Miracle-dz/UniFAR-FTUs huggingface dataset:
How pandemic-related changes in global attitudes toward the scientific community shape “post-pandemic” environmental opinion

Low public concern about anthropogenic climate change (ACC)—due in part to distrust in the scientific community—may decrease demand for policies aimed at mitigating its deleterious effects. Encouragingly, though, recent research finds that experiences with the COVID-19 pandemic has elevated trust in scientific expertise worldwide. We explore the possibility that positive attitudes toward the medical community are “spilling over” to increase ACC acceptance via globally representative survey data from 107 countries (N = 119,088) conducted during the COVID-19 pandemic. We show that trust in medical experts’ handling of the COVID-19 pandemic is associated with increased ACC acceptance, worldwide. Problematically, though, we also show that the effects of trust in medical professionals is strongest in countries experiencing the most positive change in attitudes toward the scientific community, which tend to be disproportionately wealthy, and less likely to bear the unequal effects of climate change.
Tailoring Powder Strengths for Enhanced Quality of Cold Sprayed Al6061 Deposits

By annealing of as-atomized powder, its strength is reduced by about 60%, which allows decreasing the critical velocity for a successful deposition. Experimental results demonstrate that powder strength-based calculation of quality parameter g allows for a more realistic description of microstructural characteristics and deposits properties. The single-particle impact morphologies as well as the detachment features of adhering splats by cavitation tests visualize the respective deposition characteristics and bonding behaviors. The lower critical velocities by annealing contribute to better single splat adhesion, lower porosity, higher electrical conductivity, as well as improved tensile strength of deposits. The direct correlation of powder pre-annealing and strength in combination with cold spraying parameter variation allows deﬁning effective strategies for improving deposit properties
Trajectory Tracking of Autonomous Vehicle Based on Model Predictive Control With PID Feedback

The simplified vehicle model often results in inaccuracy with respect to the conventional model predictive control (MPC) as it causes steady error in tracking control, which has negative implications for vehicle cornering. This study presents a trajectory planning and tracking framework, which applies artificial potential to obtain target trajectory and MPC with PID feedback to effectively track planned trajectory. The experimental and simulation results are then presented to demonstrate the improved performance in tracking accuracy and steering smoothness compared to that of the conventional MPC control. Especially during negotiating a curve, its steady state error is close to 0.
Developing and Implementing Noninvasive Ventilator Training in Haiti during the COVID-19 Pandemic

Background Noninvasive ventilation (NIV) is an important component of respiratory therapy for a range of cardiopulmonary conditions. The World Health Organization recommends NIV use to decrease the use of intensive care unit resources and improve outcomes among patients with respiratory failure during periods of high patient capacity from coronavirus disease (COVID-19). However, healthcare providers in many low- and middle-income countries, including Haiti, do not have experience with NIV. We conducted NIV training and evaluation in Port-au-Prince, Haiti. Objectives To design and implement a multimodal NIV training program in Haiti that would improve confidence and knowledge of NIV use for respiratory failure. Methods In January 2021, we conducted a 3-day multimodal NIV training consisting of didactic sessions, team-based learning, and multistation simulation for 36 Haitian healthcare workers. The course included 5 didactic session and 10 problem-based and simulation sessions. All course material was independently created by the study team on the basis of Accreditation Council for Continuing Medical Education–approved content and review of available evidence. All participants completed pre- and post-training knowledge-based examinations and confidence surveys, which used a 5-point Likert scale. Results A total of 36 participants were included in the training and analysis, mean age was 39.94 years (standard deviation [SD] = 9.45), and participants had an average of 14.32 years (SD = 1.21) of clinical experience. Most trainees (75%, n = 27) were physicians. Other specialties included nursing (19%, n = 7), nurse anesthesia (3%, n = 1), and respiratory therapy (3%, n = 1). Fifty percent (n = 18) of participants stated they had previous experience with NIV. The majority of trainees (77%) had an increase in confidence survey score; the mean confidence survey score increased significantly after training from 2.75 (SD = 0.77) to 3.70 (SD = 0.85) (P < 0.05). The mean knowledge examination score increased by 39.63% (SD = 15.99%) after training, which was also significant (P < 0.001). Conclusion This multimodal NIV training, which included didactic, simulation, and team-based learning, was feasible and resulted in significant increases in trainee confidence and knowledge with NIV. This curriculum has the potential to provide NIV training to numerous low- and middle-income countries as they manage the ongoing COVID-19 pandemic and rising burden of noncommunicable disease. Further research is necessary to ensure the sustainability of these improvements and adaptability to other low- and middle-income settings.
Distributed Fixed-Time Resource Allocation Algorithm for the General Linear Multi-Agent Systems

