Handling System Research Articles

Fabrication and electroadhesion properties of parylene-coated carbon fiber arrays

Parylene-coated carbon fiber (CF) arrays with tunable inclination angles and heights were fabricated using oxygen plasma etching of composite wafers with embedded parallel CFs, followed by parylene coating via chemical vapor deposition. The effective elastic modulus of the CF arrays was found to decrease approximately in proportion to the square of the fiber length (5-60μm), with the parylene coating (∼2μm) further slightly reducing the modulus. Both experimental measurements and finite element simulations indicated that CF arrays with inclination angles below 75° exhibit ideal contact with glass wafers during electrostatic adhesion. However, the measured electrostatic adhesion between CF arrays and A4 paper was significantly lower than the predicted value for ideal contact, likely due to the porous nature of the paper. Electrostatic chuck prototypes based on the parylene-coated CF arrays demonstrated effective pick-and-place capabilities for A4 paper, plastic films, and glass wafers at voltages ranging from 500 to 900 V, without causing surface damage or leaving residue. These results highlight the potential of the parylene-coated CF arrays for applications in high-precision manufacturing and automated handling systems.

Optimizing Fish Feed Pellets Handling Systems to Minimize Microplastics Emissions - A Pilot Test Based Approach

The fisheries and aquaculture industries are vital components of global food production, yet they also contribute to release of microplastics into marine environments, posing risks to ecosystem health and seafood safety. Among the contributing factors, the erosion of feeding pipes during the pneumatic conveyance of fish feed pellets stands out as a significant source of microplastic pollution. This study focuses on optimizing feed pellet conveying systems in fish farms to minimize microplastic emissions while maximizing pipeline lifespan and pellet integrity. Using high-density polyethylene (HDPE) pipes commonly found in aquaculture facilities, the impact of air velocity and pipeline configuration on pipe wall erosion and pellet breakage was investigated. Through pneumatic conveying tests, the effects of varying air flow rates and bend radii were assessed on pipeline wear and feed pellet integrity. The findings of the study underscored the importance of optimizing operating parameters to bring a balance between preventing pipe blockages and minimizing abrasive impacts on pellets and pipeline surfaces. Furthermore, a simulationbased approach to optimize feeding system performance is presented, integrating the experimental results into a computational model. This model allows for the evaluation of different operating conditions and pipeline configurations, offering insights into costeffective strategies for reducing microplastic emissions while maintaining efficient feed delivery to fish populations. Ultimately, this research provided practical recommendations and best practices for the aquaculture industry to mitigate microplastic pollution from feeding pipes. By optimizing feed pellet conveying systems, environmental sustainability can be enhanced, seafood quality be preserved, and bolster consumer confidence in aquaculture products.

Total RNA Extraction from Rice Vegetative Tissues Using Magnetic Beads.

Ribonucleic acid (RNA) extraction is the first critical step in gene expression analysis. In this chapter, we describe a high-throughput RNA extraction method using guanidine thiocyanate and isopropyl alcohol (HighGI). The use of carboxyl-coated paramagnetic beads, instead of silica membrane columns, enables semi-automation using a liquid handling system and high-throughput RNA extraction for large-scale transcriptome studies. Homemade mixes of paramagnetic beads and buffers make HighGI inexpensive. In addition, HighGI-extracted RNA retains low molecular weight RNA molecules less than 200bp, which is typically lost in commercial column-based kits.

Implementation of Natural Language Processing in the Reporting and Handling System of Sexual Violence Cases on Campus

Sexual violence in the campus environment is a serious problem that requires an effective reporting and handling system. This research aims to develop a Natural Language Processing (NLP)-based system that can improve the process of reporting and handling cases of sexual violence on campus. The methodology used includes the application of NLP techniques such as sentiment analysis and entity recognition to automate the identification and handling of reports. The Support Vector Machines (SVM) algorithm is used for the classification of text in this system. The data is collected from various sources, pre-processed, and used to train NLP models. The results of the study show that the system developed has an accuracy level of 91%, precision of 93%, and recall of 87%, which illustrates its effectiveness in collecting reports of sexual violence anonymously and accurately. Feedback from early adopters shows that the system improves the efficiency and accuracy of the reporting process. The conclusion of this study is that the implementation of NLP can significantly improve the reporting and handling system of sexual violence on campus. Further research is suggested to expand the scope of the system and improve its analysis capabilities.

