Miracle-dz/UniFAR-FTUs huggingface dataset:
Stress‐related neuropeptide systems as targets for treatment of alcohol addiction: A clinical perspective

Alcohol use is a major cause of disability and death globally. These negative consequences disproportionately affect people who develop alcohol addiction, a chronic relapsing condition characterized by increased motivation to use alcohol, choice of alcohol over healthy, natural rewards, and continued use despite negative consequences. Available pharmacotherapies for alcohol addiction are few, have effect sizes in need of improvement, and remain infrequently prescribed. Research aimed at developing novel therapeutics has in large part focused on attenuating pleasurable or “rewarding” properties of alcohol, but this targets processes that primarily play a role as initiation factors. As clinical alcohol addiction develops, long‐term changes in brain function result in a shift of affective homeostasis, and rewarding alcohol effects become progressively reduced. Instead, increased stress sensitivity and negative affective states emerge in the absence of alcohol and create powerful incentives for relapse and continued use through negative reinforcement, or “relief. ” Based on research in animal models, several neuropeptide systems have been proposed to play an important role in this shift, suggesting that these systems could be targeted by novel medications. Two mechanisms in this category, antagonism at corticotropin‐releasing factor type 1, and neurokinin 1/substance P receptors, have been subject to initial evaluation in humans. A third, kappa‐opioid receptor antagonism, has been evaluated in nicotine addiction and could soon be tested for alcohol. This paper discusses findings with these mechanisms to date, and their prospects as future targets for novel medications.
MÉTODOS DE FIJACIÓN DE DISPOSITIVOS DE SEGUIMIENTO PARA FALCO SPARVERIUS: ARNESES DE MOCHILA FRENTE A ARNESES PÉLVICOS

—The American Kestrel (Falco sparverius) is a small falcon whose population is declining across North America, but drivers of this decline have yet to be identified. Conservationists have called for greater understanding of kestrels’ migratory connectivity to identify potential causes of decline that may occur during various parts of their annual cycle. For many bird species, the most common and direct way to determine migratory connectivity is by attaching a tracking device that documents their locations over time. However, American Kestrels present a challenge when attaching tracking devices due to their small size and aggressive biting capabilities. We used two attachment methods (backpack and leg-loop harnesses) commonly used for raptors on a wintering population of American Kestrels in north Texas to test whether there were differences in observed return rates the following year. Of 20 American Kestrels fit with devices, significantly more of the kestrels fit with backpack harnesses were observed the following winter than the birds fit with leg-loop harnesses. Eight of the 10 kestrels we fit with backpacks were observed to return to their winter territories compared to only two of 10 kestrels fit with leg-loops. Overall return frequency data of kestrels with both harnessing methods (50%, n 1⁄4 20) was similar to the frequency we observed on marked birds without devices (52%, n1⁄4 190), though this was heavily skewed due to the high return of birds with backpack harnesses (80%). Although our sample size was limited, the disparity between the return rates of birds with the two harness types suggests that backpack harnesses are the preferred method of attaching tracking devices to American Kestrels, and also potentially to similar small raptor species.
A NON-LiNEAR CAMERA CALIBRATION ALGORITHM: DIRECT SOLUTION METHOD

Direct Linear Transfonnation (DLT) method suffers from an intrinsic hortcoming: non-orthogonal camera calibration and object space reconstruction. This is due to the fact that the eleven DLT parameters are basically deriVed from a set of ten independent camera parameters and thus are mutually dependent. Alternative algorithms such as MDLT have failed to gain popularity due to the complexity and inconsistency in performance. The purpose of this study Is to develop a non-linear orthogonal camera calibration algorithm with the capability to correct the optical distortion errors.
A 60-GHz CMOS Balanced Power Amplifier with Miniaturized Quadrature Hybrids Achieving 19.0-dBm Output Power and 24.4% Peak PAE

