Miracle-dz/UniFAR-FTUs huggingface dataset:
Perspective on Pulsed Electric Field Treatment in the Bio-based Industry

The bio-based industry is urged to find solutions to meet the demands of a growing world population. In this context, increased resource efficiency is a major goal. Pulsed electric field (PEF) processing is a promising technological solution. Conventional PEF and the emerging area of nanosecond PEF (nsPEF) have been shown to induce various biological effects, with nsPEF inducing pronounced intracellular effects, which could provide solutions for currently faced challenges. Based on the flexibility and continuous operation of PEF and nsPEF processing, the technology can be integrated into many existing cultivation systems; its modularity provides an approach for inducing specific effects. Depending on the treatment conditions, selective inactivation, continuous extraction without impeding cell viability, as well as the stimulation of cell growth and/or cellular compound stimulation are potential applications in the bio-based industry. However, continuous treatment currently involves heterogeneous energy inputs. Increasing the homogeneity of PEF and nsPEF processing by considering the flow and electric field heterogeneity may allow for more targeted effects on biological cells, further increasing the potential of the technology for bio-based applications. We provide an overview of existing and potential applications of PEF and nsPEF and suggest that theoretical and practical analyses of flow and electric field heterogeneity may provide a basis for obtaining more targeted effects on biological cells and for further increasing the bio-based applications of the technology, which thereby could become a key technology for circular economy approaches in the future.
Balsacone C, a New Antibiotic Targeting Bacterial Cell Membranes, Inhibits Clinical Isolates of Methicillin-Resistant Staphylococcus aureus (MRSA) Without Inducing Resistance

New options are urgently needed for the treatment of methicillin-resistant Staphylococcus aureus (MRSA) infections. Balsacone C is a new dihydrochalcone extracted from Populus balsamifera that has been reported previously as being active against Staphylococcus aureus. Here, we evaluate the antibacterial activity of balsacone C against MRSA. Thirty-four (34) MRSA isolates were obtained from hospitalized patients; these isolates were then characterized for their resistance. Most of these MRSA (>85%) were resistant to penicillin, amoxicillin/clavulanic acid, ciprofloxacin, moxifloxacin, levofloxacin, clindamycin, erythromycin, and cefoxitin as well as being sensitive to linezolid, trimethoprim/sulfamethoxazole, rifampicin, and gentamicin. When tested against all MRSA isolates and various gram-positive bacteria, the antibacterial activity of balsacone C produced a MIC of 3–11.6 mg/mL. We observed no resistant isolates of MRSA (against balsacone C) even after 30 passages. Microscopy fluorescence showed that bacteria cell membrane integrity was compromised by low concentrations of balsacone C. Scanning electron microscope (SEM) confirmed balsacone C–provoked changes in the bacterial cell membrane and we find a dose-dependent release of DNA and proteins. This loss of cellular integrity leads to cell death and suggests a low potential for the development of spontaneous resistance.
A Novel Proxy Re-encryption Scheme Based on Identity Property and Stateless Broadcast Encryption Under Cloud Environment

Due to low eﬃciency of traditional proxy re-encryption scheme in cloud environment, we propose a novel proxy re-encryption scheme based on identity property under cloud environment in this paper. The new scheme makes full use of the advantages of identity property encryption, proxy re-encryption and stateless broadcast encryption to provide safe and reliable cloud storage. Identity property encryption utilizes the user’s identity property as public key that can reduce the process of certiﬁcate validation. Proxy re-encryption can realize the ﬁne-grain access con-trol. In addition, stateless broadcast encryption can completely resist fully collusion resistant (i.e. Though one cancels the cooperation between users, they cannot de-crypt the message). Finally, experimental results demonstrate that the new scheme not only reduces the consumption of system, but realizes the encryption eﬃciency and security.
Annual Protist Community Dynamics in a Freshwater Ecosystem Undergoing Contrasted Climatic Conditions: The Saint-Charles River (Canada)

Protists are key stone components of aquatic ecosystems, sustaining primary productivity and aquatic food webs. However, their diversity, ecology and structuring factors shaping their temporal distribution remain strongly misunderstood in freshwaters. Using high-throughput sequencing on water samples collected over 16 different months (including two summer and two winter periods), combined with geochemical measurements and climate monitoring, we comprehensively determined the pico- and nanoeukaryotic community composition and dynamics in a Canadian river undergoing prolonged ice-cover winters. Our analysis revealed a large protist diversity in this fluctuating ecosystem and clear seasonal patterns demonstrating a direct and/or indirect selective role of abiotic factors, such as water temperature or nitrogen concentrations, in structuring the eukaryotic microbial community. Nonetheless, our results also revealed that primary productivity, predatory as well as parasitism lifestyles, inferred from fine phylogenetic placements, remained potentially present over the annual cycle, despite the large seasonal fluctuations and the remodeling of the community composition under ice. In addition, potential interplays with the bacterial community composition were identified supporting a possible contribution of the bacterial community to the temporal dynamics of the protist community structure. Our results illustrate the complexity of the eukaryotic microbial community and provide a substantive and useful dataset to better understand the global freshwater ecosystem functioning.
Hemozoin From the Liver Fluke, Opisthorchis felineus, Modulates Dendritic Cell Responses in Bronchial Asthma Patients

