Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Algorithms based Voiceprint Recognition System in Noisy Environment

Voiceprint Recognition (VPR) is the mechanism by which a user’s so-called identity is determined using characteristics taken from their voice, where this-technique is one of the world’s most useful and common biometric recognition techniques particularly the fields-relevant to security. These can be used for authentication, monitoring, forensic identification of speakers, and a variety of related activities. In this work, an attempt is applied to create a system that recognizes human speaker identity using Convolutional Neural Network (CNN). Two methods are used in this work which are MFCC-CNN and RW-CNN. The first method is standard method using MFCC, to use the features in the audio, where these features are will be entered into CNN to perform a process. The training CNN will take input as a picture and then the process of training via the proposed CNN is beginning. The second method, RW-CNN, the same steps as the first method, but without going through the MFCC phases where direct entry to CNN. In which, the same CNN structure was used in both methods. In this work, a 96% accuracy gained for both RW-CNN and MFCC-CNN. Both methods are similar in their results, either with or without noise, but the performance is mixed. This system can deep learn a large amount of human voices with high accuracy and minimum processes requirement.
Abordaje mínimamente invasivo para el tratamiento de tumores espinales intradurales extramedulares: Nota Técnica

Describir la técnica de abordaje mínimamente invasiva para el tratamiento de tumores intradurales extramedulares en los diferentes segmentos espinales.Se detallan la planificación, posicionamiento, marcación, pasos técnicos del abordaje mínimamente invasivo, exéresis lesional y cierre de lesiones ID-EM a nivel cervical, dorsal, lumbar y sacro. Se proporcionan recomendaciones para descomplejizar maniobras quirúrgicas, acortar el tiempo operativo y evitar potenciales complicaciones.El abordaje MISS es una opción segura y eficaz para el tratamiento quirúrgico de determinados tumores ID-EM.
Reinforcement learning based self-adaptive moving target defense against DDoS attacks

In the research of moving target defense technology, the formulation and evaluation of network offensive and defensive game strategy have become a current research hotspot, but there are still some urgent problems to be solved, such as how to adjust the defense if the attacker’s strategy is constantly changing during the offensive and defensive game strategies to deal with its changes and how to balance system security and system performance when system resources are limited. Aiming at DDoS attacks, this paper proposes a moving target defense adaptive strategy based on reinforcement learning, which can adaptively adjust defense strategies according to changes in environmental conditions, while balancing system security and system performance through parameter adjustments to adapt to different scenarios. The simulation experiment results show that the proposed model and method are feasible and effective.
Two-velocity hydrodynamics in fluid mechanics: global existence for 2D case

In this paper, we consider a compressible–incompressible two-velocity hydrodynamic system studied by Bresch et al (2015 J. Math. Pure Appl. 104 762–800) and Lions (1996 Mathematical Topics in Fluid Mechanics (Oxford: OUP)). When the density ρ is a small perturbation of a constant, we establish a priori estimates by using some delicate structure of the nonlinear terms and Hardy space. By using these a priori estimates, we prove the existence of global strong solutions and weak solutions. Our results do not require any constraint between the viscosity and the conductivity and improve the results of Bresch et al (2015) and Lions (1996) in the two-dimensional case. As an application of our results we also establish global strong solutions and weak solutions for a model of gaseous mixture and the ghost effect system.
Urban Parking Scheme in Hangzhou Based on Reinforcement Learning

With the increasing number of motor vehicles in China, the traditional parking schemes have become inefficient. Traditional parking mainly relies on the driver’s experience and judgment, which makes the search for a parking space difficult; at the same time it will cause traffic congestion nearby. The current parking system in Hangzhou, which includes smart payment, smart service, smart supervision and big data analysis, has largely alleviated the aforementioned difficulties. However, the existing smart parking system still has many shortcomings, such as insufficient coverage of hardware, and the software used is only an information display interface, which cannot make any judgements. This paper discusses a parking scheme based on reinforcement learning, including the application of Q-learning and DQN, in order to improve the performance of the parking system. Q-learning is the most likely method for smart parking, and DQN can also be used to improve real-time judgment. This paper compares traditional parking, smart parking, and smart parking with reinforcement learning, and lists their advantages and disadvantages respectively. The comparison shows that smart parking systems are overall more beneficial than traditional systems.
Adoption PPP model as an alternative method of government for funding abandoned construction projects in Iraq

