Miracle-dz/UniFAR-FTUs huggingface dataset:
Path Planning for a 6 DoF Robotic Arm Based on Whale Optimization Algorithm and Genetic Algorithm

-The trajectory planning for robotic arms is a significant area of research, given its role in facilitating seamless trajectory execution and enhancing movement efficiency and accuracy. This paper focuses on the development of path planning algorithms for a robotic arm with six degrees of freedom. Specifically, three alternative approaches are explored: polynomial (cubic and quantic), Whale Optimization Algorithm (WOA), and Genetic Algorithm (GA). The comparison of outcomes between different methods revealed that polynomial methods were found to be more straightforward to implement, albeit constrained by the intricacy of the pathway. Upon examining the functioning of the WOA, it has been shown that it is well suited for all types of pathways, regardless of their level of complexity. In addition, when GA is applied, it has been shown less smoothness than WOA but also less complexity. In brief, WOA is deemed superior in the path planning process since it is more thorough in determining the optimal path due to the conical spiral path technique it employs in offering optimized path planning. in comparison to GA, WOA is better in implementation speed and accuracy. However, GA is smoother in start and finish path.
Frequency Stability Enhancement of Microgrid Using Optimization Techniques-Based Adaptive Virtual Inertia Control

In recent years, a sharp increase in integration of renewable energy sources (RESs) in power system network has been observed. High penetration of RES interfaced with power electronics converters-inverters with reduced or no inherent inertia compromises modern power system’s overall stability. Due to low inertia, voltage and frequency deviations far off the allowable threshold occur. To overcome this challenge, an adaptive inertia control strategy based on optimization technique is proposed. The improved particle swarm optimization (PSO) and genetic algorithm (GA) optimization techniques-based PID controller has been used to generate the appropriate virtual inertia coefficient for effective emulation of inertia in the presence of energy storage system. The conventional PSO suffers local optima stagnation, resulting in premature convergence during searching process in order to achieve global and local position. To address this issue, the velocity update equation was modified on inertia weight (w) using an additional exponential term with linear decreasing inertia weight PSO (LDIW-PSO). In this paper, exponential power is taken strategically instead of squaring it in order to reduce the number of iterations for faster convergence. Finally, a microgrid based on wind and solar energy is simulated using MATLAB/Simulink where three cases, 2% disturbance, 3% disturbance, and 4% disturbance, have been considered. Here, the evaluation of proposed system is carried out based on four main performance indices (ITAE, IAE, ISE, and ITSE). Furthermore, validation was done through hardware prototype to get experimental results in real time. The results from MATLAB simulation and experimental setup are in sync.
Finding the polarizability of dielectric half-ellipse

This article studies the electrostatic response of a two-dimensional dielectric half-ellipse. A semianalytical method is presented for solving the polarizability of a half-ellipse as a function of its relative permittivity. The polarizability depends on the direction of incident field, and therefore, it is written as a dyadic with two perpendicular components. These are called series and parallel polarizability, and they are solved separately. The solution requires a series expansion of the potential function, and the coefficients must be obtained numerically, through an inversion of a finite matrix equation. Numerical results are presented for the polarizability components of the half-ellipse as functions of its geometry and permittivity.
Wireless Antenna Sensors for Biosimilar Monitoring Toward Cyber-Physical Systems: A Review of Current Trends and Future Prospects

The integration of wireless antenna sensors for cyber-physical systems has become increasingly prevalent in various biosimilar applications due to the escalating need for monitoring techniques that are efficient, accurate, and reliable. The primary objective of this comprehensive investigation is to offer a scholarly examination of the present advancements, challenges, and potentialities in the realm of wireless antenna sensor technology for monitoring biosimilars. Specifically, the focus will be on the current state of the art in wireless antenna sensor design, manufacturing, and implementation along with the discussion of cyber security trends. The advantages of wireless antenna sensors, including increased sensitivity, real-time data gathering, and remote monitoring, will next be discussed in relation to their use in a variety of biosimilar applications. Furthermore, we will explore the challenges of deploying wireless antenna sensors for biosimilar monitoring, such as power consumption, signal integrity, and biocompatibility concerns. To wrap things off, there will be a discussion about where this subject is headed and why collaborative work is essential to advancing wireless antenna sensor technology and its applications in biosimilar monitoring. Providing an in-depth overview of the present landscape and potential developments, this article aims to be an asset for academics and professionals in the fields of antenna sensors, biosimilar development, wireless communication technologies, and cyber physical systems.
An Integrated Time Series Prediction Model Based on Empirical Mode Decomposition and Two Attention Mechanisms

