Miracle-dz/UniFAR-FTUs huggingface dataset:
PENGARUH KEPERCAYAAN, KEMUDAHAN DAN PERSEPSI RISIKO TERHADAP KEPUTUSAN PEMBELIAN ONLINE

Penelitian ini ditujukan kepada masyarakat di daerah Kabupaten Pasaman Barat. Tujuan dari penelitian ini adalah: (1) Untuk mengetahui Pengaruh Kepercayaan Terhadap Keputusan Pembelian Online, (2) Untuk mengetahui Pengaruh Kemudahan Terhadap Keputusan PembelianOnline, (3) Untuk mengetahui Pengaruh Persepsi Risiko Terhadap Keputusan PembelianOnline, dan (4) Untuk mengetahui Pengaruh Kepercayaan, Kemudahan dan Persepsi Risiko Terhadap keputusan Pembelian Online Pada situs Tokopedia.com di Kabupaten Pasaman Barat. Populasi yang digunakan sebanyak 100 sanpel, karena peneliti menggunakan metode sensus, sehingga jumlah populasi yang dijadikan sampel sebanyak 100 orang. Metode pengumpulan data yang digunakan oleh peneliti yaitu melalui kuesioner sedangkan metode analisis yang digunakan adalah analisis deskriptif dan regresi linear berganda dengan menggunakan SPSS for Windows Realasa 16.0. Hasil penelitian ini menunjukkan adanya hubungan antara variabel independen terhadap variabel dependen. Dan dijelaskan bahwa: (1) Variabel Kepercayaan berpengaruh positif dan tidak signifikan terhadap Keputusan Pembelian Di Tokopedia.com, dengan koefesien regresi 0.144 dengan signifikan terhadap Keputusan Pembelian 0.886 (>0.05), (2) variabel Variabel Kemudahan berpengaruh positif dan signifikan terhadap Keputusan Pembelian dengan koefesien regresi 5.852 dengan nilai signifikan 0.000 (<0.05), (3) Variabel Persepsi Risiko berpengaruh secara positif dan signifikan terhadap Keputusan Pembelian dengan nilai koefesien regresi 3.352 dengan nilai signifikan 0.001, (4) Variabel Independen secara bersamaan berpengaruh positif dan signifikan terhadap keputusan pembelian dengan nilai koefesien regresi 75.130 dengan nilai signifikan 0.000 (<0.05). Nilai R2 menunjukan 0.701 dan sisanya sebesar 0.299 dipengaruhi oleh variabel lain yang tidak diikut sertakan dalam penelitian ini.
Uncoupling Protein-1 Modulates Anxiety-Like Behavior in a Temperature-Dependent Manner

A strong bidirectional link between metabolic and psychiatric disorders exists; yet, the molecular basis underlying this interaction remains unresolved. Here we explored the role of the brown adipose tissue (BAT) as modulatory interface, focusing on the involvement of uncoupling protein 1 (UCP-1), a key metabolic regulator highly expressed in BAT, in the control of emotional behavior. Male and female constitutive UCP-1 knock-out (KO) mice were used to investigate the consequences of UCP-1 deficiency on anxiety-related and depression-related behaviors under mild thermogenic (23°C) and thermoneutral (29°C) conditions. UCP-1 KO mice displayed a selective enhancement of anxiety-related behavior exclusively under thermogenic conditions, but not at thermoneutrality. Neural and endocrine stress mediators were not affected in UCP-1 KO mice, which showed an activation of the integrated stress response alongside enhanced fibroblast-growth factor-21 (FGF-21) levels. However, viral-mediated overexpression of FGF-21 did not phenocopy the behavioral alterations of UCP-1 KO mice and blocking FGF-21 activity did not rescue the anxiogenic phenotype of UCP-1 KO mice. No effects of surgical removal of the intrascapular BAT on anxiety-like behavior or FGF-21 levels were observed in either UCP-1 KO or WT mice. We provide evidence for a novel role of UCP-1 in the regulation of emotions that manifests as inhibitory constraint on anxiety-related behavior, exclusively under thermogenic conditions. We propose this function of UCP-1 to be independent of its activity in the BAT and likely mediated through a central role of UCP-1 in brain regions with converging involvement in energy and emotional control. SIGNIFICANCE STATEMENT In this first description of a temperature-dependent phenotype of emotional behavior, we propose uncoupling protein-1 (UCP-1), the key component of the thermogenic function of the brown adipose tissue, as molecular break controlling anxiety-related behavior in mice. We suggest the involvement of UCP-1 in fear regulation to be mediated through its expression in brain regions with converging roles in energy and emotional control. These data are important and relevant in light of the largely unexplored bidirectional link between metabolic and psychiatric disorders, which has the potential for providing insight into novel therapeutic strategies for the management of both conditions.
Event-Triggered Adaptive Output Feedback Control for Stochastic Nonlinear Systems With Time-Varying Full-State Constraints

