Miracle-dz/UniFAR-FTUs huggingface dataset:
Journal of Mechatronics, Electrical Power, and Vehicular Technology

The demand for implementing robots into our daily lives has surged in recent years, necessitating safe grasping for effective interaction with the environment. However, a majority of researchers rely on commercial grippers for their experimental studies, which are typically expensive and not accessible to everyone. Despite the existence of open-source designs, the assembly process is often challenging and requires modifications to enhance secure grasping. This paper presents a simple, compact, and low-cost gripper to offer an accessible and readily deployable solution for research and education. The gripper utilizes a parallel four-bar linkage mechanism, minimizing the number of components and incorporating off-the-shelf parts for straightforward assembly. Furthermore, to enhance its capabilities, the proposed gripper implements a soft skin tactile sensor on its fingertips. These sensors offer three-directional measurements using Hall effect sensing and embedded silicone. By controlling fingertip force based on information from the tactile sensors, the gripper achieves safe grasping. The gripper is evaluated to grasp daily life objects with different properties such as shapes, sizes, and levels of deformability. Evaluation results showcase the gripper's versatility, enabling it to securely grasp various objects, including fragile items. This outcome underscores the gripper's effectiveness, versatility, and safety in practical use. Copyright ©2023
Journal of Mechatronics, Electrical Power, and Vehicular Technology

Micro hydro power plants (MHPP) is one of the renewable energy that can be utilized as a distributed generation with controllable power output. One common issue in MHPP systems is the non-constant rotation of the generator caused by load fluctuations. This instability leads to variable frequencies, which can potentially harm electrical equipment. To address this problem, the volume of water entering through the governor can be adjusted to synchronize the turbine and generator rotation with the load. This approach helps dampen frequency oscillations and ensures that the system operates within desired limits. Therefore, there is a need for technology that can enhance the performance of micro hydro power plant units, specifically load frequency control (LFC). This research proposes the application of the grey wolf optimizer (GWO) algorithm to optimize the PID controller parameters for MHPP LFC. MHPP has been modeled in both isolated and grid-connected modes using Simulink MATLAB R2020a. The best cost function value for an isolated mode system was obtained with ISE im , yielding a value of 0.067653, while for a grid-connected mode system, it was achieved with ISE gm , with a value of 0.015861. The results of the frequency deviation response performance of the LFC using GWO indicate that the fastest settling time was achieved with the cost function ITAE im in isolated mode, and with IAE gm in grid-connected mode. The cost function that produces the smallest peak overshoot and peak undershoot parameter values varied depending on changes in the system load. Copyright ©2023 National Research and Innovation Agency. This is an open access article under the CC BY-NC-SA license (https://creativecommons.org/licenses/by-nc-sa/4.0/).
Efﬁcient Support for Interactive Operations in Multi-resolution Video Servers

In this paper, we present a placement algorithm that interleaves multi-resolution video streams on a disk array and enables a video server to efﬁciently support playback of these streams at different resolution levels. We then combine this placement algorithm with a scalable compression technique to efﬁciently support interactive scan operations (i.e., fast-forward and rewind). We present an analytical model for evaluating the impact of the scan operations on the performance of disk-array-based servers. Our experiments demonstrate that: (1) employing our placement algorithm substantially reduces seek and rotational latency overhead during playback, and (2) exploiting the characteristics of video streams and human perceptual tolerances enables a server to support interactive scan operations without any additional overhead.
Rolling Horizon Co-evolution in Two-player General Video Game Playing

Artiﬁcial Intelligence for General Video Game Playing (GVGP) is challenging not only because agents must adapt to a range of different games, but they must also make decisions within the time constraints of real-time video games. The General Video Game Ar-tiﬁcial Intelligence framework (GVGAI) is a popular framework for GVGP. It features a two-player track where two agents play a game together, either competitively or cooperatively, which poses the additional challenge of considering another player. Commonly, agents only consider their own moves in these two-player games. In this paper we discuss and assess Rolling Horizon Co-evolutionary Planning (a modiﬁcation to Rolling Horizon Evolutionary Algorithms) for two player GVGAI. We present experimental results on its effectiveness against other agents playing GVGAI games and show that co-evolution can improve results compared to a RHEA agent.
dan Uji SPF Sediaan Sunscreen

The repeated use of sunscreen makes many types and dosage forms of sunscreen. Sunscreen powder is a good alternative form of sunscreen because their use does not damage the make-up on the face. Moringa leaf extract containing flavonoids such as quercetin has antioxidant activity that can inhibit free radicals. These antioxidant compounds are very important in the content of active substances in a
Clinical Evaluation of Collagen-Induced Arthritis in Female Lewis Rats: A Comprehensive Analysis of Disease Progression and Severity