Machine vision system for automatic defect detection of ultrasound probes

Industry 4.0 conceptualizes the automation of processes through the introduction of technologies such as artificial intelligence and advanced robotics, resulting in a significant production improvement. Detecting defects in the production process, predicting mechanical malfunctions in the assembly line, and identifying defects of the final product are just a few examples of applications of these technologies. In this context, this work focuses on the detection of ultrasound probes’ surface defects, with a focus on Esaote S.p.A.’s production line probes. To date, this control is performed manually and therefore biased by many factors such as surface morphology, color, size of the defect, and by lighting conditions (which can cause reflections preventing detection). To overcome these shortfalls, this work proposes a fully automatic machine vision system for surface acquisition of ultrasound probes coupled with an automated defect detection system that leverage artificial intelligence. The paper addresses two crucial steps: (i) the development of the acquisition system (i.e., selection of the acquisition device, analysis of the illumination system, and design of the camera handling system); (ii) the analysis of neural network models for defect detection and classification by comparing three possible solutions (i.e., MMSD-Net, ResNet, EfficientNet). The results suggest that the developed system has the potential to be used as a defect detection tool in the production line (full image acquisition cycle takes ~ 200 s), with the best detection accuracy obtained with the EfficientNet model being 98.63% and a classification accuracy of 81.90%.

Identification of inhibitors of human ChaC1, a cytoplasmic glutathione degrading enzyme through high throughput screens in yeast.

The cytosolic glutathione-degrading enzyme, ChaC1, is highly up-regulated in several cancers, with the up-regulation correlating to poor prognosis. The ability to inhibit ChaC1 is therefore important in different pathophysiological situations, but is challenging owing to the high substrate Km of the enzyme. As no inhibitors of ChaC1 are known, in this study we have focussed on this goal. We have initially taken a computational approach where a systemic structure-based virtual screening was performed. However, none of the predicted hits proved to be effective inhibitors. Synthetic substrate analogs were also not inhibitory. As both these approaches targeted the active site, we shifted to developing two high-throughput, robust, yeast-based assays that were active site independent. A small molecule compound library was screened using an automated liquid handling system using these screens. The hits were further analyzed using in vitro assays. Among them, juglone, a naturally occurring naphthoquinone, completely inhibited ChaC1 activity with an IC50 of 8.7 µM. It was also effective against the ChaC2 enzyme. Kinetic studies indicated that the inhibition was not competitive with the substrate. Juglone is known to form adducts with glutathione and is also known to selectively inhibit enzymes by covalently binding to active site cysteine residues. However, juglone continued to inhibit a cysteine-free ChaC1 variant, indicating that it was acting through a novel mechanism. We evaluated different inhibitory mechanisms, and also analogues of juglone, and found plumbagin effective as an inhibitor. These compounds are the first inhibitor leads against the ChaC enzymes using a robust yeast screen.

Evaluation of microplate handling accuracy for applying robotic arms in laboratory automation

An inexpensive single-arm robot is widely utilized for recent laboratory automation solutions. The integration of a single-arm robot as a transfer system into a semi-automatic liquid dispenser without a transfer system can be realized as an inexpensive alternative to a fully automated liquid handling system. However, there has been no quantitative investigation of the positional accuracy of robot arms required to transfer microplates. In this study, we constructed a platform comprising aluminum frames and digital gauges to facilitate such measurements. We measured the position repeatability of a robot arm equipped with a custom-made finger by repeatedly transferring microplates. Further, the acceptable misalignment of plate transfer was evaluated by adding an artificial offset to the microplate position using this platform. The results of these experiments are expected to serve as benchmarks for the selection of robot arms for laboratory automation in biology. Furthermore, all information for replicating this device will be made publicly available, thereby allowing many researchers to collaborate and accumulate knowledge, hopefully contributing to advances in this field.

