Miracle-dz/UniFAR-FTUs huggingface dataset:
The Lusophone World in French Political and Internet Discourse

The paper aimed to examine the terms related to the concept of Lusophony in the French political and internet discourse to better understand the role of the Lusophone world in French-speaking society. We conducted a grammatical and contextual analysis of the words Portugal, Brésil, and lusophonie (and their adjectives) as they are used in the Europarl corpus and the FrTenTen corpora. The results show interesting differences between the two discourse genres. In political discourse, the examined words are related mostly to discussions about the economy, environmental issues, and food exports. On the other hand, in online discourse the analysed terms are most frequently applied to the topic of football, but also to discussions about colonization and beauty treatments (like Brazilian hair straightening). Utilizing the analysis presented in this study we could not detect the topic of culture, which is an unexpected result with regard to the global importance that Lusophone culture has in the field of music (e.g. fado and bossa nova), dance (e.g. samba and capoeira), and literature (e.g. Fernando Pessoa and José Saramago).
Deep and Spatio-Temporal Detection for Abnormal Traffic in Cloud Data Centers

Current interactions of network traffic through cloud data centers have become an important process of network services. Precise and real-time detection and prediction of network traffic can assist system operators in effectively allocating resources, and assessing network performance based on actual service requirements, and analyzing network health. However, sources and distribution of network traffic are different, which makes accurate warnings of network attack traffic become a difficult problem. In recent years, neural networks have been proven to be effective in predicting time series data, particularly long short-term memory networks for capturing temporal features and convolutional methods for capturing spatial features. This work proposes a Deep Hybrid Spatio-Temporal (DHST) network method for abnormal traffic detection in cloud data centers, which combines a cooperative temporal convolutional network, an attention mechanism and a random inactivation method to capture the network traffic data's spatio-temporal features. It improves accuracy of abnormal traffic detection, and realizes classification of normal traffic and abnormal one. It achieves higher accuracy than typical detection methods when applied to a real-life dataset collected from Yahoo Webscope S5.
Bilingual Spatial Cognition: Spatial Cue Use in Bilinguals and Monolinguals

Structural plasticity changes and functional differences in executive control tasks have been reported in bilinguals compared to monolinguals, supporting a proposed bilingual ‘advantage’ in executive control functions (e.g., task switching) due to continual usage of control mechanisms that inhibit one of the coexisting languages. However, it remains unknown whether these differences are also apparent in the spatial domain. The present fMRI study explores the use of spatial cues in 15 bilinguals and 14 monolinguals while navigating in an open-field virtual environment. In each trial, participants had to navigate towards a target object that was visible during encoding but hidden in retrieval. An extensive network was activated in bilinguals compared to monolinguals in the encoding and retrieval phase. During encoding, bilinguals activated the right temporal and left parietal regions (object trials) and left inferior frontal, precentral, and lingual regions more than monolinguals. During retrieval, the same contrasts activated the left caudate nucleus and the right dorsolateral prefrontal cortex (DLPFC), the left parahippocampal gyrus, as well as caudate regions. These results suggest that bilinguals may recruit neural networks known to subserve not only executive control processes but also spatial strategies.
Aerial Acoustic Targeting Orientation Estimation Based on Deconvolved Conventional Beamforming

The line spectrum detection of helicopter sound localization only considers the frequency band of the signal fundamental frequency and has low energy utilization efficiency for harmonic frequencies. Moreover, using classic beamforming algorithms (e.g., minimum variance distortionless response (MVDR)) to scan the fundamental frequency, the azimuth spectrum beam obtained is relatively wide, and the results are not ideal. To improve the resolution of helicopter sound signals, this paper adopts harmonic frequency direction estimation based on deconvolved conventional beamforming. Firstly, the output power spectrum of conventional beamforming is modeled as convolutional form, and three deconvolution methods are used to post-process the azimuth spectrum of conventional beamforming in the frequency band where the helicopter sound signal is fundamental. Then, based on the frequency doubling relationship between the fundamental frequency and harmonics of the helicopter sound signal, the superposition of harmonic frequency energy is used to ensure the peak value of the target signal in the frequency domain and azimuth. This method improves the resolution and accuracy of signals, and MATLAB simulations and experiments have shown its good performance.
Research and application of coal seam pulse hydraulic fracturing technology

In order to improve the gas drainage efficiency of high gas and low permeability coal seams,the pulse hydraulic fracturing technology was proposed,hydraulic fracturing experiments and mechanical analysis were taken under different pressures and frequencies,and fatigue damage characteristics of coal and the mechanism of high pressure pulse hydraulic fracturing were analyzed too.The results show that alternating stress is formed at the end of coal fracture under the action of the intense pulse water,and fatigue damage failure can be formed in the coal due to repeated action of compression-expansion-compression.The extension of fracture and weak plane in the coal is transformed into interlaced transfixion fracture network.Compared with general methods of hydraulic fracturing,the results of industrial experiments show that pulse hydraulic fracturing generates a better effect of pressure relieving and permeability improving,meanwhile,the concentration and flow of drilling gas extraction are remarkably improved.
Dominance and the dynamics of phenotype-limited distribution in common cranes

