Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnosis and Orthodontic Treatment of Obstructive Sleep Apnea Syndrome Children—A Systematic Review

Obstructive sleep apnea syndrome (OSAS) is a respiratory illness that is associated with recurrent episodes of either partial or full obstruction of the upper airways, or apnea, among other sleep disorders. This study aims to analyze, through a literature review, whether orthodontic treatment can be a good treatment strategy for this type of disorder. We performed a database search on Scopus, Web of Science, and Pubmed with the keywords OSA(S) and orthodontics to select the papers under evaluation. The criteria for inclusion were articles related to OSA(S) children undergoing an orthodontic treatment and clinical studies or case series, excluding systematic reviews, narrative reviews, meta-analyses, adult studies, animal models, and in vitro studies. The screening phase ended with the selection of 16 publications for this work. RME, or rapid maxillary expansion, turned out to be the preferred orthodontic treatment in cases of pediatric OSAS. The goal of this orthodontic procedure is to increase the hard palate’s transverse diameter by reopening the mid-palatal suture. Children with maxillary contraction and dental malocclusion typically undergo such a procedure and have excellent results. However, OSAS is a multifactorial disorder; it does not seem related to the morphology of the oral cavity, and therefore, it is not always possible to cope with this problem exclusively through orthodontic treatment.
1Design and Research of Active Inductor for Wireless Power Transfer System

Due to the unavoidable factors such as temperature, manufacturing tolerance and so on, the value of capacitance in the wireless power transfer (WPT) resonant network will be shifted, which will cause the sharp decline of the system transmission performance. In view of this, a topology of WPT based on active inductor is proposed in this paper. This topology only needs to fix the duty cycle of the MOSFETs, then it will improve the output power capacity and efficiency of the system. In order to verify the feasibility of the proposed concept and ensure the size and power density of the receiving side, the proposed topology is placed in the primary side of the WPT system. A 100kHz simulation model and a prototype are built, and the experiment shows that the active inductor topology can improve the efficiency of the detuning system by 7.3% under the condition of full load.
ECOLOGY AND PARTIAL RESTORATION OF MONE WETLAND FOR FISH PRODUCTIVITY

Shatiya wetland, a threatened water body of eutrophicated nature in the Gopalganj district of Bihar was studied for its degradation and possible restoration practices. There high rate of sedimentation and agricultural activities causing significant changes in water quality and local biotic populations. Agricultural activities have led to high input of N and P fertilizers along with pesticides being used by the farmers. The positives response of restoration practices was observed with partial improvement in fish-productivity due to hindrance factors acting upon severe fish species.
A Development of Time-Varying Weight Model Predictive Control for Autonomous Vehicles

Autonomous vehicles, commonly known as self-driving cars, are rapidly gaining popularity due to their numerous advantages, such as reducing traffic, pollution, and emissions while increasing safety, convenience, and transportation connectivity. In order to accurately track the motion signal, these vehicles are now utilising advanced control techniques, such as model predictive control (MPC). However, the efficiency of MPCs heavily relies on properly tuning their weights. The primary function of the MPC is to recalculate the optimal values for the vehicle control commands, such as desired speed, steering angle, etc., while considering the dynamic model of the autonomous vehicle. The existing linear MPC models cannot reach higher efficiency because of using fixed weights without considering the error. This paper introduces a novel approach for developing an MPC model with a time-varying weights algorithm for autonomous vehicles. The study aims to minimise motion tracking errors such as lateral position and yaw angle errors. Relevant MPC weights are calculated online using fuzzy logic-based units considering the lateral position and yaw angle errors. The proposed linear time-varying MPC was designed and developed using MATLAB software, resulting in improved motion tracking performance with 31.62% and 20.89% reduction of the root means square error of lateral position and yaw angle.
AGE AND CREATIVITY: EFFECT OF CHRONOLOGICAL AGE ON MANAGER’S CREATIVITY

Aim of the present investigation is to find out the relationship between managerial personnel’s chronological age on creativity inorganizations. To find out this relationship, present study was conducted on 206 managers working in different private sector organizations in India. Creativity was assessed by the help of Creative Behaviour Questionnaire. Obtained data were analysed in the term of correlation analysis and hierarchical regression analysis. Results show that age has an inverse relationship with creativity.
Evaluation of ridge planting systems on a poorly drained lake plain soil

