Miracle-dz/UniFAR-FTUs huggingface dataset:
A Systematic Review of the Association of Internet Gaming Disorder and Excessive Social Media Use With Psychiatric Comorbidities in Children and Adolescents: Is It a Curse or a Blessing?

Internet gaming and social media usage (SMU), particularly among children and teenagers, have witnessed a remarkable surge over the past decade. However, it remains uncertain whether this widespread usage has a positive or negative impact. The primary objective of this systematic review was to investigate the diverse effects of excessive video game playing and extensive SMU, both favorable and detrimental, on the psychological and mental well-being of children and adolescents. To assess the influence of internet gaming disorder (IGD) and disordered SMU on the mental health of children aged 6-12 years and adolescents aged 13-18 years, we conducted a systematic review of 20 studies on the subject. These studies utilized a substantial sample size of 48,652 participants, encompassing online and in-person questionnaires administered to children, teenagers, and their parents in educational institutions, healthcare facilities, and online platforms. Our findings suggest that multiple factors contribute to the intricate relationship between SMU, video game playing, and mental health outcomes. The majority of research indicates that excessive gaming or SMU among children and teenagers leads to adverse consequences on their mental well-being. Furthermore, certain studies have even reported fatal consequences, while others have identified a worsening of preexisting mental health issues. A few studies have explored the potential positive impacts of SMU and gaming on individuals and society at large. In light of this, we have concluded that it is inappropriate to categorize internet gaming and SMU as solely beneficial or detrimental without considering the broader context and the interplay of various factors.
Design of A Smart Microgrid for Predictive Energy Generation

The exploration of artificial neural networks (ANNs) in the realm of renewable energy-based smart grids is the primary focus of this study. The difficulties associated with the integration of renewable energy sources into the electrical grid, primarily due to their inherent variability and intermittency, are identified as the main problem. The smart grid technologies development is recognized as a solution to enhance the grid’s efficiency and reliability. It is suggested that ANNs could significantly contribute to the smart grid system performance improvement. An examination of renewable energy-based smart grid key features, integration-related challenges, and potential benefits of incorporating ANNs are presented. Research on ANNs’ application in various smart grid systems components, such as energy demand forecasting, energy generation and storage optimization, and grid stability management, are reviewed. Data division for this study was accomplished through random subsampling, with 80% of the data forming the training set and the remaining 20% comprising the testing set. A comparison between these two groups was subsequently performed. Following the computations to determine the Mean Squared Errors (MSEs) for a percentage range between 60% and 90%, the study concludes by highlighting areas requiring additional research to fully exploit the potential of ANNs in renewable energy-based smart grids. In this study, the main objective is to understand the role of ANNs in optimizing the performance of renewable energy-based smart grids and identify areas for future research.
Unravelling the diversity in water usage among wild banana species in response to vapour pressure deficit

The rise in global temperature is not only affecting plant functioning directly, but is also increasing air vapour pressure deficit (VPD). The yield of banana is heavily affected by water deficit but so far breeding programs have never addressed the issue of water deficit caused by high VPD. A reduction in transpiration at high VPD has been suggested as a key drought tolerance breeding trait to avoid excessive water loss, hydraulic failure and to increase water use efficiency. In this study, stomatal and transpiration responses under increasing VPD at the leaf and whole-plant level of 8 wild banana (sub)species were evaluated, displaying significant differences in stomatal reactivity. Three different phenotypic groups were identified under increasing VPD. While (sub)species of group III maintained high transpiration rates under increasing VPD, M. acuminata ssp. errans (group I), M. acuminata ssp. zebrina (group II) and M. balbisiana (group II) showed the highest transpiration rate limitations to increasing VPD. In contrast to group I, group II only showed strong reductions at high VPD levels, limiting the cost of reduced photosynthesis and strongly increasing their water use efficiency. M. acuminata ssp. zebrina and M. balbisiana thus show the most favourable responses. This study provides a basis for the identification of potential parent material in gene banks for breeding future-proof bananas that cope better with lack of water.
Weed Recognition Using Image-Processing Technique Based on Leaf Parameters

Weeds normally grow in patches and spatially distributed in field. Patch spraying to control weeds has advantages of chemical saving, reduced cost and environmental pollution. Advent of electro-optical sensing capabilities has paved the way of using machine vision technologies for patch spraying. Machine vision system has to acquire and process digital images to make control decisions. Proper identification and classification of objects present in image holds the key to make control decisions and use of any spraying operation performed. Recognition of objects in digital image may be affected by background, intensity, image resolution, orientation of the object and geometrical characteristics. A set of 16, including 11 shape and 5 texture-based parameters coupled with predictive discriminating analysis has been used to identify the weed leaves. Geometrical features were indexed successfully to eliminate the effect of object orientation. Linear discriminating analysis was found to be more effective in correct classification of weed leaves. The classification accuracy of 69% to 80% was observed. These features can be utilized for development of image based variable rate sprayer.
Antibacterial Properties of the Antimicrobial Peptide Gallic Acid-Polyphemusin I (GAPI)