Liquid Handling Technologies: A Study through Major Discoveries and Advancements

Liquid handling is an essential process in laboratories involving automatically transferring accurate amounts of liquids for various task completions such as sample preparation, pipetting, dilution, and mixing. Automated liquid handling systems (ALHS) help to minimize the errors, complications, and difficulties that are faced with manual methods of liquid handling by efficiently enhancing the accuracy, precision, and reproducibility of experimental workflows and have transformed life sciences aiding research in various fields. Processes like Nuclear Magnetic Resonance (NMR) spectrometry, mass spectrometry, and acoustic droplet ejection with ALHS showcase tremendous potential. Outdated manual machinery is being replaced rapidly with automated tools. We have observed that these laboratory setups are sophisticated and costly. It is challenging to access such technologies, especially in poor or developing countries. At the nanoscale level, handling and evaporation of biomaterials becomes a great issue. Multitudinous devices can combat the cost issue along with utilization of single equipment without the enhancement of add-on modules. Liquid handling devices enhance the quality of experiments contributing to efficient dealing of circumstances like COVID-19. They aid in genomics and proteomics and play a pivotal role in liquid manipulation, drug screening, quantifications, forensic studies and many more. Liquid handling devices with robotic equipment can perform multiple tasks and can assist in a wide range of fields. We anticipate that in the coming years, our laboratories will be furnished with advanced, robotic technologies and cause a revolution in the scientific scenery.

B-025 Revolutionizing NGS-driven Laboratory Developed Tests: The impact of automated liquid handling on plate-based bead clean-up

Abstract Background Automated liquid handling systems are proving to be a transformative technology in the landscape of Laboratory Developed Tests (LDTs), especially those utilizing Next-Generation Sequencing (NGS). By enhancing accuracy, boosting efficiency, and offering scalable solutions, these systems enable labs to meet current and future challenges more effectively. The right system not only improves the quality of diagnostics but also positions labs to be agile and adaptive in a rapidly advancing healthcare environment. firefly® is a well-suited liquid handling solution for diagnostics labs developing LDTs due to its compact footprint, user-friendly software and exceptional versatility. Validated protocol templates that support NGS library preparation are readily available from the firefly cloud and can be adapted to meet specific lab needs. For example, automated bead-based clean-ups designed for 96- and 384- NGS applications are available on firefly. These remove DNA fragments of undesirable sizes, enzymes or primers rom the amplified DNA after a PCR reaction. Automating this process facilitates higher throughput and scaling. firefly has a unique combination of 6 positive displacement dispense heads and a 96/384-well air-displacement pipetting head, making it ideal to distribute the reagents required for bead clean-ups. Methods firefly protocols were developed and optimized to be compatible with a range of elution volumes and bead ratios. Protocol performance was tested using library DNA (∼300bp). The same input DNA was plated out and quantified using the QuantiFluor dsDNA system (Promega) before and after running the protocol. This data was used to assess any reduction in DNA concentration and uniformity across the plate. The protocol's ability to successfully modulate the fragment size distribution was demonstrated using a 50bp DNA ladder and different bead ratios. Data was obtained using a Fragment Analyzer (Agilent, DNF-474 HS NGS Fragment Kit). Well-to-well contamination was assessed by running no-template-controls (NTCs) in wells adjacent to library DNA, then analyzed by qPCR using a LightCycler 480 System (Roche, KAPA Library Quantification kit). Results 96 format has a setup time of 10 minutes, run time of 43 and %CV library concentration of <6%. 384 format has a setup time of 10 minutes, run time of 47 and %CV library concentration of <8%. Uniformity of concentration and fragment sizes was seen across the plates. Comparison of the library concentration before and after the clean-up showed a 5% reduction in DNA concentration after the 96-well plate clean-up and an 11% reduction in library concentration when using the 384-well plate clean-up. Fragment size analysis showed that fragment sizes can be successfully modulated on firefly by changing the bead ratio of the firefly run and no detectable well-to-well contamination was detected by qPCR. Conclusions We have demonstrated that automated bead clean-ups on firefly are uniform show uniformity across both 96- and 384-well plates, with no detectable well-to-well contamination, to deliver the consistent and robust results required for an LDT. Bead-based cleanups on firefly offer a fast, 100% walkaway solution. By running 96-samples at once on firefly, we see approximately a 6-fold increase in throughput than when performed manually by a single user.

