Miracle-dz/UniFAR-FTUs huggingface dataset:
Association between illness perception and medication adherence in patients with diabetes mellitus in North Shoa, Zone: cross-sectional study

Background Although the impact of illness perception on medication adherence is well-established, its specific influence on medication adherence in Ethiopia remains unclear. Consequently, the objective of this study was to examine the association between illness perception and medication adherence among patients with diabetes mellitus in the North Shoa Zone. Methods An institution-based cross-sectional study was conducted from 24 May to 25 June 2022 in the North Shoa zone. The study included a random sample of 552 individuals with diabetes from four public hospitals. Data was collected and entered into Epi Data V.3. 1, and analysis was performed using SPSS version 22. Descriptive statistics were used to summarize continuous variables as means with standard deviations, while categorical variables were presented as percentages. The study variables were analyzed using binary logistic regression models to assess the associations between illness perception and medication adherence. In the bivariable analysis, variables with p-values less than 0.20 were entered into a multivariable logistic regression model. Associations with a p-value ≤0.05 and an odds ratio with a 95% confidence interval were considered statistically significant. Results The study results revealed that medication adherence was 64.4% (95% CI: 60.1, 67.9), while illness perception was 54.7% (95% CI, 41.2, 49.4). There was a significant and strong association between illness perception and medication adherence (p < 0.0001). In the adjusted model, the illness perception components of consequence showed a significant association with medication adherence (AOR = 3.10, 95% CI: 2.11, 4.55). Similarly, personal control (AOR = 1.77, 95% CI: 1.20, 2.61) and emotional representation of diabetes (AOR = 2.26, 95% CI: 1.54, 3.32) were also significantly associated with medication adherence in patients with diabetes. Conclusion The findings of this study indicate a positive association between higher illness perception and increased medication adherence and practice. Therefore, when engaging in discussions about diabetic self-management, diabetes educators should employ psychoeducational approaches that take into account the illness perceptions of patients.
Biopolymers as a Potential Alternative for the Retention of Pollutants from Vinasse: An In Silico Approach

Vinasse, a waste from the bioethanol industry, presents a crucial environmental challenge due to its high organic matter content, which is difficult to biodegrade. Currently, no sustainable alternatives are available for treating the amount of vinasse generated. Conversely, biopolymers such as cellulose, carboxymethylcellulose, and chitosan are emerging as an interesting alternative for vinasse control due to their flocculating capacity against several organic compounds. This study seeks to determine the thermodynamic behavior of in silico interactions among three biopolymers (cellulose, carboxymethylcellulose, and chitosan) regarding 15 organic compounds found in vinasse. For this, the Particle Mesh Ewald (PME) method was used in association with the Verlet cutoff scheme, wherein the Gibbs free energy (ΔG) was calculated over a 50 ns simulation period. The findings revealed that cellulose showed a strong affinity for flavonoids like cyanidin, with a maximum free energy of −84 kJ/mol and a minimum of −55 kJ/mol observed with phenolic acids and other flavonoids. In contrast, chitosan displayed the highest interactions with phenolic acids, such as gallic acid, reaching −590 kJ/mol. However, with 3-methoxy-4-hydroxyphenyl glycol (MHPG), it reached an energy of −70 kJ/mol. The interaction energy for flavonoid ranged from −105 to −96 kJ/mol. Finally, carboxymethylcellulose (CMC) demonstrated an interaction energy with isoquercetin of −238 kJ/mol, while interactions with other flavonoids were almost negligible. Alternatively, CMC exhibited an interaction energy of −124 kJ/mol with MHPG, while it was less favorable with other phenolic acids with minimal interactions. These results suggest that there are favorable interactions for the interfacial sorption of vinasse contaminants onto biopolymers, indicating their potential for use in the removal of contaminants from the effluents of the bioethanol industry.
A lightweight network for improving wheat ears detection and counting based on YOLOv5s

