Experimental Phase Research Articles

Search for charged excited states of dark matter with KamLAND-Zen

Particle dark matter could belong to a multiplet that includes an electrically charged state. WIMP dark matter (χ0) accompanied by a negatively charged excited state (χ−) with a small mass difference (e.g. < 20 MeV) can form a bound-state with a nucleus such as xenon. This bound-state formation is rare and the released energy is O(1−10) MeV depending on the nucleus, making large liquid scintillator detectors suitable for detection. We searched for bound-state formation events with xenon in two experimental phases of the KamLAND-Zen experiment, a xenon-doped liquid scintillator detector. No statistically significant events were observed. For a benchmark parameter set of WIMP mass mχ0=1 TeV and mass difference Δm=17 MeV, we set the most stringent upper limits on the recombination cross section times velocity 〈σv〉 and the decay-width of χ− to 9.2×10−30cm3/s and 8.7×10−14 GeV, respectively at 90% confidence level.

Analysis of MR–VR tele-operation methods for legged-manipulator robots

The development of immersive technologies in recent years has facilitated the control and execution of tasks at a high level of complexity in robotic systems. On the other hand, exploration and manipulation tasks in unknown environments have been one of the main challenges in search and rescue (SAR) robotics. Due to the complexity and uncertainty involved in autonomous manipulation tasks in unstructured environments, these are usually tele-operated initially. This article addresses a comparative study between Mixed Reality (MR—Hololens) and Virtual Reality (VR—HTC-Vive) methods for teleoperating legged-manipulator robots in the context of search and rescue. For this purpose, a teleoperation robotics method was established to address the comparison, developing VR–MR interfaces with the same contextualization and operational functionality for mission management and robot control of a robotic set composed of a quadrupedal robot equipped with a 6 degrees of freedom (6DoF) manipulator, by a user using hand gestures. A set of metrics is proposed for the comparative evaluation of the interfaces considering parameters that allow analyzing operability in the context of the mission (latencies, physical parameters of the equipment, etc.), as well as from the aspect of operator performance (required training, confidence levels, etc.). The experimental phase was conducted using both on-site and remote operations to evaluate and categorize the advantages and disadvantages of each method.

Development of a Premium Tea-Picking Robot Incorporating Deep Learning and Computer Vision for Leaf Detection

Premium tea holds a significant place in Chinese tea culture, enjoying immense popularity among domestic consumers and an esteemed reputation in the international market, thereby significantly impacting the Chinese economy. To tackle challenges associated with the labor-intensive and inefficient manual picking process of premium tea, and to elevate the competitiveness of the premium tea sector, our research team has developed and rigorously tested a premium tea-picking robot that harnesses deep learning and computer vision for precise leaf recognition. This innovative technology has been patented by the China National Intellectual Property Administration (ZL202111236676.7). In our study, we constructed a deep-learning model that, through comprehensive data training, enabled the robot to accurately recognize tea buds. By integrating computer vision techniques, we achieved exact positioning of the tea buds. From a hardware perspective, we employed a high-performance robotic arm to ensure stable and efficient picking operations even in complex environments. During the experimental phase, we conducted detailed validations on the practical application of the YOLOv8 algorithm in tea bud identification. When compared to the YOLOv5 algorithm, YOLOv8 exhibited superior accuracy and reliability. Furthermore, we performed comprehensive testing on the path planning for the picking robotic arm, evaluating various algorithms to determine the most effective path planning approach for the picking process. Ultimately, we conducted field tests to assess the robot’s performance. The results indicated a 62.02% success rate for the entire picking process of the premium tea-picking robot, with an average picking time of approximately 1.86 s per qualified tea bud. This study provides a solid foundation for further research, development, and deployment of premium tea-picking robots, serving as a valuable reference for the design of other crop-picking robots as well.

