for a more sustainable future. It is focused on the bacterial production of PHA, highlighting the current limitations of the production process and, consequently, its implementation in the industry, as well as reviewing the alternatives to turn the production of bioplastics into a sustainable and circular economy.
Theory of current collection of moving cylindrical probes.

The theory of current collection of a moving cylindrical probe is investigated. Volt‐ampere relations are derived for two distinct cases: (i) The general‐ion current for accelerating collector potential and its special cases, including general‐ion current to the stationary probe, orbital‐motion‐limited current to the moving and the stationary probes, and sheath‐area‐limited current to the moving and the stationary probes; (ii) the general‐electron current for the retarding collector potential and its special cases, including general‐electron current to the stationary probe and random‐electron current to the moving and the stationary probes. Orientation of the cylinder with respect to the drift velocity vector is taken into account. Volt‐ampere characteristics are included for illustrating the functional behavior of the current relations.
Lower Circulating Cell-Free Mitochondrial DNA Is Associated with Heart Failure in Type 2 Diabetes Mellitus Patients

Cell-free nuclear (cf-nDNA) and mitochondrial (cf-mDNA) DNA are released from damaged cells in type 2 diabetes mellitus (T2DM) patients, contributing to adverse cardiac remodeling, vascular dysfunction, and inflammation. The purpose of this study was to correlate the presence and type of cf-DNAs with HF in T2DM patients. A total of 612 T2DM patients were prescreened by using a local database, and 240 patients (120 non-HF and 120 HF individuals) were ultimately selected. The collection of medical information, including both echocardiography and Doppler imagery, as well as the assessment of biochemistry parameters and the circulating biomarkers, were performed at baseline. The N-terminal brain natriuretic pro-peptide (NT-proBNP) and cf-nDNA/cf-mtDNA levels were measured via an ELISA kit and real-time quantitative PCR tests, respectively. We found that HF patients possessed significantly higher levels of cf-nDNA (9.9 ± 2.5 μmol/L vs. 5.4 ± 2.7 μmol/L; p = 0.04) and lower cf-mtDNA (15.7 ± 3.3 μmol/L vs. 30.4 ± 4.8 μmol/L; p = 0.001) than those without HF. The multivariate log regression showed that the discriminative potency of cf-nDNA >7.6 μmol/L (OR = 1.07; 95% CI = 1.03–1.12; p = 0.01) was higher that the NT-proBNP (odds ratio [OR] = 1.10; 95% confidence interval [CI] = 1.04–1.19; p = 0.001) for HF. In conclusion, we independently established that elevated levels of cf-nDNA, originating from NT-proBNP, were associated with HF in T2DM patients.
A Novel Study on a Generalized Model Based on Self-Supervised Learning and Sparse Filtering for Intelligent Bearing Fault Diagnosis

Recently, deep learning has become more and more extensive in the field of fault diagnosis. However, most deep learning methods rely on large amounts of labeled data to train the model, which leads to their poor generalized ability in the application of different scenarios. To overcome this deficiency, this paper proposes a novel generalized model based on self-supervised learning and sparse filtering (GSLSF). The proposed method includes two stages. Firstly (1), considering the representation of samples on fault and working condition information, designing self-supervised learning pretext tasks and pseudo-labels, and establishing a pre-trained model based on sparse filtering. Secondly (2), a knowledge transfer mechanism from the pre-training model to the target task is established, the fault features of the deep representation are extracted based on the sparse filtering model, and softmax regression is applied to distinguish the type of failure. This method can observably enhance the model’s diagnostic performance and generalization ability with limited training data. The validity of the method is proved by the fault diagnosis results of two bearing datasets.
Image Analysis System for Early Detection of Cardiothoracic Surgery Wound Alterations Based on Artificial Intelligence Models

Cardiothoracic surgery patients have the risk of developing surgical site infections which cause hospital readmissions, increase healthcare costs, and may lead to mortality. This work aims to tackle the problem of surgical site infections by predicting the existence of worrying alterations in wound images with a wound image analysis system based on artificial intelligence. The developed system comprises a deep learning segmentation model (MobileNet-Unet), which detects the wound region area and categorizes the wound type (chest, drain, and leg), and a machine learning classification model, which predicts the occurrence of wound alterations (random forest, support vector machine and k-nearest neighbors for chest, drain, and leg, respectively). The deep learning model segments the image and assigns the wound type. Then, the machine learning models classify the images from a group of color and textural features extracted from the output region of interest to feed one of the three wound-type classifiers that reach the final binary decision of wound alteration. The segmentation model achieved a mean Intersection over Union of 89.9% and a mean average precision of 90.1%. Separating the final classification into different classifiers was more effective than a single classifier for all the wound types. The leg wound classifier exhibited the best results with an 87.6% recall and 52.6% precision.
Deep Learning for Multi-Level Detection and Localization of Myocardial Scars Based on Regional Strain Validated on Virtual Patients