Introduction Recognizing wheat ears plays a crucial role in predicting wheat yield. Employing deep learning methods for wheat ears identification is the mainstream method in current research and applications. However, such methods still face challenges, such as high computational parameter volume, large model weights, and slow processing speeds, making it difficult to apply them for real-time identification tasks on limited hardware resources in the wheat field. Therefore, exploring lightweight wheat ears detection methods for real-time recognition holds significant importance. Methods This study proposes a lightweight method for detecting and counting wheat ears based on YOLOv5s. It utilizes the ShuffleNetV2 lightweight convolutional neural network to optimize the YOLOv5s model by reducing the number of parameters and simplifying the complexity of the calculation processes. In addition, a lightweight upsampling operator content-aware reassembly of features is introduced in the feature pyramid structure to eliminate the impact of the lightweight process on the model detection performance. This approach aims to improve the spatial resolution of the feature images, enhance the effectiveness of the perceptual field, and reduce information loss. Finally, by introducing the dynamic target detection head, the shape of the detection head and the feature extraction strategy can be dynamically adjusted, and the detection accuracy can be improved when encountering wheat ears with large-scale changes, diverse shapes, or significant orientation variations. Results and discussion This study uses the global wheat head detection dataset and incorporates the local experimental dataset to improve the robustness and generalization of the proposed model. The weight, FLOPs and mAP of this model are 2.9 MB, 2.5 * 109 and 94.8%, respectively. The linear fitting determination coefficients R2 for the model test result and actual value of global wheat head detection dataset and local experimental Site are 0.94 and 0.97, respectively. The improved lightweight model can better meet the requirements of precision wheat ears counting and play an important role in embedded systems, mobile devices, or other hardware systems with limited computing resources.
Preliminary Quantitative Data on Behavioural Responses of Australian Pelican (Pelecanus conspicillatus) to Human Approach on Penguin Island, Western Australia

The heightened popularity of islands amongst tourists along with increasing recreational activity on small nearshore islands has resulted in the need to acquire data on the potential effects recreational activities may be having on the breeding behaviour and ultimately the reproductive success of seabird colonies. This paper quantifies the differences in behaviour elicited from two colonies of Australian pelican (Pelecanus conspicillatus) at two different stages of incubation on Penguin Island, Western Australia when approached repeatedly by a researcher. Three response indicators were chosen for comparison: (1) behaviour during an approach; (2) the pre-flight initiation distances; and (3) behaviour following an approach. Although preliminary, these data are strongly indicative of major behavioural changes by nesting pelicans in response to human approach. Observations also indicated that breeding Australian pelicans exhibited short-term habituation to human approach over both phases of the breeding season tested. Results from this study suggest that setback distances, that are designed to separate humans from Australian pelicans, should be precautionary, being based upon the longest pre-flight distance recorded during the most sensitive stage of the breeding season.
Real-Time Detection of an Undercarriage Based on Receptive Field Blocks and Coordinate Attention

Currently, aeroplane images captured by camera sensors are characterized by their small size and intricate backgrounds, posing a challenge for existing deep learning algorithms in effectively detecting small targets. This paper incorporates the RFBNet (a coordinate attention mechanism) and the SIOU loss function into the YOLOv5 algorithm to address this issue. The result is developing the model for aeroplane and undercarriage detection. The primary goal is to synergize camera sensors with deep learning algorithms, improving image capture precision. YOLOv5-RSC enhances three aspects: firstly, it introduces the receptive field block based on the backbone network, increasing the size of the receptive field of the feature map, enhancing the connection between shallow and deep feature maps, and further improving the model’s utilization of feature information. Secondly, the coordinate attention mechanism is added to the feature fusion network to assist the model in more accurately locating the targets of interest, considering attention in the channel and spatial dimensions. This enhances the model’s attention to key information and improves detection precision. Finally, the SIoU bounding box loss function is adopted to address the issue of IoU’s insensitivity to scale and increase the speed of model bounding box convergence. Subsequently, the Basler camera experimental platform was constructed for experimental verification. The results demonstrate that the AP values of the YOLOv5-RSC detection model for aeroplane and undercarriage are 92.4% and 80.5%, respectively. The mAP value is 86.4%, which is 2.0%, 5.4%, and 3.7% higher than the original YOLOv5 algorithm, respectively, with a detection speed reaching 89.2 FPS. These findings indicate that the model exhibits high detection precision and speed, providing a valuable reference for aeroplane undercarriage detection.
Development of Joint Technology on Superconducting Cables for Feeder

