Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of actual performance in humidification among different high-flow nasal cannula devices: a bench study

Background The physiological effects of HFNC devices are closely related to temperature and humidity. HFNC devices from different manufacturers may have varied performances. It is unclear whether there are differences in the humidification performance of different HFNC devices and the degree of differences. Methods Four integrated HFNC devices (AIRVO 2, Fisher & Paykel Healthcare, Auckland, New Zealand; TNI softFlow 50, TNI Medical AG, Würzburg, Germany; HUMID-BH, RESPIRACARE, Shenyang, China; OH-70C, Micomme, Hunan, China) and a ventilator with an HFNC module (bellavista 1000, Imtmedical, Buchs, Switzerland) were evaluated using their matching circuits. The dew point temperature was set at 31, 34, and 37°C (set-DP). In MR850, it was set to non-invasive mode (34°C/−3°C) and invasive mode (40°C/−3°C), respectively. At each level of set-DP, the flow was set from 20 L/min up to its maximum set limit at a gradient of 5 L/min or 10 L/min. After stabilization, the dew point temperature, temperature, relative humidity, and flow rate of the delivered gas from the cannulas were recorded. Results There were significant differences in actual-DP among these devices at any set-DP (p < 0.001). The actual-DP of OH-70C and TNI softFlow 50 was lower than set-DP, and the difference between the actual-DP and the set-DP of these two devices increased with the increase of set-DP. AIRVO 2, bellavista 1000 (MR850), and HUMID-BH can provide the nominal humidity at 37°C. The actual-DP increased with the increase of set-flow under each set-DP in AIRVO 2, TNI softFlow 50 and bellavista 1000 (MR850), but decreased when the set-flow was greater than 60 L/min. The actual-T of the delivered gas was higher than actual-DP in all devices and was higher than set-DP in AIRVO 2 and HUMID-BH. Conclusion Set-flow, set-DP, and types of devices will affect the actual temperature and humidity of the delivered gas. AIRVO 2, bellavista 1000 (MR850), and HUMID-BH can provide the nominal humidity at 37°C and may be more suitable for tracheotomy patients. The flow rate over 60 L/min should be set with caution.
Association of leisure sedentary behavior and physical activity with the risk of nonalcoholic fatty liver disease: a two-sample Mendelian randomization study

Introduction Previous observational studies have demonstrated the relationship between leisure sedentary behavior, physical activity, and nonalcoholic liver disease (NAFLD). However, whether these associations are causal or confounding factors remains unclear. Methods Pooled genetic data from the UK Biobank and other large genome-wide association studies (GWAS) were used to extract instrumental variables representing sedentary television watching, computer use, driving, vigorous physical activity (VPA), and moderate-to-vigorous physical activity (MVPA). The two-sample Mendelian randomization (MR) method was used to explain the causal relationship between them and NAFLD. The inverse variance of the weighted method was used as the main analysis method, and MR-Egger, weighted median, MR-PRESSO, and other supplementary methods were also used. A sensitivity analysis was also performed. Simultaneously, the common risk factors for NAFLD were further analyzed for potential mediating associations. Results We observed that sedentary television viewing (odds ratio (OR): 1.84; 95% confidence interval (CI): 1.09–3.10; p = 0.021) and genetically predicted VPA duration (OR: 0.0033; 95% CI: 0.000015–0.70; p = 0.036) were suggestively associated with the risk of NAFLD. Using a computer (OR: 1.51; 95% CI: 0.47–4.81; p = 0.484), driving (OR: 0.78; 95% CI: 0.05–11.94; p = 0.858), and MVPA time (OR: 0.168; 95% CI: 0.01–2.81; p = 0.214) were not significantly associated with NAFLD. The role of heterogeneity versus pleiotropy was limited in all the analyses. Discussion This study supports the association between sedentary television watching and an increased risk of NAFLD, along with vigorous physical activity as a possible protective factor for NAFLD.
Development of Global 30-minute grid Potential Evapotranspiration Data Set

Evapotranspiration is a key element in climate related studies on all spatial and temporal scales. Recent studies have shown that evapotranspiration can be estimated with some degree of precision using semiempirical and analytical models. By this study, a method for the estimation of evapotranspiration using the available global data sets has been proposed.Monthly global potential evapotranspiration (PET) on 30-minute latitude-longitude grid was estimated based on the Priestley-Taylor method using global data sets including air temperature, albedo, cloudiness, elevation, which are parts of Global Ecosystems Database supplied by NOAA-EPA.
Effects of sources and levels of dietary supplementary manganese on growing yak’s in vitro rumen fermentation