A Deep Learning Model Based on the Introduction of Attention Mechanism is Used to Predict Lithium-Ion Battery SOC

In order to enhance the accuracy of state of charge (SOC) prediction for lithium-ion batteries, this paper developed a deep learning model optimized by slime mold algorithm (SMA). This model combines convolutional neural networks, bidirectional gated recurrent units, and attention mechanisms. Through SMA optimization of critical parameters in the model, the predictive performance has been significantly improved. In the experimental phase, the paper collected discharge data from an 18650 battery pack under 8 different operating conditions, totaling 10596 sets of data. These data were used to fully train and validate the model. The test results show that the new model demonstrates exceptional accuracy in SOC prediction, with average absolute error, root mean square error, and mean absolute percentage error reaching 0.46462%, 0.56406%, and 6.8028%, respectively. Moreover, the decision coefficient R reaches 0.962. This result not only surpasses single models and unoptimized models but also provides important technical support for improving the battery life and driving range of electric vehicles.

Sacral Magnetic Neuromodulation with Intermittent Theta Burst Waveform Enhances Overactive Bladder: In Vivo Study

BackgroundThe current treatment options for overactive bladder (OAB) continue to pose challenges for refractory cases and may involve invasive procedures. To assess the potential benefit of non-invasive repetitive peripheral magnetic stimulation (rPMS) on sacral roots using intermittent theta burst stimulation (iTBS) as treatment option for OAB. The study involved a total of 33 rats, which were divided into three different experimental phases. Materials and methodsTo induce bladder overactivity rats were pretreated with a continuous transvesical infusion of 0.5% acetic acid (AA). During bladder infusion, the intravesical pressure was recorded using cystometrography (CMG) to investigate the effects of AA pretreatment and the therapeutic intervention of acute sacral rPMS using iTBS. ResultsPre-application of rPMS with iTBS at a 100% intensity significantly extended the mean first voiding time (Tv) in normal healthy rats to 132%. Acute rPMS iTBS at a 100% intensity resulted in a significant increase of the inter-contraction interval (ICI) to 121%. An AA model was established with continuous saline infusion after 0.5% AA treatment and resulted in significant reductions of Tv to 42% and ICI to 56% of the corresponding control values. Subsequently, rPMS iTBS at a 100% intensity on the sacral nerve effectively inhibited AA-induced bladder overactivity and significantly increased the ICI to 167%∼222%. No significant changes in maximum bladder pressure (Pmax) were found. ConclusionsSacral nerve rPMS with iTBS demonstrated the ability to suppress AA-induced bladder overactivity. This promising modality could be developed as an alternative approach to enhance bladder continence in OAB syndrome patients.

A novel approach for handling missing data to enhance network intrusion detection system

Managing missing data is a critical challenge in Intrusion Detection System (IDS) datasets, significantly affecting the performance of deep learning models. To address this issue, we introduce DeepLearning_Based_MissingData_Imputation (DMDI), a novel method designed to enhance the quality of input data by efficiently handling missing values. Our approach employs the Random Missing Value (RMV) algorithm to simulate missing data, enabling thorough testing and comparison of various imputation techniques. The DMDI method integrates a stacked denoising autoencoder with Gradient Boosting to improve imputation accuracy. We evaluated the effectiveness of our approach through three experimental phases: generating missing data, imputing missing values, and assessing imputation models. Using the NSL-KDD and UNSW-NB15 datasets, our results demonstrate significant improvements in the performance of five different classifiers (SVM, KNN, Logistic Regression, Decision Tree, and Random Forest) after imputation. On average, our method achieved accuracy improvements ranging from 0.95 to 0.97 across these classifiers compared to baseline imputation methods. Detailed analysis using Python 3 validates our findings, demonstrating enhanced model performance and robustness. This study underscores the necessity of precise missing data imputation for enhancing deep learning tasks, particularly in anomaly detection systems. It provides a reliable solution for managing missing data in IDS datasets.

Self-organized vortex phases and hydrodynamic interactions in Bos taurus sperm cells.