Electric railways have several problems due to electrical resistance of feeder lines such as a transmission loss and a voltage drop. In order to solve these problems, we have been developing superconducting feeder cable, which enables zero-resistance power transmission. Now we have been able to obtain government approval to conduct commercial operation using superconducting cables. It has been begun the commercial operations on passengers using superconducting cable for railway. One of the challenges of the superconducting feeder cable is its joining technique. To introduce superconducting feeder cable between substations, it is required to join superconducting feeder cable to each other at a railway site. First, we conducted an internal observation of the superconducting solder joint using X-ray CT and evaluated the state of the joint. It was found that joining under pressure is effective in reducing joint resistance. Therefore, we performed simple pressure joining using a tweezers-type soldering iron so that it could be performed at railway sites, and evaluated the current characteristics. Joining using this method yielded a low joint resistance on the order of 10−11 Ω·m2, and was found to be effective for joining at railway sites. These results give standards of the joining of superconducting feeder cable.
Fuzzy Logic Based Obstacle Avoidance Algorithm for Unmanned Systems

Researchers that create autonomous systems are very interested in obstacle avoidance. An autonomous system's ability to avoid obstacles more intelligently is a key component. Only artificial intelligence approaches can accomplish this. In this research, a smart obstacle avoidance system based on fuzzy inference system is proposed. The distance from the obstacle and the angle at which the autonomous system is facing the obstacle are the inputs to the fuzzy inference system. The suggested system's outputs include steering and speed adjustments for the autonomous vehicle to better avoid collision with obstacles. With the help of the correlation coefficient and root mean squared error, the developed fuzzy system was assessed. A correlation coefficient was obtained for the suggested system. For steering and speed adjustment, the proposed system received correlation coefficients of 0.983 and 0.974, respectively. Comparable results were found for steering and speed adjustment, with root mean squared errors of 0.0865 and 0.08633, respectively. The results of the performance review were quite successful, and the system that was created was dependable. The similar mechanism will be implemented in hardware in the future, and testing will take place.
The surface characterisation of arc erosion on a structured surface designed to reduce contact bounce

A Ag/Ni electrical contact surface has been re-evaluated for volumetric contact erosion where a new improved method is developed to describe the wear distribution. The new method satisfies the requirement that there is near zero mass loss from the anode and cathode combined surfaces for the low DC voltage and current levels investigated, (14 V, 10.4 A). The Ag/Ni contact material is processed to create a highly structured surface designed to reduce contact bounce. It is shown that the bounce time is reduced significantly. The surfaces are measured for volumetric wear using X-Ray CT and conventional optical surface profiling. A methodology is adopted to determine the wear on a structured cathode surface by measurement of a reference anode surface.
Urban Water Supply Forecasting Based on CNN-LSTM-AM Spatiotemporal Deep Learning Model