How well the heart is functioning can be quantified through measurements of myocardial deformation via echocardiography. Clinical assessment of cardiac function is generally focused on global indices of relative shortening; however, segmental strain indices have been shown to be abnormal in regions of myocardial disease such as scarring. In this work, we propose a single framework to predict myocardial scars at global, territorial, and segmental levels using regional myocardial strain traces as input to a convolutional neural network (CNN). An anatomically meaningful representation of the input data from the clinically standard bullseye representation to a multi-channel 2D image is proposed, thus enabling the use of state-of-the-art neural network configurations. A Fully Convolutional Network (FCN) is trained to detect and localize myocardial scar from regional left ventricular (LV) strain traces. Simulated regional strain data from a controlled dataset of virtual patients with varying degrees and locations of myocardial scar is used for training and validation. The proposed method successfully detects and localizes the scars on 98% of the 5490 left ventricle (LV) segments of the 305 patients in the test set using strain traces only. Due to the sparse existence of scar in the dataset, only 10% of the LV segments are scarred. Taking the imbalance into account, the class balanced accuracy is calculated as 95%. The proposed method proves successful on the strain traces of the virtual cohort and offers the potential to solve the regional myocardial scar detection problem on the strain traces of the real patient cohorts.
Games that aim to promote antimicrobial stewardship: An overview of the entertainment potential

Antimicrobial resistance is one of the most pressing healthcare issues. Healthcare awareness campaigns that promote antimicrobial stewardship have shown mixed results highlighting a need to look at complementary solutions to engage the public. In this context, games are increasingly used as an alternative to engaging users with healthcare issues; antibiotic stewardship makes no exception. The focus of research on games for health is on their effectiveness, the entertainment potential not receiving as much attention. In this paper, we address this gap. This study identifies a variety of methods used to assess entertainment value such as the number of users and user self-reported enjoyment. This provides a rich overview of the entertainment potential of games. However, as different games use different methods of assessment it makes it difficult to compare games and determine which aspects of games work best.
Possible nonlinear wave-wave coupling between three or four waves in plasmas

Since even weak nonlinear coupling between coherent waves can change the stability or instability of these waves, as computed from linearized theory, more and more cases of such wave-wave coupling are investigated. Certain combinations of resonance conditions give rise to a totally different situation, as far as the equations governing the changes in the interacting amplitudes are concerned. This constatation remains valid even when one set of resonance conditions is a special case of another one. Hence it becomes necessary to look for all the different resonance conditions which yield distinct sets of amplitude equations and thus different solutions. This has been done for coupling between two, three or four waves, in second or in third order.
HW/SW Codesign for Approximation-Aware Binary Neural Networks

Binary Neural Networks (BNNs) are rapidly gaining remarkable attention due to their superiority in shrinking the model size, which outstandingly mitigates the fundamental “memory wall” bottleneck that is attributed to the existing von-Neumann architectures. This work investigates how principles from approximate computing can be effectively employed to further optimize BNNs. It demonstrates that HW/SW codesign, in which BNNs are either proactively trained in the presence of approximation-induced errors (i.e. design-time optimization) and/or augmented with an appropriate error-mitigation scheme (i.e., run-time optimization), is a key to realize energy-efficient yet robust BNNs. We unveil, for the first time, that although the underlying HW of BNNs can be implemented using simple XNOR gates, the complexity of the required “Popcount” circuit super-linearly grows with the filter kernel size. This largely impacts the area footprint, inference time, energy, and hence it severely constricts the prospective efficiency gains from BNNs. To overcome this challenge, we replace the accurate full adders constructing the Popcount with Majority gates that approximately perform the required additions. Then, our carefully-crafted error-mitigation scheme along with activations tuning considerably minimizes the induced errors. Afterward, abstracted error probabilities are derived and employed during BNN training to obtain approximation-aware BNNs, that are inherently robust against the underlying HW approximation. Differently from the typical approaches, the proposed HW/SW codesign methodology has the merit of allowing a training of the approximate BNN without the need to modify the existing software frameworks (i.e., PyTorch). This is of great importance since existing tools rely on efficient built-in functions that can be difficult and/or inefficient to be modified. An FPGA-based SoC realizing both accurate and approximation-aware BNNs is developed for validating our proposed methodology. With merely a 4.7% loss in the inference accuracy, our HW/SW codesign leads to 64% and 80.2% savings in the area and energy, respectively, at the parity of the latency. Our results are obtained using commercial EDA tool flows employing a commercial 28nm FDSOI technology node.
Identification of Key Brittleness Factors for the Lean–Green Manufacturing System in a Manufacturing Company in the Context of Industry 4.0, Based on the DEMATEL-ISM-MICMAC Method