Enhancing quantitative imaging to study DNA damage response: A guide to automated liquid handling and imaging

Laboratory automation and quantitative high-content imaging are pivotal in advancing diverse scientific fields. These innovative techniques alleviate the burden of manual labour, facilitating large-scale experiments characterized by exceptional reproducibility. Nonetheless, the seamless integration of such systems continues to pose a constant challenge in many laboratories. Here, we present a meticulously designed workflow that automates the immunofluorescence staining process, coupled with quantitative high-content imaging to study DNA damage signalling as an example. This is achieved by using an automatic liquid handling system for sample preparation. Additionally, we also offer practical recommendations aimed at ensuring the reproducibility and scalability of experimental outcomes. We illustrate the high level of efficiency and reproducibility achieved through the implementation of the liquid handling system but also addresses the associated challenges. Furthermore, we extend the discussion into critical aspects such as microscope selection, optimal objective choices, and considerations for high-content image acquisition. Our study streamlines the image analysis process, offering valuable recommendations for efficient computing resources and the integration of cutting-edge deep learning techniques. Emphasizing the paramount importance of robust data management systems aligned with the FAIR data principles, we provide practical insights into suitable storage options and effective data visualization techniques. Together, our work serves as a comprehensive guide for life science laboratories seeking to elevate their high-content quantitative imaging capabilities through the seamless integration of advanced laboratory automation.

Tempat Sampah Otomatis Menggunakan Sesnsor Ultrasonik Berbasis Arduino Uno

The importance of innovation in waste management is increasingly felt in this modern era. This research introduces an automated waste handling system utilizing Arduino Uno-based sensors. Arduino Uno, as a versatile microcontroller, is employed to control the automation process in waste bins. Integrated ultrasonic and load cell sensors assist in identifying the presence of individuals disposing of trash and monitoring the weight of the waste, triggering the automatic disposal mechanism. This implementation not only enhances the efficiency of waste management but also provides convenience for sanitation workers. The study details the design, implementation, and performance evaluation of the automated waste bin system, showcasing the potential utilization of Arduino Uno technology in creating intelligent and sustainable solutions for waste management.

Touchpoints and systems for unmanned autonomous urban ferry operation

Abstract Currently, both experimental and commercial autonomous ferry services have a safety operator onboard who monitors the ferry systems, takes care of the passengers’ safety, and gives them the necessary information. Service touchpoints are all the interaction points between the autonomous ferry and the passengers throughout the passenger journey, from planning the trip to reaching their destination. To reach a state where the ferry service can be unmanned, by moving the safety operator into a remote operation centre, several additional systems and service touchpoints are needed to support the passengers’ safety and provide them with necessary information. This article discusses (1) the service touchpoints and systems needed for an autonomous urban ferry service, (2) how these touchpoints provide the passengers with the necessary information to foster trust and perceived safety, and (3) shows some examples and design suggestions for the experimental autonomous urban ferry milliAmpere2. The service touchpoints presented are (1) a ticket system, (2) an automatic ferry ordering system for on-demand operation, (3) an automatic passenger handling system for safe boarding of the ferry, (4) a passenger information system to provide visual and audio feedback about the ferry service, and (5) a passenger-operator communication system in case contact between the ferry and the remote operation centre need to be established.

An initial approach to industry 5.0

Industry 5.0 focuses on human-system collaboration and a more integrated approach to manufacturing and services. Digital twin is a technological advancement positioned to propel Industry 5.0 by making way for newer implementations and greater cyber-physical connections within a system. Digital twins will lead the way as Industry 5.0 encourages a closer integration of digital and physical systems. They will act as a link between the digital and physical worlds, giving each, real-time information and insights. Within Industry 5.0, digital twins are essential for establishing communication between the digital and physical realms as well as for facilitating human-autonomous system cooperation and cohabitation. Industry 5.0 highlights the requirement for adaptable manufacturing to satisfy specific client needs. Manufacturing processes can be swiftly and effectively adjusted with the use of digital twins. Industry 5.0 relies heavily on digital twins to help businesses adapt to the changing manufacturing environment and use data-driven insights to increase productivity, flexibility, and human-robot collaboration. The aspects of industry such as the commissioning and the automation of the Industrial Work floor processes are to be integrated with the digital twin. The digital twin allows for the virtual commissioning and integration of humans through interactive mixed reality that transcends the user experience making the process of integration and data (instruction) transfer seamless. The main focus of the paper was on utilizing the digital twin for the connection of various aspects of an industrial factory to establish control and real time monitoring for a Cyber-physical system. The digital Twin of a material handling system was developed and used for virtual commissioning and Mixed Reality integration and exploration.