Flocking behavior is observed in biological systems from the cellular to superorganismal length scales, and the mechanisms and purposes of this behavior are objects of intense interest. In this paper, we study the collective dynamics of bovine sperm cells in a viscoelastic fluid. These cells appear not to spontaneously flock, but transition into a long-lived flocking phase after being exposed to a transient ordering pulse of fluid flow. Surprisingly, this induced flocking phase has many qualitative similarities with the spontaneous polar flocking phases predicted by Toner-Tu theory, such as anisotropic giant number fluctuations and nontrivial transverse density correlations, despite the induced nature of the phase and the clearly important role of momentum conservation between the swimmers and the surrounding fluid in these experiments. We also find a self-organized global vortex state of the sperm cells, and map out an experimental phase diagram of states of collective motion as a function of cell density and motility statistics. We compare our experiments with a parameter-matched computational model of persistently turning active particles and find that the experimental order-disorder phase boundary as a function of cell density and persistence time can be approximately predicted from measures of single-cell properties. Our results may have implications for the evaluation of sample fertility by studying the collective phase behavior of dense groups of swimming sperm.

Impact of Different Amounts of Lithium Plating on the Thermal Safety of Lithium Ion Cells

Safety tests are usually conducted on fresh cells. However, occurring lithium plating for example due to harsh aging conditions or electrode inhomogeneities can negatively affect the thermal properties of cells over their lifespan. Recent literature studies showed serious deterioration of the thermal cell properties due to lithium plating while other studies showed no impact at all. These differences are at least partly explained by different amounts of metallic lithium. Here, the impact of the amount of lithium plating on the thermal cell properties was investigated. 1 Ah LiNi0.8Co0.1Mn0.1O2 (NMC811)||artificial graphite pouch cells were aged at 0 °C between zero and ten cycles. The amount of lithium plating was found to influence the self-heating-rates reached during the initial phase of a thermal safety experiment, but did not have a major impact on the safety at higher temperatures. Despite the presence of lithium plating of up to 15% of the initial capacity, none of the cells showed exothermic self-heating for more than three consecutive measuring points below 85 °C. An impact on the onset temperature of first permanent exothermic reactions could only be reliably detected if a cell had already suffered from 10% capacity loss due to lithium plating.

Non-Contact Laser Doppler Vibrometer for Characterization of Guided Waves in Timber Structures with Varying Moisture Content and Grain Orientations

Timber structures have been widely used in critical engineering applications throughout history. The need for Non-Destructive Testing of timbers has arisen, and ultrasonic-guided waves (GWs) have proved their efficacy in studying structural integrity. The effect of the grain direction in orthotropic timber structures on the propagation characteristics of GWs is not well understood, especially in the presence of moisture. This study aims to comprehend the effects of timber-grain orientations and the moisture content (MC) on dominant GW modes, specifically, on the anti-symmetric A0 mode. The study is based on samples extracted from a Western White Pine utility pole, in three different orientations including longitudinal, tangential, and radial. The MC is introduced using a desiccator with a saturated salt solution. The studied MC conditions included dry, ambient MC, and high MCs varying from 10% to 27%. The GWs were excited using piezoelectric actuators and sensed with a Scanning Laser Doppler Vibrometer with Free-Free boundary condition. Phase velocities were obtained using the frequency-wavenumber plots of the measured line scans and were compared to the analytical curves for mode analysis. The results showed that the experimental A0-mode phase velocities match well with those generated analytically. The difference was negligible for longitudinally oriented specimens with a percentage difference ranging from 0.28% to 5.34%, as the MC varied. The phase velocity decreased by about 7% as the MC increased from 0% to 27%. A noticeable difference in the phase velocity was measured with different orientations; at dry conditions, the A0 propagated at 958 m/s in the longitudinal specimens, while it decreased in the tangential specimens to about 760 m/s, and had the lowest phase velocity in the radial direction reaching 433 m/s. The results show a high sensitivity of the propagating GWs to the direction of the fibers and the MC present in the timber.