Aims: There is a general, inverse relationship between helminth infection and allergic diseases including bronchial asthma (BA). Proteins and other mediators released from parasitic worms exert cogent downmodulation of atopic and other allergic reactivity. We investigated the immune activities of an immortalized murine dendritic cell (mDC) line (JAWSII) and of primary human dendritic cells (hDCs) collected from study participants with and without BA after Opisthorchis felineus hemozoin (OfHz) treatment. Methods and Results: in vitro, expression of lymphocyte-activating factors—T helper 1 (Th1) induction and anti-inflammatory cytokines including tumor necrosis factor alpha (TNF-α), interleukin-1beta (IL-1β), IL-10, and IL-12β–increased significantly in mDCs pulsed with OfHz. In parallel, primary dendritic cells (hDC) from cases clinically diagnosed with BA along with healthy controls were exposed ex vivo to OfHz in combination with lipopolysaccharide (LPS). Whereas no significant change in the cellular maturation markers, CD83, CD86, and CD40, was apparent in BA vs. healthy hDC, pulsing hDC from BA with OfHz with LPS induced significant increases in expression of IL-10 and IL-12β, although not of TNF-α or tumor growth factor-beta (TGF-β). Conclusions: Liver fluke hemozoin OfHz stimulated production of Th1 inducer and anti-inflammatory cytokines IL-10 and IL-12β from BA-hDC pulsed with OfHz, an outcome that enhances our understanding of the mechanisms whereby opisthorchiasis contributes to protection against the atopic disease in liver fluke infection-endemic regions.
Bayesian Filtering with Multiple Internal Models: Toward a Theory of Social Intelligence

To exhibit social intelligence, animals have to recognize whom they are communicating with. One way to make this inference is to select among internal generative models of each conspecific who may be encountered. However, these models also have to be learned via some form of Bayesian belief updating. This induces an interesting problem: When receiving sensory input generated by a particular conspecific, how does an animal know which internal model to update? We consider a theoretical and neurobiologically plausible solution that enables inference and learning of the processes that generate sensory inputs (e.g., listening and understanding) and reproduction of those inputs (e.g., talking or singing), under multiple generative models. This is based on recent advances in theoretical neurobiology—namely, active inference and post hoc (online) Bayesian model selection. In brief, this scheme fits sensory inputs under each generative model. Model parameters are then updated in proportion to the probability that each model could have generated the input (i.e., model evidence). The proposed scheme is demonstrated using a series of (real zebra finch) birdsongs, where each song is generated by several different birds. The scheme is implemented using physiologically plausible models of birdsong production. We show that generalized Bayesian filtering, combined with model selection, leads to successful learning across generative models, each possessing different parameters. These results highlight the utility of having multiple internal models when making inferences in social environments with multiple sources of sensory information.
Power-mode-aware Memory Subsystem Optimization for Low-power System-on-Chip Design

The memory subsystem is increasingly subject to an intensive energy minimization effort in embedded and System-on-Chip development. While the main focus is typically put on energy consumption reduction, there are other optimization aspects that become more and more relevant as well, e.g., peak power constraints or time budgets. In this regard, the present article makes the following contributions. Taking industrial-grade information into account, different Static Random-Access Memory (SRAM) power modes and their characteristics are presented at first. Using this information, a comprehensive optimization model with the main intention of energy minimization is defined. It is based on memory access statistics that represent the embedded software of interest, which allows for application-tailored improvements. Further, it considers different power states of the memory subsystem and enables the definition of peak power and time corridor constraints. The presented two-stage implementation of this optimization model allows the handling of large design spaces. Clearly defined interfaces facilitate the exchange of individual workflow parts in a plug-and-play fashion and further enable a neat integration of our optimization method with existing hardware/software (HW/SW) codesign synthesis flows. A general evaluation for different technology nodes yields that the optimization potential of memory low-power modes increases with advancing miniaturization but also depends on the data footprint of the embedded software. Experimental results for a set of benchmark applications confirm these findings and provide energy savings of up to 90% and over 60% on average compared to a monolithic memory layout without low-power modes.
An Unhealthy Obsession with Theory

