Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of a Bacterial Cultivation Medium via a Design-of-Experiment Approach in a Sartorius Ambr® 15 Fermentation Microbioreactor System

In the evolving landscape of sustainable biopharmaceutical process development, the utilization of bacteria in the production of various compounds via fermentation has attracted extensive attention from scientists. A successful fermentation process and the release of its associated products hinge on the synergy between an efficient bacterial strain and the formulation of a suitable growth medium. Balancing all nutrient levels of a growth medium to maximize microbial growth and the product quality is quite an intricate task. In this context, significant advancements have been achieved via the strategic implementation of design-of-experiment (DOE) methodologies and the utilization of parallel microbioreactor systems. This work presents a case study of the fermentation growth medium optimization of a Gram-negative bacterium of the Neisseriaceae family that releases outer membrane vesicles (OMVs), which represent a potential vaccine platform. To achieve this, the ability of Sartorius MODDE®13 DOE software to explore multiple variables and their interactions was combined with the functionality of a Sartorius Ambr® 15F parallel microbioreactor system. The findings reported in this study have led to the design of a well-suited fermentation medium for a Gram-negative bacterium and an improvement in the quality of the OMVs produced from it.
Investigation of Library Job Demands and Requirements through the Lens of Job Market

Abstract This article investigated job demands and requirements in the American Library Association (ALA) job website. The states with the highest number of library and information science (LIS) job openings were California, Texas, Illinois, New York, and Washington. Academic libraries had the highest number of job openings, followed by Public libraries and Government libraries, respectively. The most sought-after LIS job categories were Administration/Management, Special Collections/Archives, Subject Specialist/Liaison, Information Literacy and Instruction, and Collection Development/Acquisitions. A significant majority of library-related jobs required a Master’s degree. These findings benefit LIS researchers, educators, professionals, and job seekers.
Reinventing the wheel? Factors influencing relationship: links between sustainability and financial performance. European evidence

Purpose This study aims to examine the factors that influence the relationship between sustainability and financial performance (FP) of the European listed companies. Design/methodology/approach This study analyzed data from 795 companies in 21 European countries by applying linear mixed-effects multilevel regressions, a two steps system generalized method of moments and quantile regression models to uncover the links between sustainability and FP. Findings The past four decades have witnessed abundant research to determine the relationship between corporate sustainability and FP. Thus, conducting further research in 2023 could be seen as “reinventing the wheel. ” Yet, earlier research considered firms as isolated entities with sustainability and FP being dependent only on that firm’s actions. By contrast, with the help of network governance theory, this study shows that a firm’s sustainability and FP depend on an interplay among interorganizational actors, such as institutional qualities, macroeconomic factors and an embrace of sustainability. Here, large firms play an essential role. Three significant findings are drawn. First, sustainability performance has a significant impact on FP in the European context. Second, the institutional quality (IQ) of the rule of law and control of corruption plays a crucial role in enhancing sustainability and FP, and finally the interaction of IQ and economic growth helps to increase companies’ market value (Tobin’s Q). The consistent and empirically robust findings offer key lessons to policymakers and practitioners on the interplay among multiple actors in corporate sustainability and FP. Practical implications A synergetic multifaced relationship between governmental institutions and corporations is inevitable for ensuring sustainable development. The degree of intimacy in the relationship, of course, will be determined by the macroeconomic environment. Originality/value In this research, this study theoretically and empirically identified that corporate sustainability and FP are not solely dependent on corporate operation. Rather, it is transformed, modified and shaped through an interaction of multiple actors’ trajectories in the macro business environment.
Dynamic Event-Triggered Distributed Model-Free Adaptive Control for Unknown Nonlinear MASs with Stochastic Cyber-Attacks

This paper studies the dynamic event-triggered distributed model-free adaptive control problem for unknown nonlinear multi-agent systems (MASs), in the presence of stochastic cyber-attacks. Firstly, the distributed output tracking error formula of all agents are transformed into linear model using dynamic linearization method. Then, in order to alleviate the impact of stochastic cyber-attacks on system performance, the compensation mechanism based on predictive distributed output tracking error is proposed. In addition, considering the burden of network communication resources, the dynamic event-triggered mechanism is introduced. Based on the above, a new distributed model-free adaptive control scheme is established to ensure consistent tracking of all agents. Finally, numerical example is given to verify the validity of this control scheme.
Occupational outcomes of people with multiple sclerosis during the COVID-19 pandemic: a systematic review with meta-analysis