In the prediction of time series, Empirical Mode Decomposition (EMD) generates subsequences and separates short-term tendencies from long-term ones. However, a single prediction model, including attention mechanism, has varying effects on each subsequence. To accurately capture the regularities of subsequences using an attention mechanism, we propose an integrated model for time series prediction based on signal decomposition and two attention mechanisms. This model combines the results of three networks—LSTM, LSTM-self-attention, and LSTM-temporal attention—all trained using subsequences obtained from EMD. Additionally, since previous research on EMD has been limited to single series analysis, this paper includes multiple series by employing two data pre-processing methods: ‘overall normalization’ and ‘respective normalization’. Experimental results on various datasets demonstrate that compared to models without attention mechanisms, temporal attention improves the prediction accuracy of short- and medium-term decomposed series by 15~28% and 45~72%, respectively; furthermore, it reduces the overall prediction error by 10~17%. The integrated model with temporal attention achieves a reduction in error of approximately 0.3%, primarily when compared to models utilizing only general forms of attention mechanisms. Moreover, after normalizing multiple series separately, the predictive performance is equivalent to that achieved for individual series.
Enhancing lung cancer disease diagnosis by employing ensemble deep learning approaches

Cancer is a disease that results from the unnatural proliferation of aberrant cells that infest the body’s healthy cells and spread throughout the body. Lung cancer is characterized by an imbalance in the cells of the affected organs, namely the lungs. The prediction of lung cancer at an early stage is very important, particularly in countries that are densely populated and have lower incomes. Clinically conventional approaches, such as blood tests and other types of treatments, are used by specialists. The age of artificial intelligence (AI) has begun, and today, it is feasible to construct a computer-aided diagnostic mechanism with the assistance of machine learning and deep learning algorithms. In this particular piece of research, one deep learning algorithm, an artificial neural network (ANN), has been investigated to determine whether or not lung cancer could be detected at an earlier stage. In addition to conventional ANN, ensemble ANN with weighted averaging and soft and hard voting ensemble techniques are also considered. In order to achieve this effectiveness, the state-of-the-art parameters for the proposed method using ANN are assessed and evaluated using the lung cancer dataset. The empirical analysis shows that hard voting-enabled ANN shows the highest accuracy at 97.47%.
Photodynamic Therapy-Induced Anti-Tumor Immunity: Influence Factors and Synergistic Enhancement Strategies

Photodynamic therapy (PDT) is an approved therapeutic procedure that exerts cytotoxic activity towards tumor cells by activating photosensitizers (PSs) with light exposure to produce reactive oxygen species (ROS). Compared to traditional treatment strategies such as surgery, chemotherapy, and radiation therapy, PDT not only kills the primary tumors, but also effectively suppresses metastatic tumors by activating the immune response. However, the anti-tumor immune effects induced by PDT are influenced by several factors, including the localization of PSs in cells, PSs concentration, fluence rate of light, oxygen concentration, and the integrity of immune function. In this review, we systematically summarize the influence factors of anti-tumor immune effects mediated by PDT. Furthermore, an update on the combination of PDT and other immunotherapy strategies are provided. Finally, the future directions and challenges of anti-tumor immunity induced by PDT are discussed.
Effects of Foliar Spraying of Organic Selenium and Nano-Selenium Fertilizer on Pak Choi (Brassica chinensis var. pekinensis. cv. ‘Suzhouqing’) under Low Temperature Stress

The effects of foliar spraying of organic selenium and nano-selenium fertilizer on pak choi (Brassica chinensis var. pekinensis. cv. ‘Suzhouqing’) under low temperature were investigated. The impacts on plant growth, antioxidant capacities, and nutritional profiles were studied. Exogenous selenium was applied at three rates: 5, 10, 20 mg L−1, and RNA-Seq technology was used to sequence the transcriptome of leaves. The study revealed that selenium influenced leaf weight and total selenium content through three main mechanisms. First, it protected photosynthetic pigments and boosted photosynthetic capacity by up-regulating LHca2, LHcb1, LHca1, and LHcb4. Second, it enhanced antioxidant capacity by elevating the expression of genes such as superoxide dismutase and monodehydroascorbic acid. Third, it facilitated selenium absorption through endocytosis, transported selenium using the ABC transport gene family, and regulated selenium metabolism-related genes like cysteine synthetase and glutaredoxin. Nine hub genes identified with a weighted gene co-expression network analysis were closely associated with these mechanisms. The results of a functional enrichment analysis were consistent with those of a Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis conducted on DEGs, thus confirming the reliability of these findings. Therefore, this study can provide scientific basis for pak choi production with selenium fortification by selenium application.
A Linear Predictor Based on FTIR Spectral Biomarkers Improves Disease Diagnosis Classification: An Application to Multiple Sclerosis