A novel antimicrobial peptide, GAPI, has been developed recently by grafting gallic acid (GA) to polyphemusin I (PI). The objective of this study was to investigate the antibacterial effects of GAPI on common oral pathogens. This laboratory study used minimum inhibitory concentrations and minimum bactericidal concentrations to assess the antimicrobial properties of GAPI against common oral pathogens. Transmission electron microscopy was used to examine the bacterial morphology both before and after GAPI treatment. The results showed that the minimum inhibitory concentration ranged from 20 μM (Lactobacillus rhamnosus) to 320 μM (Porphyromonas gingivalis), whereas the minimum bactericidal concentration ranged from 80 μM (Lactobacillus acidophilus) to 640 μM (Actinomyces naeslundii, Enterococcus faecalis, and Porphyromonas gingivalis). Transmission electron microscopy showed abnormal curvature of cell membranes, irregular cell shapes, leakage of cytoplasmic content, and disruption of cytoplasmic membranes and cell walls. In conclusion, the GAPI antimicrobial peptide is antibacterial to common oral pathogens, with the potential to be used to manage oral infections.
YIELD RESPONSE OF FINGER MILLET ( ELEUSINE CORACANA L.) TO POULTRY MANURE APPLICATION IN WESTERN KENYA

Declining soil fertility is affecting finger millet production in western Kenya and inorganic fertilizer use is expensive for most small holder farmers. With over 90% of farmers keeping poultry, its manure appears to be a potential alternative source of fertilizer for finger millet production. An experiment was conducted at KALRO - Kakamega to evaluate the yield response of finger millet ( Eleusine coracana L.) to different rates of poultry manure (0, 5, 10, 15, 20, 25 and 30 t/ ha). This was carried out during the long rains season (March - August) and short rains season (September - December) of 2016. Experimental layout was a Randomized Complete Block Design with three replications. Results indicated significant yield increase from 1,148 kg/ ha (control 0 t/ ha rate) to 1,925.9 kg/ ha (5 t/ ha rate), which was significantly different from the 10 t/ ha rate (2,463 kg/ha) during the long rains season. No significant yield increase was observed beyond 10 t/ ha. Similarly, no significant yield difference was recorded with increasing rates of poultry manure during the short rains season. This clearly indicated that, during the short rains season, finger millet yield may have been affected by other factors besides poultry manure rates. Significantly higher mean yields were recorded during the long rains (2,431.7 kg/ ha) when compared to the short rains season (936.5 kg/ ha). The positive yield response of finger millet to increased rates of poultry manure application during the long rains season indicates that smallholder farmers can consider using poultry manure (5-10 t/ha) as an alternative fertilizer for finger millet production.
What Does A Typical CNN “See” In An Emotional Facial Image?

- The objective of this research is to understand the current capabilities of artificial neural network algorithms and contrast them to the human visual system, in order to identify the most effective features to support affective automation. This can, in turn, aid in optimisation of resources used for storage and transmission by understanding which level of information can be used to augment and potentially accelerate accurate identification of emotional facial expressions. For the first part of our experiment, which we present in this paper, we focused on evaluating feature selection of facial expression images using machine learning. 70 (10 examples of each basic emotion) randomly selected from the NIMSTIM dataset images were used, which were split into train (56) and test (14) sets. The testing images were then processed using Singular Vector Decomposition to vary the levels of information shown in the image. Next, the training dataset was used to train a Convolutional Neural Network algorithm with 18 layers (with convolutional, max pooling, dropout, flattening and activation layers) and 66,884,615 trainable parameters. The validation accuracy was 45% and the confusion matrix showed that the emotion disgust was predicted at almost 100% accuracy, surprise at 55%, and sorrow/happiness/neutral at 46-47%. As expected, the granularity level of the test images had an effect on the successful predictions. A feature map visualisation was performed to demonstrate what the CNN “sees” (i.e., the feature selection) in the image in order to accurately predict the emotional expression type. For the next phase of our experiment, we plan on contrasting the features and performance to that of the human visual system using an experimental design with eye tracking.
AN INTERDISCIPLINARY FORUM ON FOLKLORE AND POPULAR CULTURE

A starting point of this article is that all writing is multiplex and collaborative, creative and critical, and a form of knowledge production and representation. The article discusses poetic inquiry as a creative method and an instrument of knowledge production. Throughout the methodological discussion, poems from the author’s research project on connections between public and private urban spaces, dis/identifications, and embodiment are included as illustrations and starting points for the discussion. The author shows how poetic inquiry is part of poststructuralist theory and methodology, how poetic inquiry can access embodied experience, and such inquiry’s possibilities for multiplicity and multilayered writing.
Eating Disorders in Youth with Chronic Health Conditions: Clinical Strategies for Early Recognition and Prevention