In the context of Industry 4.0, the lean–green manufacturing system has brought many advantages and challenges to industrial participants. Security is one of the main challenges encountered in the new industrial environment, because smart factory applications can easily expose the vulnerability of manufacturing and threaten the operational security of the whole system. It is difficult to address the problem of the brittleness factor in manufacturing systems. Therefore, building on vulnerability theory, this study proposes a vulnerability index system for lean–green manufacturing systems in a manufacturing company in the context of Industry 4.0. The index has four dimensions: human factors, equipment factors, environmental factors, and other factors. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach was used to calculate the degree of influence, the degree of being influenced, and the centrality and causes of the factors. The causal relationships and key influences between the factors were identified. Then, the dependence and hierarchy of each of the key influencing factors were analyzed using the Matrix-Based Cross-Impact Multiplication Applied to Classification (MICMAC) and Interpretative Structural Model (ISM) methods, and a hierarchical structural model of the factors was constructed. Finally, an intelligent manufacturing system that produces a micro-acoustic material and device was used as an example to verify the accuracy of the proposed method. The results show that the method not only identifies the key brittleness factors in a lean–green manufacturing system but can also provide a guarantee for the safe operation of a manufacturing system. This study provides theoretical guidance for the effective management of intelligent manufacturing systems; moreover, it lays a foundation and provides a new methodology for assessing the vulnerability of manufacturing systems.
Between emotion and occupation: employment promoting practice from social workers’ perspective

ABSTRACT Recent years have seen growing research interest in and application of the social work subfield of employment promotion practices (EPPs), designed to help populations coping with poverty and unemployment to integrate in the labour market. The aim of this study was to identify and conceptualise professional EPP in social work. Data were collected using in-depth semi-structured interviews with sixteen EPP social workers in Israel, who offered direct, practice-oriented knowledge regarding these practices. The results indicated that despite differences between employing agencies, three common themes could be identified as characterising this subfield. First, the content of professional interventions, including an inherent dilemma between emotional therapy and task-oriented occupational interventions. Second, intervention processes, including five main stages: occupational intake, setting occupational objectives, dealing with obstacles and skills improvement, work placement, and on-the-job supervision. Third, the results showed that EPP social workers focused mainly on casework and individual counselling, to the neglect of macro practices aiming to change unjust labour policies. The findings enable conceptualisation of the EPP social worker’s practices and contribute to the developing body of knowledge in this emerging subfield. Implications for further professional development and policy are presented and discussed.
Non‐precipitating cumulus convection and its parameterization

This paper discusses the thermodynamic transports of heat, liquid water and (briefly) water vapour by non-precipitating cumulus convection. It is shown that because of the irreversible mixing between cloud and environment, there is a downward transport of enthalphy in the cumulus layer. A lapse-rate adjustment model relates stratification to the life-cycle of a model cloud parcel. A sub-cloud layer model specifies the lower boundary of the lapse-rate model, and the convective transports through cloud-base. Budget equations together with the lapse-rate model, and its time dependent boundary conditions, predict the time development of the cumulus layer, and show the dependence on large-scale mean vertical motion, cloud-base variations, and the surface sensible heat flux.
Energy, Exergy, and Economic (3E) Analysis of Transcritical Carbon Dioxide Refrigeration System Based on ORC System

