Miracle-dz/UniFAR-FTUs huggingface dataset:
QoS Management for XR Traffic in 5G NR: A Multi-Layer System View and End-to-End Evaluation

Extended reality (XR) applications are gaining a lot of momentum in the industry and consumer market. XR applications are characterized by multiple simultaneous data flows with different Quality-of-Service (QoS) requirements, imposing the need for advanced QoS management in 5G networks. Compared to 4G, the 5G NR technology has been designed with enhanced QoS support, by introducing the concept of QoS flows and an additional layer for QoS handling. However, recent studies show that additional QoS enhancements are needed in 5G to support the massive adoption of XR services. 3GPP envisions to include some enhancements for XR traffic in 5G-Advanced. In this article, we review the current 5G QoS frame-work and study potential design improvements to fulfill XR QoS requirements in future cellular networks. We study QoS control procedures for XR traffics at different layers of the 5G NR protocol stack, including the proper configuration of radio access and core networks. In particular, we analyze various architectures for handling the peculiarities of XR, such as time-constraint and multi-flow applications. Also, we present XR traffic adaptation mechanisms at the application layer, to properly adapt XR traffic statistics to the actual 5G radio environment, and QoS schedulers for the MAC layer, leading to cross-layer performance optimization between XR application and 5G radio access network. Finally, we provide a validation study and end-to-end evaluation of QoS control mechanisms by simulating realistic mixed traffic scenarios in a 5G NR system-level simulator based on ns-3.
An Ontology Based Approach with Intelligent Decision for Off-Line Diagnosis of ECU

In order to meet the urgent need for diagnosis of ECU (Electronic Control Unit) function, this paper proposed an simulation approach for ECU intelligent off-line diagnosis based on ontology. A simulation diagnostic system for ECU was established based on this approach. In this system, the minimum numbers of input and output signals to maintain the normal operation of ECU was provided. With the knowledge model constructed in OWL ontology language, the system can simulate the operation environment of ECU and reason the best next operation step. On this basis, the input signals were intelligent regulated. We validated the proposed approach. Proposed approach could help diagnosis engineer to fast diagnose the fault of ECU, and provide valuable reference to development and test of the control strategy of ECU.
Ségmentation bayésienne hiérarchique de processus MA constant par morceaux

On utilise dans ce travail une methode bayesienne pour traiter une probleme de segmentation de processus MA par morceaux. La complexite des lois a posteriori ainsi que la structure particuliere de l'espace des parametres amene a utiliser la methode de simulation de type Monte Carlo par Chaines de Markov a sauts reversibles. Les sorties de l'agorithme sont utilisees pour obtenir plusieurs types d'estimateur des parametres d'interet : Maximum Marginal a Posteriori, et Moyenne Marginale a Posteriori. La delicat probleme du reglage des hyperparametres est contourne en munissant des hyperparametres de loi, utilisant ainsi une structure bayesienne hierarchique. Mots-Cles : Simulation de Monte Carlo par Chaine de Markov a Sauts Reversibles, Segmentation de processus MA.
A influência do fotopolimerizador sobre a microdureza das resinas compostas

Objetivo: Analisar na literatura a influência das técnicas de fotopolimerização na microdureza das resinas compostas. Revisão bibliográfica: O nível de intensidade de um fotopolimerizador pode ser medido através do sistema de MW (megawatt). A partir disso, é possível avaliar sua influência sobre as resinas em termos comparativos de diferentes pontos de vista: diante de técnicas fotopolimerizadoras, luz gradativa, densidade da luz aplicada, comprimento da onda, tempo de exposição e questionamentos acerca da cor da resina em relação a aplicação do fotopolimerizador. A principal abordagem narrativa, trata-se do grau da microdureza e a relação direta desses compósitos sobre o esmalte ao longo do tempo. Dessa forma, deve-se realizar um plano de tratamento viável e personalizado para cada paciente, respeitando suas características anatômicas-estéticas, evitar grande depósitos resinosos, atentar-se da calibração do dispositivo e a distância aplicada, respeitando os limites anatômicos considerados para cada caso. Considerações finais: Estudos da relação do dispositivo fotopolimerizador sobre resinas compostas são necessários para dimensionar a abrangência da dentística. Pois, o mercado odontológico evolui juntamente com a tecnologia, tornando-se uma área relevante e exige atualizações literárias acerca da influência de fotoestimuladores e polímeros e estudos pertinentes sobre a esfera odontológica.
Vulnerability Assessment of Power Grids with High Penetration of Renewable Energy