Tamanhos de parcelas e amostras para variáveis biométricas em mudas de alface

ABSTRACT: In order to ensure reliable inferences in scientific experiments, sampling criteria should be determined and defined during the experimental design phase. Determining plot and sample sizes without scientific criteria could result in resource and labor inefficiencies with no gain in experimental precision. In this work we estimate plot and sample sizes for biometric variable analyses in lettuce seedlings. The seedlings were sown in four expanded polystyrene foam transplant trays with 128 and 200 cells. Foliar area, stem diameter, shoot height, root length, root volume, root area, fresh and dry shoot and root masses. The methods described by Paranaiba (PARANAIBA et al., 2009) and Cochran (COCHRAN, 1977) were used for plot size and sample size estimations, respectively. Sample size varied according to the variables analysed and transplant tray cell number. Root variables required the highest number of samples. For a 10% margin of error, 12 seedlings were necessary for evaluations in a 128 cell tray, while 11 seedlings were required for analysis in 200 cell trays.

Realidad aumentada en educación universitaria

This study presents the “Augmented Reality in University Education” project, whose objective was to develop and evaluate mobile applications with augmented reality (AR) to improve the learning of Information Technology students at the Technological University of Nayarit. The literature review offers a clear definition of AR and documents international research on its use in education, with a specific analysis in the state of Nayarit. The research methodology was of a mixed nature, with a quasi-experimental design, in the development of the applications the agile Scrum methodology was used, subsequently implementing an experimental evaluation phase to verify the functionality and effectiveness of the apps in the educational environment; Questionnaires, observations and interviews were used to collect data on usability, motivation and improvement in student learning. Three mobile applications were created for the Data Structure and Databases subjects. The results showed that the applications are functional and were valued by students in usability, motivation and usefulness as an educational complement. The conclusions highlight that the applications allow students to use interactive models to understand abstract concepts, which improves their learning experience.

Digital transformation, blockchain, and the music industry: A review from the perspective of performers’ collective management organizations

This study examines the challenges related to the music industry's digital transformation and potential role of blockchain from the perspective of collective management organizations (CMOs). Building on desk research and primary data from semi-structured surveys conducted with C-level executives and managers, this empirical analysis identifies major projects, their state of development, and prospects for digital transformation based on blockchain in the music industry. The findings reveal that there are a limited number of blockchain projects led by and/or with the relevant participation of CMOs. However, most are just research projects, proofs of concept, or pilots, showing that blockchain is currently in an experimental phase of development on the periphery of the music industry's digital transformation. This is not very different from analysts' understanding of the current situation and perspectives on the mass adoption of blockchain in other industries. In summary, blockchain is neither at the core of the music industry's digital transformation nor a priority for CMOs leading this process from the perspective of intellectual property rights management. The limited quality of music metadata, sparsity of sound data sources, and absence of a common data governance framework among different stakeholders within the industry are the main impediments to transforming blockchain into a global solution with transformative potential. Overall, the results provide a snapshot of the current status and possible future trajectories of blockchain adoption as a paradigm for intellectual property rights exchange within the music industry.

Numerical Analysis of Flow Velocity and Performance Test of Cross-Flow Turbine (500 W)

The purpose of this study is to analyze the flow velocity and assess the performance of a cross-flow turbine through numerical simulations and performance tests. Using computational fluid dynamics (CFD), the flow patterns and velocity distributions around the turbine blades to gain insights into the turbine's hydrodynamic behavior. In the experimental phase, constructed a prototype of the cross-flow turbine and tested it in a water flume, measuring key performance metrics such as power output and efficiency.The test were performed under design flow condition. The numerical results were validated with performance tests. The numerical analysis revealed critical areas of flow separation and turbulence, which were consistent with the observed experimental data.

A Review on Engineering Behaviour of Sustainable Concrete with Steel Mill Scale and the Effect of Admixture on Workability Improvement

