Miracle-dz/UniFAR-FTUs huggingface dataset:
Traffic Classification in IP Networks Through Machine Learning Techniques in Final Systems

Data centers in higher education institutions, as well as those of large corporations, face challenges in terms of traffic flow management. In some cases, due to the limited hardware resources used for this purpose, and in others, despite having enough high-performance equipment, the centers lag behind when the traffic flow grows exponentially due to the memory limitations of the devices, which slows down the network performance. The contribution of this investigation work is the implementation of a classifying elephant and mice system using machine learning techniques for the early detection with the first flow based on the dynamic calculation of the threshold, according to the input parameters of the final system. In the first instance, training algorithms are used to determine the best performance, then the proposed algorithm determines the model with the best prediction, obtained from the supervised learning algorithm trained in off line mode. Finally in the phase of online prediction, the algorithm is capable of predicting with high precision the type of traffic in terms of the input flow, and updates in a dynamic way the threshold to determine whether the traffic is elephant or mice. With this information the network hardware can decide then to route the flows according to their characterization. According to the results, the model that best generates predictions is the decision tree with a 100% confidence level.
Medication Adherence and Contraceptive Counseling

Combined oral contraceptives (COC) are a very popular form of birth control. Incorrect use and lack of adherence to treatment reduce the effectiveness of this method. Having a standard tool to identify poor-adherence profiles quickly and objectively can be helpful for midwives and potentially for COC users. The MMAS-4 adherence scale has been used in various medical fields, but there is little evidence of its potential in contraceptive consultation. This paper presents a piece of multicenter observational research based on a sample of 327 women who had attended contraceptive counselling in Spain and were COC users or had informed the midwife that they want to start to use this method. Two interviews were conducted: at the time of consultation and after one year. In our research, the MMAS-4 identified high-risk behaviors: during the 1-year follow-up period, COC users classified as poorly adherent had a significantly higher risk of missed contraceptive pills, more incidents and problems related to the method of contraception, as well as a lower degree of satisfaction with the contraceptive method. One case of unplanned pregnancy and two cases of emergency contraception were identified, all of them corresponding to poorly adherent women. The use of MMAS-4 in consultation can improve midwives’ contraceptive counselling.
Finite Element Discretization of the Helmholtz Equation in an Underwater Acoustic Waveguide

The numerical solution of time-harmonic problems governed by the Helmholtz equation remains a task of central importance and an active area of research for over thirty years. In this chapter we are mainly interested in applications in underwater acoustics, where the Helmholtz equation models the propagation of the sound field in the sea at any angle and taking into account backscattering effects. We consider specifically an axially symmetric waveguide and we use a cylindrical coordinate system, whose vertical axis passes through the source. The waveguide consists of two fluid layers of different acoustic parameters and is bounded in depth by a rigid bottom while it is unbounded in range. The precise boundary-value problem that we consider is stated in the next section. We are interested in solving the problem numerically with the finite element method. This choice is reasonable when one has to deal with range-dependent features such as complex interface and bottom topographies. Of course it is impossible to discretize an unbounded domain, so the original problem has
Thermoplastic-based microfluidic chip bonding with PES hot melt adhesive film

ABSTRACT In this study, a low-cost and rapid bonding method for thermoplastic-based microfluidic devices is introduced. The thermoplastic substrate and cover plate were directly bonded with PES copolyester hot melt adhesive film. Compared with conventional bonding methods for thermoplastic chips, e.g. thermal fusion and chemical-assisted bonding, the proposed bonding process can be finished within minutes without sophisticated instruments or volatile chemical solvents. Several microfluidic devices were also fabricated and tested, the measured bonding strength is comparable to the conventional thermal fusion bonding method. The biocompatibility of the proposed bonding method was also evaluated by culturing bacteria in the PES adhesive covered culture dish. The proposed bonding method for thermoplastic-based microfluidic is especially suitable for the massive fabrication of POCT devices with advantages in processing speed, instrument requirement, cost, and bonding strength.
Time-resolved x-ray photoelectron spectroscopy at FLASH

The technique of time-resolved pump–probe x-ray photoelectron spectroscopy using the free-electron laser in Hamburg (FLASH) is described in detail. Particular foci lie on the macrobunch resolving detection scheme, the role of vacuum space-charge effects and the synchronization of pump and probe lasers. In an exemplary case study, the complete Ta 4f core-level dynamics in the layered charge-density-wave (CDW) compound 1T-TaS2 in response to impulsive optical excitation is measured on the sub-picosecond to nanosecond timescale. The observed multi-component dynamics is related to the intrinsic melting and reformation of the CDW as well as to extrinsic pump-laser-induced vacuum space-charge effects.
B-APFDQN: A UAV Path Planning Algorithm Based on Deep Q-Network and Artificial Potential Field