This brief focuses on the distributed resource allocation problem (RAP) for a general linear heterogeneous multi-agent system (MAS), in which each agent adopts different structure parameters. By means of multi-agent consensus approach and symbolic-function-based fixed-time control theory, an initialization-free distributed resource allocation algorithm is designed. Moreover, the equality constraint is solved based on the proportional-integral (PI) control idea and the output of all agents tracks to the optimal solution within fixed-time. Finally, we reveal the effectiveness and fast convergence performance of our proposed algorithm by a simulation example.
On Recognition for Traffic Signs Based on Neural Network

The basic theory of a neural network classifier was introduced. For three kinds of traffic signs,such as indicative signs,warning signs and prohibitive signs, a method based neural network classifier was developed to recognize traffic signs. The classifier consists of two network layers. The front network is made up of a BP networks and will do a rough classification task. The back network consists of three BP networks and will fine the classification done before. The whole process completes the recognition task. Compared with a conventional single layer classifier,the training speed and the ratio of recognition of this classifier are greatly increased. It accords with the fact that the neural network has higher correctness and fast training speed when it solves small-scale problems. At the same time,when additional training samples are added, only the corresponding network needs traineing again but the whole network. The experimental results show that the automatic recognition method based on neural network has good effect. 
Improved DC-Free Run-Length Limited 4B6B Codes for Concatenated Schemes

In this letter, we introduce a class of improved DC-free 4B6B codes in terms of error correction capabilities for a serially concatenated architecture. There are billions of different codebooks that can be derived from the 16 codewords contained in the traditional 4B6B code as per the IEEE 802.15. 7 standard for visible light communication (VLC). These codebooks can be classified based on distances properties which determine their error correction performances. The traditional 4B6B code is suitable for hard-decision decoding, however, when a soft decoder is used like in a serially concatenated architecture, that code becomes obsolete. Simulations show that the proposed 4B6B code concatenated with forward error correction (FEC) codes, has better performance compared to state-of-the-art schemes such as the original 4B6B code, the enhanced Miller code, the Manchester code, the 5B10B code and the (0,4) 2/3 RLL code.
Kreativnost kao temelj poduzetništva Creativity as the foundation of entrepreneurship

The basic objective of this paper is to explore the creativity of students of specialist graduate studies of Entrepreneurship at the Polytechnic of Rijeka and to point out the importance of creating a positive environment that would stimulate the creativity of individuals. Without creativity and innovation, there is no entrepreneurship. Since the students of specialist studies have chosen entrepreneurship as a sector of their specialization, it is only to be expected they would eventually endeavour on their own business ventures. In
Employee Pro-Environmental Behavior: The Impact of Environmental Transformational Leadership and GHRM

We investigated the impact of environmental transformational leadership (ETL) on pro-environmental behavior (PEB) of employees in Kazakhstan. The study also examined the potential mediating effect of environmental awareness and green human resource management (GHRM) as a moderating effect, using the theory of planned behavior. An online cross-sectional survey was conducted with 268 Kazakh employees from private and public organizations. Structural equation modeling was used to test the theory-driven model. The results show that environmental leadership predicted employees’ PEB, environmental awareness mediated the relationship, and GHRM partially moderated the relationship. The results of bootstrapping tests reveal that environmental awareness played successive mediating roles in the link between ETL and PEBs, but the simple slope test did not support the moderating effect of GHRM. Environmental awareness and GHRM were identified as fundamental mechanisms that accounted for the connection between ETL and PEBs.
Optimale Versuchsplanung zur Modellierung des Wachstums von Pflanzenzellen in Submerskultur