The high penetration of renewable energy source (RES) has changed the operation condition of the modern power system. In order to evaluate the vulnerability of power grids with high penetration of RES, an evaluation method based on the complex network theory and cascading fault propagation model is proposed in this paper, and the fluctuation of RES output and power load is also considered. A four-level vulnerability assessment indicator of the load loss ratio, maximum remaining subgroups, number of remaining subgroups, and number of isolated islands was established to assess the system operation risk. A 197-node grid in a northwest region is applied to demonstrate the effectiveness of the proposed model, and the validity and practicability of the conclusion are verified.
Area Coverage Optimization using Networked Mobile Robots with State Estimation

In this paper, we present a solution to the area coverage problem using a team of mobile robots with state estimation. A group of autonomous mobile robots is deployed in a two-dimensional area of interest, for example, where communication among robots is limited or noisy as expected in a real life scenario. Each robot estimates its own state (position and orientation) using noisy range and bearing information received from other robots in its operating range. The area of interest is then divided into multiple sub-area using a voronoi tessellation. Using the classical Lloyd's algorithm, each robot employs distributed action command to move towards the centroid of the voronoi cell that it belongs yielding the maximum coverage of the area. Here we emphasize that the network of autonomous robots deployed in the environment is unknown a priori. A set of computer experiments is conducted to validate the fact that the area coverage is still possible under noisy communication among robots deployed in a two-dimensional area.
Metrology Characteristics Estimation for Digitally Modulated Signals Measurement Instruments

The precision signal source with regulated digital modulation quality parameters has been investigated. It has been developed a method for the reproducing of the signals with a predefined value of the error vector magnitude (EVM). Several cases of application of the method are considered, when amplitude and phase modulation are used as normalized noise. The possibility of using a vector signal generator or an arbitrary waveform generator (AWG) as an EVM parameter calibrator based on the developed method has been experimentally confirmed.
Evaluation of Semecarpus kurzii Engler from Bay Islands for oxidative DNA damage protective activity and in vitro antioxidant potential

Introduction and Aim: Harnessing traditional knowledge of medicinal plants is important for the betterment of mankind. We must safeguard traditional knowledge from misuse by miscreants living in modern societies and knowledge must be available in the public domain for use in drug designing in the healthcare system. Since the beginning of recorded history, Indians have used plants as a source of medicine, and their cultural past is incredibly rich. This study was conducted to evaluate the oxidative stress-induced DNA damage prevention and antioxidant potential of Semecarpus kurzii found in the Bay Islands. Materials and Methods: The scavenging of superoxide, hydroxyl radicals, nitric oxide (NO), 2,2-diphenyl-2-picrylhydrazyl hydrate (DPPH) radicals, ferric reducing power (FRAP), and lipid peroxidation inhibition activity were tested to determine antioxidant activity. DNA damage inhibition test was used to evaluate the protection against oxidative DNA damage. Results: The S. kurzii extract showed dose-dependent scavenging of DPPH, superoxide anion, nitric oxide, hydroxyl radical and reducing power evaluated by comparing with standard antioxidants (ascorbic acid, alpha-tocopherol and BHT). However, the nitric oxide scavenging (IC50 =186.47) and superoxide scavenging (IC50 = 678.32) was more than that of DPPH (IC50 =28.03) and Hydroxyl radical (IC50 =89.10) scavenging capacity. In addition, S. kurzii extract and ascorbic acid showed lipid peroxidation inhibition activity. The extract also exhibited DNA protection which was confirmed by the DNA damage inhibition assay. Conclusion: Our results corroborated that, the methanolic extract of S. kurzii bark has substantial antioxidant activity and DNA damage inhibition. The potential antioxidant and oxidative DNA damage preventive activity could be due to the existence of polyphenolic compounds in the S. kurzii bark.
Coffee Shop Marketing Communication Strategy to Increase Sales (Case Study at Kedai Kopi Inspirasi Bogor)