Objective: The collagen-induced arthritis (CIA) model is the most commonly studied autoimmune model of rheumatoid arthritis (RA). In this study, we investigated the usefulness of collagen type II emulsified in Freund's incomplete adjuvant (CII/IFA) as a suitable method for establishing RA in Lewis rats. The aim of the present study was to present a straightforward and effective method for inducing CIA in rats. Materials and Methods: In this experimental study, animals were divided into two equal groups (n=5); control and CIA. Five rats were injected intradermally at the base of the tail with a 0.2 ml CII/IFA emulsion. On the seventh day, a 0.1 ml CII/IFA emulsion booster was injected. Arthritis symptoms that arose were evaluated at clinical, histological, radiological, and at protein expression levels to find out if the disease had been induced successfully. Results: Our finding showed a decreasing trend in the body weight during the RA induction period, while the arthritis score and paw thickness were increased during this period. The results of the enzyme-linked immunosorbent assay (ELISA) for serum samples revealed that the levels of proinflammatory cytokines, interleukin (IL)-1β, IL-6, IL-17, and tumor necrosis factor (TNF)-α and anti-CII IgG were significantly increased in CIA rats compared to the control group. After CIA induction, the level of anti-inflammatory protein IL-10 was decreased significantly. Radiographic examination of the hind paws showed soft tissue swelling, bone erosion, and osteophyte formation in CIA rats. Additionally, based on histological evaluations, the hind paws of the CIA group showed pannus formation, synovial hyperplasia, and bone and cartilage destruction. Conclusion: It seems that CII/IFA treatment can be an appropriate and effective method to induce RA disease in Lewis rats. This well-established and well-characterized CIA model in female Lewis rats could be considered to study aspects of RA and develop novel anti-arthritic agents.
New approaches for human epidermal growth factor receptor 2-low and human epidermal growth factor receptor 2-overexpressing metastatic breast cancer

Purpose of review In recent years, there has been a flurry of activity in the human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer space. New, powerful drugs like trastuzumab deruxtecan have challenged our fundamental definition of what HER2 expression means as a predictive biomarker. Recent findings Recent approvals of multiple agents in the second line-metastatic setting have given patients access to a variety of new agents, but also raise questions with regard to optimal sequencing. Summary This review will explore current issues with HER2 testing, recently approved drugs in the HER2+ and HER2 low spaces, as well as novel agents/combinations on the horizon.
Peripheral membrane proteins mediate binding of vacuolar storage proteins to membranes of the secretory pathway of developing pea cotyledons

In developing pea cotyledons, storage proteins are sorted via dense vesicles into the protein storage vacuole. Formation of these unique transport vesicles is characterized by aggregation of their cargo proteins. Protein sorting into dense vesicles is pH dependent. In order to gain insight into the molecular basis of storage protein sorting, a membrane binding assay was developed which allows for a detailed biochemical analysis of binding events. Employing this assay it was possible to show that storage proteins bind in a pH-dependent manner to the membranes of the secretory pathway with a pH optimum in the range of the lumenal pH of the Golgi cisternae. Through reconstitution experiments, it was possible to demonstrate further that this recruitment occurs via the interaction of peripheral rather than intrinsic membrane proteins. Results of co-immunoprecipitation experiments point to interactions between different storage proteins in the secretory system. These results are discussed in terms of the aggregation-mediated sorting of storage proteins into maturing dense vesicles.
Involvement of the thylakoidal NADH-plastoquinone-oxidoreductase complex in the early responses to ozone exposure of barley ( Hordeum vulgare L . ) seedlings

Apossible implicationof theplastidNADH-plastoquinoneoxidoreductase (Ndh) complex in the response against ozone-mediated oxidative stress in barley (Hordeum vulgareL.) leaveswas investigated.After a 4h treatment, exposure of barley seedlings to moderate ozone concentrations produced leaf-age-dependent increases in lipid peroxidation, peroxidase, and Ndh complex activities in the thylakoid membranes. A significant amount and activity of the Ndh complex were detected in mature barley leaves, but not in young barley leaves. In fact, youngbarley leaves behaved likendh-deficient leaves in most of the aspects studied. When plants were exposed to photo-oxidative light after ozone fumigation, the recoveryofFv/Fmwas lower in young leaves than inmature leaves. Ozone treatment significantly decreased nonphotochemical quenching (qN) in young leaves, but not in mature leaves. Mature leaves showed higher levels of the energy (DlH1) dependent (qE) component of qN. Treatment with antimycin A, an inhibitor of cyclic electron flow, increased the decay of qN produced by ozone in young leaves, but not in mature ones. The reduction state of plastoquinone increased after ozone treatment in mature dark-adapted leaves and was strongly quenched by far red light. It is proposed that the function of the Ndh complex helps the maintenance of qN, probably through the poising of the redox steady-state level of the intersystem carriers and then by optimizing the rate of cyclic electron flow. This should constitute an age-dependent early response in barley leaves, by contributing to minimize photoinhibition in the presence of ozone and high light.
DigitalCommons@SHU DigitalCommons@SHU

