Manual Setup Research Articles

An Ubiquitous Framework forElectronic Dd with Near Field Communication Standard

Intelligent automation now permeates almost every aspect of living in the modern world. Intelligent automation for the issue of demand draughts, which has historically been a labor-intensive operation, is one example of such an application. Demand draughts (DDs) are only occasionally available during bank business hours, making it difficult to get one manually. We suggest an automated solution that fixes the drawbacks of the present manual setup to get around these restrictions. This cutting-edge device, which is put in bank branches, is similar to an ATM. The Near Field connectivity (NFC) technology, RFID scanners, and GSM connectivity are all included in our creative solution and are all managed by an Arduino Uno microcontroller. An RFID tag or NFC card Credit or Acts as a debit card in this arrangement. The RFID reader identifies the information on the card when a user swipes it at the terminal and sends it to the microcontroller. To create an electronic demand draught, the microcontroller processes this data, and encryption techniques are used to assure safe transactions. The specified amount is then sent immediately to the appropriate accounts, and both parties involved receive credit/debit information via SMS, further encouraging an easy and quick process.

Good practices for the automated production of 18F-SiFA radiopharmaceuticals

BackgroundThe positron emitting isotope fluorine-18 (18F) possesses almost ideal physicochemical properties for the development of radiotracers for diagnostic molecular imaging employing positron emission tomography (PET). 18F in its nucleophilic anionic 18F− form is usually prepared by bombarding an enriched 18O water target with protons of various energies between 5 and 20 MeV depending on the technical specifications of the cyclotron. Large thick-target yields between 5 and 14 GBq/µA can be obtained, enough to prepare large batches of radiotracers capable to serve a considerable contingent of patients (50 + per clinical batch). The overall yield of the radiotracer however depends on the efficiency of the 18F labeling chemistry. The Silicon Fluoride Acceptor chemistry (SiFA) has introduced a convenient and highly efficient way to provide clinical peptide-based 18F-radiotracers in a kit-like procedure matching the convenience of 99mTc radiopharmaceuticals.Main bodyA radiotracer’s clinical success primarily hinges on whether its synthesis can be automated. Due to its simplicity, the SiFA chemistry, which is based on isotopic exchange (18F for 19F), does not only work in a manual setup but has been proven to be automatable, yielding large batches of 18F-radiotracers of high molar activity (Am). The production of SiFA radiotracer can be centralized and the radiopharmaceutical be distributed via the “satellite” principle, where one production facility economically serves multiple clinical application sites. Clinically validated tracers such as [18F]SiTATE and [18F]Ga-rhPSMA-7/-7.3 have been synthesized in an automated synthesis unit under good manufacturing practice conditions and used in large patient cohorts. Communication of common guidelines and practices is warranted to further the dissemination of SiFA radiopharmaceuticals and to give easy access to this technology.ConclusionThis current review highlights the most recent achievements in SiFA radiopharmaceutical automation geared towards large batch production for clinical application. Best practice advice and guidance towards a facilitated implementation of the SiFA technology into new and already operating PET tracer production facilities is provided. A brief outlook spotlights the future potential of SiFA radiochemistry within the landscape of non-canonical labeling chemistries.

The accuracy of virtual setup in simulating treatment outcomes in orthodontic practice: a systematic review

ObjectivesTo evaluate the accuracy of virtual orthodontic setup in simulating treatment outcomes and to determine whether virtual setup should be used in orthodontic practice and education.Materials and MethodsA systematic search was performed in five electronic databases: PubMed, Scopus, Embase, ProQuest Dissertations & Theses Global, and Google Scholar from January 2000 to November 2022 to identify all potentially relevant evidence. The reference lists of identified articles were also screened for relevant literature. The last search was conducted on 30 November 2022.ResultsThis systematic review included twenty-one articles, where all of them were assessed as moderate risk of bias. The extracted data were categorized into three groups, which were: (1) Virtual setup and manual setup; (2) Virtual setup and actual outcomes in clear aligner treatment; (3) Virtual setup and actual outcomes in fixed appliance treatment. There appeared to be statistically significant differences between virtual setups and actual treatment outcomes, however the discrepancies were clinically acceptable.ConclusionThis systematic review supports the use of orthodontic virtual setups, and therefore they should be implemented in orthodontic practice and education with clinically acceptable accuracy. However, high-quality research should be required to confirm the accuracy of virtual setups in simulating treatment outcomes.