This brief investigates the issue of event-triggered adaptive output feedback control for stochastic nonlinear systems with time-varying full-state constraints. Firstly, the unmeasurable states are estimated by the fuzzy observer. Secondly, the quartic time-varying barrier Lyapunov functions are constructed to avoid the violation of time-varying constraints. Thirdly, the command filter technique and error compensation mechanism are incorporated into the controller design to get over the issue of “explosion of complexity” and compensate the filtered errors. Then, the event-triggered mechanism is introduced to improve the efficiency in resource utilization. It is shown that the tracking error can converge to the desired neighborhood of the origin and all the signals in the closed-loop systems are bounded. Finally, the validity of the control strategy is demonstrated by a physical example.
Recombinant Mammalian Prions: The “Correctly” Misfolded Prion Protein Conformers

Generating a prion with exogenously produced recombinant prion protein is widely accepted as the ultimate proof of the prion hypothesis. Over the years, a plethora of misfolded recPrP conformers have been generated, but despite their seeding capability, many of them have failed to elicit a fatal neurodegenerative disorder in wild-type animals like a naturally occurring prion. The application of the protein misfolding cyclic amplification technique and the inclusion of non-protein cofactors in the reaction mixture have led to the generation of authentic recombinant prions that fully recapitulate the characteristics of native prions. Together, these studies reveal that recPrP can stably exist in a variety of misfolded conformations and when inoculated into wild-type animals, misfolded recPrP conformers cause a wide range of outcomes, from being completely innocuous to lethal. Since all these recPrP conformers possess seeding capabilities, these results clearly suggest that seeding activity alone is not equivalent to prion activity. Instead, authentic prions are those PrP conformers that are not only heritable (the ability to seed the conversion of normal PrP) but also pathogenic (the ability to cause fatal neurodegeneration). The knowledge gained from the studies of the recombinant prion is important for us to understand the pathogenesis of prion disease and the roles of misfolded proteins in other neurodegenerative disorders.
A Deep Learning Model for Remaining Useful Life Prediction of Aircraft Turbofan Engine on C-MAPSS Dataset

In the era of industry 4.0, safety, efficiency and reliability of industrial machinery is an elementary concern in trade sectors. The accurate remaining useful life (RUL) prediction of an equipment in due time allows us to effectively plan the maintenance operation and mitigate the downtime to raise the revenue of business. In the past decade, data driven based RUL prognostic methods had gained a lot of interest among the researchers. There exist various deep learning-based techniques which have been used for accurate RUL estimation. One of the widely used technique in this regard is the long short-term memory (LSTM) networks. To further improve the prediction accuracy of LSTM networks, this paper proposes a model in which effective pre-processing steps are combined with LSTM network. C-MAPSS turbofan engine degradation dataset released by NASA is used to validate the performance of the proposed model. One important factor in RUL predictions is to determine the starting point of the engine degradation. This work proposes an improved piecewise linear degradation model to determine the starting point of deterioration and assign the RUL target labels. The sensors data is pre-processed using the correlation analysis to choose only those sensors measurement which have a monotonous behavior with RUL, which is then filtered through a moving median filter. The updated RUL labels from the degradation model together with the pre-processed data are used to train a deep LSTM network. The deep neural network when combined with dimensionality reduction and piece-wise linear RUL function algorithms achieves improved performance on aircraft turbofan engine sensor dataset. We have tested our proposed model on all four sub-datasets in C-MAPSS and the results are then compared with the existing methods which utilizes the same dataset in their experimental work. It is concluded that our model yields improvement in RUL prediction and attains minimum root mean squared error and score function values.
Applications of Wavelet Bases to The Numerical Solutions of Fractional PDEs