Multiple sclerosis (MS) is a neurodegenerative disease of the central nervous system that can lead to long-term disability. The diagnosis of MS is not simple and requires many instrumental and clinical tests. Sampling easily collected biofluids using spectroscopic approaches is becoming of increasing interest in the medical field to integrate and improve diagnostic procedures. Here we present a statistical approach where we combine a number of spectral biomarkers derived from the ATR-FTIR spectra of blood plasma samples of healthy control subjects and MS patients, to obtain a linear predictor useful for discriminating between the two groups of individuals. This predictor provides a simple tool in which the contribution of different molecular components is summarized and, as a result, the sensitivity (80%) and specificity (93%) of the identification are significantly improved compared to those obtained with typical classification algorithms. The strategy proposed can be very helpful when applied to the diagnosis of diseases whose presence is reflected in a minimal way in the analyzed biofluids (blood and its derivatives), as it is for MS as well as for other neurological disorders.
GEOGRAPHICAL SIMULATION SYSTEMS FOR URBAN PLANNING

GIS have been widely applied in urban planning because of their powerful spatial data han- dling capabilities. This paper develops the framework and methodologies of geographical simulation sys- tems (GSS). GSS will provide the powerful simula- tion functions that are quite weak in existing GIS. The core of GSS is cellular automata and multi-agent systems. GSS can be used to simulate, predict, opti- mize and analyze complex natural systems. It can be used to facilitate the modeling of complex human- environment interactions in solving land use problems.
Using the OptiBand to Increase the Long-Range Spatial Perception of People with Vision Disabilities

Mobility aids such as the white cane provide close-range information to help people with vision disabilities navigate the world. However, this technology has a limited sensing range and does not provide long-distance scene awareness. This paper proposes a vibrotactile feedback device to fill this gap: the OptiBand, which was developed based on design criteria from a blind stakeholder. The presented user study ($N=27$) compared the OptiBand to a proxy for existing shorter-range mobility aids, considered two potential sensed distance-to-vibration mapping strategies, and covered the use cases of locating and approaching objects of interest. Results of the object-locating trials showed that using the OptiBand led to faster and more successful performance, as well as lower task load and more satisfaction with the device, compared to using a proxy state-of-the-art device. A final trial with the original stakeholder demonstrated that the design criteria were met and supplied insights for the next iteration of participatory design for the OptiBand. Those who are interested in assistive devices for people with vision disabilities can benefit from this work.
Modelling The Impulsive Breakdown Characteristics of Sub-mm to mm Spheroidal Voids

Solid insulation is widely used in power and pulsed power systems. In the past, great attention has been paid to gas-filled voids found in solid dielectrics within the context of partial discharges for power equipment, such as high voltage cables. However, it is equally important to consider the effect of such voids on the degradation of solid insulation within pulsed power systems. Discharge activity within gas-filled voids, or across cavities at solid-solid interfaces, can potentially threaten equipment longevity and durability. Therefore, the transient ionization processes initiated inside these voids, particularly under fast-rising impulsive energization, are of great importance. Yet, the additional complexities of transient stress compared to steady-state conditions typically render classical steady-state breakdown models inapplicable, and necessitates a different approach for the impulsive regime. In this work, an approach for the breakdown voltage and time-to-breakdown estimation of spheroidal air-filled voids, subjected to fast-rising high voltage impulses has been developed. The proposed model links the time-dependent electric field developed within a spheroidal void to gas ionization and breakdown criteria, allowing the estimation of the formative time-to-breakdown and breakdown voltage of the void. This study investigated the effects of dV/dt on estimated void breakdown parameters, and the approach may be of use for the further characterization of solid insulation under impulsive energization, aiding in the future development of novel insulation technologies for current and future pulsed power systems.
Collaborators Needed: Predictors of Self-Assessed Teamwork Competence in Agricultural Faculty

Despite an abundance of research in the past 20 years on the importance of teamwork in higher education, little data exists on what factors predict faculty self-evaluations of teamwork competence. This is important for leadership educators because as research on the science of team science has increased there has been a proliferation of different training programs for faculty in academia to improve collaboration, with little regard for what variables predict self-perceived teamwork skill. This study used a cross-sectional survey design to collect faculty self-evaluations of teamwork competence from a national sample of agricultural faculty. Regression analysis was used to determine what factors, identified as potentially important from prior research, predicted positive self-evaluations of teamwork competence. Results showed that discipline self-efficacy, feelings of impostorism, team participation, the presence of prior training, and gender were all significant predictors. The implications of these findings and avenues for future research for teamwork in higher education are discussed.
Real-Time Object Detection Overview: Advancements, Challenges, and Applications

