Miracle-dz/UniFAR-FTUs huggingface dataset:
Gang Membership among Malaysian Indian Young Adults

Young people involvement in gang crime is not only a problem in Western countries but also in middle and low-income countries. Globally, gangsterism has become a threat to the well-being of young adults. Therefore, the present study aims to explore the issue of gang membership, especially among Indian young adults in Kuala Garing Park, in Rawang. An in-depth interview approach was used to identify the demographic profile of the gang members and explore the risk factors that promote gang membership. The present study’s findings show that the majority of the respondents were single, male, drug dealers, aged between 23 and 24 years old, and had completed their education up to lower secondary level. The findings also show that the respondents were involved in gangsterism due to various reasons such as peer influence, poor relationships with parents, financial deprivation, and a desire to gain power and esteem in society. Since gangsterism is a sophisticated and multidimensional phenomenon, it is therefore recommended that collaboration and synergy among families, schools, social support groups, and law enforcement agencies are essential to create a comprehensive approach to tackle gangsterism issues. Through early intervention, community engagement, and targeted enforcement we can work towards reducing the allure of gangs and creating safer and healthier communities for everyone.
Frequency-Domain-Equalization-based Full-Duplex Receiver with Passive-Frequency-Shifting N-Path Filters Achieving >53 dB SI Suppression Across 160 MHz BW

Wideband full-duplex (FD) transceivers pose a significant challenge as they require >100dB of self-interference cancellation (SIC) over large bandwidths. This work utilizes (i) a near-zero-power rotary clock-path passive frequency shifting technique for N-path filters while requiring only a single common LO signal across all filters, and (ii) a closed-loop adaptation algorithm to find the optimal configurations of various RF canceler filters using analytical modelling of tap non-idealities caused by frequency shifting and quality factor variations. The FD receiver achieves (i) tunable operation from 200MHz to 1GHz, (ii) wideband SI suppression of up to 53dB across 160MHz BW when operating at 720MHz (4.44x more fractional bandwidth (FBW) compared to [1]), (iii) a power consumption of 1.8mW/DoF (degree of freedom for each tap, almost 2x better than [1]), while (iv) handling TX power of up to +15dBm across an initial circulator isolation of 26dB.
An Adaptive Interval Construction Based GRU Model for Short-Term Wind Speed Interval Prediction Using Two Phase Search Strategy

The application of wind power is greatly restricted due to the volatility and intermittency of wind. It is a challenging task to quantify the uncertainty of wind speed prediction. To tackle such a challenge, an adaptive interval construction-based gated recurrent unit (GRU) model is proposed for directly generating short-term wind speed prediction intervals in this article, using the two phase search strategy to search the model parameters. Different from the traditional interval prediction techniques, in the proposed model an adaptive interval construction method is designed, where the target values of wind speed are characterized by two interval width adjustment variables which are used to determine the lower and upper bounds of the interval of wind speed. A two phase search strategy is designed to optimize the parameters. In Phase I, the dynamic inertia weight particle swarm optimization algorithm is used to search the two interval width adjustment variables. In Phase II, the GRU networks are trained using the root mean square prop (RMSProp) algorithm (an effective gradient-based optimizer) to fit the upper and lower bounds of the constructed intervals, respectively. The two phases are executed alternately, so as to obtain optimal prediction intervals. The performance of the proposed method is compared with eight other machine learning and deep learning methods, and the experimental results show that the proposed method outperforms the compared methods. It indicates that the proposed method can generate satisfactory and better prediction intervals compared with other methods.
Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications

Above-ground biomass (AGB) serves as an indicator of crop growth status, and acquiring timely AGB information is crucial for estimating crop yield and determining appropriate water and fertilizer inputs. Unmanned Aerial Vehicles (UAVs) equipped with RGB cameras offer an affordable and practical solution for efficiently obtaining crop AGB. However, traditional vegetation indices (VIs) alone are insufficient in capturing crop canopy structure, leading to poor estimation accuracy. Moreover, different flight heights and machine learning algorithms can impact estimation accuracy. Therefore, this study aims to enhance wheat AGB estimation accuracy by combining VIs, crop height, and texture features while investigating the influence of flight height and machine learning algorithms on estimation. During the heading and grain-filling stages of wheat, wheat AGB data and UAV RGB images were collected at flight heights of 30 m, 60 m, and 90 m. Machine learning algorithms, including Random Forest Regression (RFR), Gradient Boosting Regression Trees (GBRT), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso) and Support Vector Regression (SVR), were utilized to construct wheat AGB estimation models. The research findings are as follows: (1) Estimation accuracy using VIs alone is relatively low, with R2 values ranging from 0.519 to 0.695. However, combining VIs with crop height and texture features improves estimation accuracy, with R2 values reaching 0.845 to 0.852. (2) Estimation accuracy gradually decreases with increasing flight height, resulting in R2 values of 0.519–0.852, 0.438–0.837, and 0.445–0.827 for flight heights of 30 m, 60 m, and 90 m, respectively. (3) The choice of machine learning algorithm significantly influences estimation accuracy, with RFR outperforming other machine learnings. In conclusion, UAV RGB images contain valuable crop canopy information, and effectively utilizing this information in conjunction with machine learning algorithms enables accurate wheat AGB estimation, providing a new approach for precision agriculture management using UAV remote sensing technology.
Understanding of Interactions for Bubbles Generated at Neighboring Nucleation Sites