A Novel Setup Technique for Radiation Treatment of a Severely Obese Patient With a Primary Brain Tumor.

Modern external beam radiation therapy (EBRT) techniques rely on the accurate positioning of the patient using the treatment couch. These motorized couches have weight limits that have decreased over time and are not able to support severely obese patients requiring EBRT. We aimed to develop a technique to support obese patients who are above their weight tolerance while accurately delivering radiation treatment to a brain tumor. This technique was used on a patient receiving adjuvant radiation for gliosarcoma, a variant of glioblastoma. The patient was CT scanned, and the 3D conformal radiation therapy plan was created. A custom treatment couch was created using a transport stretcher, Styrofoam, a CT couch-top, and an IMRT board, which allowed for a thermoplastic mask to be used for a reproducible setup. AP and lateral portal films were taken prior to each treatment to confirm the accuracy of the manual daily setup of the patient on the custom couch. The patient received 60 Gy in 30 daily fractions of 3DCRT in a reproducible fashion. The average deviation from the isocenter fell within the 10 mm and 8 mm planning margins applied to the clinical target volume (CTV) for the initial and boost fields, respectively. The average daily shifts in the anterior-posterior (AP) direction for the patient were 7.97 (-16.19 to 12.04) mm and 1.98 mm (-1.1 to 4.3) mm for the initial and boost treatments, respectively. The average daily shifts in the superior-inferior (SI) direction were 2.2 (-5.08 to 9.04) mm and 3.88 (-2.9 to 8.0) mm for the initial and boost treatments, respectively. This novel approach allowed treatment at 60 Gy for a gliosarcoma patient who had previously been denied treatment due to his weight. By utilizing readily available materials within the department, our team was able to create a reproducible setup technique to safely treat the patient.

Robotic Positioning for Quality Assurance of Feature-Sparse Components Using a Depth-Sensing Camera

In this paper, a novel method for crawler positioning is presented utilizing an onboard depth-sensing camera which can operate in semi-structured, self-similar environments, using only measurements of the sample under inspection to navigate. Non-destructive evaluation at manufacture is a vital aspect of assuring the fitness for purpose of high-value marine assets, moreover, it is almost always a regulatory requirement to ensure build quality standards have been met. Traditionally these inspections were deployed manually by a trained operator in a laborious and time-consuming manner. More recently, robotic crawler-based solutions have become available on the marketplace, however, these solutions are limited in their capabilities and still require significant manual intervention and set-up for each application. Additionally, GPS or prior knowledge of their surroundings which are critical to their operation are often unavailable in an active work environment. An autonomous, self-localizing system would provide significant benefits in these situations, but certain challenges arise from limited situational awareness and poor positional accuracy. The accuracy and robustness of the novel method were assessed and experimentally validated through ground truth readings from a Vicon motion capture system. The localization algorithm’s ability to function on different materials and under various lighting conditions was also explored. Using the example of the receipt inspection of steel plate under 240 lux lighting, the system proved capable of positioning the crawler at the desired position within 5.7 mm.

Development of a Quick-Install Rapid Phenotyping System.

In recent years, there has been a growing need for accessible High-Throughput Plant Phenotyping (HTPP) platforms that can take measurements of plant traits in open fields. This paper presents a phenotyping system designed to address this issue by combining ultrasonic and multispectral sensing of the crop canopy with other diverse measurements under varying environmental conditions. The system demonstrates a throughput increase by a factor of 50 when compared to a manual setup, allowing for efficient mapping of crop status across a field with crops grown in rows of any spacing. Tests presented in this paper illustrate the type of experimentation that can be performed with the platform, emphasizing the output from each sensor. The system integration, versatility, and ergonomics are the most significant contributions. The presented system can be used for studying plant responses to different treatments and/or stresses under diverse farming practices in virtually any field environment. It was shown that crop height and several vegetation indices, most of them common indicators of plant physiological status, can be easily paired with corresponding environmental conditions to facilitate data analysis at the fine spatial scale.