Real-time object detection is a crucial aspect of computer vision with applications spanning autonomous vehicles, surveillance, robotics, and augmented reality. This study examines real-time object detection techniques, highlighting their significance in artificial intelligence. The primary goal is swift and accurate object identification in images or video streams. Traditional methods like sliding windows and region-based approaches had limitations in computational efficiency. Deep learning, particularly Convolutional Neural Networks (CNNs), revolutionized object detection. Models like SSD, YOLO, and Faster R-CNN excel in accuracy and speed. They employ anchor boxes, feature pyramid networks, and non-maximum suppression to balance precision and processing speed. Hardware accelerators like GPUs, TPUs, and FPGAs facilitate real-time inference. Challenges in real-time object detection include occlusion, scale variations, and cluttered environments. Researchers must navigate the trade-offs between accuracy and speed. Real-time object detection is pivotal in computer vision, enabling intelligent systems across diverse applications. The continuous evolution of deep learning algorithms and hardware capabilities pushes the boundaries of this field, making it a dynamic research domain in artificial intelligence.
Millimeter-Wave Gallium Nitride Maturation of 40nm T3 Gallium Nitride Monolithic Microwave Integrated Circuit Process

Future microelectronics systems will rely on gallium nitride (GaN) devices able to operate at higher frequencies. Smaller gate length nodes enable such applications but have their own manufacturing challenges. HRL Laboratories offers a 40nm T3 GaN process with fT and fmax of 160/360GHz, power density up to 2W/mm, and noise figure of 1.8dB at 94GHz. Under internal and DARPA funding, this process has been matured for improved performance, yield, and cycle time. The process is monitored through three standard evaluation circuits covering X-, Ka-, and W-band and their performance correlates well with pulsed-current measurements. HRL has also matured the metal-embedded chiplet assembly for microwave integrated circuit (MECAMIC) technology and built demonstration circuits.
BIOAVAILABILITY ENHANCEMENT STRATEGIES FOR RIVAROXABAN: A NOTEWORTHY REVIEW

This review article discusses Rivaroxaban (RXB), an anticoagulant that has gained much attention due to its ability to prevent blood clots from forming in the body. However, one of the major challenges pharmaceutical companies face is the low water solubility of RXB, which can lead to difficulties in formulating the drug for oral administration and affect the drug's bioavailability. However, to the best of our knowledge, limited studies have explored enhancing the bioavailability of the RXB. Most of these studies have been purely academic and impractical for industrial use. Therefore, this review article aims to discuss successful studies that have increased the bioavailability of RXB. The goal is to inspire researchers to develop this new drug further. The article covers seven strategies for enhancing the bioavailability of RXB, including microspheres, liposomes, self-nano emulsifying drug delivery system, solid lipid nanoparticles, cocrystals, sustained release, and solid dispersion. The studies discussed in this review offer valuable insights into developing novel drug delivery systems that can help overcome the limitations of existing drugs.
The Nature of the Spark Is a Pivotal Element in the Design of a Miller–Urey Experiment

Miller and Urey applied electric sparks to a reducive mixture of CH4, NH3, and water to obtain a complex organic mixture including biomolecules. In this study, we examined the impact of temperature, initial pressure, ammonia concentration, and the spark generator on the chemical profile of a Miller–Urey-type prebiotic broth. We analyzed the broth composition using Gas Chromatography combined with Mass Spectroscopy (GC/MS). The results point towards strong compositional changes with the nature of the spark. Ammonia exhibited catalytic properties even with non-nitrogen-containing compounds. A more elevated temperature led to a higher variety of substances. We conclude that to reproduce such a broth as well as possible, all the studied parameters need to be tightly controlled, the most difficult and important being spark generation.
Application of vermicompost in improving the growth, water content, and vase life of LA hybrid lily

Abstract One of the limitations of soilless cultivation is its high cost. Considering this issue, this technique is cost-effective when the obtained product is of high quality from a commercial point of view. Lilies are the world’s fourth most popular cut flowers in terms of commercial importance. It is crucial to provide the lily with a culture medium that will extend its vase life and enhance its morphological and marketable characteristics. Vermicompost (VC) improves plant growth and performance with better access to water and nutrients. This study investigated the effect of different VC levels on LA hybrid lilies’ growth parameters and vase life. LA hybrid lily bulbs were planted at 20, 35, 50, and 65% VC levels on a cocopeat and perlite bed. First, growth traits such as the number of leaves and flowers, leaf area, plant height, petals’ anthocyanin content, some plant organs’ water content, and finally, vase life were measured. It was found that only the 20% VC treatment resulted in an increase in leaf number (8.6%) and flower number (3.6%). Furthermore, only in this treatment did water absorption in cut flowers and vase life increase by 43.5% and 9.8%, respectively. This research showed that the 20% VC treatment (V1) performed better in producing compact cut flowers with freshness and longer vase life.