Un modele de culture cellulaire de vegetal base sur le metabolisme, les taux limitants, les balances de masse et d'energie, ainsi que les equations du reacteur, est developpe et utilise comme application d'un algorithme. Il est demontre qu'on peut obtenir ainsi une augmentation significative de la precision de l'estimation des parametres
Phenotyping Mediterranean Durum Wheat Landraces for Resistance to Zymoseptoria tritici in Tunisia

Durum wheat landraces have huge potential for the identification of genetic factors valuable for improving resistance to biotic stresses. Tunisia is known as a hot spot for Septoria tritici blotch disease (STB), caused by the fungus Zymoseptoria tritici (Z. tritici). In this context, a collection of 3166 Mediterranean durum wheat landraces were evaluated at the seedling and adult stages for STB resistance in the 2016–2017 cropping season under field conditions in Kodia (Tunisia). Unadapted/susceptible accessions were eliminated to reach the final set of 1059 accessions; this was termed the Med-collection, which comprised accessions from 13 countries and was also screened in the 2018–2019 cropping season. The Med-collection showed high frequency of resistance reactions, among which over 50% showed an immune reaction (HR) at both seedling and adult growth stages. Interestingly, 92% of HR and R accessions maintained their resistance levels across the two years, confirming the highly significant correlation found between seedling- and adult-stage reactions. Plant Height was found to have a negative significant effect on adult-stage resistance, suggesting that either this trait can influence disease severity, or that it can be due to environmental/epidemiological factors. Accessions from Italy showed the highest variability, while those from Portugal, Spain and Tunisia showed the highest levels of resistance at both growth stages, suggesting that the latter accessions may harbor novel QTLs effective for STB resistance.
Influence of Glucose on Candida albicans and the Relevance of the Complement FH-Binding Molecule Hgt1 in a Murine Model of Candidiasis

Candidiasis is common in diabetic patients. Complement evasion is facilitated by binding complement factor H (FH). Since the expression of high-affinity glucose transporter 1 (Hgt1), a FH-binding molecule, is glucose-dependent, we aimed to study its relevance to the pathogenesis of Candida albicans. Euglycemic and diabetic mice were intravenously challenged with either Candida albicans lacking Hgt1 (hgt1-/-) or its parental strain (SN152). Survival and clinical status were monitored over 14 days. In vitro, Candida albicans strains were grown at different glucose concentrations, opsonized with human serum, and checked for C3b/iC3b and FH deposition. Phagocytosis was studied by fluorescein isothiocyanate-labeled opsonized yeast cells incubated with granulocytes. The murine model demonstrated a significantly higher virulence of SN152 in diabetic mice and an overall increased lethality of mice challenged with hgt1-/-. In vitro lower phagocytosis and C3b/iC3b deposition and higher FH deposition were demonstrated for SN152 incubated at higher glucose concentrations, while there was no difference on hgt1-/- at physiological glucose concentrations. Despite C3b/iC3b and FH deposition being glucose-dependent, this effect has a minor influence on phagocytosis. The absence of Hgt1 is diminishing this dependency on complement deposition, but it cannot be attributed to being beneficial in a murine model.
Machine Learning Identifies Pan-Cancer Landscape of Nrf2 Oxidative Stress Response Pathway-Related Genes