A Stochastic Energy-Efficient Robust Simulation-Based Truck Dispatching Optimization for Simultaneous GHG Mitigation and Operational Excellence in Open-Pit Mines

Almost all currently active large open-pit mines across the globe heavily rely on fleet of shovels and haul trucks for material handling. This material handling system has a significant contribution not only to the production cost of the mine (+50%), but also to its energy consumption (+41%) and greenhouse gas (GHG) emissions (+37%). However, traditionally, operation management in general and truck dispatching, in particular, focused primarily on production targets and operational efficiency, neglecting environmental considerations in open-pit mines. Nowadays, growing awareness of climate change and responsible resource extraction has shifted, however, the focus towards sustainable practices throughout the mining industry, open-pit mining not excluded. The significant contribution of this study lies in the development of an integrated simulation and optimization framework that simultaneously accounts for anthropogenic GHG emissions resulting from energy (fuel) consumption and enhances the operational efficiency of the material handling system, thus yielding direct economic and environmental benefits. The paper fulfills its goals with the aim of simultaneous achievement of four interdependent objectives: minimizing the deviations from target production rates set by strategic plans, minimizing the shovel idle time, minimizing the truck wait time, and minimizing the truck fuel consumption. The performance and the robustness of the proposed framework has been evaluated in a case study at Gol-E-Gohar mine in Iran, demonstrating a successful achievement of up to 6% reduction in fuel consumption per tonne of production leading to a significant overall decrease of up to 20,000 liters in fuel consumption, equivalent to 61,000 carbon dioxide (CO2) emissions, in a 10-day operation with 12 hours of operation per day.

Risk Analysis of Conveyor Material Handling in the Production Process of Crude Palm Oil and Palm Kernel Oil with FMECA and FTA Methods

Oil palm is one type of plantation crop that occupies an important position in the agricultural sector and the plantation sector, and is a mainstay commodity of Indonesia whose development is very rapid. Good Material Handling is minimizing material handling costs, minimizing disruption or risk and delays in the production process. One of the companies engaged in the palm oil industry is PT. BSI which is a company engaged in the palm fruit processing industry. The products produced are crude palm oil (CPO), kernel, and fiber which implement a material handling system. However, the material handling process that is applied still experiences disturbances whose risks have an impact on the production process. So that researchers want to know the value of material handling risks that occur at PT.BSI with FMECA and FTA methods. The results of this study are at the risk identification stage using the FMECA method obtained six lists of risks that have an RPN value above 200 this is included in the very high and Unacceptable category. Then after further analysis using the FMECA method also that the six risks have decreased where the RPN value is less than 180 so that it is included in the high and tolerable categories. Then for the FTA analysis that the problem of these risks, namely conveyor trips and conveyor blockages, needs to be checked on a regularly scheduled conveyor leaf and implement a routine maintenance schedule and provide training to operators before carrying out work.

Peran Aviation Security dalam Pengawasan dan Penanganan Dangerous Goods dan Prohibited Item di Bandar Udara Tjilik Riwut Palangka Raya

Tjilik Riwut Palangka Raya Airport is the largest airpot in Central Kalimantan Privince. This airport has a runway size of 2,500 x 45cm. this airport is managed by PT.Angkasa Pura II. Aviation Security (AVSEC) based on the regulation of the Director General of Air Transportation with Number: SKEP 2756/XII/2010 concerning procedures for checking the security of passengers,aircraft officers,and luggage transported by aircraft and individuals. Thes purpose of this study is to determine the differences between Security Check Point1 and Security Check Point 2 and to determine the work system of supervision and handling of Dangerous Goods and Prohibited Item at Tjilik Riwut Palngka Raya Airport. This research is a qualitative research with data collection techniques,namely observasion,documentation,and interview. Data analysis techniques used are data collection,data reduction,data presentasion, and drawing conclusions. The results of the research conducted by the researcher showed that there was nosignificant difference between Security Check Point 1 and Security Check Point 2 and the work system of Aviation Security officers was in accordance with existing procedures in the supervision and handling of Dangerous Goods and Prohibited Item.