Youth with chronic health conditions face an elevated risk of eating disorders and disordered eating behaviors. Contributors to this phenomenon may include the unique threats faced by this vulnerable population to their body image, their relationships with food and eating, and their mental health and self-esteem. However, youth with chronic health conditions may also experience more severe medical complications and mortality from eating disorder behaviors because of the additional risks conveyed by their underlying conditions. In this review, clinical strategies are provided to support youth with chronic health conditions through early recognition of eating disorder behaviors and prompt referral to treatment, which is important for a better prognosis. Suggestions are also given to mitigate their risk of developing eating disorders by proactively addressing risk factors and offering thoughtful anticipatory guidance that promotes a positive relationship with food and eating.
Challenges of ultradeep drilling

To successfully produce ultradeep gas (below 25,000 ft), advances will be needed in geoscience and engineering. Combined thinking from experts in geology, geophysics, geochemistry, and engineering has outlined some of the challenges. Geologists will require assistance from geophysicists to identify the spatial distribution of sedimentary rocks. They will need to interpret the structural and tectonic framework and fit them into an integrated geological model. Characterization of potential reservoirs including knowledge of the preservation or creation of porosity will be vital. Although seismic methods have the highest resolving power for delineating structure at depth and indicating other rock properties, other geophysical methods should also be utilized for additional information on deep sediments. A better understanding of the source requirements and chemical reactions for generation of methane is required. Methane can survive at high temperatures and high pressures associated with depths of 40,000 ft in an inert dry reservoir, but the stability of methane in the presence of complex mixtures of minerals and other fluids is uncertain. Further experimental studies and theoretical work are needed to understand the constraints on methane preservation. Severe problems will be encountered in drilling, evaluation, and completion of ultradeep wells. Engineering challenges will come from higher in-placemore » stresses, temperatures, pressures, and the probability of encountering corrosive and toxic fluids. Drill strings, casing, and other tubular materials used in the drilling and well completion process will need advanced metallurgy to reach and complete wells in deep targets. The economic returns at $3.00 per Mcf can be attractive if 200 ft of gas within a 7 percent porosity reservoir can be successfully completed at 30,000 to 40,000 ft. This assumes good productivity and draining gas reserves of 125 Bcf from a single well. 6 refs., 5 figs.« less
Nutritional risks in children with food allergy

Food allergies (FA) are a growing problem in the pediatric population and clinical features differ according to the underlying immunological mechanisms. While the primary management strategy is to eliminate the culprit food from the diet, assessment of the potential nutritional risks of elimination is also an integral part of management. In cases that do not improve over time; if you have basic food allergies and multiple food allergies, this can also lead to negative nutritional consequences. The contribution of basic nutrients, economical and easily accessible foods to the diet, is critical and has an important place in meeting the daily adequate intake of many nutrients. In the presence of food allergy, it is necessary to meet the vitamins and minerals that cannot be obtained from allergic foods, with alternative sources or supplements. For example, insufficient calcium intake in cow’s milk allergy (CMA), the most common FA in early childhood, is very likely if an alternative supplement has not been introduced. In the management of CMA, choosing the appropriate formula and/or supplement for the clinical characteristics of children, when necessary, has an important place. In conclusion, nutritional risk assessment of children with FA requires a comprehensive, detailed, and multidisciplinary approach.
Abnormalities in Copper Status Associated with an Elevated Risk of Parkinson’s Phenotype Development

In the last 15 years, among the many reasons given for the development of idiopathic forms of Parkinson’s disease (PD), copper imbalance has been identified as a factor, and PD is often referred to as a copper-mediated disorder. More than 640 papers have been devoted to the relationship between PD and copper status in the blood, which include the following markers: total copper concentration, enzymatic ceruloplasmin (Cp) concentration, Cp protein level, and non-ceruloplasmin copper level. Most studies measure only one of these markers. Therefore, the existence of a correlation between copper status and the development of PD is still debated. Based on data from the published literature, meta-analysis, and our own research, it is clear that there is a connection between the development of PD symptoms and the number of copper atoms, which are weakly associated with the ceruloplasmin molecule. In this work, the link between the risk of developing PD and various inborn errors related to copper metabolism, leading to decreased levels of oxidase ceruloplasmin in the circulation and cerebrospinal fluid, is discussed.
3D/6DOF Particle Filtering Location Algorithm Based on GPU Parallel Acceleration