Kedai Kopi Inspirasi is a well-known coffee shop in Bogor Regency, as shown by the high number of visitors and exposure in various local media as one of the recommendations for coffee shops in Bogor Regency. The purpose of this research is to discover how Kedai Kopi Inspirasi may use marketing communication strategies to boost customer sales and visits in the midst of fierce competition in the present coffee shop business. The data for this research was collected through interviews with the owners and management of Kedai Kopi Inspirasi observation, and literature reviews. The theory used for this research is Integrated Marketing Communication with a Promotion Mix. Based on data analysis, it was found that Kedai Kopi Inspirasi had implemented a marketing communication strategy as part of their promotional strategies, utilizing advertising, personal selling, sales promotion, and public relations strategies to raise brand awareness, which resulted in higher customer visits and purchases. In order to attract customers in the Jabodetabek area, Kedai Kopi Inspirasi performs social media marketing. In addition, personal selling is conducted through engaging with customers as a type of service in order to encourage up-selling in every interaction. Sales promotions are performed by offering special deals on specific holidays. Simultaneously, Kedai Kopi Inspirasi works to approach particular groups and organizations for public relations purposes and may later take part in an event to attract potential customers
Determining the Accuracy of the Measurement of Arrival Time of the Useful Signal by the Meter for Non-Energy Parameters of the Signal

The analysis of the influence of multiplicative noise acting simultaneously with additive noise on the accuracy of measurement of arrival time of the useful signal by the tracking meter is carried out. The input signal is given as a sequence of coherent and incoherent pulses. It is shown that a very low level of additive noise leads to the fact that multiplicative noise does not affect the steepness of the discriminatory characteristic. It is noted that an increase in the spectrum width of the noise modulation function leads to an increase in measurement errors due to a decrease in the power of the input signal. However, the power reduction can be compensated by the effect of automatic gain control. The value of the accuracy characteristics of the tracking meter under the influence of multiplicative noise is determined by the steepness of the discriminatory characteristic, the coefficients determining the normalizing effect of automatic gain control in the presence and absence of noise, as well as the spectral density of fluctuations at the discriminator output.
Parnassin, a Novel Therapeutic Peptide, Alleviates Skin Lesions in a DNCB-Induced Atopic Dermatitis Mouse Model

Atopic dermatitis (AD) is a chronic inflammatory skin disease which requires continuous treatment due to its relapsing nature. The current treatment includes steroids and nonsteroidal agents targeting inflammation but long-term administration causes various side effects such as skin atrophy, hirsutism, hypertension and diarrhea. Thus, there is an unmet need for safer and effective therapeutic agents in the treatment of AD. Peptides are small biomolecule drugs which are highly potent and remarkably have less side effects. Parnassin is a tetrapeptide with predicted anti-microbial activity curated from Parnassius bremeri transcriptome data. In this study, we confirmed the effect of parnassin on AD using a DNCB-induced AD mouse model and TNF-α/IFN-γ-stimulated HaCaT cells. In the AD mouse model, topical administration of parnassin improved skin lesions and symptoms in AD mice, such as epidermal thickening and mast cell infiltration, similar to the existing treatment, dexamethasone, and did not affect body weight, or the size and weight of spleen. In TNF-α/IFN-γ-stimulated HaCaT cells, parnassin inhibited the expression of Th2-type chemokine CCL17 and CCL22 genes by suppressing JAK2 and p38 MAPK signaling kinases and their downstream transcription factor STAT1. Parnassin also significantly reduced the gene expression of TSLP and IL-31, which are pruritus-inducing cytokines. These findings suggested that parnassin alleviates AD-like lesions via its immunomodulatory effects and can be used as a candidate drug for the prevention and treatment of AD because it is safer than existing treatments.
Stochastic Optimization for Max-Norm Regularizer via Matrix Factorization