In this paper, a 60-GHz CMOS balanced power amplifier (PA) is proposed. Two miniaturized quadrature hybrids are utilized to realize power splitting and power combining. The compact hybrids can achieve good input and output matching performances. Transistor-layout optimization is taken into consideration to boost the output power and improve the power-added efficiency (PAE). The balanced PA is designed in 65-nm bulk CMOS and all the electromagnetic (EM) simulations of passive devices are executed in the EM tool. Post-layout simulation results show that the proposed balanced PA realizes a small-signal gain of 29.1 dB at 60 GHz with an 11-GHz bandwidth. With a 1.2-V supply voltage and 300-mW power consumption, the balanced PA achieves a saturated output power of 19.0 dBm and 24.4% peak PAE at 60 GHz, which is suitable for 60-GHz wireless systems.
Traffic velocity distributions for different spacings

Conventional traffic density-velocity models usually use deterministic functions.However,stochastic traffic flow fluctuations mean that the traffic velocity cannot be uniquely determined by the density.A more accurate description of the density-velocity relation was developed using NGSIM(next generation simulation) data to model the velocity distribution for different spacing.Test results show that at low traffic densities the velocity normally follows a log-normal distribution,while at high traffic densities the velocity follows a negative-exponential distribution.
Electric Vehicle Readiness in Sabah: Overview of Market Forecast and Adoption Challenges

Electric vehicles (EVs) have been a hot issue in recent decades as the public and business world started to pay attention to the environmental pollution caused by the use of gasoline, where carbon dioxide emissions from motor vehicle engines are claimed to be the primary cause of global warming and climate change. In Malaysia, the government has taken the initiative to regulate the use of EVs by enacting a number of regulations in order to boost the industry. Since 2014, for example, the 2014 National Automotive Policy has provided a regulatory framework for energy-efficient automobiles. In September 2022, Malaysia released the National Energy Policy (NEP) from 2022 to 2040 in an effort to hasten the execution of policies aimed at achieving zero carbon emissions by the year 2050. By realising a significant economic advantage from the energy transition in accordance with the NEP goals and linked with the ambition of the Long-Term Low Emission Development Strategy (LT-LEDS) to attain net-zero carbon emissions in 2050, Malaysia will be able to pursue the Low Carbon Nation Aspiration 2040 and keep pace with global megatrends while simultaneously seizing economic possibilities from the energy transition. Electric vehicle (EV) adoption in Malaysia is anticipated to rise from less than 1% in 2018 to 38% in 2040 under the Low Carbon Nation Aspiration 2040 objective. While EVs are becoming more popular, particularly in neighbouring countries such as Singapore, Thailand, and Indonesia, the EV adoption rate in Malaysia remains lower, owing to a variety of constraints that stand in the way of widespread EV deployment despite their multiple benefits such as zero tailpipe emissions, cheaper fuel prices, and the ease of filling up at home. This paper gives an overview of electric vehicle readiness in Sabah by assessing the EV market forecast using an econometric regression model, highlighting the challenges and barriers to EVs achieving market acceptability, and addressing concerns for the power utility in Sabah with wide-scale adoption of EVs, including the construction of EV charging stations, influence on the local distribution network, and resale of electricity by a third party.
Development of the Spatial Model of an Urban Passenger Transport Route for the Safe Movement of Passenger Vehicles with a Partially Autonomous (Robotic) Control System

This article discusses the issue of constructing a spatial analytical model of the route of a vehicle of urban passenger transport with an automated (robotic) control system. This model is necessary to ensure automatic control of the longitudinal and transverse movement of the vehicle while maintaining a safe speed in all areas, including complex spatial maneuvers. We propose to call such control systems robotic. Robotization of specific control functions inherent only to this type of ground passenger transport is a logical continuation of work on automation of vehicle control functions. The apparatus of theoretical mechanics, the theory of constructing Bezier curves is used as a methodological basis. The theory of Bezier curves allows you to describe the route of movement in three-dimensional space, which is important for countries, located in mountainous areas. This theory makes it possible to develop a compact description of the route trajectory, which can be loaded into the memory of the on-board computer via a radio channel on–line. Vehicle control system uses this information when moving along the route in a robotic mode. As a mechanism for practical implementation, it is planned to use modern software systems for transport geodesy.
UML-based Forward Database Engineering