Introduction Manganese (Mn) is an essential trace element for livestock, but little is known about the optimal Mn source and level for yak. Methods To improve yak’s feeding standards, a 48-h in vitro study was designed to examine the effect of supplementary Mn sources including Mn sulfate (MnSO4), Mn chloride (MnCl2), and Mn methionine (Met-Mn) at five Mn levels, namely 35 mg/kg, 40 mg/kg, 50 mg/kg, 60 mg/kg, and 70 mg/kg dry matter (includes Mn in substrates), on yak’s rumen fermentation. Results Results showed that Met-Mn groups showed higher acetate (p < 0.05), propionate, total volatile fatty acids (p < 0.05) levels, ammonia nitrogen concentration (p < 0.05), dry matter digestibility (DMD), and amylase activities (p < 0.05) compared to MnSO4 and MnCl2 groups. DMD (p < 0.05), amylase activities, and trypsin activities (p < 0.05) all increased firstly and then decreased with the increase of Mn level and reached high values at 40–50 mg/kg Mn levels. Cellulase activities showed high values (p < 0.05) at 50–70 mg/kg Mn levels. Microbial protein contents (p < 0.05) and lipase activities of Mn-Met groups were higher than those of MnSO4 and MnCl2 groups at 40–50 mg/kg Mn levels. Discussion Therefore, Mn-met was the best Mn source, and 40 to 50 mg/kg was the best Mn level for rumen fermentation of yaks.
Recycling optical fibers for sensing

Optical fiber sensors has become one of the most promising sensing technologies. Within all the optical fiber sensing technologies, the Fabry-Perot interferometer (FPI) micro-cavities are one of the most attractive, due to the size, linearity and higher sensitivity. In this work we present the recent results, achieved by our group, regarding the production of optical sensors, by recycling optical fibers destroyed through the catastrophic fuse effect. This enabled the production of FPI sensors, in a cost effective way, tailored for the monitoring of several physical parameters, such as relative humidity (RH), refractive index (RI) and hydrostatic pressure.
A sensory signal related to left-right symmetry modulates intra- and interlimb cutaneous reflexes during locomotion in intact cats

Introduction During locomotion, cutaneous reflexes play an essential role in rapidly responding to an external perturbation, for example, to prevent a fall when the foot contacts an obstacle. In cats and humans, cutaneous reflexes involve all four limbs and are task- and phase modulated to generate functionally appropriate whole-body responses. Methods To assess task-dependent modulation of cutaneous interlimb reflexes, we electrically stimulated the superficial radial or superficial peroneal nerves in adult cats and recorded muscle activity in the four limbs during tied-belt (equal left-right speeds) and split-belt (different left-right speeds) locomotion. Results We show that the pattern of intra- and interlimb cutaneous reflexes in fore- and hindlimbs muscles and their phase-dependent modulation were conserved during tied-belt and split-belt locomotion. Short-latency cutaneous reflex responses to muscles of the stimulated limb were more likely to be evoked and phase-modulated when compared to muscles in the other limbs. In some muscles, the degree of reflex modulation was significantly reduced during split-belt locomotion compared to tied-belt conditions. Split-belt locomotion increased the step-by-step variability of left-right symmetry, particularly spatially. Discussion These results suggest that sensory signals related to left-right symmetry reduce cutaneous reflex modulation, potentially to avoid destabilizing an unstable pattern.
“Is this blueberry ripe?”: a blueberry ripeness detection algorithm for use on picking robots

Blueberries are grown worldwide because of their high nutritional value; however, manual picking is difficult, and expert pickers are scarce. To meet the real needs of the market, picking robots that can identify the ripeness of blueberries are increasingly being used to replace manual operators. However, they struggle to accurately identify the ripeness of blueberries because of the heavy shading between the fruits and the small size of the fruit. This makes it difficult to obtain sufficient information on characteristics; and the disturbances caused by environmental changes remain unsolved. Additionally, the picking robot has limited computational power for running complex algorithms. To address these issues, we propose a new YOLO-based algorithm to detect the ripeness of blueberry fruits. The algorithm improves the structure of YOLOv5x. We replaced the fully connected layer with a one-dimensional convolution and also replaced the high-latitude convolution with a null convolution based on the structure of CBAM, and finally obtained a lightweight CBAM structure with efficient attention-guiding capability (Little-CBAM), which we embedded into MobileNetv3 while replacing the original backbone structure with the improved MobileNetv3. We expanded the original three-layer neck path by one to create a larger-scale detection layer leading from the backbone network. We added a multi-scale fusion module to the channel attention mechanism to build a multi-method feature extractor (MSSENet) and then embedded the designed channel attention module into the head network, which can significantly enhance the feature representation capability of the small target detection network and the anti-interference capability of the algorithm. Considering that these improvements will significantly extend the training time of the algorithm, we used EIOU_Loss instead of CIOU_Loss, whereas the k-means++ algorithm was used to cluster the detection frames such that the generated predefined anchor frames are better adapted to the scale of the blueberries. The algorithm in this study achieved a final mAP of 78.3% on the PC terminal, which was 9% higher than that of YOLOv5x, and the FPS was 2.1 times higher than that of YOLOv5x. By translating the algorithm into a picking robot, the algorithm in this study ran at 47 FPS and achieved real-time detection well beyond that achieved manually.
Sparse Convolutions on Lie Groups