ABSTRACT: No-till or minimum tillage planting on ridges has been suggested as a water quality management practice for poorly drained soils. Studies were conducted between 1982 and 1985 on a Hoytville silty clay (Mollic ochraqualf, fine, illitic, mesic) to evaluate corn (Zea. mays L.) and soybean (Glycine max L. Merr.) responses to ridge planting. Corn planted on ridges generally produced yields equivalent to or greater than corn planted following fall plowing for continuous corn and corn-soybean rotations, with or without tile drainage. Soybeans planted on ridges in 76-cm rows generally produced yields equivalent to soybeans planted following fall plowing in 76-cm rows, in corn-soybean rotations, but not always in continuous soybeans. Soybeans planted in 76-cm rows often produced yields lower than those planted in 18-cm rows. In 1 of 2 years in which soil moisture conditions permitted, earlier planting of corn on ridges yielded more than corn planted on fall-plowed plots.
Analysis on Soil Organic Carbon Content in Newly Planted Forest in Desert Area and Periphery Desert Soil

【Objective】 The organic carbon contents in Karamy desert soil and the soil of newly planted trees,as Populus Alba L. ,Populus.Russkii L. ,Ulmus pumila Fraxinus chinensis Roxb were analyzed to confirm the effects of planting trees on soil organic carbon and to provide basic data for land use and ecological protection. 【Method】The total organic carbon(TOC),dissolved organic carbon(DOC),active organic carbon(AOC),microbial organic carbon(MBC) and Biomass of the soil samples were analyzed. 【Result】The TOC contents of Populus Alba L.soil,Populus.Russkii L.soil,Ulmus pumila Fraxinus chinensis Roxb soil and desert soil in the layer of 0-50 cm were 10.38 g/kg,6.73 g/kg,11.16 g/kg,4.37 g/kg,respectively;The order of DOC content in the soil in the layer of 0-50 cm is desert soil Ulmus pumila Fraxinus chinensis Roxb soil Populus.Russkii L.soil Populus Alba L.soil;The order of AOC content of the soil is: Ulmus pumila Fraxinus chinensis Roxb soil Populus Russkii L.soil Populus Alba L.soil aeolian sandy desert soil;There is significant difference between the AOC contents of the desert and the three forest soils,but there is no significant difference among the three forest soil samples.The MBC contents of forest soil were higher than that of the desert soil,and the MBC contents of Populus.Russkii L.soil were lower than the other two forest soil. 【Conclusion】The TOC, MBC,AOC contents of soil increased after the desert was changed into forest,but the DOC content decreased.The changing order of TOC,MBC,AOC was Ulmus pumila Fraxinus chinensis Roxb soil Populus Alba L.soil Populus.Russkii L.soil desert soil;the biomass of herbage and arbor forest was higher than that of the desert.The biomass order of the three forests were Populus.Russkii L. Populus Alba L.Ulmus pumila Fraxinus chinensis Roxb desert soil.
A Review of Manufacturing Technologies of Soft Magnetic Composites for Electric Machines

In this article the review of the existing manufacturing technologies of the soft magnetic materials have been performed. The existing technological protocols for production of the electrically insulated powders and their further treatment towards manufacturing of the soft magnetic materials are provided. The influences of different technological processes on the mechanical, electrical and magnetic properties of the resulting soft magnetic materials have been reviewed and described. Finally, the usage and implementation of the soft magnetic composites in the field of electric motor design have been discussed.
A CNN-Based Deep Learning Approach in Anomaly-Based Intrusion Detection Systems

The growing prevalence of cybersecurity threats has increased the demand for robust intrusion detection systems (IDSs). Deep learning techniques have shown promising results in detecting and mitigating these threats, making them an increasingly popular choice in IDS design. However, evaluating the performance of deep learning-based IDSs can be challenging due to the complexity of the models and the lack of standardized evaluation metrics. This review paper presents an overview of the most common evaluation metrics used in deep learning-based IDSs, including precision, confusion metrics, accuracy, F1 score, Area Under Curve (AUC), and recall. Several studies have applied machine-learning classic algorithms like Random Forest, Decision Tree, Logistic Regression, and others, but for this paper, we used a Convolutional Neural Network (CNN) that would be independent of the features in the dataset. The studied papers did not provide AUC and none of them balanced the dataset based on the feature's proportion. The dataset utilized in this study is the CSE-CIC-IDS2018 dataset, which underwent meticulous cleansing and normalization procedures to ensure the inclusion of legitimate and useful data. Furthermore, a weighting mechanism was introduced to balance the dataset and mitigate the potential for bias in the Machine Learning process.
Multi-Parametric Model Predictive Control Strategies for a Rotary Tablet Press in Pharmaceutical Industry