The paper presents the most recent achievements in developing AMADEOS - the first online web-based tool aimed at business process model-driven database design. The preexisting AMADEOS was able only to derive an initial conceptual database model automatically, while other design phases were not supported. The most recent development efforts resulted in the complete coverage of the database design process, from conceptual model to physical database, by using the standard UML notation.
Real-Time Video Fusion Implemented In GStreamer Framework

In this paper we present real-time pixel level video fusion implemented in GStreamer framework as a plugin. The processing is performed in two steps. The first step is image registration, which is compensation of the effect of different viewing angles and perspective of two cameras that are observing the same scene. The second step is pixel-level fusion using Laplacian pyramid method or simple addition. The OpenVX standard is used to accelerate the algorithm. We also present experimental performance results of video fusion of thermal and visible light camera.
An Energy-Efficient Mixed-Bit ReRAM-based Computing-in-Memory CNN Accelerator with Fully Parallel Readout

Computing-In-memory (CIM) accelerators have the characteristics of storage and computing integration, which has the potential to break through the limit of Moore's law and the bottleneck of Von-Neumann architecture. However, the performance of CIM accelerators is still limited by conventional CNN architectures and inefficient readouts. To increase energy-efficient performance, optimized CNN model is required and low-power fully parallel readout is necessary for edge-computing hardware. In this work, an ReRAM-based CNN accelerator is designed. Mixed-bit 1~8-bit operations are supported by bitwidth configuration scheme for implementing Neural Architecture Search (NAS)-optimized multi-bit CNNs. Besides, energy-efficient fully parallel readout is achieved by variation-reduction accumulation mechanism and low-power readout circuits. Benchmarks show that the proposed ReRAM accelerator can achieve peak energy efficiency of 2490.32 TOPS/W for 1-bit operation and average energy efficiency of 479.37 TOPS/W for 1~8-bit operations when evaluating NAS-optimized multi-bitwidth CNNs.
Dielectric-Metal Nanostructures for Enhanced Scattering and Absorption in Solar Cells

The primary feature of the metallic nanostructures is their localized surface response at optical frequency resulting in enhanced scattering and absorption. Among these nanostructures, nanoparticles and nanoshells are known for their three-dimensional symmetry, with stand-alone responses naive to the state of incident wave polarization. Nanoparticles can confine and trap light and are often used as a basic building block in metamaterials and metasurfaces employed for silicon solar cells, which require light enhancement due to their narrow bandgap and limited absorption. However, they have shown a limited ability for enhanced scattering and absorption of light. Nanoshells also known as coated spheres have shown superior optical response compared to nanoparticles. In this paper, we propose the nanoshell as a basic building block for metasurfaces and demonstrate their superior scattering and absorption response for improved energy harvesting in solar cells. The proposed model derives expressions to maximize energy absorption and forward scattering using basic concepts from applied mathematics and linear circuit theory. The superior response of the proposed solution is demonstrated using a numerical field solver and Mie theory to evaluate specific examples with various radius and interparticle distances. The developed solution is highly efficient, with improved scattering and absorption, which is well-suited for next-generation solar cells.
Malscanner – File Behavior Analysis using Machine Learning

MalScanner is a tool that aims to provide a simple, effective, and user-friendly method of scanning files for malicious behavior. Furthermore, MalScanner scans a file and extracts features to be used in machine learning assisted static malware analysis and inspects the file’s behavior dynamically. This tool also implements a blockchain database to store analysis results. The solution will be presented to the user in a straightforward manner via web application.
A Deep Learning Approach for Ocular Disease Detection

The early identification of ocular disease (OD) detection is essential in preventing complete blindness. Although much information is available online, ophthalmologists have valuable information to diagnose the condition. Still, it also creates many challenges due to the increase in the variation in fundus images. The diagnosis of disease using hand-crafted techniques on a manual basis is time-consuming. It is unsuitable in countries like India, where the blind population is approximately 16 million. In this paper, we proposed an approach to diagnose OD automatically, where detection was done in two steps. The MobileNet architecture is used for feature extraction since it is suitable for smartphone/iPhone users those does not have computer systems at home. The architecture is faster than other available architectures, such as VGG and RESNET. The network was trained on the data of over 3500 patients and tested over 1500 patients giving an accuracy of 95.68% when validated.
A Comprehensive Analysis of Biofeedback Treatment in Migraine