As the flagship journal of the Association for Information Systems (AIS), the Journal of the Association for Information Systems ( JAIS ) invites the incoming president of AIS to write an editorial about issues facing AIS and its members. In this editorial, I argue that over the past 30 years, information systems research has shifted to what I see as an unhealthy obsession with one particular type of theory to the exclusion of other types. I believe this obsession is stifling new research and is preventing us, as a discipline, from leading technology innovation. The solution is simple: return to our roots and embrace other types of theory. The opinions in this editorial are solely mine, and do not reflect the official policy of AIS, or the opinions of my colleagues at AIS or those who serve at JAIS .
Consumer Segmentation Using Case Based Reasoning Approach To Printing Company

Every print produced by the printing machine in printshop was recorded in the machine log. This machine log contains a variety of information about the printed product produced. By analyzing the login, it is expected to find patterns that can be used to find out customer needs. Case-based Reasoning is a knowledge transformation technique used in the context of a new algorithm. The way to find out the case-based reason is to use data mining techniques. One technique is the C4.5 method, called the tree data mining method which is often used in classifying and making rules based on transactional data or facts. The purpose of this study is to determine the rules or segmentation of customers. So that in the future the rules generated from this context can be used in system development.
Tackling Class Imbalance Problem in Software Defect Prediction Through Cluster-Based Over-Sampling With Filtering

In practice, Software Defect Prediction (SDP) models often suffer from highly imbalanced data, which makes classifiers difficult to identify defective instances. Recently, many techniques were proposed to tackle this problem, over-sampling technique is one of the most well-known methods to address class imbalance problem. This technique balances the number of defective and non-defective instances by generating new defective instances. However, these approaches would generate non-diverse synthetic instances, and many unnecessary noise instances at the same time. Motived by this, we propose a Cluster-based Over-sampling with noise filtering (KMFOS) approach to tackle class imbalance problem in SDP. KMFOS firstly divides defective instances into $K$ clusters, and new defective instances are generated by interpolation between instances of each two clusters. After this, these new defective instances would diversely spread in the space of defective dataset. Then, we extend this cluster-based over-sampling through the Closest List Noise Identification (CLNI) to clean the noise instances. We do extensive experiments on 24 projects to compare KMFOS with some over-sampling approaches such as SMOTE, Borderline-SMOTE, ADASYN, random over-sampling (ROS), K-means SMOTE, SMOTE + IPF, SMOTE + ENN and SMOTE + Tomek Links using five prediction classifiers. At the same time, we also compare KMFOS with other state-of-the-art class-imbalance methods including balancebaggingclassifier, RUSboostclassifier, InstanceHardnessThreshold and cost-sensitive methods. Experimental results indicate our KMFOS can obtain better Recall and bal values than other over-sampling methods and other compared class-imbalance methods. Hence, KMFOS is an efficient approach to generate balanced data for SDP and improves the performance of predicting models.
Complex Dynamic Event Participant in an Event-Based Social Network: A Three-Dimensional Matching

In recent years, <underline>e</underline>vent-<underline>b</underline>ased <underline>s</underline>ocial <underline>n</underline>etworks (EBSNs), such as Meetup, Eventbrite and Douban, have emerged as a popular new type of social network on which online users can organize and register for offline social events. Existing approaches focus mainly on arrangement strategies that include users and events on an EBSN platform under an offline scenario, where all information is known in advance. However, these strategies ignore the importance of the organizer, and the offline scenarios can be impractical since all elements appear dynamically in reality. Therefore, we consider not only the user and event in the EBSN platform under the online scenario but also the importance of the organizer. In this paper, we study the complex dynamic event participant in an EBSN that considers three elements: users, events and organizers. We first formalize the event arrangement problem in the online scenario through <underline>e</underline>vent <underline>a</underline>rrangement through <underline>t</underline>hree-<underline>d</underline>imensional <underline>m</underline>atching (EATDM). Then, we propose a straightforward solution of the EATDM problem, a greedy algorithm with a competitive ratio, and further develop a basis solution, a random algorithm. We also propose a threshold-based algorithm and a weighted-threshold algorithm with a tighter competitive ratio. Finally, we verify the validity and practicability of the proposed algorithms on synthetic and real datasets, and we find that the weighted-threshold algorithm performs better than the random algorithm, the greedy algorithm and the threshold-based algorithm in terms of the total utility value.
A Proposed Effective Framework for Elderly with Dementia Using Data Mining