We studied the behavior of 13 radiotagged cranes dispersing from a communal roost over days when they changed their main daily foraging area be- tween consecutive days during two winter seasons. In- dividuals went to a new foraging zone when on the previous day their morning food intake had fallen below their mean morning food intake measured over the whole winter. Food intake on the day before a change in foraging area was positively correlated with dominance rank. Dominant cranes changed to new zones with higher numbers of birds and food density, while subor- dinate cranes went to new zones with lower numbers of birds. As a result, all birds increased their food intake over that of the previous day. Dominant cranes re- mained more faithful to their most preferred foraging zone, where they spent 69% of the mornings, while subordinate birds were more mobile, switching among zones frequently. Dominant birds left the roost later than subordinate birds on the days they changed to a new zone, which could be used to track the main de- parting flows. The results suggest that the dynamics that led to a truncated phenotype-limited distribution were determined by social dominance and food abundance, with dominant cranes shifting to a new zone to maintain their high intake levels and subordinates changing more frequently whenever their daily intake did not reach the minimum metabolic requirements.
HEART Rate Variability Biofeedback for LOng COVID Dysautonomia (HEARTLOC): Results of a Feasibility Study

Introduction: Post-COVID-19 syndrome, or Long Covid (LC) refers to symptoms persisting 12 weeks after the COVID-19 infection. LC comprises a wide range of dysautonomia symptoms, including fatigue, breathlessness, palpitations, dizziness, pain and brain fog. This study tested the feasibility and estimated the efficacy, of a Heart Rate Variability Biofeedback (HRV-B) programme via a standardised slow diaphragmatic breathing technique in individuals with LC. Methods: LC patients underwent a 4-week HRV-B intervention for 10 minutes twice daily for 4 weeks using the Polar H10 ECG (Electrocardiogram) chest strap and Elite HRV phone application. Outcome measures C19-YRSm (Yorkshire Rehabilitation Scale modified), Composite Autonomic Symptom Score (COMPASS-31), WHO Disability Assessment Schedule (WHODAS), EQ5D-5L (EuroQol 5 Dimensions) and Root Mean Square of Successive Differences between heartbeats (RMSSD) using a Fitbit device were recorded before and after the intervention. The study was pre-registered at clinicaltrials.gov NCT05228665. Results: A total of 13 participants (54% female, 46% male) completed the study with high levels of independent use of technology, data completeness and intervention adherence. There was a statistically significant improvement in C19YRS-m (P = .001), COMPASS-31 (P = .007), RMSSD (P = .047), WHODAS (P = .02) and EQ5D Global Health Score (P = .009). Qualitative feedback suggested participants could use it independently, were satisfied with the intervention and reported beneficial effects from the intervention. Conclusion: HRV-B using diaphragmatic breathing is a feasible intervention for LC. The small sample size limits generalisability. HRV-B in LC warrants further exploration in a larger randomised controlled study.
Reindeer husbandry : a practical decision-tool for adaption of herds to rangelands

This decision-tool is focused on the adaptation of reindeer herds to available food resources in a district, i.e. to the availability and quality of winter and summer pastures. Previous studies have found that practical management is complicated by the dynamics involved and by lack of precise information. Hence one is faced with an information problem. The decision-tool captures the essence of optimisation models and should be sufficiently simple to be used in practice. In short, the decision-tool helps organize raw time-series data such that they become directly useful for decision-making. The intention is that the tool shall stimulate to reflection and discussions that may lead to increased mutual understanding, better collection and use of data, and to better strategies. Case studies from the Nordic countries indicate that the tool produces new and interesting insights from existing data. Feel free to ask the authors for a demonstration of the tool.
A Transferable Multi-Agent Reinforcement Learning Method for Distribution Service Restoration

The occurrence of extreme events, which has increased the risk of major outages in the grid, makes the quick and efficient recovery of load in the distribution network become a key issue. The data-driven deep reinforcement learning method has great potential in providing fast decision-making. However, a large number of agents lead to the curse of dimensionality, making it inefficient to obtain effective control strategies. When tackling similar tasks in different power gird, the retraining of multiple agents will bring us great cost. To solve this problem, we propose a transferable multi-agent reinforcement learning framework that employs model reload and buffer reuse methods to transfer control strategies from small-scale simple scenes to large-scale complex scenes. It also utilizes attention mechanisms to aggregate observation features and handles the problem of variable observation dimensions. Finally, the distribution service restoration problem is modeled as a Markov decision process and solved using the QMIX algorithm. The performance of the proposed method has been verified in IEEE 34-node and IEEE 123-node distribution systems.
“Success was Actually Having Learned:” University Student Perceptions of Ungrading