Max-norm regularizer has been extensively studied in the last decade as it promotes an effective low rank estimation of the underlying data. However, max-norm regularized problems are typically formulated and solved in a batch manner, which prevents it from processing big data due to possible memory bottleneck. In this paper, we propose an online algorithm for solving max-norm regularized problems that is scalable to large problems. Particularly, we consider the matrix decomposition problem as an example, although our analysis can also be applied in other problems such as matrix completion. The key technique in our algorithm is to reformulate the max-norm into a matrix factorization form, consisting of a basis component and a coefficients one. In this way, we can solve the optimal basis and coefficients alternatively. We prove that the basis produced by our algorithm converges to a stationary point asymptotically. Experiments demonstrate encouraging results for the effectiveness and robustness of our algorithm.
Parental knowledge of children’s developmental milestones in Al-Ahsa, Saudi Arabia

Background: Milestones refer to a child’s typical development by a certain age. Children’s development varies considerably in different countries; therefore, milestones are not fixed by specific ages and have a normal range of variation. This study aimed to determine parents’ knowledge of children’s developmental milestones in Al-Ahsa, Saudi Arabia. Method: A cross-sectional study was conducted between January and March 2022 using a pre-designed validated questionnaire among parents who were > 18 years, living in Al-Ahsa and had at least one child aged from birth to 14 years; 372 parents responded via local virtual and social media. Result: Of the parents, 71% and 62.9% correctly reported the age at which children typically start to crawl and walk, respectively. Meanwhile, 9.7% of mothers had a good knowledge level regarding milestones compared to 3.2% of fathers (P = 0.048). Primary information sources were internet sites (81.4%), parents/relatives (81.4%) and pediatricians or general practitioners (68.3%). Conclusion: Parents had poor knowledge of children’s developmental milestones. Healthcare providers should ensure that parents receive accurate information about child development by educating them during routine visits.
Technology-Integrated Computing Education in Early Childhood: A Systematic Literature Review

Technology has become an integral part of teaching and learning, but there is still limited understanding of how it is utilized to support computing education in early childhood. To address this knowledge gap, this review investigated the current implementation of computing technologies in early childhood settings, the implementation of computing activities, the learning outcomes achieved by students, and the utilization of assessment strategies to evaluate student learning. Through a systematic review and synthesis of 31 empirical studies published between 2014 and 2020, this review identified: (1) Twenty-two computing technologies that feature three types of computing environments; (2) Eight types of learning activities to engage children in computing; (3) A variety of learning outcomes accomplished in cognitive and non-cognitive dimensions; (4) A wide range of assessment strategies to evaluate students’ outcomes in different dimensions. This review strengthened the evidence base for the benefits of teaching computing with technology to children, informed the design of age-appropriate computing technology and learning activities, and identified research gaps to inform future research. Implications were provided to inform the future design and delivery of computing instruction to early childhood learners.
Dynamic mixing probability measures of mixtures

ABSTRACT Lifetime of heterogeneous population can be modeled as mixture of a family of lifetime distributions according to a mixing probability measure. With the help of dynamic mixing measure, the hazard rate of the mixture can also be expressed as the mixture of the hazard rates of the lifetime distributions. Various local stochastic orderings are defined in this article. Applying these local stochastic orderings, we can explore the behavior of the dynamic mixing measures locally and then compare the hazard rates of two heterogeneous populations in both the local and global ways.
Predicting Personality Types using Machine Learning and the Myers-Briggs Inventory