For describing the probability distribution of the positions and times of particles performing anomalous motion, fractional PDEs are derived from the continuous time random walk models with waiting time distribution having divergent first order moment and/or jump length distribution which has divergent second order moment. It can be noted that the fractional PDEs are essentially dealing with the multiscale issues. Generally the regularity of the solutions for fractional PDEs is weak at the areas close to boundary and initial time. This paper focuses on developing the applications of wavelet bases to numerically solving fractional PDEs and digging out the potential benefits of wavelet methods comparing with other numerical methods, especially in the aspects of realizing preconditioning, adaptivity, and keeping the Toeplitz structure. More specifically, the contributions of this paper are as follows: 1. the techniques of efficiently generating stiffness matrix with computational cost $\mathcal{O}(2^J)$ are provided for first, second, and any order bases; 2. theoretically and numerically discuss the effective preconditioner for time-independent equation and multigrid method for time-dependent equation, respectively; 3. the wavelet adaptivity is detailedly discussed and numerically applied to solving the time-dependent (independent) equations. In fact, having reliable, simple, and local regularity indicators is the striking benefit of the wavelet in adaptively solving fractional PDEs (it seems hard to give a local posteriori error estimate for the adaptive finite element method because of the global property of the operator).
Building Climate Resilience in Coastal City Living Labs Using Ecosystem-Based Adaptation: A Systematic Review

Climate change leads to an unequivocal rise in the intensity and frequency of natural disasters. This necessitates mainstreaming of climate adaptation strategies in the global movement on climate action. Ecosystem-Based Adaptation (EBA) has become popular as an effective means of climate adaptation, which can be resilient and flexible compared to hard engineering-based measures. However, ecosystem-based approaches in disaster risk reduction still remain under-researched despite their growing popularity. This study delves into the utility of EBA in the context of the living lab, using a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) based Systematic Literature Review. A living lab (LL) is a participatory tool invented to foster innovation through real-life testing environments, such as individual cities. This study focuses on European coastal regions, as these are both highly populated and vulnerable to climate change impacts such as sea-level rise, storms, flooding and erosion. This study identified multiple synergies between the EBA concepts, living lab and disaster-risk reduction and concludes that EBA schemes can be highly effective in the living lab set-up. It also demonstrates that increased stakeholder engagement and the consideration of socio-economic co-benefits as part of the EBA-LL model can lead to successful disaster risk reduction.
Remembering for Relationships: Brief Cognitive-Reminiscence Therapy Improves Young Adults’ Perceptions About Self and Others in Social Relationships