Abstract: The integration of chemical admixtures and alternative materials in concrete production has become a focal point in advancing construction technologies. This study investigates the combined effects of superplasticizers and mill scale on the workability and compressive strength of M30 grade concrete. Three different superplasticizers—Auramix 200, SikaPlast®-3069 NS, and Master Polyheed 8126—are evaluated for their performance enhancements in concrete. Additionally, mill scale, an industrial by-product, is utilized to partially replace sand in varying proportions (0%, 20%, 40%, 60%, 80%, and 100%) to assess its viability as a sustainable aggregate alternative. The experimental phase is divided into three stages: initially assessing the workability and compressive strength of M30 concrete without admixtures; subsequently incorporating mill scale to observe its effect on concrete properties; and finally combining mill scale with each superplasticizer to maintain workability while evaluating the compressive strength. Slump cone tests are conducted to measure workability, and compressive strength tests are performed at 7, 14, and 28 days. Results indicate that the inclusion of superplasticizers significantly enhances workability, with Auramix 200 showing the highest improvement. The addition of mill scale up to 60% replacement level increases compressive strength, with a notable peak at 60% replacement for both plain and superplasticizer mixes. However, higher replacement levels (80% and 100%) result in reduced strength and workability. Among the superplasticizers, Auramix 200 exhibits the best overall performance in conjunction with mill scale. This study concludes that the strategic use of superplasticizers and mill scale can produce high-performance, sustainable concrete. Future research should explore long-term durability aspects and optimize mix designs for broader applications in construction

Engineering Behaviour of Sustainable Concrete with Steel Mill Scale and the Effect of Admixture on Workability Improvement

Abstract: The integration of chemical admixtures and alternative materials in concrete production has become a focal point in advancing construction technologies. This study investigates the combined effects of superplasticizers and mill scale on the workability and compressive strength of M30 grade concrete. Three different superplasticizers—Auramix 200, SikaPlast®-3069 NS, and Master Polyheed 8126—are evaluated for their performance enhancements in concrete. Additionally, mill scale, an industrial by-product, is utilized to partially replace sand in varying proportions (0%, 20%, 40%, 60%, 80%, and 100%) to assess its viability as a sustainable aggregate alternative. The experimental phase is divided into three stages: initially assessing the workability and compressive strength of M30 concrete without admixtures; subsequently incorporating mill scale to observe its effect on concrete properties; and finally combining mill scale with each superplasticizer to maintain workability while evaluating the compressive strength. Slump cone tests are conducted to measure workability, and compressive strength tests are performed at 7, 14, and 28 days. Results indicate that the inclusion of superplasticizers significantly enhances workability, with Auramix 200 showing the highest improvement. The addition of mill scales up to 60% replacement level increases compressive strength, with a notable peak at 60% replacement for both plain and superplasticizer mixes. However, higher replacement levels (80% and 100%) result in reduced strength and workability. Among the superplasticizers, Auramix 200 exhibits the best overall performance in conjunction with mill scale. This study concludes that the strategic use of superplasticizers and mill scale can produce high-performance, sustainable concrete. Future research should explore long-term durability aspects and optimize mix designs for broader applications in construction.

TARGET IDENTIFICATION AND TRACKING IN COMPLEX ENVIRONMENT

This study focuses on developing advanced methods for the identification and classification of objects in complex environments. Over the past two years, there has been an increase in the use of advanced technologies in various challenging scenarios. This research is centered on accurately identifying targets and tracking them. The study addresses challenges related to object detection in multi-dimensional and intricate settings, taking into account natural conditions like rain and fog, as well as technical limitations such as camera capabilities. Special emphasis is placed on data collection for training the identification model, followed by extensive data preprocessing, including cleaning, labeling, and augmentation. The research employs YOLO and Deep Sport machine learning algorithms, focusing on improving the accuracy and reliability of target recognition and increasing data processing speed to minimize misidentification risks. The integration of YOLO, known for its quick real-time object detection, with Deep Sport, which excels in detailed feature extraction and classification, forms the basis of our methodology. This fusion is a complex combination of both models' strengths, with YOLO quickly identifying relevant objects and Deep Sport performing an in-depth analysis. The experimental phase involves extensive testing of the models in varied weather conditions and settings to evaluate performance under challenging circumstances. This work aims to enhance object identification techniques in complex environments, a critical aspect for the effectiveness of various advanced operations. The findings are expected to significantly contribute to the field by enabling quicker and more accurate target identification.