Wear Parameter Diagnostics of Industrial Milling Machine with Support Vector Regression

Modern industrial machine applications often contain data collection functions through automation systems or external sensors. Yet, while the different data collection mechanisms might be effortless to construct, it is advised to have a well-balanced consideration of the possible data inputs based on the machine characteristics, usage, and operational environment. Prior consideration of the collected data parameters reduces the risk of excessive data, yet another challenge remains to distinguish meaningful features significant for the purpose. This research illustrates a peripheral milling machine data collection and data pre-processing approach to diagnose significant machine parameters relevant to milling blade wear. The experiences gained from this research encourage conducting pre-categorisation of data significant for the purpose, those being manual setup data, programmable logic controller (PLC) automation system data, calculated parameters, and measured parameters under this study. Further, the results from the raw data pre-processing phase performed with Pearson Correlation Coefficient and permutation feature importance methods indicate that the most dominant correlation to recognised wear characteristics in the case machine context is perceived with vibration excitation monitoring. The root mean square (RMS) vibration signal is further predicted by using the support vector regression (SVR) algorithm to test the SVR’s overall suitability for the asset’s health index (HI) approximation. It was found that the SVR algorithm has sufficient data parameter behaviour forecast capabilities to be used in the peripheral milling machine prognostic process and its development. The SVR with Gaussian radial basis function (RBF) kernel receives the highest scoring metrics; therefore, outperforming the linear and polynomial kernels compared as part of the study.

Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with Sparrow.

Semi-empirical quantum chemical approaches are known to compromise accuracy for the feasibility of calculations on huge molecules. However, the need for ultrafast calculations in interactive quantum mechanical studies, high-throughput virtual screening, and data-driven machine learning has shifted the emphasis toward calculation runtimes recently. This comes with new constraints for the software implementation as many fast calculations would suffer from a large overhead of the manual setup and other procedures that are comparatively fast when studying a single molecular structure, but which become prohibitively slow for high-throughput demands. In this work, we discuss the effect of various well-established semi-empirical approximations on calculation speed and relate this to data transfer rates from the raw-data source computer to the results of the visualization front end. For the former, we consider desktop computers, local high performance computing, and remote cloud services in order to elucidate the effect on interactive calculations, for web and cloud interfaces in local applications, and in world-wide interactive virtual sessions. The models discussed in this work have been implemented into our open-source software SCINE Sparrow.

SurgAI3.8K: A Labeled Dataset of Gynecologic Organs in Laparoscopy with Application to Automatic Augmented Reality Surgical Guidance

We focus on explaining the concepts underlying Artificial Intelligence (AI), using Uteraug, a laparoscopic surgery guidance application based on Augmented Reality (AR), to provide concrete examples. AI can be used to automatically interpret the surgical images. We are specifically interested in the tasks of uterus segmentation and uterus contouring in laparoscopic images. A major difficulty with AI methods is their requirement for a massive amount of annotated data. We propose SurgAI3.8K, the first gynaecological dataset with annotated anatomy. We study the impact of AI on automating key steps of Uteraug. We constructed the SurgAI3.8K dataset with 3800 images extracted from 79 laparoscopy videos. We created the following annotations: the uterus segmentation, the uterus contours and the regions of the left and right fallopian tube junctions. We divided our dataset into a training and a test dataset. Our engineers trained a neural network from the training dataset. We then investigated the performance of the neural network compared to the experts on the test dataset. In particular, we established the relationship between the size of the training dataset and the performance, by creating size-performance graphs. University PATIENTS: NA INTERVENTION: NA MEASUREMENTS AND MAIN RESULTS: The size-performance graphs show a performance plateau at 700 images for uterus segmentation and 2000 images for uterus contouring. The final segmentation scores on the training and test dataset were 94.6% and 84.9% (the higher, the better) and the final contour error were 19.5% and 47.3% (the lower, the better). These results allowed us to bootstrap Uteraug, achieving AR performance equivalent to its current manual setup. We describe a concrete AI system in laparoscopic surgery with all steps from data collection, data annotation, neural network training, performance evaluation, to final application.

A Novel System to Increase Yield of Manufacturing Test of an RF Transceiver through Application of Machine Learning