The histologic transformation (HT) of Waldenström macroglobulinemia (WM) into diffuse large-cell lymphoma is an uncommon but poor-prognostic event for which there is no standard therapy. Knowledge of this entity is mainly derived from largely retrospective studies, which report abysmal average survival rates even with the utilization of first-line chemoimmunotherapy and especially in patients who meet the high-risk criteria based on prognostic indices used for WM. We present the case of a 75-year-old man with high-risk, transformed WM who was ineligible for standard chemoimmunotherapy (due to pancytopenia and multiple comorbidities) and was consequently treated with tafasitabmab, an anti-CD19 monoclonal antibody plus lenalidomide. Tafasitamab plus lenalidomide (TAF/LEN) is a recently approved therapy for relapsed or refractory de novo diffuse large-cell lymphoma (DLCL) but has not been previously studied in transformed low-grade lymphomas or WM. We show that TAF/LEN resulted in a complete and durable response of the DLCL by PET/CT and a complete bone marrow response of lymphoplasmacytoid cells, including the normalization of complex cytogenetic abnormalities. The extraordinary response of our patient to TAF/LEN suggests that this combination may be an effective and tolerable therapy for transformed WM as well as relapsed or refractory non-transformed WM. Clinical trials of TAF/LN for the treatment of Waldenström macroglobulinemia are recommended.
Design of Horizontally Aligned Six-Plate Capacitive Power Transfer for EV Charging Applications

Researchers have shown interest in wireless power transfer (WPT) technology for EV charging due to its salient features of safety, convenience, and the possibility of opportunity charging. Inductive power transfer (IPT) has been used commonly; however, it has an issue of misalignment intolerance and high cost. Alternatively, the capacitive power transfer (CPT)-based charging systems can be utilized, which offers better misalignment, lower cost, and lightweight. However, it displays lesser power density and efficiency than IPT systems. Therefore, the design of the CPT coupler is crucial to improving the CPT system performance. This paper presents a group of horizontally aligned six-plate couplers to achieve higher power transfer capability with better electric field shielding. The performance of these CPT couplers is holistically evaluated to get the best CPT coupler structure through finite-element analysis (FEA) in ANSYS-MAXWELL software. The results shows that HCC3 coupler performs better in terms of electric field emissions and HCC1 is suitable for high power EV charging.
Performance Analysis of a Predictive Controller Using Recursive and Non-Recursive Models Applied to a Speed System

In recent years, control techniques are in continuous improvement, in order to fulfill the performance requirements and guarantee the robustness and stability of the systems. Among the predictive controllers, the Generalized Predictive Control (GPC) controller stands out, which uses a process model to estimate the future response, aiming to determine the control signal. In this work, the goal was to verify the performance of this type of controller when applied in a speed control system, using both the Recursive Least Squares Method (RLSM) and Non-Recursive Least Squares Method (NRLSM) estimators to compute the coefficients of Controlled Auto-Regressive Integrated Moving Average model (CARIMA) used in GPC. Finally, this study analysed the performance of the GPC controller and compared to the classic Proportional Integral Derivative (PID) and all controllers had a satisfactory performance in relation to tracking references without overshoot, with emphasis on the GPC-RLSM, which had lower rise time than others.
Innovation in Livestock Surveillance: Applying the YOLO Algorithm to UAV Imagery and Videography

With the advancement of time, many aspects of human life are developing, one of which is the field of animal husbandry which is currently getting larger, and with the increasing size of farms, a livestock monitoring system is now needed to monitor cattle that are not in their cage, such task is expected to be achieved by using YOLOv5 on images taken by drones. YOLOv5 is an object detection model used to detect cows on farms through images taken by drones or UAVs, to achieve this detection capability, training is carried out using a dataset of cow images and also changing the hyperparameter configuration in the algorithm to get optimal detection results. The dataset used in this training is 3131 cow images and 836 non-cow images. The most optimal hyperparameter configuration obtained is batch 32, learning rate 0.01, and epoch 350 with the results of precision 0.943, Recall 0.925, and mAP 0.831. From the accuracy obtained at five meters and drone not moving is 75%, height of ten meters has an average accuracy of 69.1% and 15 meters with an average accuracy of 47.2%. In the condition of the drone moving at the speed of 0.1 m/s the accuracy results obtained are 75% for five meters, 0% for ten meters and 25% for 15 meters.
Uncovering the Share Fake News on Social Media During Crisis