These days, migraines are a prevalent issue for almost everyone. A migraine strike can be brought on by a stressful job, the effects of genetics on children from parents, changes in lifestyle, unusual dietary habits, weather changes, excessive caffeine consumption, overuse of medication, and the stress and tension of day to day life. To demonstrate the proposed work, a standard migraine dataset is taken to show how these processes are helpful in real. In order to learn, data set Kostecki-Dillon obtained from the UCI machine learning repository, which includes 4152 records on 133 participants for 9 attributes, headaches reports set down in a therapy program were separated from Tammy Kostecki-records Dillon’s on migraine handling. According to numerous experimental research, biofeedback therapy is quite beneficial for migraine headaches. Muscle discomfort is recorded by biofeedback devices, which then display those levels to the patient. Muscles are kept adequately relaxed because the patient learns to connect precise levels of tension with sensations from the muscles. This study focuses on the range of current migraine treatment methods as well as how biofeedback works to counteract the negative side effects of allopathic drugs. This work also focused on biofeedback processes for the general population depending on gender, age, and other characteristics.
0.7-6 GHz Programable Gain Push-Pull Driver PA Based on Dual-Loop Biases

A 28 nm 0.7-6 GHz programable push-pull driver power amplifier (DPA) based on dual-loop biases for Sub-6 GHz band is presented. Based on the dual-loop biases, the DPA can dynamically adjust two gate biases to ensure low output HD2 and HD3, in addition the DPA is robust over process, voltage, and temperature (PVT) variations. The DPA achieves wideband frequency coverage with off-chip matching network. The DPA consists of thermometer-weight transconductors and binary-weighted transconductors. The transmitter prototype with the DPA achieves 32 dB dynamic gain range, 0.5 dB accuracy in 1 dB step over the Sub-6 GHz band. Fabricated in 28 nm CMOS technology, the DPA shows 30.5 dBm OIP3 while consuming 62 mA from 1.8 V power supply for high gain setting.
Third-Harmonic-Type Modulation Minimizing the DC-Link Energy Storage Requirement of Isolated Phase-Modular Three-Phase PFC Rectifier Systems

A three-phase ac-dc converter with high-frequency isolation can be realized as a phase-modular system by using three single-phase Power Factor Correction (PFC) rectifier modules with isolated dc-dc converter output stages, which advantageously allows to cover a wide input voltage range by module reconfiguration from a star-(Y)- to a delta-(<inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>)-arrangement. However, the main limitation of a phase-modular topology is the fact that the input power of each PFC rectifier module pulsates at twice the mains frequency such that large dc-link capacitors are required. Recent literature predicts a substantial single-phase power pulsation reduction enabled by means of third-(3 <inline-formula> <tex-math notation="LaTeX">$^{\mathrm{ rd}}$ </tex-math></inline-formula>)-harmonic common-mode (CM) voltage (Y) or current (<inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>) injection modulation. This paper experimentally verifies and extends the dc-link energy storage requirement reduction of the 3 <inline-formula> <tex-math notation="LaTeX">$^{\mathrm{ rd}}$ </tex-math></inline-formula>-harmonic injection modulation concepts: In a first step, the derivation of the harmonic injection concept is recapitulated and suitable control methods are discussed for both CM voltage (Y) and CM current (<inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>) injection. Further, an alternative CM voltage injection strategy with simplified reference generation based only on the instantaneous grid voltage measurements is presented and compared to the pure 3 <inline-formula> <tex-math notation="LaTeX">$^{\mathrm{ rd}}$ </tex-math></inline-formula>-harmonic injection modulation. Measurement results obtained from a 6 kW prototype reveal a dc-link voltage variation and/or energy buffering reduction by up to 38.6 % enabled by the harmonic injection modulation compared to conventional operation without 3 <inline-formula> <tex-math notation="LaTeX">$^{\mathrm{ rd}}$ </tex-math></inline-formula>-harmonic injection modulation.
Integrated Circuits for Wireless Communications: Research Activities at the University of California, San Diego: Circuits Research for Wireless Communications at the University of California, San Diego

