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1. Empirical assessment of laser safety for photoacoustic-guided liver surgeries

   https://doi.org/10.1364/BOE.415054  

   Huang, Jiaqi; Wiacek, Alycen; Kempski, Kelley_M; Palmer, Theron; Izzi, Jessica; Beck, Sarah; Lediju_Bell, Muyinatu_A (February 2021, Biomedical Optics Express)

   Photoacoustic imaging is a promising technique to provide guidance during multiple surgeries and procedures. One challenge with this technique is that major blood vessels in the liver are difficult to differentiate from surrounding tissue within current safety limits, which only exist for human skin and eyes. In this paper, we investigate the safety of raising this limit for liver tissue excited with a 750 nm laser wavelength and approximately 30 mJ laser energy (corresponding to approximately 150 mJ/cm²fluence). Laparotomies were performed on six swine to empirically investigate potential laser-related liver damage. Laser energy was applied for temporal durations of 1 minute, 10 minutes, and 20 minutes. Lasered liver lobes were excised either immediately after laser application (3 swine) or six weeks after surgery (3 swine). Cell damage was assessed using liver damage blood biomarkers and histopathology analyses of 41 tissue samples total. The biomarkers were generally normal over a 6 week post-surgicalin vivostudy period. Histopathology revealed no cell death, although additional pathology was present (i.e., hemorrhage, inflammation, fibrosis) due to handling, sample resection, and fibrous adhesions as a result of the laparotomy. These results support a new protocol for studying laser-related liver damage, indicating the potential to raise the safety limit for liver photoacoustic imaging to approximately 150 mJ/cm²with a laser wavelength of 750 nm and for imaging durations up to 10 minutes without causing cell death. This investigation and protocol may be applied to other tissues and extended to additional wavelengths and energies, which is overall promising for introducing new tissue-specific laser safety limits for photoacoustic-guided surgery. 

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2. Supervised machine learning approaches for leak localization in water distribution systems: impact of complexities of leak characteristics

   Basnet, Lochan; Bril, Downey E; Ranjithan, Ranji S; Mahinthakumar, Kumar (May 2023, Journal of water resources planning and management)

   Localizing pipe leaks is a significant challenge for water utilities worldwide. Pipe leaks in water distribution systems (WDSs) can cause the loss of a large amount of treated water, leading to pressure loss, increased energy costs, and contamination risks. What makes localizing pipe leaks challenging is the underground location of the water pipes and the similarity in impact on hydraulic properties (e.g., pressure, flow) due to leaks as compared to the effects of WDS operational changes. Physical methods to locate leaks are expensive, intrusive, and heavily localized. Computational approaches such as data-driven machine learning models provide an economical alternative to physical methods. Machine learning models are readily available and easily customizable to most problems; therefore, there is an increasing trend in their application for leak localization in WDSs. While several studies have applied machine learning models to localize leaks in single pipes and small test networks, these studies have yet to thoroughly test these models against the different complexities of leak localization problems, and hence their applicability to real-world WDSs is still unclear. The simplicity of the WDSs, the oversimplification of leak characteristics, and the lack of consideration of modeling and measuring device uncertainties adopted in most of these studies make the scalability of their proposed approaches questionable to real-world WDSs. Our study addresses this issue by devising four study cases of different complexity that account for realistic leak characteristics and model- and measuring device-related uncertainties. Two established machine learning models—multilayer perceptron (MLP) and convolutional neural network (CNN)—are trained and tested for their ability to localize the leaks and predict their sizes for each of the four study cases using different simulated hydraulic inputs. In addition, the potential benefit of combining different types of hydraulic data as inputs to the machine learning models in localizing leaks is also explored. Pressure and flow, two common hydraulic measurements, are used as inputs to the machine learning models. Further, the impact of single and multiple time point input in leak localization is also investigated. The results for the L-Town network indicate good accuracies for both the models for all study cases, with CNN consistently outperforming MLP. 