Reminiscence-based interventions involve the guided recall and interpretation of autobiographical memories to promote adaptive thinking. This study involved secondary analyses of a recent trial of a positively focused, group-based, three-session version of cognitive-reminiscence therapy (CRT) on perceptions of general relationship quality and interpersonally related psychological resources in young adults. A community sample (N = 62, Mage = 24.6 (SD = 3.1), 71% identified as cisgender women and 29% identified as cisgender men) of young adults were randomised into a CRT and wait-list condition. Participants completed assessments measuring perceived general relationship quality (relationship satisfaction, emotional intimacy, commitment, and trust) and perceptions of self and others within relationships (relationship self-esteem, relationship self-efficacy, relationship optimism and meaning in relationships). The CRT group, relative to the control group, scored significantly higher on perceived relationship quality (d = .62), and higher on generalized relationship self-efficacy (d = .70), relationship self-esteem (d = .59), and relationship optimism (d = .57) at the follow-up. Group differences for relationship meaning were not statistically significant (small to moderate). A brief, positive-focused intervention of guided recall of autobiographical memories led to more adaptive perceptions about self and others within the context of relationships in young adults. Replication studies with larger samples are needed, and outside of a pandemic lockdown context. Future research may expand CRT to target other relationship variables, assess the impacts of different doses, explore relationships in specific populations, and better understand the mechanisms for change.
HRV-Guided Training for Elders after Stroke: A Protocol for a Cluster-Randomized Controlled Trial

There are many consequences associated with having a stroke, all of which are important factors affecting long-term rehabilitation outcomes; these become important health issues for those of advanced age and require dedicated health strategies. High-intensity interval training (HIIT) is an effective training protocol used in cardiac rehabilitation programs; however, owing to the inter-individual variability in physiological responses to training associated with cardiovascular diseases, the exercise regimen given to each patient should be closely controlled and individualized to ensure the safety and efficiency of the exercise program. Heart rate variability (HRV) is currently being used for this purpose, as it is closely linked to parasympathetic nervous system activation, with high HRV scores associated with good cardiovascular adaptation. The objective of this protocol is to determine the effect of HIIT compared to HRV-guided training in terms of cardiorespiratory fitness, heart rate variability, functional parameters, body composition, quality of life, inflammatory markers, and cognitive function in patients who have had a stroke, as well as to assess the feasibility of patients undertaking an 8-week cardiac rehabilitation program, evaluating its safety and their adherence. The proposed protocol involved cluster-randomized controlled design in which the post-stroke patients are assigned either to an HRV-based training group (HRV-G) or a HIIT-based training group (HIIT-G). HIIT-G will train according to a predefined training program, whereas HRV-G will train depending on the patients’ daily HRV. The outcomes considered are peak oxygen uptake (VO2peak), endothelial and work parameters, heart rate variability, functional parameters, relative weight and body fat distribution, quality of life, inflammatory markers and cognitive function, as well as exercise adherence, feasibility, and safety. It is expected that this HRV-guided training protocol will improve functional performance in patients following a stroke and be safer, more feasible, and generate improved adherence relative to HIIT, providing an improved strategy for to optimize cardiac rehabilitation interventions.
APPLICATION OF BOX-BEHNKEN, ANN, AND ANFIS TECHNIQUES FOR IDENTIFICATION OF THE OPTIMUM PROCESSING PARAMETERS FOR FDM 3D PRINTING PARTS

Fused Deposition Modeling, among the various 3D printing approaches, is becoming more and more popular because of its capacity to produce complicated parts quickly. The tensile strength of parts printed with polylactic acid (PLA) showed a significant variation of many factors such as printing speed, printing temperature, printing angle and infill pattern. This study presented an experimental investigation of collecting data with four input factors namely printing speed, printing temperature, printing angle and infill pattern with the tensile strength response. The research methodology of the RSM Box-Behnken DOE method, ANN (Artificial neural network), and ANFIS (Adaptive neuro-fuzzy inference systems) has been used to determine the optimum process 3D printing parameters. The obtained results based on RSM, ANN and ANFIS methods are used to predict the tensile strength of 3D printed FDM details. The best tensile value is 7,03303 MPa corresponding to print speed of 30,0003 mm/s, printing temperature of 211,594℃, printing angle of 90° with Honeycomb” infill printing pattern. Moreover, the results also highlighted that ANFIS is potential approach for forecasting the tensile strength of 3D printing parts more competitively.
Plasmonic generation of attosecond pulses and attosecond imaging of surface plasmons