Autonomous navigation ability is considered to be one of the important functions of intelligent mobile robot system. The localization method based on particle filter is widely used in the localization field of mobile robot. Under the background of traditional single thread computing method, the positioning accuracy and real-time performance of particle filter location algorithm are often difficult to be guaranteed simultaneously. To solve this problem, we propose to use graphics processing units (GPU) to carry out large-scale parallel computation on particles, so as to achieve the acceleration effect. Specifically, we analyse the parallelism of the prediction, observation and resampling aspects of the mcl_3dl algorithm, and do parallel acceleration of the particle weight update in the observation part. The acceleration effect of the proposed algorithm was tested in pose tracking experiments and global localization experiments. The results show that the particle weight updating speed of the algorithm in indoor scene and outdoor scene is improved by 16.7% and 37.8%, respectively. In the indoor corridor scene, the convergence speed of the global localization is increased by 29.1%.
Expression Level Changes in Serotonin Transporter are Associated with Food Deprivation in the Pond Snail Lymnaea stagnalis

In the pond snail Lymnaea stagnalis, serotonin (5-HT) plays an important role in feeding behavior and its associated learning (e.g., conditioned taste aversion: CTA). The 5-HT content in the central nervous system (CNS) fluctuates with changes in the nutritional status, but it is also expected to be influenced by changes in the serotonin transporter (SERT) expression level. In the present study, we identified SERT in Lymnaea and observed its localization in 5-HTergic neurons, including the cerebral giant cells (CGCs) in the cerebral ganglia and the pedal A cluster neurons and right and left pedal dorsal 1 neurons in the pedal ganglia by in situ hybridization. Real-time PCR revealed that the SERT mRNA expression level was lower under severe food deprivation than under mild food deprivation in the whole CNS as well as in a single CGC. These results inversely correlated with previous data that the 5-HT content in the CNS was higher in the severely food-deprived state than in the mildly food-deprived state. Furthermore, in single CGCs, we observed that the 5-HT level was significantly increased in the severely food-deprived state compared with the mildly food-deprived state. Our present findings suggest that changes in the SERT expression level associated with food deprivation may affect 5-HT signaling, probably contributing to learning and memory mechanisms in Lymnaea.
Leader-Following Consensus for a Class of Nonlinear State Time-delays Multiagent Systems

This paper focuses on the consensus tracking problem for a class of nonlinear multiagent systems with state time-delays and dead-zone input. Based on the backstepping technique, a consensus control protocol is proposed, where the nonlinear terms in the system are approximated with the radial basic function neural networks (RBFNNs). Combining the Lyapunov-Krasovskii functional and Young’s inequalities, the adverse effects of time-delays have been eliminated. At the same time, the nonlinear input problem is solved by converting the dead-zone to a combination of linear terms and bounded disturbance terms. The proposed control protocol guarantees that the convergence of the consensus error will converge to a limited neighborhood around the origin. Finally, the numerical results further illustrate the feasibility of the theoretical results.
ANN-NDI-based Quadrotor UAV Large-Angle Maneuver Attitude Controller Design

This paper proposes an adaptive neural network (ANN) and nonlinear dynamic inversion (NDI) based controller for large-angle maneuver attitude control problems in quadrotor UAVs, which aims to overcome the challenges posed by model uncertainty and external disruptions on control effectiveness. The NDI serves as the primary controller, while the ANN acts as the compensation controller. Firstly, the attitude dynamic model of the quadrotor UAV is presented in the paper. Then, a control method based on dynamic inversion theory is developed to achieve large-angle maneuver control for the quadrotor UAV, which takes into account the coupling and nonlinearity of the attitude dynamic model. Additionally, in order to address inaccuracies caused by model uncertainty and unidentified external disruptions, compensation control signals are evaluated and generated by using an adaptive neural network. The update rate of neural network weights is designed using Lyapunov theory to achieve online adaptive control. Ultimately, to evaluate the effectiveness of the proposed control method, numerical simulations are conducted. According to the numerical simulation results, the proposed controller exhibits superior performance compared to traditional controllers in terms of both robustness and attitude tracking accuracy.
International Journal of Research

Post-disaster reconstruction plays a vital role in restoring communities devastated by natural or human-made disasters. This study explores strategies and approaches for rebuilding and designing resilient communities after such events. The process of reconstruction extends beyond physical infrastructure, encompassing social, economic, and environmental factors crucial for community resilience. Immediate relief efforts addressing basic needs are followed by long-term recovery with disaster risk reduction measures integrated into infrastructure design. Community engagement is pivotal, empowering affected populations and incorporating local knowledge in reconstruction decisions. Promoting inclusivity and equity in reconstruction can address underlying vulnerabilities and inequalities. The study's background emphasizes the growing importance of resilience and community-based approaches in post-disaster reconstruction, calling for further research and evidence-based practices. By examining successful case studies and theoretical frameworks, this research aims to contribute valuable insights for policymakers, practitioners, and researchers, fostering the development of resilient and adaptive communities