Deep Q-network (DQN) is one of the standard methods to solve the Unmanned Aerial Vehicle (UAV) path planning problem. However, the way agent deepens its cognition of the environment through frequent random trial-and-error leads to slow convergence. This paper proposes an optimized DQN with Artificial Potential Field (APF) as prior knowledge called B-APFDQN for path planning. Replacing the traditional neural network which has only one Q-value output with a multi-output neural network to promote the training process in combination with APF. Furthermore, a SA- $\varepsilon $ -greedy algorithm that can automatically adjust the stochastic exploration frequency with steps and successes is proposed in order to prevent the agent from falling into local optimum. We remove the nodes that do not affect the path connectivity and apply the B-spline algorithm to make the path shorter and smoother. Simulation experiments show that the proposed B-APFDQN algorithm performs better than the classical DQN, has a strong ability to avoid falling into local optimum, and the obtained paths are smooth and shorter, which proves the effectiveness of B-APFDQN in the UAV path planning problem.
HAPTIC RENDERING OF THIN AND SOFT OBJECTS

In this work, we take up the problem of modeling deformations, both local and global, along with stable translations for radially-thin virtual objects especially like that of a wire or a spaghetti noodle, during haptic manipulation. To achieve this we recommend the use of mass-spring systems rather than geometric models like vertex based and free form deformation as they do not model the physics behind the interactions. Finite Equation Methods (FEMs) are also not chosen as they are computationally expensive for fast haptic interactions and force feedback. We have explored different types of distribution of masses within the volume of the object, in order to come up with a suitable distribution of masses and network of springs and dampers so that the simulations mimic the behavior of a real object. We also model the constraint forces like normal and frictional forces between the object and the plane on which it is kept. Further, we simulate the effect of a varying temperature distribution of the object and discuss how anisotropic deformation of an object may be effected. We demonstrate through experimentations that it is indeed possible to haptically interact with virtual soft objects.
Multi-objective optimization of committee selection for hierarchical Byzantine fault tolerance-based consensus protocols

Decentralized platforms like blockchain have been attracting significant attention in recent years, especially in the context of financial and payment systems. They are designed to provide a transparent, secure, and reliable environment for digital transactions without the need for a central authority. The core of a decentralized platform like blockchain is a consensus layer that allows all participants (called Workers), who properly operate and follow all network protocols and have access to the same state of the distributed ledger, to coordinate their actions and arrive at the same decisions. However, some Workers may be temporarily offline at their own discretion, without any confirmation, or their work may be faulty due to technical circumstances, resulting in unpredictable behavior. The goal of this article is to present an approach for multi-objective optimizing of Byzantine fault tolerance (BFT)-based consensus protocols, to reduce the impact on the network of faulty participants. Two criteria were considered – minimization of the number of sent service messages, and maximization of the mathematical expectation of the number of produced blocks. The result is a method to determine the optimal committee size and distribution of Workers, depending on their total number in the network and the expected proportion of Byzantine faulty nodes. All protocol amendments presented in this work are tested with corresponding simulation models and have demonstrated notable enhancements in the performance of the system and decreased the load on network nodes. These improvements will be implemented to the consensus protocol Gozalandia on the Waterfall platform, enhancing its overall reliability, performance, and security. In addition, the presented optimizing algorithm can be applied to a wide range of consensus protocols in blockchains, where blocks must be signed by randomly selected committees to confirm their validity
Nurturing the nurturer: enacting transformative infant-toddler teacher preparation through reflective practice

ABSTRACT This study followed three preservice student teachers as they navigated a semester-long infant-toddler practicum designed to enact a relationship-based approach to teaching and learning. Selected participants were in particular and contrasting placement settings, which served as a catalyst for engaging in critical reflection in multiple ways. Critical reflection was integrated into all course assignments and highlighted within an interactive journaling process. Throughout the practicum, students met together in a weekly seminar with the course instructor, who also served as their field supervisor. Findings uncover multiple ways that critical self-reflection was stimulated in preservice students and the value and interconnection of multiple mentoring practices, including traditional, collaborative, peer and reverse mentoring. While each preservice student teacher engaged in critical reflection in unique ways, they all benefitted from the supportive infrastructure and socially shared, collective reflection with others. This study highlights how critical reflection supported by collaborative dialogue and mentoring can influence transformative practice in caring for infants. Implications for early childhood teacher education and insights for the field are discussed.
ON CERTAIN CLASSES OF p-VALENT ANALYTIC FUNCTIONS