The Iraqi construction sector is facing complex issues and a number of problems. Abandoned construction projects are considered one of the most common and serious problems in the construction industry in Iraq due to a lack of funding, which reflected by exceeding the time limit for completion of the projects. The number and value of the projects involved are usually large. reconstruction, maintenance, and operation of unfinished projects in Iraq require better coordination between these issues, and the country’s other affairs, this goal, cannot be accomplished unless the project is financing in the most successful way. This study aims to use the analytic hierarchy process (AHP)technique to select the appropriate type of the Public-Private Partnership (PPP) contracts to finance abandoned projects in Iraq. using a questionnaire survey to select the most significant criteria for choosing an appropriate financing approach for the abandoned project. The reliability of the questionnaire is achieved, by using the Cronbach Alpha calculated by SPSS software (Version 26). The results obtained illustrate the final weight of the Build, Operate and Transfer (BOT) contract from AHP is 0.261 is the best type of the (PPP) contracts in the alternatives suggested to finance the project.
Application of a D Number based LBWA Model and an Interval MABAC Model in Selection of an Automatic Cannon for Integration into Combat Vehicles

A decision making procedure for selection of a weapon system involves different, often contradictory criteria and reaching decisions under conditions of uncertainty. This paper proposes a novel multi-criteria methodology based on D numbers which enables efficient analysis of the information used for decision making. The proposed methodology has been developed in order to enable selection of an efficient weapon system under conditions when a large number of hierarchically structured evaluation criteria has to be processed. A novel D number based Level Based Weight Assessment – Multi Attributive Border Approximation area Comparison (D LBWA-MABAC) model is used for selection of an automatic cannon for integration into combat vehicles. Criteria weights are determined based on the improved LBWA-D model. The traditional MABAC method has been further developed by integration of interval numbers. A hybrid D LBWA-MABAC framework is used for evaluation of an automatic cannon for integration into combat vehicles. Nine weapon systems used worldwide have been ranked in this paper. This multicriteria approach allows decision makers to assess options objectively and reach a rational decision regarding the selection of an optimal weapon system. Validation of the proposed methodology is performed through sensitivity analysis which studies how changes in the weights of the best criterion and the elasticity coefficient affect the ranking results.
Numerical Simulation of the Fracture Characteristics of Copper EFP with Different Constitutive Models

With three different constitutive model, LS-DYNA hydro code is applied to simulate the forming and fracture characteristics of Copper EFP. By comparison of numerical results of different models, the appropriate constitutive model to simulate the complete and fracture EFPs can be obtained. By comparison of experimental and numerical results, it can be concluded that Steinberg-Guinan constitutive model gives the best result to predict the complete EFP’s forming shape with minimum error, while Johnson-Cook constitutive model with failure model and h-adaptive algorithm is the most suitable method to predict the fracture of EFP in axial direction. The research results are important and significant in the deign of shaped charge, which could provide a guideline to predict the fracture of EFP in numerical simulation.
PREDICTION OF THE SOLUBILITY OF CAFFEIC ACID IN WATER USING AN ACTIVITY COEFFICIENT MODEL

Solubility of solid compounds is one of the most widely used physicochemical properties in chemical engineering design and experiments. Experimental works for solubility are not always possible because of the small amount of yield available in the phytochemicals extraction. Thus, one interesting perspective is the used of thermodynamic models, which are usually employed for predicting the activity coefficients in the case of solid–liquid equilibria (SLE). Phytochemical compound used in this study is caffeic acid where a comparative study of the MPP-UNIFAC and Pharma Modified UNIFAC were used to predict the solubilities of this phytochemical. The performances of these two activity coefficient models were compared using the experimental solubilities data obtained from the literature in the temperature range of 288 to 323 K and were evaluated by analysing the absolute relative errors (ARE) between the experimental and the predicted values. Moreover, the model errors were also discussed according to the functional groups of the molecules and water as the solvent. In general, the MPP UNIFAC showed better accuracy as compared to the Pharma Modified UNIFAC in predicting the solubility of caffeic acid in water. Nevertheless, both models give very poor qualitative predictions.
Network intrusion detection system using deep neural networks

