Miracle-dz/UniFAR-FTUs huggingface dataset:
5 easy pieces: How fishing impacts marine ecosystems

‘‘5 Easy Pieces’’ refers to five high-profile papers by Professor Daniel Pauly and colleagues that are reprinted in this book along with critiques and press coverage, and linked by Pauly’s commentary. The book is somewhat of a hybrid and seems to juggle what I will refer to as ‘‘3 Tricky Purposes’’: (1) a commentary on the science of global fisheries sustainability, (2) a case study of the intersection of science and media and (3) a scientific autobiography. These ‘‘3 Tricky Purposes’’ link roughly to the ‘‘3 Potential Audiences’’ of the book suggested by Pauly as (1) environmental activists, (2) graduate students of fisheries science and (3) students of epistemology and the nature of science. The autobiographical purpose is justified by Pauly’s status as perhaps the most well-known contemporary fisheries scientist. He has pioneered various methods and projects such as FishBase that have become staple resources for fisheries biologists, written prolifically, and increasingly advocated for action to limit the negative ecological impacts of fishing. He coined concepts such as ‘‘fishing down the foodweb,’’ ‘‘shifting baseline syndrome’’ and ‘‘malthusian overfishing,’’ which have entered the lexicon of fisheries, and even general environmental science. The first of the Easy Pieces (and the most technical of the chapters) involves calculation of the proportion of all aquatic primary productivity that accrues to fisheries (8%, and quadruple previous estimates). It is accompanied by an interesting appendix that describes diverse attempts over the years to estimate the global maximum marine fish catch. Piece 2, published in 2001 introduces the concept of ‘‘fishing down the foodweb. ’’ The article found a downward trend in mean trophic level (MTL) of global catches. This was an eye-catching indicator of ecosystem change, and for Pauly, a clear indication of unsustainability. Not everyone was convinced, and the use of MTL as a sustainability indicator remains controversial (see Branch et al. 2010). Pauly’s commentary on the debate is uncompromisingly partisan, illustrating that each critique of the original article turned out to be a ‘‘judo argument,’’ ultimately reinforcing Pauly et al.’s original claims (of declining MTL). This (along with an Appendix of 27 cases of declining MTL) is taken as a defence of MTL as an ecosystem indicator. I was somewhat confused by this defence of MTL as an indicator given that several of the ‘‘judo arguments’’, while emphasising impacts on ecosystem structure, also seemed to illustrate that MTL of catches can underestimate such changes. Piece 3 is perhaps the greatest contribution of Pauly and his colleagues to changing perceptions of global marine fisheries. It showed that catches at the turn of Journal of Integrative Environmental Sciences Vol. 8, No. 4, December 2011, 299–301
The promise of training deep neural networks on CPUs: A survey

This survey presents a comprehensive analysis of the potential benefits and challenges of training deep neural networks (DNNs) on CPUs, summarizing existing research in the field. Five distinct DNN models are examined: Ternary Neural Networks (TNNs), Binary Neural Networks (BNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and a novel method called Sub-Linear Deep Learning Engine (SLIDE), specifically designed for CPU-based network training. The survey emphasizes the advantages of using CPUs for DNN training, such as low cost, compact size, and broad applicability across various domains. Furthermore, the survey collects concerns related to CPU acceleration, including the absence of a unified programming model and the inefficiencies in DNN training due to increased floating-point operations. The survey explores algorithmic and hardware optimization strategies, incorporating compressed network structures, innovative techniques like SLIDE, and the RISC-V instruction set to tackle these issues. According to the survey, CPUs are more likely to become the alternative for developers with limited resources in the future. Through continued algorithm optimization and hardware enhancements, CPUs can provide more cost-efficient neural network training solutions, excelling in areas such as mobile servers and edge computing.
Tip Leakage Experiment - Part One: Aerodynamic And Acoustic Measurements