Convolutional neural networks have proven very successful for a wide range of modelling tasks. Convolutional layers embed equivariance to discrete translations into the architectural structure of neural networks. Extensions have generalised continuous Lie groups beyond translation, such as rotation, scale or more complex symmetries. Other works have allowed for relaxed equivariance constraints to better model data that does not fully respect symmetries while still leveraging on useful inductive biases that equivariances provide. How continuous convolutional filters on Lie groups can best be parameterised remains an open question. To parameterise sufficiently flexible continuous filters, small MLP hypernetworks are often used in practice. Although this works, it typically introduces many additional model parameters. To be more parameter-efficient, we propose an alternative approach and define continuous filters with a small finite set of basis functions through anchor points. Regular convolutional layers appear as a special case, allowing for practical conversion between regular filters and our basis function filter formulation, at equal memory complexity. The basis function filters enable efficient construction of neural network architectures with equivariance or relaxed equivariance, outperforming baselines on vision classification tasks.
Verified Optimization (work in progress)

Optimization is used extensively in engineering, industry, and finance, and various methods are used to transform problems to the point where they are amenable to solution by numerical methods. We describe progress towards developing a framework, based on the Lean interactive proof assistant, for designing and applying such reductions in reliable and flexible ways.
Study on hologram mosaic algorithm based on Harris corners

To solve the problem of the small field of the view caused by CCD in the process of the hologram record, the hologram mosaic algorithm based on the Harris corners is proposed. The Harris corners in multi-scale are extracted and the mismatching points are removed. The final homography is calculated by using the improved RANSAC algorithm based on L-M algorithm. Finally, the stitched hologram with high quality can be obtained based on the weighted average fusion algorithm. It can overcome the influence to the hologram that the incident angles of the object beam are not consistent. Two experiments carried out with different reconstructed distance demonstrate that the proposed algorithm can realize the measurement of the big object by using the hologram method. Furthermore, it has high accuracy and strong robustness.
Local governance, inter-governmental funding and sustainable socio-economic development in the Gomoa West District of Ghana

The governance and pursuit for socio-economic growth at the lowest level has become de rigueu in sustainability and development discourse. Framed from the public finance theory and the qualitative approach, this study examined the inter-governmental funding system and implications for sustainable socio-economic development in the Gomoa West District. About 50 official respondents were drawn utilising the purposive approach. Self-designed interviews were the methods for data collection. The district heavily depends on government revenue transfers for local development. Though partner in sustainable development, the district was excluded from decision making process regarding its share of government funds. Delayed release of funds and deductions at source were setbacks to availability of funds for socio-economic development. Transfer of the District Assemblies Common Fund (DACF) was erratic while equalisation factor in the DACF formula had no indicators. Service pressure and responsiveness factors could deepen development gaps. Indicators in the DACF should be reviewed. Funding capacities should be increased and DACF disbursement should be needs-based and pro-poor development oriented. An effectively decentralised governance system should enable transfer of resources and galvanising support for development. Local self-governance helps to break the monopoly of power at the national level, enables transparency in funding allocation and delivery of public goods to local people. Funding allocation decisions should not be centrally-driven as it defeats the purposes of decentralisation. Participation of the districts in government funding decisions will strengthen responsiveness and accountability to citizens, improve the monitoring of performance and provision of goods and services at the district level.
Research on DQN Stock Trading Strategy Based on Investor's Compound Sentiment

Both individual investor and institutional investor sentiment affect investors' stock trading decisions. Most existing investor sentiment analyses ignore institutional investor sentiment and treat individual investor sentiment as overall investor sentiment. Quantifying the two types of investor sentiment and expressing them as comprehensive investor sentiment, then exploring their impact on stock investment strategies are conducive to optimizing investment decisions and their investment returns. Based on individual and institutional investors in the stock market, this paper proposes a stock composite sentiment score to measure overall investor sentiment through the text mining method and VADER sentiment analysis. It constructs a DQN single stock trading model based on the composite investor sentiment score using the reinforcement learning DQN algorithm. Through experimental comparison with buy-and-hold and DQN strategies on real stock data, the results show that investor sentiment can effectively optimize stock trading strategies and improve investment returns; compared with individual investor sentiment, comprehensive investor sentiment is better optimized.
EEG Source Imaging Method Improves the Detection Perfor-mance of Emergency Braking Intention in Man-machine Hybrid Driving