The COVID-19 pandemic has been accompanied by an unprecedented amount of information and misinformation, making it challenging for individuals to discern accurate information from false information. To understand the spread of false information during the COVID-19 pandemic, researchers conducted a study using structural equation modeling among a sample of 176 students from the Faculty of Mass Communication at United Arab Emirates University. The study found that the spread of false information during the COVID-19 pandemic is influenced by several factors, including people's susceptibility to misinformation, their trust in traditional and social media, and their attitudes toward the pandemic. Specifically, the study found that people who are more susceptible to misinformation are more likely to believe and spread false information. Additionally, people who have low trust in traditional media and high trust in social media are more likely to believe and spread false information. Finally, people who hold skeptical attitudes toward the severity of the pandemic are also more likely to believe and spread false information.
Toward Project Success Forecasting in Reward-based Crowdfunding through Wide-and-Deep Computational Learning

While reward-based crowdfunding platforms centered like Kickstarter in the United States, allow entrepreneurs or well-off investors like industrial owners to gather assets and seek patron support, they have evolved into an option for generating funds via an internet platform. Conversely, these crowdfunding initiatives pose innovation challenges due to the uncertainty of receiving support and success. Both project developers and supporters may benefit from it for better knowledge and more accurate forecasting of project success. Analyzing a completed project information, this study aims to identify critical success characteristics of crowdfunding. It investigates the effectiveness of wide-and-deep learning in predicting models. These findings can help fundraisers understand the likelihood of successful fundraising initiatives.
Multiple-level Omics Data based Cancer Subtypes Prediction by Multi-view Subspace Learning

Accurate forecast of cancer subtypes is rather important in the malignancy diagnosis. Disease etiology is sophisticated, which manifests in diverse omics levels. Therefore, the elaborate fusion of multiple-level omics is very helpful to the analysis of cancer subtypes. In this paper, multiple-level omics data based cancer subtypes prediction is treated as a multi-view subspace clustering problem, where integrating the complemental information among multiple omics data to build up a unified representation is a pivotal challenge. Due to the fact that the great inconsistency lies among diverse omics levels, which leads to an unsatisfactory integrative effect via traditional multi-view clustering. To address this issue, different data on omics levels is fused under the spectral embedding domain, since even the high-inconsistency data across all omics level still have a rather analogous spectral block structure. Owning to the fact that the spectral block structure across all omics levels satisfies the great consistency, the data across all omics levels would be melted into a common representation with slight information damage. Apart from that, to enhance the global structure of subspace across all omics levels, an innovative low-rank relaxation is established by resorting to the the logarithmic determinant function. Finally, experimental outcome exhibits that the raised approach achieves superior behavior than existing prediction schemes.
Automated Driving Capabilities using advanced software tools to improve optimization for Autonomous Vehicle in Industry 4.0

Autonomous vehicles are still evolving in Industry 4.0 and moving closer to becoming a reality. As technology advances in the context of intelligent world capabilities for self-driving cars, systems become more complicated and humans as engineers must employ several techniques to produce a product with fewer flaws. The graphical representation of technological developments is rising quickly over time and will reach its apex soon. Since it is extremely difficult to create a product from zero in this market, developers may benefit greatly from the industry's cutting-edge tools like MATLAB & Simulink for creation, analysis, simulation, testing, and verification. These tools for software development offer excellent solutions at every stage of creating a strong and efficient targeted goal. The self-driving car and its advancements, including ADAS levels, functional building blocks, smart sensors, sensor fusion capabilities, and the integration of these physical systems with cutting-edge software tools to address problems that arise in the real world, are the focus of this research. Additionally, autonomous cars can use interdisciplinary elements to strengthen them. Finally, we have proposed a strategy that entails building an effective architecture, employing data optimization techniques for efficient data processing, using the V-Model testing and verification cycle for autonomous cars, and integrating Auto Code generation into independent standalone application deployment features.
Crops Classification Using Machine Learning And Google Earth Engine

The primary objective of this study was to address the critical challenge of obtaining accurate information regarding the spatial distribution and classification of crops in agricultural areas. The aim was to enhance agricultural decision-making and management, especially in regions with limited water resources, where crop productivity and sustainability are crucial for achieving food security and sustainable development. To achieve this objective, this study utilized machine-learning classification algorithms in conjunction with Landsat and Sentinel satellite imagery. The classification of different crops was based on Normalized Difference Vegetation Index (NDVI) phenology . The classification process and post-processing were conducted using the Google Earth Engine (GEE) platform , as well as utilizing Python, and scikit-learn library. Ground-truth data provided by local experts, along with the EUROMAP 2018 dataset in South Spain, were used to label the classification results . The findings of this study demonstrated a classification accuracy of 72% for certain crop types, indicating significant implications for sustainable agricultural practices and land use planning.