An aeroacoustic characterisation of Low Mach number (∼ 0.2) tip leakage flows is carried out at the anechoic wind tunnel facility of the Ecole Centrale de Lyon. Results suggest that the pressure fluctuations induced on the airfoil by the tip leakage vortex are scattered into sound at the trailing edge whereas another high frequency source seems to be related to the jet-like leakage flow impinging the main flow on the suction side. These features are evidenced by PIV, unsteady wall and far field pressure measurements as well two points PIV-PIV, PIV-pressure, HWA-Pressure and pressure-pressure cross-statistics in the gap region. The experiment is carried out on a single airfoil located in the potential core of a flanged jet in a medium at rest. The leakage flow is obtained by loading the high camber foil and a parametric study of the angle of attack, the gap size and the flow velocity shows that the two former have a significant impact onto the flow structure whereas the velocity mainly sets the magnitudes of the unsteady flow patterns.
Exploring UX Factors through the Dogfooding Approach: An Experience Report

The User Experience (UX) is an important quality attribute for software products, as it addresses aspects related to usage and subjective elements, such as affection, sensations, and emotions. Given its significance, various approaches, methods, and techniques that have been developed to evaluate UX during software development. In this context, Dogfooding is one of the approaches that companies adopt to assess the UX of their products, as seen in companies like Apple and Google. Dogfooding proposes that company employees use their own products or services in their daily lives, allowing them to understand both positive and negative aspects based on user perception. However, it is essential to comprehend how the perceptions reported by users in UX evaluations are analyzed from the viewpoint of UX factors, which encompass characteristics that define such experiences. This article presents the account of an action research aimed at identifying UX factors in the context of mobile devices through the Dogfooding approach. The results revealed different factors that characterize the UX, contributing to a better understanding for teams involved in the development of mobile devices concerning this quality attribute. The identification of these factors guides the creation of new ways to analyze UX in future projects in the context of mobile devices.
CALM - Un système de gestion de données de CApteurs à Localisation Mobile

Nowadays, moving objects databases are an important research topic. A lot of work dealt with modelling, querying and indexing objects that move freely or in networks. However, a moving object - such as a vehicle - could be associated to additional measures (e.g. gas consumption). Managing such data is of major interest for some applications such as analyzing driving behaviour or reconstructing the circumstances of an accident. But, this is not hold using existing approaches. In this paper, we propose a new data model and a management system that handle mobile location sensor data. We then give preliminary results in a real application context. MOTS CLE : Base de donnees spatiotemporelle, Modelisation, Objets Mobiles, Langage de requetes, Flux de donnees des capteurs.
The genomic and epidemiological virulence patterns of Salmonella enterica serovars in the United States

The serovars of Salmonella enterica display dramatic differences in pathogenesis and host preferences. We developed a process (patent pending) for grouping Salmonella isolates and serovars by their public health risk. We collated a curated set of 12,337 S. enterica isolate genomes from human, beef, and bovine sources in the US. After annotating a virulence gene catalog for each isolate, we used unsupervised random forest methods to estimate the proximity (similarity) between isolates based upon the genomic presentation of putative virulence traits We then grouped isolates (virulence clusters) using hierarchical clustering (Ward’s method), used non-parametric bootstrapping to assess cluster stability, and externally validated the clusters against epidemiological virulence measures from FoodNet, the National Outbreak Reporting System (NORS), and US federal sampling of beef products. We identified five stable virulence clusters of S. enterica serovars. Cluster 1 (higher virulence) serovars yielded an annual incidence rate of domestically acquired sporadic cases roughly one and a half times higher than the other four clusters combined (Clusters 2–5, lower virulence). Compared to other clusters, cluster 1 also had a higher proportion of infections leading to hospitalization and was implicated in more foodborne and beef-associated outbreaks, despite being isolated at a similar frequency from beef products as other clusters. We also identified subpopulations within 11 serovars. Remarkably, we found S. Infantis and S. Typhimurium subpopulations that significantly differed in genome length and clinical case presentation. Further, we found that the presence of the pESI plasmid accounted for the genome length differences between the S. Infantis subpopulations. Our results show that S. enterica strains associated with highest incidence of human infections share a common virulence repertoire. This work could be updated regularly and used in combination with foodborne surveillance information to prioritize serovars of public health concern.
A Filtering System for the Large-Scale Dial-A-Ride Problem With Shared Autonomous Vehicles