A large body of evidence shows that many ungrading practices are as good or better than conventional approaches at supporting learning outcomes. Much of the research on student perceptions of ungrading, however, is based on individual case studies which, although informative, are often anecdotal, not systematically implemented, and tend to emphasize the instructor’s perspectives. Building on this literature, we offer a systematic study that asks: how do students perceive pedagogical practices designed by instructors to support an ungrading strategy? To answer this question, we conducted a survey of students across a range of disciplines and a variety of ungrading approaches to assess how they perceive their learning experiences in these courses as compared to others. Findings indicate that students generally perceive that ungrading practices improve their relationship with their instructor; enhance their engagement, agency, enjoyment, and interest; foster their intrinsic motivation and focus on learning; and facilitate their creativity. While many students reported reduced stress, others reported that the unfamiliarity and uncertainty of ungrading increased their stress. Gaining a better understanding of how students react to these pedagogical techniques can help instructors improve their practices.
Engineering Analysis of One-sided State Transitions for the Derivation of Dual-Task Conditions

The purpose of this study is to determine the intervention method among individuals by analyzing and categorizing how the performance on a dual task decreases when the required attentional load increases through a validation test. The validation test consisted of a dual task with two different tasks. As a result of the validation test, we hypothesized that there exists a one-sided state in which response delays occur in the low-priority task during the transition from the dual-task state, where the task is performed stably, to the saturation state, where all tasks are disrupted. Therefore, considering the one-sided state, we classified participants' attention distribution state into four states during the dual-task and analyzed the transition of the collapse model for individual. As a result, we were able to identify the one-sided state, classify the collapse models of the dual-task into three patterns, and propose appropriate timing of intervention methods for each pattern.
Hypoxia and Activation of Neutrophil Degranulation-Related Genes in the Peripheral Blood of COVID-19 Patients

Severe COVID-19 is characterized by systematic hyper-inflammation and subsequent damage to various organs. Therefore, it is critical to trace this cascade of hyper-inflammation. Blood transcriptome has been routinely utilized in the interrogation of host immune response in COVID-19 and other infectious conditions. In this study, consensus gene dysregulation in the blood was obtained from 13 independent transcriptome studies on COVID-19. Among the up-regulated genes, the most prominent functional categories were neutrophil degranulation and cell cycle, which is clearly different from the classical activation of interferon signaling pathway in seasonal flu. As for the potential upstream causal factors of the atypical gene dysregulation, systemic hypoxia was further examined because it is much more widely reported in COVID-19 than that in seasonal flu. It was found that both physiological and pathological hypoxia can induce activation of neutrophil degranulation-related genes in the blood. Furthermore, COVID-19 patients with different requirement for oxygen intervention showed distinctive levels of gene expression related to neutrophil degranulation in the whole blood, which was validated in isolated neutrophils. Thus, activation of neutrophil degranulation-related genes in the blood of COVID-19 could be partially attributed to hypoxia. Interestingly, similar pattern was also observed in H1N1 infection (the cause of Spanish flu) and several other severe respiratory viral infections. As for the molecular mechanism, both HIF-dependent and HIF-independent pathways have been examined. Since the activation of neutrophil degranulation-related genes is highly correlated with disease severity in COVID-19, early detection of hypoxia and active intervention may prevent further activation of neutrophil degranulation-related genes and other harmful downstream hyper-inflammation. This common mechanism is applicable to current and future pandemic as well as the severe form of common respiratory infection.
Transcriptomic Analysis Reveals Sixteen Potential Genes Associated with the Successful Differentiation of Antibody-Secreting Cells through the Utilization of Unfolded Protein Response Mechanisms in Robust Responders to the Influenza Vaccine

The seasonal influenza vaccine remains one of the vital recommended infection control measures for the elderly with chronic illnesses. We investigated the immunogenicity of a single dose of influenza vaccine in 123 seronegative participants and classified them into four distinct groups, determined by the promptness of vaccine response, the longevity of humoral immunity, and the likelihood of exhibiting cross-reactivity. Subsequently, we used transcriptional profiling and differential gene expression analysis to identify potential genes directly associated with the robust response to the vaccine. The group of exemplary vaccine responders differentially expressed 16 genes, namely: MZB1, MYDGF, TXNDC5, TXNDC11, HSP90B1, FKBP11, PDIA5, PRDX4, CD38, SDC1, TNFRSF17, TNFRSF13B, PAX5, POU2AF1, IRF4, and XBP1. Our findings point out a list of expressed proteins that are related to B cell proliferation, unfolded protein response, and cellular haemostasis, as well as a linkage of these expressions to the survival of long-lived plasma cells.
Geometric Model and Algorithm for Passive Location of Unmanned Aerial Vehicles