ABSTRACT Bubble dynamics and site interaction are studied numerically by solving conservation of mass, momentum, and energy equations for the liquid and vapor phases. The liquid-vapor interface is tracked by a volume of fluid method. The effects of applied mean heat flux, site-site interactions, orientation of the boiling surface, inclination of nucleating tunnels, and reentrant cavity shape on the bubble dynamics are investigated. With the increase of mean heat flux three separate regimes namely surface tension driven interaction, no interaction and instability driven interaction between the bubbles were shown. Closer nucleation sites fascilitates bubble merging whereas sites away from each other behaves like individual bubble release. Critical spacing between the sites are predicted bifurcating interaction and no interaction zones separately. Site-site interaction patterns are also tested on inclined planes and critical inclination angle has been reported beyond which columnar bubble formation is observed as a result of merging. Using numerical simulation efforts have been also made to check suitability of site inclination and base pocket at the end of the tunnel to generate high heat transfer coefficient. Proposal of reentrant cavity base geometry matches well with literature.
A new scaling for divertor detachment

The ITER design, and future reactor designs, depend on divertor ‘detachment,’ whether partial, pronounced or complete, to limit heat flux to plasma-facing components and to limit surface erosion due to sputtering. It would be valuable to have a measure of the difficulty of achieving detachment as a function of machine parameters, such as input power, magnetic field, major radius, etc. Frequently the parallel heat flux, estimated typically as proportional to Psep/R or PsepB/R, is used as a proxy for this difficulty. Here we argue that impurity cooling is dependent on the upstream density, which itself must be limited by a Greenwald-like scaling. Taking this into account self-consistently, we find the impurity fraction required for detachment scales dominantly as power divided by poloidal magnetic field. The absence of any explicit scaling with machine size is concerning, as Psep surely must increase greatly for an economic fusion system, while increases in the poloidal field strength are limited by coil technology and plasma physics. This result should be challenged by comparison with 2D divertor codes and with measurements on existing experiments. Nonetheless, it suggests that higher magnetic field, stronger shaping, double-null operation, ‘advanced’ divertor configurations, as well as alternate means to handle heat flux such as metallic liquid and/or vapor targets merit greater attention.
Dynamic attentional mechanisms of creative cognition

In popular imagination creativity requires us to surrender control. Yet, attention is at the heart of control, and many studies show attention to play a key role in the creative process. This is partly due to the selective nature of attention—creative cognition consists of two phases, idea generation and idea evaluation, and selective processes are essential for both phases. Here, we investigate attentional (i.e., selective) mechanisms underlying each phase, using the framework of two major attention taxonomies: top-down/bottom-up and internal/external attention. We argue that creative cognition is supported by a dynamic interplay between the typically opposing sides of each taxonomy. Further, we argue that this dynamic relationship is reflected in interactions across three large-scale brain networks: the default mode (DMN), frontoparietal control (FPN), and salience (SN) networks. Our review of the evidence suggests that creative cognition is best achieved through the flexible use of multiple forms of attention, rather than through reduced attention. We thus propose a two-dimensional space, including one dimension for top-down/bottom-up attention and another for internal/external attention, which can sufficiently capture the flexibility and diversity of attentional mechanisms underlying different stages and components of creative cognition.
Measurements of plasma density profile evolutions with a channel-guided laser

Abstract The discharged capillary plasma channel has been extensively studied as a high-gradient particle acceleration and transmission medium. A novel measurement method of plasma channel density profiles has been employed, where the role of plasma channels guiding the advantages of lasers has shown strong appeal. Here, we have studied the high-order transverse plasma density profile distribution using a channel-guided laser, and made detailed measurements of its evolution under various parameters. The paraxial wave equation in a plasma channel with high-order density profile components is analyzed, and the approximate propagation process based on the Gaussian profile laser is obtained on this basis, which agrees well with the simulation under phase conditions. In the experiments, by measuring the integrated transverse laser intensities at the outlet of the channels, the radial quartic density profiles of the plasma channels have been obtained. By precisely synchronizing the detection laser pulses and the plasma channels at various moments, the reconstructed density profile shows an evolution from the radial quartic profile to the quasi-parabolic profile, and the high-order component is indicated as an exponential decline tendency over time. Factors affecting the evolution rate were investigated by varying the incentive source and capillary parameters. It can be found that the discharge voltages and currents are positive factors quickening the evolution, while the electron-ion heating, capillary radii and pressures are negative ones. One plausible explanation is that quartic profile contributions may be linked to plasma heating. This work helps one to understand the mechanisms of the formation, the evolutions of the guiding channel electron-density profiles and their dependences on the external controllable parameters. It provides support and reflection for physical research on discharged capillary plasma and optimizing plasma channels in various applications.
Stochastic graph models with phase type distributed edge weights