In this paper, we study a prospective transportation system in which shared autonomous vehicles provide dial-a-ride service for large passenger demands in urban and rural areas. We address this very large-scale problem using a greedy insertion heuristic. Accompanying this heuristic, we propose a filtering system to speed up routing process. The approach includes two specific data structures used as filtering devices, three different filtering modules, and a halt mechanism to control the computation effort spent in exploring the search space for candidate vehicles. The approach also relies on an original route encoding that exploits the topology of the road network. Experimental results show that en-route vehicles can be significantly reduced in such a system, decreasing the vehicle number by more than . And thanks to the filtering system, the execution time can be reduced by almost compared to the classic best-fit insertion heuristic while maintaining a comparable level of solution quality.
Lysicamine Reduces Protein Kinase B (AKT) Activation and Promotes Necrosis in Anaplastic Thyroid Cancer

Anaplastic thyroid cancer (ATC) is an aggressive form of thyroid cancer (TC), accounting for 50% of total TC-related deaths. Although therapeutic approaches against TC have improved in recent years, the survival rate remains low, and severe adverse effects are commonly reported. However, unexplored alternatives based on natural compounds, such as lysicamine, an alkaloid found in plants with established cytotoxicity against breast and liver cancers, offer promise. Therefore, this study aimed to explore the antineoplastic effects of lysicamine in papillary TC (BCPAP) and ATC (HTH83 and KTC-2) cells. Lysicamine treatment reduced cell viability, motility, colony formation, and AKT activation while increasing the percentage of necrotic cells. The absence of caspase activity confirmed apoptosis-independent cell death. Necrostatin-1 (NEC-1)-mediated necrosome inhibition reduced lysicamine-induced necrosis in KTC-2, suggesting necroptosis induction via a reactive oxygen species (ROS)-independent mechanism. Additionally, in silico analysis predicted lysicamine target proteins, particularly those related to MAPK and TGF-β signaling. Our study demonstrated lysicamine’s potential as an antineoplastic compound in ATC cells with a proposed mechanism related to inhibiting AKT activation and inducing cell death.
The Effectiveness of Internet Addiction Mobile Application on Sleep Quality of Internet-Addicted Students

ABSTRACTExcessive internet use causes addiction leading to obsessive-compulsive behavior, depression, anxiety, juvenile delinquency, and sleep disorders. Poor sleep quality will reduce the ability to think, increase the risk of accidents, hormonal metabolism disorders during puberty and menstruation, excessive fatigue during the day, and impaired activities in work, school, and social functions. Internet addiction therapy is often carried out after complaints of severe symptoms such as social isolation, depression, and the risk of suicide. Patients must be hospitalized, which takes a long time and cost. Thus, developments are carried out to overcome the problem of internet addiction by preventing, screening, and intervening in internet addiction in a practical, effective, safe, and economical way using mobile applications. This study aimed to identify the effectiveness of internet-addiction mobile applications on internet-addicted students' sleep quality. A quasi-experimental using non-equivalent control group design with pre-test and post-test. The research instrument used was the Internet Addiction Test (IAT) and the Pittsburgh Sleep Quality Index (PSQI). This study used a consecutive sampling of 46 people for the intervention and control groups. Data were analyzed using the McNemar test. Internet-Addiction Mobile Application significantly improved sleep quality for students who experienced internet addiction (60,9%) experienced good sleep quality. Furthermore, Internet-Addiction Mobile Application can significantly reduce internet addiction (p-value = 0.04). Internet-Addiction Mobile Application effectively improves students' sleep quality who experience internet addiction. This information can be used as data to prevent and intervene in sleep quality problems in people with internet addiction.
UvA-DARE ( Digital Academic Repository ) Application of traversability maps in the Virtual Rescue competition