The pharmaceutical manufacturing industry has been undergoing a paradigm change from the standard batch pharmaceutical manufacturing towards continuous manufacturing, which is a quicker, more effective approach. Even so, most work in continuous tablet manufacturing has focused on classic model predictive control strategies. Multi-parametric model predictive control strategies are a better fit for assuring a quality-by-control (QbC) approach and progressing towards embracing the Industry 4.0 revolution. In this work an advanced multi-parametric model based predictive control strategy for the control of a continuous manufacturing process for solid dosage forms in the pharmaceutical industry is developed. The model used to develop the advanced control strategies is validated and calibrated using real data from the Pilot Plant. This will lead to better process performances including increased process efficiency, expanded process flexibility, and decreased environmental impact despite the presence of process uncertainties, measurement noise and disturbances.
Strengths and limitations of non-disclosive data analysis: a comparison of breast cancer survival classifiers using VisualSHIELD

Preserving data privacy is an important concern in the research use of patient data. The DataSHIELD suite enables privacy-aware advanced statistical analysis in a federated setting. Despite its many applications, it has a few open practical issues: the complexity of hosting a federated infrastructure, the performance penalty imposed by the privacy-preserving constraints, and the ease of use by non-technical users. In this work, we describe a case study in which we review different breast cancer classifiers and report our findings about the limits and advantages of such non-disclosive suite of tools in a realistic setting. Five independent gene expression datasets of breast cancer survival were downloaded from Gene Expression Omnibus (GEO) and pooled together through the federated infrastructure. Three previously published and two newly proposed 5-year cancer-free survival risk score classifiers were trained in a federated environment, and an additional reference classifier was trained with unconstrained data access. The performance of these six classifiers was systematically evaluated, and the results show that i) the published classifiers do not generalize well when applied to patient cohorts that differ from those used to develop them; ii) among the methods we tried, the classification using logistic regression worked better on average, closely followed by random forest; iii) the unconstrained version of the logistic regression classifier outperformed the federated version by 4% on average. Reproducibility of our experiments is ensured through the use of VisualSHIELD, an open-source tool that augments DataSHIELD with new functions, a standardized deployment procedure, and a simple graphical user interface.
Exploring Hierarchical Changes in Functional Brain Network Hubs Through Brain-Activity Prediction with Convolutional Neural Networks

This study aims to clarify how functional network hubs change during hierarchical visual processing in the human brain through the estimation of brain states from features extracted using a convolutional neural network (CNN), a hierarchical model of image processing. We used representational similarity analysis for brain states predicted through encoding models based on feature representations at each layer of the CNN, and applied the PageRank algorithm to matrices converted from the generated representational dissimilarity matrices to capture the hub characteristics of brain region-related systems. This succeeded in capturing changes in the hubness of interregional brain coordination during hierarchical information processing in the human cerebral cortex in visual processing. Specifically, we found that the hubness of the occipital visual cortex increased in the early phase of visual processing, and that the hubness of the prefrontal cortex and temporal lobe increased in the late phase of visual processing. From the above, we found that our proposed method allows us to capture hierarchical changes in the hubness of interregional coordination.
The Influence of GPX1 Pro198Leu, CAT C262T and MnSOD Ala16Val Gene Polymorphisms on Susceptibility for Non-Hodgkin Lymphoma and Overall Survival Rate at Five Years from Diagnosis

Abstract Objective: The aim of the current study was to investigate possible associations between catalase C262T (CAT C262T), glutathione peroxidase 1 Pro198Leu (GPX1 Pro198Leu), manganese superoxide dismutase Ala16Val (MnSOD Ala16Val) gene polymorphisms and non-Hodgkin Lymphoma risk (NHL) in a Romanian population and the five-year overall survival rate of the NHL patients. Methods: We included in this case-control study 406 individuals, divided into two groups: the control group (n=315) and the patients group (n=91). The DNA was extracted from peripheral blood and amplified using specific techniques. Results: The variant homozygous genotype of GPX1 Pro198Leu represents a risk factor for NHL development and no associations regarding the risk for NHL were found for MnSOD Ala16Val and CAT C262T gene polymorphisms. Two of the studied polymorphisms were associated with the overall survival rate thus: negative association regarding MnSOD Ala16Val, associated with higher overall survival rate and a positive one regarding CAT C262T, associated with lower overall survival rate. Conclusions: According to our results, the mentioned polymorphisms may be considered as susceptible markers of the five-year overall survival rate for NHL patients. Future studies with a larger number of patients are needed to confirm our results.
Finding tau rhythms in EEG: An independent component analysis approach