Most stochastic shortest path problems include an assumption of independent weights at edges. For many practical problems, however, this assumption is often violated indicating an increased number of applications with stochastic graphs where edge weights follow a distribution that has a possible correlation with weights at adjacent edges. Real-world information in conjunction with existing dependencies in networks can significantly contribute to the computation of the optimal solution to stochastic shortest path problems. For example, the knowledge of a congestion arising on the current road results in a better traveler’s choice of an alternative route. Markov dependability models describing the correlation in the length of availability and unavailability intervals of technical components could support optimal decisions to avoid high maintenance costs. In this thesis, an innovative model class for stochastic graphs with correlated weights at the edges is introduced. In the developed model edge weights are modeled by PH distributions and correlations between them can be encoded using transfer matrices for PH distributions of adjacent edge weights. Stochastic graph models including correlations are important to describe many practical situations where the knowledge about system parameters like traveling times and costs is incomplete or changes over time. Based on PH-Graphs efficient solution methods for Stochastic Shortest Path Problems with correlations can be developed. Competing paths from origin to destination in a PH-Graph can be interpreted as CTMDPs. Optimal solutions to different shortest path problems can be obtained from finding an optimal policy in a CTMDP. For example, the problem of finding the reliable shortest path to maximize the probability of arriving on time under realistic assumptions can be efficiently treated. Formulations of shortest path problems with correlations as well as solution methods from the CTMDP field are presented. We address the parameterization of PH-Graphs based on measured data from simulated systems. Fitting methods for parameterization of transfer matrices are adopted from MAP fitting approaches. Also similarity transformations for order 2 acyclic PHDs in composition are considered. Our experiments and examples show that correlation has a significant impact on shortest paths in stochastic weighted networks and that our solution methods are effective and usable in online decision senarios.
Toeplitz block circulant matrix optimized with particle swarm optimization for compressive imaging

Compressive imaging is an imaging way based on the compressive sensing theory, which could achieve to capture the high resolution image through a small set of measurements. As the core of the compressive imaging, the design of the measurement matrix is sufficient to ensure that the image can be recovered from the measurements. Due to the fast computing capacity and the characteristic of easy hardware implementation, The Toeplitz block circulant matrix is proposed to realize the encoded samples. The measurement matrix is usually optimized for improving the image reconstruction quality. However, the existing optimization methods can destroy the matrix structure easily when applied to the Toeplitz block circulant matrix optimization process, and the deterministic iterative processes of them are inflexible, because of requiring the task optimized to need to satisfy some certain mathematical property. To overcome this problem, a novel method of optimizing the Toeplitz block circulant matrix based on the particle swarm optimization intelligent algorithm is proposed in this paper. The objective function is established by the way of approaching the target matrix that is the Gram matrix truncated by the Welch threshold. The optimized object is the vector composed by the free entries instead of the Gram matrix. The experimental results indicate that the Toeplitz block circulant measurement matrix can be optimized while preserving the matrix structure by our method, and result in the reconstruction quality improvement.
Effect of inter-tissue inductive coupling on multi-frequency imaging of intracranial hemorrhage by magnetic induction tomography

Magnetic induction tomography (MIT) is a promising technique for continuous monitoring of intracranial hemorrhage due to its contactless nature, low cost and capacity to penetrate the high-resistivity skull. The inter-tissue inductive coupling increases with frequency, which may lead to errors in multi-frequency imaging at high frequency. The effect of inter-tissue inductive coupling was investigated to improve the multi-frequency imaging of hemorrhage. An analytical model of inter-tissue inductive coupling based on the equivalent circuit was established. A set of new multi-frequency decomposition equations separating the phase shift of hemorrhage from other brain tissues was derived by employing the coupling information to improve the multi-frequency imaging of intracranial hemorrhage. The decomposition error and imaging error are both decreased after considering the inter-tissue inductive coupling information. The study reveals that the introduction of inter-tissue inductive coupling can reduce the errors of multi-frequency imaging, promoting the development of intracranial hemorrhage monitoring by multi-frequency MIT.
THE EFFECT OF OVERLOAD ON THE DESIGN OF LIFE OF ROAD PAVEMENT (CASE STUDY: KOTI ROAD, JAYAPURA CITY)