Attosecond pulses are ultrashort radiation bursts produced via high harmonic generation (HHG) during a highly nonlinear excitation process driven by a near infrared (NIR) laser pulse. Attosecond pulses can be used to probe the electron dynamics in ultrafast processes via the attosecond streaking technique, with a resolution on the attosecond time scale. In this thesis it is shown that both the generation of attosecond (AS) pulses and the probing of ultrafast processes by means of AS pulses, can be extended to cases in which the respective driving and streaking fields are produced by surface plasmons excited on nanostructures at NIR wavelengths. Surface plasmons are optical modes generated by collective oscillations of the surface electrons in resonance with an external source. In the first part of this thesis, the idea of high harmonic generation (HHG) in the enhanced field of a surface plasmon is analyzed in detail by means of numerical simulations. A NIR pulse is coupled into a surface plasmon propagating in a hollow core tapered waveguide filled with noble gas. The plasmon field intensity increases for decreasing waveguide radius, such that at the apex the field enhancement is sufficient for producing high harmonic radiation. It is shown that with this setup it is possible to generate isolated AS pulses with outstanding spatial and temporal structure, but with an intensity of orders of magnitude smaller than in standard gas harmonic arrangements. In the second part, an experimental technique for the imaging of surface plasmonic excitations on nanostructured surfaces is proposed, where AS pulses are used to probe the surface field by means of photoionization. The concept constitutes an extension of the attosecond streak camera to ``Attosecond Photoscopy'', which allows space- and time-resolved imaging of the plasmon dynamics during the excitation process. It is numerically demonstrated that the relevant parameters of the plasmonic resonance buildup phase can be determined with subfemtosecond precision. Finally, the method used for the numerical solution of the Maxwell's equations is discussed, with particular attention to the problem of absorbing boundary conditions. New insights into the mathematical formulation of the absorbing boundary conditions for Maxwell's equations are provided.
The Transport Properties of Semi-Crystalline Polyetherimide BPDA-P3 in Amorphous and Ordered States: Computer Simulations

The effect of polymer chain ordering on the transport properties of the polymer membrane was examined for the semi-crystalline heterocyclic polyetherimide (PEI) BPDA-P3 based on 3,3′,4,4′-biphenyltetracarboxylic dianhydride (BPDA) and diamine 1,4-bis [4-(4-aminophenoxy)phenoxy]benzene (P3). All-atom Molecular Dynamics (MD) simulations were used to investigate the gas diffusion process carried through the pores of a free volume several nanometers in size. The long-term (~30 μs) MD simulations of BPDA-P3 were performed at T = 600 K, close to the experimental value of the melting temperature (Tm ≈ 577 K). It was found during the simulations that the transition of the PEI from an amorphous state to an ordered one occurred. We determined a decrease in solubility for both the gases examined (CO2 and CH4), caused by the redistribution of free volume elements occurring during the structural ordering of the polymer chains in glassy state (Tg ≈ 481 K). By analyzing the diffusion coefficients in the ordered state, the presence of gas diffusion anisotropy was found. However, the averaged values of the diffusion coefficients did not differ from each other in the amorphous and ordered states. Thus, permeability in the observed system is primarily determined by gas solubility, rather than by gas diffusion.
Effectiveness of a Multimodal Intervention on Social Climate (School and Family) and Performance in Mathematics of Children with Attention Deficit/Hyperactivity Disorder

This study analyzes the differential efficacy of a multimodal versus pharmacological intervention in isolation to improve the social climate (school and family) and the performance in mathematics of a sample of 20 children with ADHD aged 7 to 9 years. The multimodal intervention was based on a training program for 20 parents and 20 teachers in the management of ADHD during a school year, in combination with stimulant medication. The results evidenced the superiority of the multimodal intervention compared to the isolated pharmacological intervention to improve various variables of the family climate (Cohesion; Expressiveness; Autonomy and Control), of the school climate (Help; Tasks; Competitiveness; Organization, Clarity and Control), as well as their academic performance in the curricular area of mathematics. Our findings support the need to intervene in the significant contexts in which children with ADHD develop in order to improve their quality of life.