This paper used the energy, exergy, and economic analysis of a carbon dioxide (CO2) transcritical two-stage compression system based on organic Rankine cycle (ORC) waste heat recovery technology. When the intermediate pressure and high-pressure compressor outlet pressure were changed, respectively, this study simulated the change in system energy efficiency by adding the ORC for waste heat recovery, calculated the ratio of exergy loss of each component, and performed an economic analysis of the coupled system. The results show that adding waste heat recovery can effectively increase the energy efficiency of the system, and among all components, the heat exchanger had the largest exergy loss, while the evaporator had the highest capital investment and maintenance costs.
THE EFFECT OF CENTRAL STARBURSTS ON THE INTERSTELLAR MEDIUM OF DWARF GALAXIES

Major starburst events can last tens of millions of years, and in the process they can deposit significant amounts of energy into the surrounding interstellar medium. This energy from supernova and stellar winds imparts enough momentum to the interstellar medium (ISM) that portions of the ISM can become unbound and leave the parent galaxy, taking the metal-enriched stellar debris along. In dwarf galaxies, starbursts can produce enough total energy to unbind most or all of the ambient ISM. Whether this actually occurs is a strong function of the ellipticity of the ISM distribution, with flat disks and spheres being the limiting cases. We calculate whether 'blow out' along the symmetry axis of 'blow away' of the entire ISM occurs during a central starburst in dwarf galaxies as a function of galactic mass, starburst energy, ISM density, and ISM ellipticity. The calculations cover a range of 10(exp 7) to 10(exp 9) solar mass for dwarf galaxies and include 'normal' galaxies of 10(exp 11) solar mass as well. No massive dark matter halos are assumed to be present. We find that for physically reasonable values of total ISM mass and starburst energy a blow out along the symmetry axis occurs in the majority of cases, though a significant fraction of small dwarf galaxies can lose most of their ISM. As no dark matter halos or clumpy ISM distributions are included, it is apparent that the ISM in most dwarf galaxies may be generally resistant to significant disruption by a central starburst event. The effects of this range of behavi or on the metallicities that would be observed in these galaxies is discussed.
Optimal pricing policies of an inventory model for deteriorating items with discounts

Inventory models create a lot of interest due to its ready applicability at various practical situations. One of the most important considerations in developing the inventory models for deteriorating items is that the deteriorated item is thrown as waste. But in some practical situations at food and vegetable markets, the deteriorated items are sold for certain discounts. In this paper, we developed and analysed an economic order quantity model for deteriorating items with seconds sale. Here, it is assumed that the lifetime of the commodity is random and follows a two-component mixture of Weibull distribution. It is further assumed that the deteriorated items are sold with certain discounts. Assuming that the demand is a linear function of the selling price, the instantaneous state of inventory is derived by maximising the profit rate function. The optimal ordering and pricing policies are determined. The sensitivity of the model with respect to the parameters and costs is also studied. It is observed that the second sale has a significant impact on the optimal ordering quantity and pricing policies. This model also includes many of the earlier models as particular cases for specific values of the parameters.
Parameters to Predict the Outcome of Severe and Critical COVID-19 Patients when Admitted to the Hospital

Manifestations of COVID-19 are diverse and range from asymptomatic to severe, critical illness and death. Cases requiring hospital care (in severe and critical illnesses) are associated with comorbidities and hyperactivation of the immune system. Therefore, in this exploratory observational study, we analyzed which parameters are associated with mortality. We evaluated: demographic characteristics (age, sex and comorbidities), laboratory data (albumin, leukocytes, lymphocytes, platelets, ferritin), days of hospital stay, interleukins (IL-2, IL-6, IL-7, IL-10, IL-17) and sP-selectin in 40 Mexican patients admitted to medical emergencies with a confirmed diagnosis of COVID-19, a complete clinical record, and who signed the informed consent. Twenty severe (they required intermediate care with non-invasive ventilation) and twenty critically ill patients (they required mechanical ventilation) were classified, and these were subsequently compared with healthy and recovered subjects. A significant difference was found between the hospitalized groups in the parameters of age, ferritin, days of hospital stay and death with p values = 0.0145, p = 0.0441, p = 0.0001 and p = 0.0001, respectively. In the determination of cytokines and P-selectin, a significant difference was found between the following groups: recovered patients and healthy volunteers compared with hospitalized patients in severe and critical condition. Importantly, IL-7 remained elevated one year later in recovered patients. Taken together, these values determined at the time of hospital admission could be useful to monitor patients closely and evaluate in-hospital progress, hospital discharge, and out-of-hospital progress.