This paper describes an exploration behavior which has been designed to explore large indoor areas. While exploring, traversability information is saved onto a layer in the map. This information is used in an A* algorithm has been implemented and is used for path planning. This approach allows the robot to learn from its experience and to find in the long run the “ideal line” to driving through the indoor environment.
WiFi-based Positioning System with k-means Clustering and Outlier Removal: Evidence from Multiple Datasets

Indoor positioning systems are receiving much attention due to their practical applications in our daily lives. WiFi Fingerprinting is the technique that is commonly used for indoor positioning due to its utilization of the WiFi infrastructure which is ubiquitously deployed in modern buildings. However, this technique suffers from the computational burden when determining the user's position in big areas that consist of lots of fingerprints and reference points. To reduce the search space as well as computing time, the clustering algorithm can be applied. In this paper, we evaluate the efficacy of k-means clustering in the WiFi Fingerprinting-based positioning system. Moreover, k-means clustering with outlier removal is applied in several public datasets, in which the WiFi data was collected in different environmental conditions, to show how much the clustering algorithm can help in reducing the computing time as well as improving the positioning accuracy. From the experimental results, compared to popular deterministic-based algorithms, the clustering algorithm can significantly reduce the computing time by up to 79.07%. Nevertheless, it cannot surpass the positioning accuracy of the compared algorithms.
Organizational environments, work characteristics and employee innovative behavior in the digital age: an fsQCA approach

Purpose For start-ups or growing firms, to effectively navigate the unpredictable nature of digital development and achieve superior innovative performance, it is crucial to have a workforce comprised of creative and innovative employees. Drawing upon the principles of social information processing theory, this study aims to investigate whether specific combinations of organizational internal and external environments, as well as work characteristics in the digital age, can foster a high level of employee innovative behavior. Design/methodology/approach By collecting a multilevel and multisource data set comprising 693 employees and 88 CEOs from 88 start-ups or growing firms, this study used fuzzy-set qualitative comparative analysis to examine the distinctive configurations associated with achieving a high level of employee innovative behavior. Findings The study found that six solutions enabled employees to innovate more effectively, but six solutions led to the absence of employee innovative behavior. Research limitations/implications The findings of this study offer important theoretical and practical implications to motivate employee innovative behavior in Chinese enterprises. Originality/value First, this study contributes to the literature on employee innovative behavior by addressing the need to explore the impact of the digital context on promoting innovation among employees. Second, this study adds to the existing literature on employee innovation and entrepreneurship by examining multiple organizational contexts and their influence on innovative behavior. Third, this study makes a significant contribution to the field of employee innovative behavior by examining the macroenvironment surrounding digital transformation within enterprises and integrating both internal and external organizational factors.
Heritable responses to stress in plants

Abstract Abstract Most plants are adapted to their environments through generations of exposure to all elements. The adaptation process involves the best possible response to fluctuations in the environment based on the genetic and epigenetic make-up of the organism. Many plant species have the capacity to acclimate or adapt to certain stresses, allowing them to respond more efficiently, with fewer resources diverted from growth and development. However, plants can also acquire protection against stress across generations. Such a response is known as an intergenerational response to stress; typically, plants lose most of the tolerance in the subsequent generation when propagated without stress. Occasionally, the protection lasts for more than one generation after stress exposure and such a response is called transgenerational. In this review, we will summarize what is known about inter- and transgenerational responses to stress, focus on phenotypic and epigenetic events, their mechanisms and ecological and evolutionary meaning.
Memory B cells dominate the early antibody-secreting cell response to SARS-CoV-2 mRNA vaccination in naïve individuals independently of their antibody affinity