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3. BLeak: automatically debugging memory leaks in web applications

   https://doi.org/10.1145/3422598  

   Vilk, John; Berger, Emery D. (October 2020, Communications of the ACM)

   Memory leaks in web applications are pervasive and difficult to debug. Leaks degrade responsiveness by increasing garbage collection costs and can even lead to browser tab crashes. Previous leak detection approaches designed for conventional applications are ineffective in the browser environment. Tracking down leaks currently requires intensive manual effort by web developers, which is often unsuccessful. This paper introduces BLEAK (Browser Leak debugger), the first system for automatically debugging memory leaks in web applications. BLEAK'S algorithms leverage the observation that in modern web applications, users often repeatedly return to the same (approximate) visual state (e.g., the inbox view in Gmail). Sustained growth between round trips is a strong indicator of a memory leak. To use BLEAK, a developer writes a short script (17-73 LOC on our benchmarks) to drive a web application in round trips to the same visual state. BLEAK then automatically generates a list of leaks found along with their root causes, ranked by return on investment. Guided by BLEAK, we identify and fix over 50 memory leaks in popular libraries and apps including Airbnb, AngularJS, Google Analytics, Google Maps SDK, and jQuery. BLEAK'S median precision is 100%; fixing the leaks it identifies reduces heap growth by an average of 94%, saving from 0.5MB to 8MB per round trip. 

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4. A Continuum Lung Stapler Leveraging Phase Changing Metal for Dexterity and Stiffness

   https://doi.org/10.31256/HSMR2024.31  

   Esser, Daniel S; Ertop, Tayfun Efe; McCabe, Emily; Maldonado, Fabien; Gillaspie, Erin A; Kuntz, Alan; Webster_III, Robert J (June 2024, The Hamlyn Symposium on Medical Robotics)

   Lung cancer claims over 130,000 lives per year in the USA. For those with malignant tumors requiring resection, minimally invasive thoracic surgery via a video assisted or robotic approach is an alternative to highly invasive open thoracotomy (in which the chest is “cracked” open). This involves the insertion of 3-5 ports through the chest wall and the use of a camera and instruments mounted to rigid shafts, which are used to resect tissue in a deflated lung. One of these tools is typically a stapler which is able to simultaneously cut and seal the lung tissue. Tendon-driven continuum robots (TDCRs) are capable of curvilinear motions, which can add useful dexterity in constrained anatomical regions like the chest. However, the inherent flexibility of TDCRs presents challenges for integrating stapler-type end effectors. Lung staplers today are typically rigid tools because they require large axial forces to be transmitted along the tool shaft to fire staples. Such forces would apply large loads to curved continuum devices, changing their shapes and moving the end effector undesirably during staple firing. Low melting point alloys (LMPA) have been explored to stiffen substantially soft robots and compliant surgical devices. Here, we propose their use in a TDCR stapler to stiffen the tool shaft before staples are fired. Prior to stiffening, tendon actuation can provide enhanced maneuverability by curving the backbone compared to rigid staplers to position the stapler at the desired location. 

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5. Map-Assisted Millimeter Wave Localization for Accurate Position Location

   https://doi.org/10.1109/globecom38437.2019.9013365  

   Kanhere, Ojas; Ju, Shihao; Xing, Yunchou; Rappaport, Theodore S. (December 2019, 2019 IEEE Global Communications Conference)

   null (Ed.)

   Accurate precise positioning at millimeter wave frequencies is possible due to the large available bandwidth that permits precise on-the-fly time of flight measurements using conventional air interface standards. In addition, narrow antenna beamwidths may be used to determine the angles of arrival and departure of the multipath components between the base station and mobile users. By combining accurate temporal and angular information of multipath components with a 3- D map of the environment (that may be built by each user or downloaded a-priori), robust localization is possible, even in non-line-of-sight environments. In this work, we develop an accurate 3-D ray tracer for an indoor office environment and demonstrate how the fusion of angle of departure and time of flight information in concert with a 3-D map of a typical large office environment provides a mean accuracy of 12.6 cm in line-of-sight and 16.3 cm in non-line-of-sight, over 100 receiver distances ranging from 1.5 m to 24.5 m using a single base station. We show how increasing the number of base stations improves the average non-line-of-sight position location accuracy to 5.5 cm at 21 locations with a maximum propagation distance of 24.5 m. Index Terms—localization; positioning; position location; navigation; mmWave; 5G; ray tracing; site-specific propagation; map-based 

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