The continuing demand for improved wireless connectivity and enhanced data rates has spurred worldwide research in microwave and millimeter (mm)-wave circuits and systems within academia, government, and industrial centers. At the University of California, San Diego (UCSD), the Center for Wireless Communications (CWC) was established more than two decades ago and has contributed to analog, microwave, and millimeter circuits and systems research; communication and information theory; coding; and application studies for 4G, 5G, and 6G wireless systems. This article highlights recent UCSD research efforts, emphasizing the microwave and mm-wave circuits and systems and accompanying analog circuit techniques, carried out in conjunction with the CWC.
Performance Analysis of Planar Transformer for DC-DC Converter with Ansys Maxwell

Recent developments in the field of power electronics are majorly focusing on innovating power converters with high power density and high operating frequency. These developments have revealed number of drawbacks in the use of conventional wire-wound machines especially transformers. Planar transformer provides many enhanced features compared to conventional transformer such as high efficiency and compact size. Therefore, planar transformers (PT) are ideally suited in electric vehicle application such as battery management systems and military systems. But designing planar transformers within desired size limit with accurate analysis has always been a challenge. Hence a new methodology is required. Ansys Maxwell (Maxwell 2D and 3D) have shown their unmatchable ability of electromagnetic analysis with good accuracy in planar transformer design. Hence, this paper aims at FEM based simulation of planar transformer for DC-DC Forward Converter in Ansys Maxwell. Here, initially 2D Model of Planar transformer is designed in Ansys PExprt. This model is imported in Ansys Maxwell and its performance is analyzed. While designing planar transformer, the most important factors for planar transformer (PT) design which includes winding loss, core loss, leakage inductance has been individually studied. And overall efficiency of planar transformer is 98.82 %. Issues arising and typical phenomena encountered are also discussed in detail.
4 Bit and 8 Bit Convolution Using Vedic Multiplier

One of the fundamental processes in digital signal processing is convolution. The mathematical process of mixing two signals to create a third signal is called convolution. Utilizing a Vedic multiplier, Urdhava Triyagbhyam Sutra or UT sutra, based on one of the sixteen Vedic mathematics sutras, linear and circular convolution processing is accelerated. The Urdhava Triyagbhyam multiplier compared to other traditional multipliers offers faster results. Multiplier having pipelining architecture is also used to compute the convolution (circular and linear) of two sequences because it speeds up the multiplication process. Xilinx ISE Design suite 14.7 software is used to carry out the simulation and synthesis using VHDL. The area and delay for 4 bit and 8 bit circular and linear convolution is computed.
Wildland Fire Detection and Monitoring using a Drone-collected RGB/IR Image Dataset

Drone-based Unmanned Aerial Systems (UAS) provide an efficient means for early detection and monitoring of remote wildland fires due to their rapid deployment, low flight altitudes, high 3D maneuverability, and ever-expanding sensor capabilities. Recent sensor advancements have made side-by-side RGB/IR sensing feasible for UASs. The aggregation of optical and thermal images enables robust environmental observation, as the thermal feed provides information that would otherwise be obscured in a purely RGB setup, effectively "seeing through" thick smoke and tree occlusion. In this work, we present Fire detection and modeling: Aerial Multi-spectral image dataset (FLAME 2) [1], the first ever labeled collection of UAS-collected side-by-side RGB/IR aerial imagery of prescribed burns. Using FLAME 2, we then present two image-processing methodologies with Multi-modal Learning on our new dataset: (1) Deep Learning (DL)-based benchmarks for detecting fire and smoke frames with Transfer Learning and Feature Fusion. (2) an exemplary image-processing system cascaded in the DL-based classifier to perform fire localization. We show these two techniques achieve reasonable gains than either single-domain video inputs or training models from scratch in the fire detection task.