In recent decades, rapid development in the world of technology and networks has been achieved, also there is a spread of Internet services in all fields over the world. Piracy numbers have increased, also a lot of modern systems were penetrated, so the developing information security technologies to detect the new attack become an important requirement. One of the most important information security technologies is an Intrusion Detection System (IDS) that uses machine learning and deep learning techniques to detect anomalies in the network. The main idea of this paper is to use an advanced intrusion detection system with high network performance to detect the unknown attack package, by using a deep neural network algorithm, also in this model, the attack detection is done by two ways (binary classification and multiclass classification). The proposed system has shown encouraging results in terms of the high accuracy (99.98% with multiclass classification and with binary classification).
Human Mesenchymal Stem Cells being Encapsulated in Alginate/reduced Geraphen Oxide Improving Infarct Expansion, and Inducing Neovasculariziation Formation in Ischemic Myocardium

Currently, one of the new therapeutic strategies is injection of hydrogel and cells to myocardial infarction (MI) patients, which has some limitations such as lack of electromechanical properties and neovascularization. In this study, we investigated the therapeutic potential of new electroactive hydrogel [Reduced graphene oxide (rGO)/Alginate (ALG)] encapsulated human bone marrow mesenchymal stem cell (BMSC) in different experimental groups. The study was done in rat model of chronic ischemic cardiomyopathy by ligating the left anterior descending coronary artery (LAD). Echocardiograms were analyzed at 4 and 8 weeks after MI induction. Experimental groups particularly (BMSC) encapsulated in rGO-ALG increased significantly improvement of fractional shortening (FS) and ejection fraction (EF) compared to the control group. Eighth week after injection, Eosin (H&E) staining was performed. MSC-ALG-rGO demonstrated that significantly increasing vascularization and connective tissue in comparison with control. Masson's trichrome staining indicated reduced scarring/collagen deposition and improved remodeling LV thickness in the MSC-ALG-rGo group. Anti-CD31 antibody applied to detect neovascularization in the experimental groups. Microvessel density was significantly higher in MSC-ALG-rGO treatment than other groups (MSC-ALG and control) (p<0.01). This study demonstrates, use of rGO/ALG a bio-electroactive hydrogel appears safe for intramyocardial injection, improving (LV) function, neovascularization, adjust the electrical properties following MI.
On Hyper-Chordal graphs

Triangulated graphs have many interesting properties (perfection, recognition algorithms, combinatorial optimization algorithms with linear complexity). Hyper-triangulated graphs are those where each induced subgraph has a hyper-simplicial vertex. In this paper we give the characterizations of hyper-triangulated graphs using an ordering of vertices and the weak decomposition. We also offer a recognition algorithm for the hyper-triangulated graphs, the inclusions between the triangulated graphs generalizations and we show that any hyper-triangulated graph is perfect.
Solar Powered Battery Charging System Using Optimized PI Controller for Buck Boost converter

The basic framework for tuning Genetic Algorithm (GA) is to bring the system to stable condition with proportional Integral controller for DC-DC converter (Buck -Boost Converter) which is applied to PV source. Due to its non-linear characteristics, the performance of the controller needs to be improved in order to improve its efficiency. The gating pulses of the converter uses Pulse Width Modulation (PWM) technique in order to reduce its THD level. The main purpose of PI controller is to bring down the large values of instabilities in the system response and to reduce steady state error which in turns improves the gain and accuracy. Genetic Algorithm (GA) is a search algorithm to solve optimization problems. The controller parameters are optimized to produce reasonable transient response without affecting the stable operation. Output voltage magnitude of DC–DC converter is either greater than or lesser than the input voltage magnitude. Variable DC voltage from PV panel is applied to DC – DC converter to get constant DC output voltage. The modelling of the Proposed system is modelled using MATLAB/SIMULINK and its results are verified.
Photoinjection of High Potential Holes into Cu5Ta11O30 Nanoparticles by Porphyrin Dyes