Background Oxidative stress produced a large amount of reactive oxygen species (ROS), which played a pivotal role in balanced ability and determining cell fate. The activated Nrf2 signaling pathway that responds to the excessive ROS regulated the expressions of antiapoptotic proteins, antioxidative enzymes, drug transporters, and detoxifying factors. Methods The Nrf2 signaling pathway-related genes that had a direct relationship with Nrf2, including ATF4, BACH1, CREBBP, CUL3, EIF2AK3, EP300, FOS, FOSL1, GSK3B, JUN, KEAP1, MAF, MAFF, MAFG, MAFK, MAPK1, MAPK3, MAPK7, MAPK8, MAPK9, PIK3CA, PRRT2, and RIT1, were selected to do a systematic pan-cancer analysis. The relationship of Nrf2 signaling pathway-related gene expressions with tumor mutation burden, microsatellite status, clinical characteristics, immune system, cancer stemness index, and drug sensitivity was calculated by the Spearson correlation analysis across 11,057 subjects representing 33 cancer types. The prognosis models in lung squamous carcinoma, breast cancer, and stomach cancer were constructed with the Cox multivariate regression analysis and least absolute shrinkage and selection operator (Lasso) regression. Results Many Nrf2 signaling pathway-related genes were differently expressed between tumor and normal tissues. PIK3CA showed high mutation rate in pan-cancer. The expressions of Nrf2 signaling pathway-related genes were significantly related to tumor mutation burden, copy number variant, microsatellite instability score, survival rate, pathological stage, immune phenotype, immune score, immune cell, cancer stemness index, and drug sensitivity. The prognosis models were significantly associated with survival rate in lung squamous carcinoma, breast cancer, and stomach cancer; and the prognosis model-based riskscore was significantly associated with clinicopathological characteristics of each cancer. Conclusions The study provided a comprehensive pan-cancer landscape of Nrf2 pathway-related genes. Based on the same Nrf2 pathway-related genes, the different prognosis models were constructed for different types of cancers.
Super‐Assembled Hierarchical Cellulose Aerogel‐Gelatin Solid Electrolyte for Implantable and Biodegradable Zinc Ion Battery

Transient devices represent an emerging type of electronics whose main characteristic is the constituent materials that could fully or partially dissolute or disintegrate by chemical or physical processes after completing the mission, and are considered as new research directions for implantable devices. However, research on transient devices is still in its infancy and there are many challenges to overcome, especially the development of transient energy devices is relatively slow. Here, an implantable, biodegradable transient zinc ion battery (TZIB) that assembles a carefully designed cellulose aerogel‐gelatin (CAG) solid electrolyte. The new fully degradable CAG solid electrolyte allows TZIB to achieve controlled degradation and stable electrochemical performance, at the same time maintaining excellent mechanical properties. The entire battery device can be completely degraded within 30 days in the buffered proteinase K solution. More importantly, TZIB has excellent electrochemical performance while meeting controlled degradation, providing a specific capacity of 211.5 mAh g−1 at a current of 61.6 mA g−1 and a wide voltage range (0.85–1.9 V). These results demonstrate the potential of TZIB in future clinical applications and provide a new platform for transient electronic technology.
Antiandrogen therapy immediately after surgery for prostate cancer improved survival

M a i n r e s u l t s Analysis was by intention to treat. At a median follow-up of 7.1 years, more men in the observation group had died from any cause (hazard ratio [HR] 3.0, 95% CI 1.2 to 7.3; P = 0.02) or from prostate cancer (HR 6.2, CI 1.8 to 21.5; P < 0.01) than had men who received immediate antiandrogen therapy (Table). Disease recurrence was greater in the observation group than in the immediate antiandrogen therapy group (HR 12.2, CI 5.1 to 29.1; P < 0.001) (Table). Baseline characteristics or type of antiandrogen therapy did not affect survival.
PepNet: A Fully Convolutional Neural Network for De novo Peptide Sequencing