Background People with Multiple Sclerosis (PwMS) are vulnerable to unfavorable occupational outcomes and the COVID-19 pandemic brought major consequences on people’s professional lives. In this view, we decided to investigate the occupational outcomes of PwMS during the COVID-19 pandemic. Methods We performed a systematic review with meta-analysis searching key terms in four databases. We initially included any peer-reviewed original article that enrolled adult patients with the diagnosis of MS and assessed any occupational variable during the COVID-19 pandemic. There were no time limits and no language restrictions. The primary outcomes were the prevalence of unemployment, retirement and employment status change among people with MS during the COVID-19 pandemic. Other outcomes included the modality and characteristics of work: type of work, full-time work, part-time work and remote work. We also searched for data from studies that addressed any change in the work status due to the COVID-19 outbreak. Results We identified 49 eligible articles comprising a total sample size of 17,364 individuals with MS. The pooled prevalence of unemployment and retirement was 0.47 (95% CI = 0.42–0.53). The pooled prevalence of PwMS who were unemployed or retired was positively associated with the progressive phenotype of the disease (p = 0.017) and the use of glatiramer acetate (p = 0.004), but negatively associated with hospitalization due to COVID-19 (p = 0.008) and the use of immunosuppressants (p = 0.032), siponimod (p < 0.001), and cladribine (p = 0.021). The pooled proportion of PwMS that reported any change of the employment status during the COVID-19 pandemic was 0.43 (95% CI = 0.36–0.50) while the pooled prevalence of PwMS who worked remotely during this period was 0.37 (95% CI = 0.15–0.58). The change in employment status was negatively associated with the duration of MS (p = 0.03) but positively associated with the progressive phenotype of the disease (p < 0.001). Conclusion Our seminal review may serve as an example of how patients with neurological diseases or disabilities in general may have their jobs impacted in a pandemic and foster the context of global socio-economic crisis.
P2X3 Receptor Antagonism Reduces the Occurrence of Apnoeas in Newborn Rats

Apnoeas of prematurity (AoP) are a common problem in neonates. The mechanisms underlying this condition are not understood. We hypothesised hyperreflexia of afferent systems regulating breathing as a potential cause. Since P2X3 receptors are present on afferents controlling respiration and can mediated hyperreflexia (e.g. carotid body; Pijacka et al. 2016. Nat Med, 22, 1151–1159), we tested whether their antagonism would reduce AoP in new born rats. Rats were subjected to whole‐body plethysmography to record respiratory frequency (fR) and tidal volume (VT); minute ventilation (VE) was calculated (fR × VT). The frequency of apnoeas, which included both AoP and post‐sigh apnoeas, was counted over 10 min epochs at 5 and 15 min following administration of either a non‐brain penetrant P2X3 antagonist ‐ AF‐454 (15 mg/kg s.c.) or vehicle in separate rats. In 7 rats, basal apnoea frequency was 6.42 ± 1.32 per 10 min; AF‐454 reduced significantly the occurrence of these apnoeas at both 5 (2.42 ± 0.61; P>0.01) and 15 min post‐drug injection (3.4 ± 0.67; P>0.05). There was neither a change in fR, VT or MV after AF‐454 nor an effect from injection of vehicle (n=4). These data suggest that P2X3 receptor antagonism is a novel approach to reduce the occurrence of apnoeas in new born rats. We propose P2X3 receptor mediated afferent hyperreflexia as a mechanism triggering breathing dysfunction at birth.
An Intelligent Scheduling Strategy in Fog Computing System Based on Multi-Objective Deep Reinforcement Learning Algorithm