Electronic manufacturing and design companies maintain test sites for a range of products. These products are designed according to the end-user requirements. The end user requirement, then, determines which of the proof of design and manufacturing tests are needed. Test sites are designed to carry out two things, i.e., proof of design and manufacturing tests. The team responsible for designing test sites considers several parameters like deployment cost, test time, test coverage, etc. In this study, an automated test site using a supervised machine learning algorithm for testing an ultra-high frequency (UHF) transceiver is presented. The test site is designed in three steps. Firstly, an initial manual test site is designed. Secondly, the manual design is upgraded into a fully automated test site. And finally supervised machine learning is applied to the automated design to further enhance the capability. The manual test site setup is required to streamline the test sequence and validate the control and measurements taken from the test equipment and unit under test (UUT) performance. The manual test results showed a high test time, and some inconsistencies were observed when the test operator was required to change component values to tune the UUT. There was also a sudden increase in the UUT quantities and so, to cater for this, the test site is upgraded to an automated test site while the issue of inconsistencies is resolved through the application of machine learning. The automated test site significantly reduced test time per UUT. To support the test operator in selecting the correct component value the first time, a supervised machine learning algorithm is applied. The results show an overall improvement in terms of reduced test time, increased consistency, and improved quality through automation and machine learning.

Evaluating the Accuracy of Automated Orthodontic Digital Setup Models

Orthodontic tooth setup provides the orthodontist an insight into the possibilities and limitations of treatment and enables visualization of the final goal of treatment. Lately, artificial intelligence techniques have been applied to the digital setup software to automate the process of tooth segmentation and alignment. The purpose of this study was to evaluate the accuracy and efficiency of automated digital setup software. The diagnostic digital impression data of 30 patients (mean age, 29.8 ± 14.4 y) who underwent non-extraction orthodontic treatment from 2020 to 2022 were collected. Three automated digital setup software programs, Autolign (Diorco, Yongin, Korea), Outcome Simulator Pro (Align Technology, Inc., Arizona, USA), and Ortho Simulation (Medit, Seoul Korea) were tested and compared to the manual setup model using the Maestro 3D Dental Studio software (AGE Solutions S.r.l., Pontedera, Italy). The linear and angular transposition with respect to six dimensions of tooth movements, including occlusal-gingival, facial-lingual, and mesial-distal bodily movement, as well as crown rotation, mesial-distal crown tip, and facial-lingual crown torque, were calculated. The mean error for linear and angular movement ranged 0.39 – 1.40 mm and 3.25 – 7.80 °, respectively. The effectiveness of automated digital setup systems varies among software, tooth type, and the dimension of movement. Although the setup results have shown improvement in the occlusion indices, further manual adjustments may be needed for clinical use, such as indirect bonding and clear aligner therapy.

Efficient computation of ship’s wave-making resistance using michell’s integral

The aim of the present paper is to find efficient method of computation the wave-making resistance of a ship using mitchell’s integral. The computational schemes described in publications suggest the use of simple quadratures (trapezoidal and Simpson’s rule) with a fixed number of integration’s intervals. This approach assumes manual setup of the algorithm for calculating the wave-making resistance for each new ship and makes it difficult to estimate the error of the obtained results. It is shown that the use of these simple quadratures makes it possible to obtain reliable results, but at the cost of tens of billions of calculations of the ship's surface function. The applicability of more advanced universal quadratures for calculating the mitchell’s integral is investigated: adaptive Newton-Cotes rules, Gauss-Kronrod rules and Clenshaw-Curtis quadratures. As a result, it is established that the Clenshaw-Curtis quadrature provides a reliable and efficient calculation of the mitchell’s integral. The computational scheme using this quadrature allows you to build an automatic algorithm for calculating the ship's wave-making resistance by type ship method.

EMBN-MANET: A method to Eliminating Malicious Beacon Nodes in Ultra-Wideband (UWB) based Mobile Ad-Hoc Network

Recent breakthroughs in microelectromechanical systems, microsystem technologies, and wireless communications have made energy-efficient, inexpensive, off-the-shelf devices that unambiguously carry data without communication overhead practical. UWB radio technology employs minimal energy for short-range, high-bandwidth communications throughout a large section of the radio. Mobile computing organizations now explore mobile ad-hoc networks. Decentralized networks communicate. Peer nodes enable wireless device-to-device communication. Location-based and location-aided routing improve ad hoc routing. Physical or involuntary configuration places mobile nodes during deployment. Large networks need too much manual setup. GPS receivers for each sensor unit would significantly increase network installation costs. It inspired non-GPS and non-physical configuration localization. Most localization systems use beacon nodes. Beacon nodes are unique. Standard security approaches block secure beacon nodes, which might compromise localization. This research investigates beacon node localization issues and attacks. We constructed a statistical model to detect rogue beacon nodes in the ad hoc network and studied how various attacks affect localization. Bogus beacon nodes find mobile nodes. We enabled malicious beacon node communication. Trilateration and Received Signal Strength Indicator found unknown nodes. Trilateration detects compromised beacon nodes. The algorithm finds malicious beacon nodes. Our experiments removed 90% of compromised beacon nodes. Our flexible localization technique allows several algorithms and range methods without time complexity. Location identification affected fake beacon nodes.