The roads hold a vital role in the transportation system in the Province of Papua, especially in the Jayapura area. However, real road conditions often experience a decrease in performance (damage) due to construction failures or deviant utilization. On Koti Road in Jayapura, sometimes vehicles passing it do not match the maximum permissible load. Consequently, excessive loading on the pavement can directly affect the design life of a road section. Therefore, this study aims to analyze the percentages of actual overload on each vehicle class, the increase in the cumulative VDF value, and the decrease in the design life on Koti Road, Jayapura. The research results indicated that the actual overload on Koti Road, Jayapura, obtained 81.06% for group 6a, 43.13% for group 6b, and 40.42% for group 7a. According to the Bina Marga method (1987), the cumulative VDF value increased by 133.8% due to actual excessive load in the field, while based on the NAASRA method (2004), it was 121.2%. In terms of the actual overload effects, the design life decreased by 9,273 years, or 46,365%, from the 20-year design life using the Bina Marga (1987) method, while based on the NAASRA (2004) method, the decrease in design life was 8.7898 years, or 43.949%, from the 20-year life plan. Keywords: Importance Performance Analysis; Facility Feasibility; Field Show Terminal
Exploration of Design / Layout Tradeoffs for RF Circuits using ALIGN

The extended manual layout process for RF and analog/mixed-signal design restricts design space exploration and limits design productivity. This work demonstrates the efficacy of an automated layout generator versus a manual approach using a state-of-the-art MIMO receiver. Multiple smaller floorplans of the layout are generated automatically in hours compared to weeks for a single manual layout. Measured results from an automatically generated layout fabricated in TSMC 65nm CMOS show performance numbers comparable to the manual design. Measured in-band/in-notch IIP3 and out-of-band/in-notch IIP3 are 18.3dBm and 23.64dBm, respectively.
A combined backstepping and dynamic surface control to adaptive fuzzy state‐feedback control

This paper studies an adaptive fuzzy dynamic surface control for a class of nonlinear systems with fuzzy dead zone, unmodeled dynamics, dynamical disturbances, and unknown control gain functions. The unknown system functions are approximated by the Takagi‐Sugeno–type fuzzy logic systems. There are 3 main features for the presented systematic design scheme. First, by adopting an integrated method, a novel adaptive fuzzy controller is constructed for the nonlinear system with fuzzy dead zone. Second, only 3 online learning parameters need to be tuned, which significantly reduces the computation burden. Third, the possible controller singularity problem in some of the existing adaptive control methods with feedback linearization techniques can be avoided. On the basis of the backstepping technique and dynamic surface control, all the signals of the closed‐loop system are guaranteed to be semiglobally uniformly ultimately bounded. Finally, 2 simulation examples are provided to illustrate the effectiveness of the proposed scheme.
Tunable resonant Goos–Hänchen and Imbert–Fedorov shifts in total reflection of terahertz beams from graphene plasmonic metasurfaces

Highly tunable enhanced lateral displacements in the center of gravity of a totally reflected light beam from a graphene plasmonic metasurface are investigated. Multiple reflections of the incident beam, and the resonance coupling between the incident beam and the surface modes of the graphene metasurface in each reflection, are employed to enhance the Goos–Hanchen and Imbert–Fedorov shifts in the proposed structure. It is shown that spatial Goos–Hanchen and Imbert–Fedorov shifts as high as 1089λ0 and −44. 66λ0 (λ0: incident wavelength) are achievable in the proposed structure. The effects of different parameters, including the incident beam waist, temperature, the scattering time, and the chemical potential of the graphene, on the shift values are then studied. Because of the strong light confinement in the surface modes of the graphene metasurface, the dispersion properties of these modes, and, therefore, the coupling strength between the incident beam and these modes, are highly sensitive to the parameters of the reflecting structure and the incident beam itself. The high sensitivity of the coupling strength between the incident beam and the surface modes is then exploited to tune the shift values. It is shown that by introducing a small change of ΔμC=0.02 eV in the chemical potential of the graphene, the spatial Goos–Hanchen and Imbert–Fedorov shift variations of 855λ0 and −31λ0 can be achieved, respectively. The wide range of lateral shift variations along with the relatively small required actuation power support the application of the proposed structure in the realization of optical devices, such as temperature sensors and switches.
Rate of Same-Day Antiretroviral Therapy Initiation and its Predictors among Newly-Diagnosed HIV-Positive Patients in a District Hospital in Douala, Cameroon

Aim: The fight against HIV/AIDS remains a global health priority. In 2015, in a bid to control the pandemic, the World Health Organization adopted the Treatment for All strategy aimed at guaranteeing the placement of all persons living with HIV (PLWHIV) on Antiretroviral Therapy (ART)