The objective of the present paper is to introduce a certain general class P(p,α,β)(p∈N={1,2,3,…}, 0≤α<p and β≥0) of p-valent analytic functions in the open unit disk U and we prove that if f∈P(p,α,β) then Jp,c(f), defined by Jp,c(f)=c
THE POLICY WHICH MAXIMISES LONG-TERM SURVIVAL OF AN ANIMAL FACED WITH THE RISKS OF STARVATION AND PREDATION

This paper presents a simple model of the decision problems which face many animals in the wild. Animals have an upper limit to the amount of energy reserves which can be stored as body fat. They constantly use energy and find food as a stochastic process. Death occurs if energy reserves fall to zero or if the animal is taken by a predator. Typically animals have a range of behavioural options which differ in the distribution of food gained and in the associated predation risk. There is often a trade-off between starvation and predation in that animals have to expose themselves to higher predation risks in order to gain more food. The above situation is modelled as a finite-state, finite-time-horizon Markov decision problem. The policy which maximises long-term survival probability is characterised. As special cases two trade-offs are analysed. It is shown that an animal should take fewer risks in terms of predation as its reserves increase, and that an animal should reduce the variability of its food supply at the expense of its mean gain as its reserves increase.
Neuroimaging in Alzheimer's Disease for Early Diagnosis: A Comprehensive Review

Alzheimer's disease (AD) is the most common cause of dementia in the elderly, affecting roughly half of those over the age of 85. We briefly discussed the risk factors, epidemiology, and treatment options for AD. The development of therapeutic therapies operating very early in the disease cascade has been spurred by the realization that the disease process begins at least a decade or more before the manifestation of symptoms. Thus, the clinical significance of early diagnosis was emphasized. Using various keywords, a literature search was carried out using PubMed and other databases. For inclusion, pertinent articles were chosen and reviewed. This article has reviewed different neuroimaging techniques that are considered advanced tools to aid in establishing a diagnosis and highlighted the advantages as well as disadvantages of those techniques. Besides, the prevalence of several in vivo biomarkers aided in discriminating affected individuals from healthy controls in the early stages of the disease. Each imaging method has its advantages and disadvantages, hence no single imaging approach can be the optimum modality for diagnosis. This article also commented on a better approach to using these techniques to increase the likelihood of an early diagnosis.
DBT-PAHSE Intervention for Reduce Emotion Dysregulation and Suicide Behavior in Mexican Early Adolescents: A Longitudinal Study

This study aimed to evaluate the effect of time on suicidal behavior, associated risk factors, and protective factors in early Mexican adolescents. Method: With a two-year longitudinal design, which included 18 of 34 adolescents who had previously participated in a DBT skills training program (DBT-PAHSE). The study evaluated ideation, suicide attempt, depression, emotional dysregulation, and psychological resources. Results: We observed differentiating significant differences over time in emotional dysregulation (F = 2.36 p = 0.04, η2= 0.12, β = 0.72), affective resources (F = 3.94, p = 0.01, η2 = 0.18, β = 0.82), and suicidal ideation. (F = 2.55, p = 0.03, η2= 0.13, β = 0.77). In conclusion, the DBT-PAHSE program prevented deaths by suicide. It showed a reduction in emotional dysregulation up to two years after the end of treatment and maintained an increase in emotional and social resources. However, improvements are required to reduce depression over time and strengthen psychological resources.
Subjective Happiness, Frequency of Laughter, and Hypertension: A Cross-Sectional Study Based on the Japan Gerontological Evaluation Study (JAGES)

In recent years, positive psychological factors, such as subjective happiness and laughter, have been reported to be associated with cardiovascular disease. In this study, we examined the relationship of hypertension with subjective happiness and frequency of laughter using the data from the Japan Gerontological Evaluation Study (JAGES). Of the 138,294 respondents, 26,368 responded to a version of the self-administered questionnaire that included a question about the frequency of laughter in the JAGES 2013. In total, 22,503 (10,571 men and 11,932 women) were included in the analysis after excluding those with missing responses regarding a history of hypertension, frequency of laughter, and subjective happiness. The prevalence of hypertension in this study was 10,364 (46.1%). Multivariate logistic regression analysis showed that age, female sex, obesity, infrequent chewing, former and current drinker, seeing three to five friends, and the absence of hobbies were positively associated with hypertension. However, infrequent laughter/high level of subjective happiness, frequent laughter/high level of subjective happiness, being underweight, and current smoker were negatively associated with hypertension. As per the findings of this study, it was determined that subjective happiness was negatively associated with hypertension. Therefore, this study suggests that having more opportunities to feel happiness may be important in preventing hypertension.
Cross-Frequency Coupling and Intelligent Neuromodulation

Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field.
The eect of Minimal Dark Matter annihilation on the hydrogen recombination process

The Minimal Dark Matter model states that the dark matter is a quintuplet of WIMPs added to the Standard Model. No other new physics beyond the Standard Model is added. There is one neutral component in the multiplet (without electrical and weak charge) has a mass of 9.6 TeV/c. The other components are charged and more massive due to a mass splitting ∆M = 166 MeV/c. The neutral component can annihilate into two W -bosons. The bare annihilation cross section of this annihilation is 〈σv〉 = 4.1 · 10−33 cm s−1. The annihilation cross section is enhanced by the Sommerfeld enhancement: a summation over ladder-diagrams corresponding to gauge boson exchanges prior to the annihilation. The Sommerfeld enhancement factor is 1 · 10. Using Pythia we analyze the decay of the two W -bosons produced in the annihilation. Applying the so-called on-the-spot approximation we conclude that 40% of the energy involved in the annihilation process, is transferred on to the electrons and photons of the intergalactic medium during recombination. We modify RecFast to incorporate the extra energy from the dark matter annihilations and calculate the evolution of the free electron fraction during the hydrogen recombination. We obtain a result in which the free electron fraction with dark matter annihilation is not different from the free electron fraction without the annihilation taken into account (e xe ∼ 10 −5). Hence, the annihilation of Minimal Dark Matter does not change the recombination process. The energy release from the annihilations is too small because of a very small annihilation cross section. Adding physics beyond the Standard Model could increase the energy released from dark matter annihilations.
Energy-Efficient Beamforming Design for RIS-Aided MIMO Downlink Communication With SWIPT

This paper investigates an energy/power-efficient simultaneous wireless information and power transfer (SWIPT)-enabled reconfigurable intelligent surface (RIS)-assisted multi-input multi-output (MIMO) communication network, where a base station (BS) communicates with multiple information receivers (IRs) and energy receivers (ERs) simultaneously with the aid of a RIS. We formulate a power minimization problem and jointly optimize the active beamforming matrix at the BS and passive beamforming matrix at the RIS subject to the constraint of the minimum required rate at each IR while ensuring the minimum energy harvesting requirement of ERs. Due to the non-convex nature of the formulated problem, we transform the minimum rate constraint into a simpler form by adopting a mean square error (MSE) based approach. Next, an alternating optimization (AO) method is adopted to transform the main non-convex problem into two simpler sub-problems, for obtaining the optimal active and passive beamforming matrices, respectively. Thereafter, computationally efficient algorithms are proposed to determine the optimal solutions of each sub-problem utilizing optimization tools such as Lagrangian dual decomposition, sub-gradient method, majorization-minimization (MM) and pricing-based approaches. Then, we also propose an AO-based iterative algorithm that obtains the optimal values of both matrices iteratively by repeatedly using the aforementioned sub-optimal solutions. We also discuss the impact of imperfect channel state information (CSI) on the performance of the considered network and demonstrate the effectiveness of the proposed algorithm in providing an efficient beamforming design for the same. In addition to this, we highlight the impact of key system parameters such as the number of reflecting elements, and the minimum rate constraint. Finally, we establish that the use of an RIS in such a network along with the proposed algorithm provides a power saving of approximately 10% −26% compared to the other traditional schemes to achieve the same quality of service.
Implications of Stochastic Singularity in Linear Multivariate Rational Expectations Models

In general, linear multivariate rational expectations models do not have a unique solution. This paper reviews some procedures for determining whether there exists a solution, whether it is unique, and infers on the dimension of indeterminacy and the number of free parameters in a parametrization thereof. A particular emphasis is given to stochastic singularity, i.e. the case in which the number of outputs is strictly larger than the number of (stochastic) inputs. First, it is shown that assuming stochastic singularity of the exogenous driving process has the same effects as (but is more natural than) assuming that some variables are predetermined, i.e have trivial one-step-ahead prediction error. Second, the dimension of the solution set is in general different from the one derived in the case where the number of outputs and inputs coincide. We derive this result in both the framework of [37, 34] (which impose nonexplosiveness conditions) and [9, 11] (which do not impose non-explosiveness conditions). In this context, the results of [34] and [11] are corrected and extended. Last, we note that the framework of [11] can be adjusted to incorporate non-explosiveness conditions and lends itself to an identifiability analysis of dynamic stochastic general equilibrium (DSGE) models.