Fog computing (FC) has recently emerged as a promising new paradigm that provides resource-intensive Internet of Things (IoT) applications with low-latency services at the network edge. However, the limited capacity of computing resources in fog colonies poses great challenges for scheduling and allocating application tasks. In this paper, we propose an intelligent scheduling strategy algorithm in an FC system based on multi-objective deep reinforcement learning (MODRL) to select nodes for task processing (fog nodes or cloud nodes) based on three objectives: node current load, node distance, and task priority. The proposed model addresses two main problems: task allocation and task scheduling. We employ three deep reinforcement learning (DRL) agents based on a deep Q network (DQN), one for each objective. However, this is a more challenging scenario because there is a trade-off among these objectives, and eventually, each algorithm may select different processing nodes according to its own objective, which brings us to a Pareto front problem. To solve this problem, we propose using multi-objective optimization, a multi-objective evolutionary algorithm based on decomposition (MOEA/D), and a non-dominated sorting genetic algorithm (NSGA2), which are multi-objective optimization algorithms that can choose the optimal node by considering three objectives. The simulation results show that our proposed intelligent scheduling strategy could achieve better outcomes for the various employed performance, efficiency, and adaptability metrics: Task Completion Time, Makespan, Queueing Delay, Propagation Delay, Transmission Delay, Processing Delay, Computational Delay, Latency, CPU Load, and Storage Utilization, with an average value of 2.02 ms, 10 ms, 2 ms, 9.9 ms, 25 ms, 1.0 ms, 3.5 ms, 10 %, and 99 %, respectively, compared with the existing related research studies.
Exploring Men’s Experiences with Follow-Up Care following Primary Treatment for Prostate Cancer in Atlantic Canada: A Qualitative Study

Prostate cancer is a common and life-altering condition among Canadian men, yet little is known about how follow-up care is provided to those who have completed treatment. Despite improving survival rates, survivors experience ongoing needs and are often not provided with support to manage them. This study sought to investigate the post-treatment experiences and needs of prostate cancer survivors and to determine if and how these needs are being met. Using a qualitative description design, prostate cancer survivors who had completed treatment took part in semi-structured interviews. The interviews were recorded and analyzed thematically. The participants experienced varying levels of satisfaction with their follow-up care. While primary care providers played significant roles, continuity of care and specialist involvement varied. Most participants felt unprepared to manage the long-term effects of their cancer due to a lack of information and resources from their healthcare providers. Instead, participants turned to their peers for support. Ongoing physical and psychosocial needs went unmet and had significant impacts on their daily lives. Participants felt that support for these issues should be automatically integrated into their follow-up care. In summary, this study revealed the importance of integrated, patient-centered follow-up care for prostate cancer in Atlantic Canada.
STB: synthetic minority oversampling technique for tree-boosting models for imbalanced datasets of intrusion detection systems

Attacks on the Intrusion Detection System (IDS) can result in an imbalanced dataset, making it difficult to predict what types of attacks will occur. A novel method called SMOTE Tree Boosting (STB) is proposed to generate synthetic tabular data from imbalanced datasets using the Synthetic Minority Oversampling Technique (SMOTE) method. In this experiment, multiple datasets were used along with three boosting-based machine learning algorithms (LightGBM, XGBoost, and CatBoost). Our results show that using SMOTE improves the content accuracy of the LightGBM and XGBoost algorithms. Using SMOTE also helps to better predict computational processes. proven by its accuracy and F1 score, which average 99%, which is higher than several previous studies attempting to solve the same problem known as imbalanced IDS datasets. Based on an analysis of the three IDS datasets, the average computation time required for the LightGBM model is 2.29 seconds, 11.58 seconds for the XGBoost model, and 52.9 seconds for the CatBoost model. This shows that our proposed model is able to process data quickly.
Weekly precipitation cycles? Lack of evidence from United States surface stations

Previous work has inferred a relationship between human activity and the occurrence and amount of precipitation through examining possible weekly cycles in precipitation. Daily precipitation records for 219 surface observing stations in the United States for the 42‐year period 1951–1992 are investigated for weekly cycles in precipitation. Results indicate that neither the occurrence nor amount of precipitation significantly depends upon the day of the week.
ABORDANDO EL DESEQUILIBRIO DE DATOS EN CLASIFICACIÓN DE ATAQUES DE DENEGACIÓN DE SERVICIO DISTRIBUIDO (DDOS)