Excited-state hole injection into the valence band of Cu5Ta11O30 nanoparticles (NP-Cu5Ta11O30) was investigated through sensitization with zinc porphyrin dyes using simulated solar irradiance. The Cu5Ta11O30 nanoparticles were prepared by a flux-mediated synthesis and found to have an average particle size of ∼10–15 nm by DLS and TEM. The zinc 4-(10,15,20-tris(4-pyridinyl)-porphin-5-yl)phenylphosphonic acid (D1) and its analogue, in which the pyridine groups are methylated (D2), were synthesized and found to have excited-state reduction potentials appropriate for p-type dye sensitization of the nanoparticles. The dye-sensitized NP-Cu5Ta11O30 exhibited fluorescence quenching consistent with electron transfer from the NP-Cu5Ta11O30 to the dye; forward and recombination rates were obtained by transient absorption measurements. Hole injection times of 8 ps and <100 fs were observed for D1 and D2, respectively. Nanoparticulate films of Cu5Ta11O30 were prepared and evaluated in dye-sensitized solar cells under .
EFEKTIFITAS EDUKASI HEALTH BELIEF MODELS DALAM PERUBAHAN PERILAKU PASIEN HIPERTENSI: LITERATUR REVIEW

Hipertensi adalah keadaan dimana tekanan darah meningkat secara kronis karena jatung memompa darah lebih kuat untuk memenuhi kebutuhan tubuh. Hipertensi dikatakan sebagai penyakit silent killer dikarenakan hipertensi ini merupakan penyakit yang terkadang tidak menunjukkan gejala namun dapat menimbulkan komplikasi yang membahayakan bahkan secara tiba-tiba dapat mengakibatkan kematian. Health Belief Model (HBM) merupakan salah satu pendekatan promosi kesehatan yang digunakan dalam perubahan perilaku yang berorientasi terhadap persepsi pasien. HBM telah lama dikembangkan, namun tampaknya hanya sampai dengan penelitian, sementara implementasi di lapangan masih jarang dilakukan. Tujuan penelitian adalah untuk mengetahui efektifitas edukasi health belief models dalam perubahan perilaku pasien hipertensi. Metode penelitian yang digunakan aadalah penelitian sekunder dengan literature reviews. Populasi sebanyak 168 artikel jurnal dengan sampel yang memenuhi kriteria inklusi sebanyak 15 artikel jurnal dan prosiding nasional dan internasional dengan search engine proquest dan Google Schoolar. Hasil penelitian menunjukkan bahwa edukasi HBM efektif dalam meningkatkan perilaku sehat pasien hipertensi. Disarankan hasil penelitian sekunder ini dapat dijadikan dasar teori bagi penelitian yang akan datang untuk dilanjutkan sebagai penelitian primer.
“A Randomized Comparison of Sample Adequacy and Diagnostic Yield of EBUS-TBNA using various Suction Pressures”

Anant Mohan (  anantmohan@yahoo.com ) All India Institute of Medical Sciences https://orcid.org/0000-0002-2383-9437 Hariharan Iyer All India Institute of Medical Sciences Karan Madan All India Institute of Medical Sciences Vijay Hadda All India Institute of Medical Sciences Saurabh Mittal All India Institute of Medical Sciences Pawan Tiwari All India Institute of Medical Sciences Deepali Jain All India Institute of Medical Sciences R M Pandey All India Institute of Medical Sciences Avneet Garg All India Institute of Medical Sciences Randeep Guleria All India Institute of Medical Sciences
Role of Probiotics and Their Metabolites in Inflammatory Bowel Diseases (IBDs)