The de novo peptide sequencing, which does not rely on a comprehensive target sequence database, provided us a way to identify novel peptides from tandem mass (MS/MS) spectra. However, current de novo sequencing algorithms suffer from lower accuracy and coverage, which hinders their applications in proteomics. In this paper, we present PepNet, a fully convolutional neural network (CNN) for high accuracy de novo peptide sequencing. It takes an MS/MS spectrum (represented as a high dimensional vector) as input, and outputs the optimal peptide sequence along with its confidence score. Our model was trained using a total of 30 million high-energy collisional dissociation (HCD) MS/MS spectra from multiple human peptide spectral libraries. The evaluation results show that PepNet significantly outperformed currently best-performing de novo sequencing algorithms (e.g. PointNovo and DeepNovo) at both peptide level accuracy and positional level accuracy. In addition, PepNet can sequence a large fraction of spectra that were not identified by database search engines, and thus could be used as a complementary tool of database search engines for peptide identification in proteomics.
Trade unions challenges in organising Polish workers: A comparative case study of British and Swiss trade union strategies

This article analyses challenges in organising Polish workers by Unison (UK) and Unia (Switzerland). Using case studies of projects delivered by those unions with the aim to enhancing inclusion of Polish workers, the article argues that the internal union dynamics, such as union identity and structures are instrumental for a sustainable organisation of migrant members. Furthermore, the example of Unia demonstrates that an important factor for successful organising of Polish workers was the recognition of their collective agency and their contribution to union revitalisation. Finally, the research demonstrates that previous membership in Polish trade unions was not a significant factor influencing their involvement in British and Swiss workers’ movements.
Table-structure Recognition Method Consisting of Plural Neural Network Modules

In academic papers, tables are often used to summarize experimental results. However, graphs are more suitable than tables for grasping many experimental results at a glance because of the high visibility. Therefore, automatic graph generation from a table has been studied. Because the structure and style of a table vary depending on the authors, this paper proposes a table-structure recognition method using plural neural network (NN) modules. The proposed method consists of four NN modules: two of them merge detected tokens in a table, one estimates implicit ruled lines that are necessary to separate cells but undrawn, and the last estimates cells by merging the tokens. We demonstrated the effectiveness of the proposed method by experiments using the ICDAR 2013 table competition dataset. Consequently, the proposed method achieved an F-measure of 0.972, outperforming those of our earlier work (Ohta et al., 2021) by 1.7 percentage points and of the topranked participant in that competition by 2.6 percentage points.
An Integrated Framework and Smart Algorithm for Vehicle Localization in Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) have emerged to use different technologies to promote safety, convenience, and efficiency of transportation networks. Many applications of ITS depend on the availability of the real-time positioning of the vehicles in the network. In this research, the two open challenges in the field of vehicle localization for ITS are introduced and addressed. First, in order to have safe and efficient transportation systems, the locations of the vehicles need to be available everywhere in a network. Conventional localization techniques mostly rely on Global Positioning System (GPS) technology which cannot meet the accuracy requirements for all applications in all situations. This work advances the study of vehicle positioning in ITS by introducing an integrated positioning framework which uses several resources including GPS, vehicle-to-infrastructure and vehicle-tovehicle communications, radio-frequency identification, and dead reckoning. These technologies are used to provide more reliable and accurate location information. The suggested framework fills the gap between the accuracy of the current vehicle localization techniques and the required one for many ITS applications. Second, different ITS applications have different localization accuracy and latency requirements. A smart positioning algorithm is proposed which enable us to change the positioning accuracy delivered by the algorithm based on different applications. The algorithm utilizes only the most effective resources to achieve the required accuracy, even if more resources are available. In this way, the complexity of the system and the running time decrease while the desired accuracy is obtained. The adjective Smart is selected because the algorithm smartly selects the most effective connection which has the most contribution to vehicle positioning when a connection needs to be added. On the other hand, when a connection should be removed, the algorithm smartly selects the least effective one which has the least contribution to the position estimation. This study also provides an overview about the positioning requirements for different ITS applications. A close-to-real-world scenario has been developed and simulated in MATLAB to evaluate the performance of the proposed algorithms. The simulation results show that the vehicle can acquire accurate location in different environments using the suggested Integrated framework. Moreover, the advantages of the proposed Smart algorithm in terms of accuracy and running time are presented through a series of comprehensive simulations.