It is possible to study the reviews-based dataset. The validity of this paradigm was demonstrated by our data analysis. The data taken from the Twitter reviews (Myer-Briggs) dataset is subjected to behaviour analysis. To determine the user's remark, the data is evaluated. We want to use data-driven marketing technologies including supervised machine learning models, natural language processing, along with information visualisation. Logistic Regression is one of the classification techniques on which the system was constructed. The current issues with each topic are examined before the most recent fixes are provided and debated. The findings from the experiment demonstrate the accuracy, precision, recall, and F1 score. Following that, we can use the Twitter API to forecast the personality. It illustrates how openness is compared.
Distinct Profiles of Desensitization of µ-Opioid Receptors Caused by Remifentanil or Fentanyl: In Vitro Assay with Cells and Three-Dimensional Structural Analyses

Remifentanil (REM) and fentanyl (FEN) are commonly used analgesics that act by activating a µ-opioid receptor (MOR). Although optimal concentrations of REM can be easily maintained during surgery, it is sometimes switched to FEN for optimal pain regulation. However, standards for this switching protocol remain unclear. Opioid anesthetic efficacy is decided in part by MOR desensitization; thus, in this study, we investigated the desensitization profiles of REM and FEN to MOR. The efficacy and potency during the 1st administration of REM or FEN in activating the MOR were almost equal. Similarly, in β arrestin recruitment, which determines desensitization processes, they showed no significant differences. In contrast, the 2nd administration of FEN resulted in a stronger MOR desensitization potency than that of REM, whereas REM showed a higher internalization potency than FEN. These results suggest that different β arrestin-mediated signaling caused by FEN or REM led to their distinct desensitization and internalization processes. Our three-dimensional analysis, with in silico binding of REM and FEN to MOR models, highlighted that REM and FEN bound to similar but distinct sites of MOR and led to distinct β arrestin-mediated profiles, suggesting that distinct binding profiles to MOR may alter β arrestin activity, which accounts for MOR desensitization and internalization.
Low Error Floor QC-LDPC Codes Construction Using Modified Cole's Trapping Sets Enumerating

In order to count the number of trapping sets (TS), we changed Cole's Importance Sampling (IS) approach, which led to a message-passing decoder problem. Several concepts have been combined to suggest improvements to Cole's IS: utilizing short cycles and simultaneous TS impulse tree decomposition, un-wrapping of message passing iterations Tanner Graph/Forney's Normal Graph symmetry's Graph Authomorphism is a complex yet simple concept. Superior Velasquez-Subramani and Karimi-Banihashemi TS enumerating techniques were supported. The proposed technique under PEG (1008, 504) Mackay code for single thread implementation is 43 times quicker than the original Cole's method and 5027 times (71463 times, multi-thread) faster than the Velasquez-Subramani LP method. For the TS enumerating problem at (2640, 1320), Margulis's code is 37958 times quicker than the suggested Velasquez-Subramani LP approach for single thread implementation, 82 times faster than Karimi-Banihashemi, and 134 times faster than Cole's original solution. On the basis of the creation of QC-LDPC codes, we demonstrate the effects of improved EMD spectrum and increased hamming distance on the TS spectrum and BER/FER error-floor level.
Enhancing Accuracy and Explainability of Recidivism Prediction Models

Predicting recidivism is a challenging task, but it helps support courts in their decision-making process. Automated prediction models suffer from low accuracy and are associated with criticism for biased and unexplainable decision-making. In this poster, we present different machine-learning models with just a few selected features that achieve accuracies as good as models that use larger sets of features. In addition, we investigate the influencing features that contribute to recidivism prediction, which can enhance the explainability of the learned models.
Numerical Simulation of a Shock Tube for Bio-dynamics Study

Abstract A bio-shock tube with non-lethal pressure levels can be used to study blast-induced primary injuries. In this paper, a finite element shock tube model was developed to simulate the shock wave and gas flow in a specific bio-shock tube. The behaviors of the gases in the tube were described with an equation of state using the multi-material arbitrary Lagrangian–Eulerian (MMALE) formulation. The FE model-predicted temporal and spatial distributions of pressures were in reasonable agreement with those obtained experimentally. The characteristics of shock wave, unloading wave and rarefaction wave were well captured by this numerical model.