Inflammatory Bowel Disease (IBD) is a term used to describe a group of complex disorders of the gastrointestinal (GI) tract. IBDs include two main forms: Crohn’s Disease (CD) and Ulcerative Colitis (UC), which share similar clinical symptoms but differ in the anatomical distribution of the inflammatory lesions. The etiology of IBDs is undetermined. Several hypotheses suggest that Crohn’s Disease and Ulcerative Colitis result from an abnormal immune response against endogenous flora and luminal antigens in genetically susceptible individuals. While there is no cure for IBDs, most common treatments (medication and surgery) aim to reduce inflammation and help patients to achieve remission. There is growing evidence and focus on the prophylactic and therapeutic potential of probiotics in IBDs. Probiotics are live microorganisms that regulate the mucosal immune system, the gut microbiota and the production of active metabolites such as Short-Chain Fatty Acids (SCFAs). This review will focus on the role of intestinal dysbiosis in the immunopathogenesis of IBDs and understanding the health-promoting effects of probiotics and their metabolites.
The family factor: How collaborative dialogue between owner managers and the owner family shapes firm-level outcomes

ABSTRACT The influence of the owner family on managerial decision-making in family firms is undisputed. However, we know relatively little about how this influence is exerted. Based on upper echelon theory, and in the context of innovation management, we explore the owner family’s influence via collaborative dialogue. Quantitative analyses based on 116 family firms, substantiated by 12 qualitative interviews within seven family firms, show that an owner family’s collaborative dialogue minimizes the influence of an owner manager’s attitude toward risk on TMT innovativeness. On the other hand, the collaborative dialogue strengthens the impact TMT innovativeness has on firm-level innovative capacity.
Antioxidant and Anti-Inflammatory Activities of Aralia Cachemirica Decne.: A Comparative Study

Background: Aralia cachemirica Decne. is an important medicinal plant species of Kashmir Himalaya. Regardless of having enormous medicinal importance little information exists on the biological activities of the species. For this reason, present work was carried out for providing comprehensive details on the antioxidant and anti-inflammatory activities of different parts of A. cachemirica.Methods: In this connection, different parts (leaves, stem and root) of the plant were examined following standard methodologies. Antioxidant potential of the plant was determined by three different standard methodologies viz. DPPH radical scavenging activity, reducing power activity and metal chelating activity. For anti-inflammatory activities two different standard methods viz. human RBC membrane stabilization method and protein denaturation method were followed.Results: The extracts exhibited concentration dependent antioxidant and anti-inflammatory activities but were weaker than the standards. Also, the highest activities were recorded in the root and least in the leaves. Also, the highest activities were recorded in the leaves and least in the stem.Conclusion: This is the first study providing information regarding antioxidant and anti-inflammatory potential of different parts of the species. The antioxidant activity of the extract could have great importance as therapeutic agents in preventing or slowing the progress of aging and age associated, oxidative stress related degenerative diseases. Plant derived anti-inflammatory agents are competent cure for inflammation caused by a range of agents, which shows the way to their soaring requirement. Exploitation of these agents requires to be regulated in order, to be sustainable and keep up with demands.
Factors Determining Mathematics Performance of High School Students: Implications for Teacher Education

The alignment between the goals of pre-service education and that of in-service education is important. Institutions which offer a degree in education must constantly connect and understand the experiences of the teachers and learners in the elementary and secondary schools so that they can offer better undergraduate programs. This study explored how critical thinking skills of secondary students contribute to their Mathematics performance. There were 236 secondary student participants in this study from a private high school in Mindanao, Philippines. Correlational design was utilized in this study. A questionnaire was used to gather data on critical thinking skills of the students. The questionnaire has undergone Rasch analysis with reliability indices of . 88 (high) for Part 1 and . 77 (moderate) for Part 2 thus establishing preliminary evidence of construct validity. Using path analysis, the model showed that critical thinking skills significantly explained the Mathematics performance of the participants. The results called for a need to address the teaching of critical thinking skills among secondary students because this will benefit the facilitation of better Mathematics performance. Critical thinking skills must be taught alongside Mathematics content. Teachers carry out multiple roles of delivering the content at the same time honing the critical thinking skills of the students. Undergraduate education specifically for teacher education has an important role to play in preparing future teachers on how to develop critical thinking skills of their students. It is a two-way task of developing future teachers to be critical thinkers and teaching them how to teach such skills to their students so that when they are in the field of teaching, they teach both content and critical thinking skills.