Assessing spatial learning and memory in mice: Classic radial maze versus a new animal-friendly automated radial maze allowing free access and not requiring food deprivation.

The radial arm maze (RAM) is a common behavioral test to quantify spatial learning and memory in rodents. Prior attempts to refine the standard experimental setup have been insufficient. Previously, we demonstrated the feasibility of a fully automated, voluntary, and stress-free eight-arm RAM not requiring food or water deprivation. Here, we compared this newly developed refined RAM to a classic manual experimental setup using 24 female 10–12 weeks old C57BL/6J mice. We used a lipopolysaccharide (LPS)-induced model of systemic inflammation to examine long-term cognitive impairment for up to 13 weeks following LPS injection. Both mazes demonstrated robust spatial learning performance during the working memory paradigm. The refined RAM detected spatial learning and memory deficits among LPS-treated mice in the working memory paradigm, whereas the classic RAM detected spatial learning and memory deficits only in the combined working/reference memory paradigm. In addition, the refined RAM allowed for quantification of an animal’s overall exploratory behavior and day/night activity pattern. While our study highlights important aspects of refinement of the new setup, our comparison of methods suggests that both RAMs have their respective merits depending on experimental requirements.

Intravenous Smart Pumps at the Point of Care: A Descriptive, Observational Study.

ObjectiveThis study was conducted to advance understanding of intravenous (IV) smart pump medication administration practices using the Baxter Spectrum IQ. The primary objective was to observe adherence with manufacturer required IV smart pump system setup at the point of care during actual clinical use.MethodsThe study was conducted in a 285-bed acute care community hospital near Boston, Massachusetts. The study design was observational and noninterventional, and all data were collected by a single observer. Observations included measurement and documentation of adherence with the Baxter Spectrum IQ system setup requirements.ResultsA total of 200 primary and secondary IV medication administration observations were included: 101 in critical care and 99 in medical-surgical. Overall adherence was found to be: 6.5% with IV smart pump position relative to the patient (aim 1); 6.5% with required position of the primary infusion bag (aim 2); and 69.5% adherence with required position of the secondary medication infusion bag (aim 3). Additional exploratory data were also collected.ConclusionsThese results add to the emerging body of knowledge, which support that adherence to required system setup for head-height dependent IV smart pumps is low and difficult to achieve during actual clinical use. Consideration of alternative human factors–designed technology to replace the current manual setup requirements is needed to improve the process of acute care IV medication administration in this very important area of patient safety.

Gellan Gum Is a Suitable Biomaterial for Manual and Bioprinted Setup of Long-Term Stable, Functional 3D-Adipose Tissue Models.

Due to its wide-ranging endocrine functions, adipose tissue influences the whole body’s metabolism. Engineering long-term stable and functional human adipose tissue is still challenging due to the limited availability of suitable biomaterials and adequate cell maturation. We used gellan gum (GG) to create manual and bioprinted adipose tissue models because of its similarities to the native extracellular matrix and its easily tunable properties. Gellan gum itself was neither toxic nor monocyte activating. The resulting hydrogels exhibited suitable viscoelastic properties for soft tissues and were stable for 98 days in vitro. Encapsulated human primary adipose-derived stem cells (ASCs) were adipogenically differentiated for 14 days and matured for an additional 84 days. Live-dead staining showed that encapsulated cells stayed viable until day 98, while intracellular lipid staining showed an increase over time and a differentiation rate of 76% between days 28 and 56. After 4 weeks of culture, adipocytes had a univacuolar morphology, expressed perilipin A, and secreted up to 73% more leptin. After bioprinting establishment, we demonstrated that the cells in printed hydrogels had high cell viability and exhibited an adipogenic phenotype and function. In summary, GG-based adipose tissue models show long-term stability and allow ASCs maturation into functional, univacuolar adipocytes.