Accurate and efficient forecasting of urban water supply is of great significance for urban water supply management. In this paper, a spatiotemporal deep learning model that integrates convolutional neural network (CNN), long short-term memory (LSTM), and attention mechanism (AM) is proposed for predicting the urban daily water supply. First, a one-dimensional CNN is used to identify the potential pattern structure in the water supply system and automatically extract the spatial features of the water supply data. Second, the feature vector output from the CNN is constructed into time series form and used as input to the LSTM network, and the parameters of the LSTM network are searched and optimized using the Bayesian algorithm. Then, the AM is introduced into the LSTM network, and the weighted sum is obtained by assigning the weights to the hidden layers of the LSTM network. Finally, the constructed CNN-LSTM-AM model captures the spatiotemporal information of the water supply data and makes an accurate prediction. Results show that the proposed CNN-LSTM-AM model reduces the mean absolute error, mean square error, and root mean square error values for two different sets of water supply data compared with the traditional LSTM, CNN-LSTM, and LSTM-AM models. The model has high forecasting accuracy and robustness, which are attributed to the excellent spatiotemporal feature extraction.
A Machine Learning Framework for Stock Prediction using Sentiment Analysis

The advent of machine learning and natural language processing techniques, sentiment analysis has emerged as a promising approach for predicting stock prices using textual data such as news headlines and social media posts. This report focuses on the application of sentiment analysis for stock prediction using two popular machine learning algorithms, Random Forest, and Multinomial Naive Bayes, and the TFIDF technique for feature extraction. The dataset used in this study consists of news headlines from the financial news website, Financial Times, and the prediction task is to classify the direction of the stock price changes as either positive or negative. The purpose of this study is to evaluate the effectiveness of sentiment analysis for stock prediction and to compare the performance of Random Forest and Multinomial Naive Bayes algorithms.
Natural Fatty Acid Guards against Brain Endothelial Cell Death and Microvascular Pathology following Ischemic Insult in the Presence of Acute Hyperglycemia

Ischemic stroke is worsened by the presence of sudden high blood sugar levels, even in individuals without pre-existing diabetes. This elevated glucose concentration hampers the ability of energy-starved brain cells to efficiently use it as a source of energy. Consequently, this leads to the production of abundant amounts of toxic glucose metabolites, which trigger oxidative stress in the brain milieu, particularly in the microvasculature of the brain. A prominent feature of this oxidative stress is the demise of endothelial cells, causing detrimental changes in blood vessels, including a reduction in their vascular diameter, a decreased efficiency of vessel proliferation, and the impaired integrity of tight junctions. These vascular pathologies contributed to an increase in the volume of damaged tissues (infarct), an exacerbation of brain swelling (edema), and a decline in cognitive and motor functions. In a mouse model of ischemic stroke with induced acute hyperglycemia, a naturally occurring saturated fatty acid provides protective cover to the microvasculature by preventing damage related to oxidative stress. Our current research revealed that lauric acid (LA) attenuated infarct volume and reduced brain edema by reducing endothelial cell death, enhancing vessels’ diameter, promoting vascular angiogenesis, and stabilizing barrier functions. Animals administered with this natural compound showed a significant reduction in 4-HNE-positive vessels. In conclusion, natural saturated fatty acids help to preserve brain microvascular functions following ischemic insults in the presence of acute hyperglycemia.
The regulation of inhibitor of apoptosis proteins (IAPs) during the apoptosis of Cotesia chilonis

Inhibitor of apoptosis proteins (IAPs) are crucial components of apoptosis that perform vital roles in the regulation of caspase activity in organisms. In this study, two IAPs genes were identified from Cotesia chilonis, the dominant parasitic wasp of Chilo suppressalis. CcIAP1 gene is a typical IAP and contains two BIR domains and a RING domain, whereas CcIAP gene is an atypical IAP1 only containing two BIR domains. Phylogenetic analysis indicated that CcIAP1 and CcIAP were grouped with other Hymenopteran IAPs and IAP1 in C. suppressalis. Real-time quantitative PCR revealed that CcIAP1 and CcIAP genes were both highly induced at −6°C and 30°C, and expression was highest at the third instar stage. The expression of CcIAP1 and CcIAP genes were significantly induced during parasitism of C. suppressalis, and the 7-d time point resulted in the highest expression levels for both genes, in which was an advanced stage of larval development of C. chilonis. RNAi experiments showed that CcIAP1 gene was the key IAP in the regulation of apoptosis of C. chilonis and its host. In conclusion, CcIAP1 and CcIAP correlate with the development of C. chilonis and their responses to temperature stress.
Acoustic Source Localization Model using Audio-Visual Learning