Assessing the carbon footprint of soybean transportation in Brazil: a network equilibrium model approach.

Brazil has historically invested few resources in its transport infrastructure, leaving gaps and reducing its efficiency. The country presents a high dependence on road transport, which results in increased operational costs and higher greenhouse gas (GHG) emissions. Consequently, the performance of cargo transportation in Brazil has been deteriorating, accompanied by a rise in the consumption of fossil fuels and noteworthy levels of GHG emissions. This article assesses the carbon intensity of soybean transport operations within Brazil. Utilizing a network equilibrium model, this study estimated the soybean transportation flows that minimize the total cost of transporting this product across the origins and destinations within the grain handling system. The modeling also calculated the GHG emissions in transportation. The results show that the transportation of soybeans produced 2.74 million tonnes of CO2 equivalent annually, with road transport accounting for 81.7% of these emissions. The state of Mato Grosso, responsible for 44.08kg CO2 equivalent per tonne of soybeans transported, contributed almost 49% of the total emissions due to the extensive distances involved. In contrast, states like Paraná and Rio Grande do Sul, located closer to southern ports, exhibited the lowest emissions, with rates of 11.55kg CO2 eq/t and 12.52kg CO2 eq/t, respectively. The analysis highlights the significant potential for reducing GHG emissions by increasing the use of rail and barge transport, particularly in high-emission regions such as Mato Grosso.

Development of a prototype gateway for data collection and transmission in material handling systems - Gate Move 4.0

Development of a prototype gateway for data collection and transmission in material handling systems - Gate Move 4.0

Artificial Intelligence in Maritime Transportation: A Comprehensive Review of Safety and Risk Management Applications

Maritime transportation is crucial for global trade but faces significant risks and operational challenges. Ensuring safety is essential for protecting lives, the environment, and economic stability. This review explores the role of artificial intelligence (AI) in enhancing maritime safety and risk management. Key AI applications include risk analysis, crew resource management, hazardous material handling, predictive maintenance, and navigation systems. AI systems identify potential hazards, provide real-time decision support, monitor hazardous materials, predict equipment failures, and optimize shipping routes. Case studies, such as Wärtsilä’s Fleet Operations Solution and ABB Ability™ Marine Pilot Vision, illustrate the benefits of AI in improving safety and efficiency. Despite these advancements, integrating AI poses challenges related to infrastructure compatibility, data quality, and regulatory issues. Addressing these is essential for successful AI implementation. This review highlights AI’s potential to transform maritime safety, emphasizing the need for innovation, standardized practices, and robust regulatory frameworks to achieve safer and more efficient maritime operations.

Parallel Computing Techniques for Solving Large-Scale Partial Differential Equation

Two-layered partial differential equations (PDEs) will be structured in this investigation using a variety of parallel mathematical techniques, such as Red Dull Gauss Seidel and Gauss Seidel. For the solution of this study, the two-layered PDEs of the elliptic and illustrated sorts were selected. Parallel Virtual Machine (PVM) is used on the correspondence among all chips of the Parallel Handling System. PVM is well-known for its item system, which enables several diverse computers to be used as flexible and easy-to-use concurrent handling resources. An evaluation of the parallel exhibition assessment including the Gauss Seidel and Red Gauss Seidel techniques in terms of execution time, speed, capability, ampleness, and transient execution will follow the graphical presentation of the mathematical results. Since the goal of this work was to create a successful Two-Dimensional PDE Solver (TDPDES), performance evaluations are simple. Numerous design and mathematical sectors will see an improvement in their examination and investigation systems thanks to this new, effective TDPDES technology..