When drones are flying in formation, in order to minimize external interference, the bearings-only passive positioning method is generally used to adjust the position of the drones. This paper studies various situations of passive localization and establishes localization models for unmanned aerial vehicles in various situations. The solutions have been successfully given for different situations and various applications. An adjustment plan for the drone has been designed. We use greedy algorithm to adjust the two drones within this triangle multiple times, except for the vertices, until the received angle information converges to 60 degrees and determine the position of the remaining drones using triangulation positioning method. It can be concluded that the scheme has good stability and accuracy through analysis.
Identification and Tracking of Main Material of Transmission Tower Based on Monocular RGB Photography

This paper investigates the recognition and tracking of transmission tower main materials in single RGB photography environment. Considering the photographic noise in complex environments, this paper models the photographic image using line features, analyzes the 3D-2D camera photographic model of transmission tower main material features, the spatial motion model of rigid bodies and the motion model of transmission tower main material line features in photographic images. A recognition and tracking method for transmission tower main materials is designed, which integrates Kalman filtering and probability modeling. Experimental results show that the proposed method can effectively track transmission tower main materials and exhibits good robustness in complex environments. At the same time, the method proposed in this paper also has high speed, accuracy and stability, and can meet the needs of tracking and recognition of transmission tower main materials in power transmission and distribution systems.
A Large-Scale Group Decision Making Model Based on Adaptive Subgroup Rescue Mechanism

This paper proposes an adaptive subgroup rescue mechanism to better balance efficiency and information loss in large-scale group decision-making. We calculate the consensus for different clusters through linguistic preferences and trust relations, find the group with the lowest consensus level, and advise the group to exit from the process. A rescue process is triggered once the group has low cohesion or can propose a new idea. A trust-based feedback mechanism is designed to ensure enough decision makers in consensus reaching. Finally, we conduct an empirical study to verify the feasibility of our mechanism.
The Research of Influencing Factors that Possibly lead to Company Bankruptcy

Previous research has established correlations between corporate bankruptcy and internal factors such as financial ratios, cash reserves, company liquidity, and capital structure, as well as external factors, such as macroeconomic conditions, industry competition, and regulatory policies. However, several unidentified factors still warrant investigation. This study employs the Binary Probit Model to analyze data from Taiwan Economic Journal, encompassing 6,819 companies over the period from 1999 to 2009. The findings indicate that corporate bankruptcy is not significantly associated with 8 variables such as Operating Gross Margin, Cash flow rate, Operating Profit Growth Rate, Total Asset Growth Rate, Current Ratio, etc. However, it exhibits particularly strong correlations with 8 variables such as Pre-tax net Interest Rate, Operating Expense Rate, ROA(C) before interest and depreciation before interest, Non-industry income and expenditure/revenue, Net Value Per Share (B), Total Asset Turnover, etc. This research provides valuable insights into factors that may lead to corporate bankruptcy, which is instrumental in identifying warning signals, mitigating risks, and formulating effective preventive measures. Moreover, it points to the direction for further research in this field.
The paranormal health beliefs scale: an evaluation using cognitive interviewing

Paranormal health beliefs denote the inclination to endorse illusory supernatural notions about well-being and treatment. These ideations are important since they potentially influence perceptions of health and allied behaviors. Noting this, researchers in Italy developed and verified the Paranormal Health Beliefs Scale (PHBS). Despite initial promising outcomes, the construct and measurement properties of the PHBS have remained under investigated. This is likely due to the fact that the instrument draws heavily on traditional Italian social, political, and religious influences and is overly culturally specific. Hence, items do not generalize well across populations and nationalities. Acknowledging these factors, this study used cognitive interviewing (think aloud protocol and concurrent probing) to assess the suitability of the PHBS for general use. Concurrently, the intention was to identify necessary modifications that would enhance scale performance. Fourteen interviewees (eight males and six females), evenly distributed across two rounds, participated. Round 1 focused on comprehension and perception of the PHBS. Cognitive interviews identified issues with culturally particular content/points of reference, phraseology, and wording. To address these a modified version of the PHBS was produced. Round 2 then examined the effectiveness of changes. Analysis revealed fewer concerns, although difficulties with ambiguity, complex terminology, and response scale appropriateness persisted. Overall, interviews indicated that a focus on illusory (rather than paranormal) health beliefs would improve scale utility. Methodologically, cognitive interviewing provided invaluable insights into the effectiveness of the PHBS and identified ways in which researchers could adapt the instrument for use with other cultures.