Efficiency of 7-dehydrocholesterol vitamin-D3 complex cream for xerosis and pruritus in elderly women

Background: Xerosis and pruritus (XP) are the most common skin conditions in the elderly, manifesting clinically as roughness, scales, fissures, and mild-to-severe itching. Vitamin D improves skin hydration, transepidermal water loss (TEWL), and regulates the immune system. We aimed to determine the efficiency of topical 7-dehydrocholesterol (DHC)-vitamin D3 complex cream in elderly patients with XP. Methods: An experimental phase III study was performed on elderly women with XP from December 2021 to March 2022 at the Geriatric Dermatology Clinic of Dr. Mohammad Hoesin General Hospital, Palembang. Inclusion criteria were age older than 55 years, healthy or with comorbidity. The exclusion criteria include the use of moisturizer within two weeks before the study. DHC-vitamin D3 cream was applied on subject’s volar arms once a day. Overall dry skin score (ODSS), visual analog scale (VAS) for pruritus and adverse effects, skin hydration, TEWL, sebum level, serum vitamin D level, and dermatology life quality index (DLQI) were assessed at baseline and after 4 weeks. Statistical analysis was done with Wilcoxon and paired T-test. Results:Eighteen elderly women were included. Most subjects were 60 to 74 years old (83,3%). Half the subjects (50%) had comorbidity. There was a significant difference in skin hydration (p Conclusion: Topical 7-DHC vitamin D3 complex cream is proven to improve skin barriers, increase serum vitamin D levels, well-tolerated, and provided satisfaction and comfort in elderly women with XP.

Optimizing Crowd Counting in Dense Environments Through Curriculum Learning Training Strategy

Counting individuals in highly crowded environments, characterized by thousands of people, has garnered significant attention in recent years, due to the high number of vertical markets wherein such algorithms can prove beneficial, ranging from smart city and transportation to retail sectors, among others. Within this context, in this paper we introduce a novel training methodology tailored for estimating the number of people, ensuring precise counting accuracy in both moderately and highly crowded scenarios. The proposed approach exploits a formulation of the problem based on point detection, where each point represents an individual’s head. Our innovative contributions center around the designing of a novel training strategy employing Curriculum Learning (CL), which aims to replicate the gradual learning process observed in human cognition, training on simpler tasks at the beginning and tackling more complex tasks as the training evolves. In order to evaluate the complexity of each sample image, we propose a novel indicator taking into account both the number of people and their distribution within the image. The experimentation phase encompassed 18 publicly available datasets; the obtained results validate the effectiveness of the proposed approach, surpassing the baseline state-of-the-art point detection by 71% and 70% in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE), respectively

Revolutionizing cultural heritage preservation: an innovative IoT-based framework for protecting historical buildings

Italy offers a cultural heritage of considerable value that needs to be protected. Indeed, natural deterioration linked to the passage of time affects ancient artifacts and buildings. Sometimes, the deterioration compromises the functionality of cultural assets, pushing them toward decay. In this scenario, effective intervention seems impossible on the various critical points because of the wide variability of factors involved and the wide range of possible treatments. However, the spread of low-cost technologies has led to the possibility of having different devices and sensors able to communicate and interact with each other and humans: the Internet of Things (IoT). In this scenario, the IoT paradigm makes it possible to map reality by defining a coherent virtual representation (Digital Twin), which could help preserve Cultural Heritage. This work introduces an IoT-based system combining monitoring, predictive maintenance, and decision-making regarding the implementable interventions for protecting cultural heritage buildings. For this purpose, deep and machine learning techniques allow for the detection and classification of damages on specific materials. The experimental phase consists of two phases: the first aims to evaluate the accuracy of the proposed architecture, and the second exploits a prototype capable of interacting with expert users. The results of the experimental campaign are promising.

Deep-learning map segmentation for protein X-ray crystallographic structure determination.

When solving a structure of a protein from single-wavelength anomalous diffraction X-ray data, the initial phases obtained by phasing from an anomalously scattering substructure usually need to be improved by an iterated electron-density modification. In this manuscript, the use of convolutional neural networks (CNNs) for segmentation of the initial experimental phasing electron-density maps is proposed. The results reported demonstrate that a CNN with U-net architecture, trained on several thousands of electron-density maps generated mainly using X-ray data from the Protein Data Bank in a supervised learning, can improve current density-modification methods.