Enhanced bio-methane and bio-hydrogen production using banana plant waste and sewage sludge through anaerobic co-digestion

The yield of bio-methane and bio-hydrogen was enhanced by co-digesting banana plant waste (BPW) and sewage sludge (SS). In stage 1, acclimatisation of the inoculum SS is performed in a continuous stirred tank reactor (CSTR), and its various parameters are analyzed daily. In stage 2, six different BPW to SS ratios is optimized and incubated in manual methane potential test setup (MMPTS) for 40 days. The highest bio-methane and bio-hydrogen yields were observed from R4 (60% BPW and 40% SS). In stage 3, the best-achieved ratio of BPW and SS are then treated with different doses of NaOH as an alkaline pre-treatment. The highest bio-methane was achieved from dose 2 (0.75 mol NaOH) as 620.8 NmL/day. The study shows that the co-digestion of the BWP with SS has promising potential for enhanced bio-methane and bio-hydrogen production.

Accuracy of 3D Tooth Movements in the Fabrication of Manual Setup Models for Aligner Therapy.

Background: The clinical outcome of aligner therapy is closely related to the precision of its setup, which can be manually or digitally fabricated. The aim of the study is to investigate the suitability of manual setups made for aligner therapy in terms of the precision of tooth movements. Methods: Six dental technicians were instructed to adjust each of eleven duplicate plaster casts of a patient models as follows: a 1 mm pure vestibular translation of tooth 11 and a 15° pure mesial rotation of tooth 23. The processed setup models were 3D scanned and matched with the reference model. The one-sample Wilcoxon signed-rank test (p < 0.05) was used for evaluation. Results: The overall precision of the translational movement covers a wide range of values from 0.25 to 2.26 mm (median: 1.09 mm). The target value for the rotation of tooth 23 was achieved with a median rotation of 9.76° in the apical-occlusal direction. Unwanted movements in the other planes also accompanied the rotation. Conclusions: A manual setup can only be fabricated with limited precision. Besides the very high variability between technicians, additional unwanted movements in other spatial planes occurred. Manually fabricated setups should not be favored for aligner therapy due to limited precision.

IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics

Benchmarking and performance analysis play an important role in understanding the behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic and evolutionary algorithms, Bayesian optimization algorithms, etc. This task, however, involves manual setup, execution, and analysis of the experiment on an individual basis, which is laborious and can be mitigated by a generic and well-designed platform. For this purpose, we propose IOHanalyzer, a new user-friendly tool for the analysis, comparison, and visualization of performance data of IOHs. Implemented in R and C++ , IOHanalyzer is fully open source. It is available on CRAN and GitHub. IOHanalyzer provides detailed statistics about fixed-target running times and about fixed-budget performance of the benchmarked algorithms with a real-valued codomain, single-objective optimization tasks. Performance aggregation over several benchmark problems is possible, for example in the form of empirical cumulative distribution functions. Key advantages of IOHanalyzer over other performance analysis packages are its highly interactive design, which allows users to specify the performance measures, ranges, and granularity that are most useful for their experiments, and the possibility to analyze not only performance traces, but also the evolution of dynamic state parameters. IOHanalyzer can directly process performance data from the main benchmarking platforms, including the COCO platform, Nevergrad, the SOS platform, and IOHexperimenter. An R programming interface is provided for users preferring to have a finer control over the implemented functionalities.

A microfluidic hanging drop-based spheroid co-culture platform for probing tumor angiogenesis.

Co-culturing of embryoid bodies (EBs) and tumor spheroids (TSs) allows mimicking tumor angiogenesis in vitro. Here, we report a microfluidic hanging drop-based spheroid co-culture device (μ-CCD) that permits the generation and co-culturing of EBs and TSs using a simple manual operation procedure and setup. In brief, uniform-sized EBs and TSs can be generated on the device in eight pairs of hanging droplets from adjacent microfluidic channels, followed by the confrontation of EB and TS pairs by merging the droplet pairs to culture the EB-TS spheroids to investigate tumor-induced angiogenic sprouting. The physical parameters of the device were optimized to maintain the long-term stability of hanging droplets for up to ten days. The mouse embryonic stem cell line ES-D3 and breast cancer cell lines MDA-MB-231 and MCF-7 were used to generate EBs, invasive TSs, and non-invasive TSs respectively. Confocal imaging results showed that the vessel percentage area and total vessel length which are linked to tumor angiogenesis increased after 6 days of co-culturing. An anti-angiogenesis drug testing on the co-cultured EB-TS spheroids was also demonstrated in the device. The μ-CCD provides a simple yet high-efficiency method to generate and co-culture cell spheroids and may also be useful for other applications involving spheroid co-culturing.