Elderly people constitute a significant percentage of the population all over the world. Patient Central Bureau of General Mobilization and Statistics (2013) reported that elderly represent 6.9% of the Egyptian population, it is estimated that the number of elderly people will be replicated in 2030. Eight percent (8%) of elders aged between 65 & 74, also about 50% aged over 80 suffer from the cognitive disease or dementia. The core of this research is to use intelligent techniques to fulfill specific needs of cognitive deficits patients and assess interaction changes based on patient’s behavior in various disease stages. The aim of this paper is to provide a usable and effective framework for elderly. The proposed framework will be divided into three main phases, evaluation phase, data mining phase and assistance phase. Patient proceeds in the three stages by behavior analysis and feedback to provide interactive evaluation and assistance for him/her.
Identification of Influencers in Social Media using Social Network Analysis (SNA)

Social media is a communication media that is often used to connect many people around the world. The inappropriate use of social media will have a negative impact. The example is the spread of hoaxes. Hoax is a topic that shared by many accounts on social media. These accounts are referred as influencers. This research aims to identify influencers in social media. This research use hoax dataset. The methods are Social Network Analysis (SNA) and weighting of SNA measurements. The results of this study are a list of SNA measurement results which are then combined using weighting. The results indicate the social media accounts that act as the major influencers and their relationships with other accounts. The relation is shown in a model to know the path of the spread.
Estimation of Sound Speed Profile from MODAS and Its Effect on Sound Propagation in the South China Sea

Satellite remotely-sensing observations are used to reconstruct the underwater environment of temperature and salinity which was named as the method Modular Ocean Data Assimilation System (MODAS) by US navy laboratory. The MODAS in the South China Sea has been developed by the South China Sea Institute of Oceanology, Chinese Academy of Sciences. Based on the system, this paper estimates the sound speed profile from MODAS and Argo and its effect on sound propagation in the South China Sea. Although the overall root mean square error (RMSE) was only 1.3m/s, the error was mainly within 200m, reaching 4.8m/s maximally. The inversion error of sound speed profile did not affect the location and width of convergence zones in deep-sea channels, but the surface duct was abnormal.
Early-onset Degradation of Thin-film Magnet Wire Insulation for Electromechanical Energy Converters

One of the predominant fault modes of electric machines is insulation failure which may lead to short circuits and catastrophic failure. Electrical insulation materials provide the vital function of turn-to-turn, phase to phase, and phase-to-ground electrical isolation for the electromagnetic coils and circuits. This paper investigates the characterisation of early-onset degradation of thin-film magnet wire insulation at elevated temperatures from 200 to 275 °C. Sample specimens were analysed after ageing for 100 hours in terms of their physical properties (surface roughness, mass), chemical properties (Fourier Transform Infra-Red (FTIR) spectroscopy), dielectric properties (capacitance, dissipation factor and impedance) and electrical properties (voltage breakdown strength and resistance). The roughness and mass increase and decrease fairly uniformly, respectively, as might be expected, with increased ageing temperature. The dielectric and electrical properties, however, do not change uniformly with ageing temperature and the results here appear not to conform to the commonly accepted Arrhenius law for insulation lifetime versus temperature. Instead we find that for slightly elevated temperatures (200 and 215◦C) the breakdown voltage performance is significantly worse than the unaged insulation, but this improves at 230–260◦C, and then finally drops to the lowest value at 275 °C. It is unclear exactly why this is the case, but we hypothesize that it could be related to build up of thermo-mechanical stress in the polymer layers which is not relaxed at the lower ageing temperatures.
WhatTheHealth: An Android Application for Consumers of Healthy Food

Currently, Non-Communicable Diseases (NCDs) have become a severe public health problem in every country. The major cause of NCDs is unhealthy eating habits. Thus, selecting food to eat should be considerably concerned. However, it is difficult to check whether nutrients and their quantities are sufficient or excessive for daily consumption. Typically, nutrition information can be examined from nutrition facts label attached to food products. Nevertheless, most consumers do not know or clearly understand nutrition information and the appropriate amount of calories and ratio of nutrients they should be consumed each day. Nowadays, a great number of smartphone users can access information on the Internet easier and more convenient via mobile applications. Therefore, the researchers have an innovative idea to develop an android application named “WhatTheHealth” to provide nutrition information of the packaged foods to consumers for making better food choices. The ultimate goal of this research project is to encourage people to consume healthy food, have a healthier life and far away from NCDs.
Critical object recognition in underwater environment

Nowadays, ocean exploration is far from complete and the development of suitable recognition systems are crucial, to allow that the robots perform inspection and monitoring tasks in diverse conditions. The online available datasets are incomplete for these kinds of scenarios and, so it is important to build datasets that covered real condition in a simulated environment. Thus, it was developed a dataset with some man-made objects presents in the underwater environment. Moreover, it is also presented the developed method (Convolutional Neural Network) and its evaluation in diverse conditions is performed. It is also presented a comparative analysis and a discussion between the proposed algorithm and the ResNet architecture. The obtained results showed that the developed method is appropriate to classify 7 critical different objects with good performance.