Electroencephalogram (EEG) is a method of recording brain activity using electrophysiological indicators. Because of its portability and ease of operation, it is widely used in brain computer interface (BCI). Emergency braking intention detection is an effective auxiliary means of man-machine hybrid drive. More and more researchers are interested in decoding the EEG of emergency braking intention. In this paper, a brain power imaging strategy for emergency braking intention detection is proposed. We built a simulated driving environment through the Carla platform. A total of 11 subjects participated in our experiment. We collected EEG of each subject driving a simulated car to complete normal driving and emergency braking tasks, and converted scalp electrode signals into source space signals through EEG source imaging (ESI) technology. We used the activation map of brain regions in the cognitive process of emergency braking to select the period of intention generation, and used three different classifiers to classify different driving conditions. The source imaging method not only improves the detection rate of emergency braking intention, but also advances the prediction time of emergency braking intention.
JRHDLSI: An Approach Towards Job Recommendation Hybridizing Deep Learning and Semantic Intelligence

The requirement of the job for people and employees for employers are al-ways in demand. This is due to the lack of proper infrastructure to reduce the unmatching job application for employers and inappropriate job recommendations for people. This chapter proposes a strategic framework with machine learning and knowledge integration to increase accuracy in the provided recommendations and increase the chance of getting a job offer. The usage of ’user’s search data intends job recommended more in liking of the users, and the machine learning helps in finding the accurate job recommendation. The machine learning technique used here is Radial Basis Function Neural Net-work for the classification and Knowledge Integrated using Analysis of Variance - Web Point Wise Mutual Information and Kullback Leibler (KL) divergence. All the job providers ads are retrieved from the top websites using beautiful soup. The proposed JRHDLSI architecture achieved an accuracy of 94.99% which outperformed the baseline models and was much superior.
BUG-T5: A Transformer-based Automatic Title Generation Method for Bug Reports

In Github, developers may not clarify and summarize the critical problems in the bug report titles due to a lack of domain knowledge or poor writing skills. Therefore, it is essential to help practitioners draft high-quality titles. In this study, we propose the BUG-T5 method automatically generating titles by fine-tuning the T5 model. In our empirical analysis, we choose a publicly available corpus from Github. After comparing BUG-T5 with four state-of-the-art baselines (i
Drivers and consequences of strategic leader indecision: an exploratory study in a complex case

PurposeThe research explores indecision of strategic leaders in a complex case organization. This research offers new insights into the drivers of indecision of upper echelons decision-makers and explores the perceived consequences of the decision-makers' indecision.Design/methodology/approachFollowing a review of literature on upper echelons theory and strategic decision-making, indecision and the antecedents and consequences of indecision, the research follows a qualitative exploratory design. Semi-structured interviews were conducted among 20 upper echelons decision-makers with responsibility across 19 Sub-Saharan African countries in a case company. Thematic analysis was used to analyze the data.FindingsThe findings reveal that specific organizational, interpersonal and personal factors work together to drive strategic leader indecision in a complex organization. Strategic leader indecision brings about several negative organizational consequences and demotivates team members.Research limitations/implicationsThe findings are based on a single-case exploratory design but represent geographical diversity.Practical implicationsThe research cautions organizations to deal with the drivers of strategic leader indecision to help avoid potential negative consequences of stifled organizational performance and team demotivation.Originality/valueThe study offers previously unknown insights into strategic leader indecision. This study builds on current literature on the antecedents and consequences of indecision and has a new research setting of strategic leader indecision in a complex organization.
Benchmarking Sentence Alignment Techniques for Automatic Review-Response Generation in the Hospitality Domain

Recently, online customer reviews have surged in popularity, placing additional demands on businesses to respond to these reviews. Conditional text generation models, trained to generate a response given an input review have been proposed to facilitate human authors in composing high quality responses. However, this approach has been shown to yield rather unsatisfying, generic responses while, in practice, responses are required to address reviews specifically and individually. We hypothesise that this issue could be tackled by changing the alignment paradigm and using sentence-aligned training data instead of document-aligned. Yet, finding correct sentence alignments in the review-response document pairs is not trivial. In this paper we investigate methods to align sentences based on computing the surface and semantic similarity between source and target pairs and benchmark performance for this rather challenging alignment problem.