Abstract Tau rhythms are largely defined by sound responsive alpha band (~8–13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of tau's elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects' data were split into 60 parallel processing pipelines entailing use of five levels of high‐pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low‐pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi‐model AMICA [mAMICA]). Tau‐related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8–13 Hz alpha band. These “tau ICs” showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high‐pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high‐pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high‐pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms.
Unravelling the Complexity of the +33 C>G [HBB:c.-18C>G] Variant in Beta Thalassemia

The +33 C>G variant [NM_000518. 5(HBB):c. -18C>G] in the 5′ untranslated region (UTR) of the β-globin gene is described in the literature as both mild and silent, while it causes a phenotype of thalassemia intermedia in the presence of a severe β-thalassemia allele. Despite its potential clinical significance, the determination of its pathogenicity according to established standards requires a greater number of published cases and co-segregation evidence than what is currently available. The present study provides an extensive phenotypic characterization of +33 C>G using 26 heterozygous and 11 compound heterozygous novel cases detected in Cyprus and employs computational predictors (CADD, RegulomeDB) to better understand its impact on clinical severity. Genotype identification of globin gene variants, including α- and δ-thalassemia determinants, and rs7482144 (XmnI) was carried out using Sanger sequencing, gap-PCR, and restriction enzyme digestion methods. The heterozygous state of +33 C>G had a silent phenotype without apparent microcytosis or hypochromia, while compound heterozygosity with a β+ or β0 allele had a spectrum of clinical phenotypes. Awareness of the +33 C>G is required across Mediterranean populations where β-thalassemia is frequent, particularly in Cyprus, with significant relevance in population screening and fetal diagnostic applications.
Management Control Systems Practices in Small and Medium Enterprises

Small and medium-sized enterprises (SMEs) comprise 99.6\% of the Philippine economy. Many studies have said that SMEs do not need and do not use management control systems (MCS) due to their simple organizational structure and limited resources. Most MCS research and their empirical evidence have nearly been completely founded on information from bigger organizations or their sub-units. It has been expressed that modern Management Accounting Practices (MAPs) and sophisticated systems may likewise be valuable for private ventures, albeit small firms may have dissimilar needs than their larger counterpart. Thus, this study determines the MCS adopted and used in SMEs in Misamis Occidental. A descriptive study using a survey questionnaire was administered to 100 randomly selected SMEs in Misamis Occidental. The questionnaires were personally delivered and administered to facilitate questions raised by the respondents regarding the items in the survey. The result showed that SMEs in Misamis Occidental utilize a wide scope of MCS practices and information. However, numerous practices are not incorporated as profoundly and systematically into their business operations. The outcome also shows that MCS is themed with financial, customer-oriented, and strategic-oriented controls. Thus, aside from designing MCS suited to SMEs, there is a need to guide them in the technical application, adoption, and utilization of MCS data. The data can be used as a base for additional scholarly examination in business.
Classification models of direct opinion holders in the space of stylometric and sentiment features of chat messages

Messaging platforms and online chat services offered by internet-based companies (such as transportation providers, taxi services, energy providers, etc.) serve as conventional channels for customers to communicate with service providers and provide valuable feedback. As research shows, the stylistic and sentimental behavior of opinion holders carries a lot of valuable information about the client and provides opportunities, in particular, to build a connection between his behavioral patterns and his preferences as a customer of the service. The authors propose classification models for the analysis of the direct thoughts of holders in the space of stylometric and sentimental features of chat messages. The study focuses on examining the characteristics of holders through the use of their writing styles and emotional components. Research has shown the existence of clearly defined segments in the space of the above-mentioned features, which made it possible to develop a classifier that effectively classifies new holders based on their writing styles, which allows for the personalization of services and optimization of interaction. Knowledge of stylometric and sentiment patterns in chat messages has practical applications in improving recommender systems, improving customer service experiences, and understanding communicative dynamics in chats. The obtained research results open up new opportunities for the further development of the field of natural language processing and sentiment analysis, and are also of practical importance for the development of recommender systems and improving the quality of customer service in the field of chat platforms.