Los ataques de denegación de servicio distribuido (DDoS) representan una amenaza significativa para instituciones y empresas que dependen de redes interconectadas. Distinguir entre ataques maliciosos y aumentos legítimos en el tráfico web es un desafío, y los sistemas de defensa existentes luchan por identificar. Este estudio explora el impacto del desequilibrio de datos en la clasificación de ataques DDoS y propone una solución utilizando datos sintéticos. La metodología involucra: recolección de datos, preprocesamiento, generación de datos sintéticos, y análisis de rendimiento. Utilizamos CICDDoS2019 dataset, contiene 22 millones de ejemplos medidos en 88 características. Generamos datos sintéticos utilizando Redes Generativa Antagónica (GANs), centrándonos en tres características del conjunto de datos: tiempo, tipo de ataque y duración. Se trabajó con tres grupos de datos del mismo dataset: manera convencional (desequilibrada), submuestreo de la clase minoritaria y utilizando GANs para generar un total adicional de 2 millones de puntos de datos. Una comparación de rendimiento entre métodos tradicionales de clasificación (CNN, KNN y XGBoost) y el uso de GANs muestra una mejora significativa. Los métodos tradicionales alcanzan tasas de precisión del 82-86%, mientras que las GANs logran consistentemente tasas de precisión del 98-99%. Estos hallazgos resaltan el impacto del desequilibrio de datos en la eficacia de la clasificación y demuestran la efectividad de las GANs para mitigar este desafío mientras mejoran la precisión. El estudio enfatiza la importancia de considerar el desequilibrio de datos y adoptar técnicas innovadoras como las GANs en el campo de la ciberseguridad.
Transcriptome and metabolome comprehensive analysis reveal the molecular basis of slow-action and non-repellency of cycloxaprid against an eusocial pest, Solenopsis invicta

The eusocial pest, red imported fire ant (Solenopsis invicta), is a highly invasive species that poses significant threats to public safety, agriculture, and the ecological environment. Cycloxaprid, a newly identified effective, slow-acting, and non-repellent insecticide against S. invicta, allows contaminated individuals to transfer the insecticide among nestmates through body contact. However, the molecular-level changes occurring in S. invicta post cycloxaprid exposure and any molecular alterations contributing to the slow demise or decreased sensitivity remain unclear. In this study, transcriptomic and metabolomic techniques were used to investigate the molecular mechanisms of S. invicta exposed to cycloxaprid. Differential analysis results revealed 275, 323, and 536 differentially expressed genes at 12, 24, and 48 h, respectively. Genes involved in lipid and energy metabolism, DNA integration, and hormone synthesis were largely upregulated at 12 h, suggesting S. invicta might actively resist cycloxaprid impacts, and predominantly downregulated at 48 h, indicating further functional impairment and impending death. Also, we observed an imbalance in olfactory perception pathways at 12 h, which may indicate a disruption in the olfactory system of S. invicta. Metabolomic results showed that the regulation of most differential metabolites (DMs) was consistent with the expression changes of their related DEGs at different time points. Our study provides insights into the mechanism underlying slow-acting and non-repellent properties of cycloxaprid against S. invicta.
The Agropyron mongolicum bHLH Gene AmbHLH148 Positively Involved in Transgenic Nicotiana benthamiana Adaptive Response to Drought Stress