Sound Source Localization provides valuable information about the spatial distribution of sound in our environment, enabling us to perceive better, communicate, and interact with the world around us. It provides crucial spatial information about the environment, allowing for better perception and interaction with the world. In literature, multiple techniques exist to extract features from an audio-visual input, such as attention models and convolutional/deep neural networks. Many pre-trained models are available for the task, such as VGG-19, Covolution 3D Networks(C3D), inflated Covolution 3DNetworks(I3D)-nonlocal, SlowFast, etc. These models are largely applied to video analysis purpose as these are good in capturing spatiotemporal features. In this paper, we summarize the recent advancements in the domain and propose a methodology in which audio-visual input is fed to the pre-trained C3D is 3-dimensional convolution networks that extract the video features based on manually generated bounding boxes. The audio feature extraction is done via the VGGish model. Both of these are then used for training a fully connected neural network. Experimental results on Audio-Visual Event Dataset and Flicker Soundnet Dataset have shown satisfactory results. The performance metrics video accuracy with the AVE dataset is 82.83%, and the cIoU score is 0.75 on the AVE dataset.
Why Post That? Re-thinking the Problem of Absent Presence within Social Media

While social media have the potential to promote positive relations online, the increasing use of contemporary apps, such as Instagram, distances many from important face-to-face interactions. In this study, we rethink the problem of ‘absent presence’ within social media by revealing the language games of Instagram. Drawing on social media literature and Gergen’s (2002) concern about the erosion of cultural norms due to the absent presence generated by new media, we engaged in a qualitative study of active Instagram users. Employing a unique ‘prompt and response’ interview approach, in which we asked participants to explain why they would (or would not) post particular statements, we gain insights into social norms of Instagram as expressed by active social media users. The results of this study show that participants base their posting decisions largely on their perception of a desired self, without much concern for who is posting or why they are posting. Because our findings reveal that posting on Instagram is done without much engagement with others and is often done only if a message corresponds with their own experiences or self-identification, this study extends the concern about absent presence. Specifically, we argue that the complex blurring of monologic and dialogic forms of communication found in contemporary social media apps generates hollow and inconsequential language games that maintain narrow concepts of self and fail to generate interactions necessary to co-create relevant and meaningful social values.
Dormant categories and spatial resolution affect the perception of land cover change model performance

Most models of land cover change predict change using physical and socio-economic factors in raster grids where temporal and spatial scales must be selected to optimize prediction and calculation time. This study tests the impacts of spatial extent and spatial resolution (cell size) on land cover change modelling. Spatial extent here is equivalent to increasing the area of a dormant category. Two extents (33.6 km² and 79.1 km²) and three resolutions (25 m, 50 m and 100 m) were tested on study zones located in SE France in the Var department. The 50 m and 100 m resolutions were downscaled back to 25 m and compared to the initial 25 m maps. Land cover maps dated from 1950, 1982, 2003 and 2011, and IDRISI’s Land Change Modeler (LCM) was used to predict 2011. Dormant category improved Cramer’s V values (1.3 to 1.5 time greater) and quantity and allocation disagreement values. Actual change predictions were similar for the two zones, but the high persistent forest in the large window artificially improved prediction statistics, so increasing dormant category area (spatial extent) artificially inflates prediction statistics. Spatial resolution appeared to have little impact at first, but upscaling/downscaling revealed that coarser cell sizes lose predictive power (1.5-2 times greater allocation errors). Dormant category area should be minimized and upscaling/downscaling should be done if data are modelled at coarser resolutions than original cell size.