Memory B cells (MBCs) formed over the individual’s lifetime constitute nearly half of the adult peripheral blood B cell repertoire in humans. To assess their response to novel antigens, we tracked the origin and followed the differentiation paths of MBCs in the early anti-S response to mRNA vaccination in SARS-CoV-2-naïve individuals on single-cell and monoclonal antibody level. Newly generated and pre-existing MBCs differed in their differentiation paths despite similar levels of SARS-CoV-2 and common corona virus S-reactivity. Pre-existing highly mutated MBCs showed no signs of germinal center re-entry and rapidly developed into mature antibody secreting cells (ASCs). In contrast, newly generated MBCs derived from naïve precursors showed strong signs of antibody affinity maturation before differentiating into ASCs. Thus, although pre-existing human MBCs have an intrinsic propensity to differentiate into ASCs, the quality of the anti-S antibody and MBC response improved through the clonal selection and affinity maturation of naïve precursors. Highlights mRNA vaccination of SARS-CoV-2 naïve individuals recruits naïve and pre-existing MBCs with similar levels of S-reactivity into the response S-reactive naïve but not pre-existing MBCs undergo affinity maturation S-reactive pre-existing MBCs dominate the early ASC response independent of their antigen affinity High-affinity S-reactive MBCs and ASCs develop over time and originate from affinity matured naïve precursors
Research and Development of a Smart Solution for Runtime Web Application Self-Protection

In contemporary times, ensuring web application security is a critical concern for organizations due to the prevalence of numerous types of attacks that serve diverse purposes. While traditional security measures such as web application firewalls (WAF) and intrusion detection systems (IDS) can help mitigate attacks, there is still a possibility of them being circumvented or compromised. A more efficacious approach is to adopt runtime application self-protection (RASP) solutions integrated within the web application. This solution has demonstrated its effectiveness by aiding in early attack detection and rapid attack mitigation. In this research, we propose a smart solution for runtime web application self-protection (RASP) to protect against vulnerabilities, attacks, and common weaknesses that have been rated among the top ten web security risks in 2021 by the Open Web Application Security Project (OWASP). The proposed solution leverages convolutional neural network (CNN) and a family of recurrent neural network (RNN) techniques. It builds a deep learning model with deep neural network architectures that scrutinizes user requests, thereby detecting potential SQL injection (SQLi), Cross-Site scripting (XSS), command injection (CMDi), and other types of attacks. The solution is designed to dynamically adapt to the application’s behavior and traffic, with the goal of minimizing false positives and preventing the blocking of legitimate traffic. Furthermore, the proposed solution, based on a microservices architecture, enhances the flexibility of the prediction module during upgrades and automated deployment. It is integrated with MLOps and DevSecOps and is also designed to be compatible with RESTful API servers. Our results have validated the efficacy of this solution in providing real-time application protection.
Influence of silanes on the hydration of cement by separating the cement into its individual phases

Cementitious materials can be treated with organosilicon compounds (silanes) to achieve a water‐repellent effect. However, the influence of silanes on cement hydration is still not fully understood. For this reason, we studied the hydration of the main Portland cement phase (C3S ‐ tricalcium silicate) and the interaction with iBTES (isobutyltriethoxysilane), iOTES (isooctyltriethxysilane) and nOTES (n‐octyltriethoxysilane) by isothermal calorimetry and in‐situ X‐ray diffraction (XRD). The results show that C3S hydration is initially retarded and subsequently accelerated by iBTES and iOTES. In addition to the hydration process, the crystal growth of the portlandites (calcium hydroxide) is also affected by the silanes. The XRD results indicate that the portlandites grow significantly more plate‐like. In this context, the influence of the silanes on the portlandite crystallization was therefore also recorded in a special experimental setup. From the scanning electron microscope images it can be shown that all portlandite crystals grown in the presence of silanes have a platelike morphology. This is due to the fact that the hexagonal surfaces of the portlandite are covered by silanes and thus the growth direction of the crystals is affected. The modified portlandite crystals indicate that silanes also influence the microstructure of the cement paste and therefore its chemical and mechanical properties.