While bHLH transcription factors have been linked to the regulation of various abiotic stressors, research on drought-related bHLH proteins and their molecular processes in Agropyron mongolicum has remained limited. In this study, a bHLH gene from A. mongolicum, designated as AmbHLH148, was successfully cloned and isolated. AmbHLH148 was exclusively localized within the nucleus. Additionally, qRT-PCR analysis demonstrated a significant upregulation of AmbHLH148 in response to drought stress. When transferred into tobacco (Nicotiana benthamiana), the heterologous expression of AmbHLH148 led to enhanced drought tolerance. Under drought stress conditions, AmbHLH148-OE transgenic tobacco plants exhibited increased activities of antioxidant defense enzymes, such as SOD (superoxide dismutase), POD (peroxidase), and CAT (catalase). These enzymes efficiently mitigated the accumulation of reactive oxygen species (ROS) compared to wild-type plants. Furthermore, AmbHLH148-OE transgenic tobacco showed elevated levels of PRO (proline) and reduced MDA (malondialdehyde) content, contributing to enhanced stability in the plant’s cell membrane system during drought stress. In summary, this study underscores that the overexpression of AmbHLH148 in transgenic tobacco acts as a positive regulator under drought stress by enhancing the plant’s antioxidant capacity. These findings shed light on the molecular mechanisms involved in bHLH transcription factors’ role in drought resistance, contributing to the discovery and utilization of drought-resistant genes in A. mongolicum for enhancing crop drought resistance.
Sparse Grid Imputation Using Unpaired Imprecise Auxiliary Data: Theory and Application to PM2.5 Estimation

Sparse grid imputation (SGI) is a challenging problem, as its goal is to infer the values of the entire grid from a limited number of cells with values. Traditionally, the problem is solved using regression methods such as KNN and kriging, whereas in the real world, there is often extra information—usually imprecise—that can aid inference and yield better performance. In the SGI problem, in addition to the limited number of fixed grid cells with precise target domain values, there are contextual data and imprecise observations over the whole grid. To solve this problem, we propose a distribution estimation theory for the whole grid and realize the theory via the composition architecture of the Target-Embedding and the Contextual CycleGAN trained with contextual information and imprecise observations. Contextual CycleGAN is structured as two generator–discriminator pairs and uses different types of contextual loss to guide the training. We consider the real-world problem of fine-grained PM2.5 inference with realistic settings: a few (less than 1%) grid cells with precise PM2.5 data and all grid cells with contextual information concerning weather and imprecise observations from satellites and microsensors. The task is to infer reasonable values for all grid cells. As there is no ground truth for empty cells, out-of-sample mean squared error and Jensen–Shannon divergence measurements are used in the empirical study. The results show that Contextual CycleGAN supports the proposed theory and outperforms the methods used for comparison.
Vision-Based SLAM for Uavs in Dynamic Environments

Visual SLAM (Simultaneous Localization And Mapping) is a technique mainly used for robot navigation and positioning. It uses vision sensors to map the environment and estimate its own location. In dynamic environment, they are inevitably affected by dynamic objects, which leads to the decrease of accuracy and stability of the system. In this paper, the visual odometer in SLAM system is studied, and the semi-direct visual odometer (SVO) algorithm is improved on the basis of ORB-SLAM framework. The basic principle of the algorithm is to treat pixel matching between adjacent image frames as an optimization problem, and use direct registration method to obtain camera pose for sparse feature blocks, avoiding a lot of feature extraction and matching. When a key frame appears, ORB (Oriented FAST and Rotated BRIEF) features of the image are extracted, and feature matching is used to track local maps to obtain more mapping relationships. Moreover, camera pose and three-dimensional structure of the scene are estimated by minimizing reprojection errors, thus improving positioning accuracy and map construction quality. Finally, experiments on several public data sets show that the improved semi-direct visual odometer scheme in this paper still has good robustness to moving objects in dynamic scenes. While maintaining high accuracy, it obtains better real-time performance by tracking visual feature points.
The Concept CTPAX ‘FEAR’: Metaphorization Dynamics in Ukrainian Fiction Texts (1980–2000 and 2001–2022)

The paper draws on applying corpus-based approach to the analysis of productive models of the concept CTPAX ‘FEAR’ based on a previous study on improving methods and tools for automatic/automated identification of metaphorization models. The material of the study is Ukrainian fiction texts of two periods: 1980-2000 and 2001–2022. The frequency of verbalizers of the concept CTPAX ‘FEAR’ is calculated to identify statistically significant verbalizers in the texts of a particular period. Using linguistic and statistical methods, the metaphorical compounds of the structural and semantic model verb + noun are identified. Quantitative results contribute to the identification of the dominant models of metaphorization and demonstrate how they dynamically extend over time.