Search

The increasing volume of personal motorized vehicles (PMVs) in cities has become a serious issue leading to congestion, noise, air pollution and high land consumption. To ensure the sustainability of urban transportation, it is imperative to transition the current transportation paradigm toward a more sustainable state. Transitions within socio-technical systems often arise from niche innovation. Therefore, this paper pursues the technical optimization of such a niche innovation by applying a technical sustainability perspective on an innovative mobility and logistics concept within a case study. This case study is based on a centrally managed connected, automated, shared and electric (CASE) vehicle fleet which might replace PMV use in urban city centers of the future. The key technical system components of the envisioned mobility and logistics concept are analyzed and optimized with regard to economic, ecological and social sustainability dimensions to maximize the overall sustainability of the ecosystem. Specifically, this paper identifies key challenges and proposes possible solutions across the vehicle components as well as the orchestration of the vehicles' operations within the envisioned mobility and logistics concept. Thereby, the case study gives an example of how different engineering disciplines can contribute to different sustainability dimensions, highlighting the interdependences. Finally, the discussion concludes that the early integration of sustainability considerations in the technical optimization efforts of innovative transportation systems can provide an important building block for the transition of the current transportation paradigm to a more sustainable state. [ABSTRACT FROM AUTHOR]

Intravenous therapy is the standard medical procedure that is used for administering medications directly into the vein. The automated drug infusion devices are designed in such a way that they provide exact medication doses with safety measures included. On the other hand, this is why they must be regularly watched by healthcare providers. This paper introduces a cloud-based IoT drug infusion system that was developed to address remote patient care needs. This system enables remote, accurate, and secure management of medication delivery. Its key contributions include allowing healthcare providers to control and monitor IV infusions remotely while maintaining safety features. The system consists of a microcontroller that is responsible for data processing, a control system that oversees infusion rate regulation, and an IoT-based framework that allows for remote monitoring and alerts via a user-friendly web interface. This new approach to care will therefore improve patient care by providing remote management of medications. [ABSTRACT FROM AUTHOR]

In the field of automated technology research and development, trajectory tracking plays a crucial role in the energy consumption of the vehicle's power battery. Reducing the deviation between the actual trajectory and the reference trajectory is the focus of trajectory tracking research. This paper proposes the use of the model predictive control (MPC) method to reduce the deviation of lateral and longitudinal position between the actual driving trajectory and the reference trajectory. First, the driving conditions of the vehicle are reflected by establishing the vehicle dynamics model. Then, the MPC trajectory tracking controller is built by designing the objective function with constraints; Finally, the feasibility of this approach was verified by a joint Carsim-Simulink simulation. The simulation results show that the MPC controller designed in this paper can track the trajectory better, and reduce the lateral and longitudinal position deviation. To a certain extent, the battery energy consumption is reduced and the accuracy of the tracking trajectory and the safety of vehicle driving are improved. [ABSTRACT FROM AUTHOR]

Retinopathy of Prematurity (ROP) is a disease of the eye and a potential source of blindness in low birth weight preterm infants. It is preventable if diagnosed and treated on time. Artificial Intelligence (AI) has played an important role in developing automated screening systems to assist medical experts. There are many traditional literature review articles available that focus on the scientific content of ROP-AI. The researchers also require a bibliometric analysis to become acquainted with the competing groups and new trends in this field. This paper gives a brief overview of ROP and AI systems for ROP screening with a statistical analysis of the Scopus database's related documents. [ABSTRACT FROM AUTHOR]

Managing the massive losses associated with large-scale disasters requires significant resources. The unexpected violence of these events generally remains a matter of casualties that urgently need to be identified in a reliable and cost-effective manner. To overcome these difficulties, many researchers have attempted to develop automated methods; moreover, a few recent research have investigated the applicability of artificial intelligence (AI)-based methods using skull, dental, and maxillofacial forensic imaging. In this review, we speculate on the advancement and potential of AI in Dental and Maxillofacial imaging that can help simplify person or victim identification and speed up the process with good accuracy. Using a few prefix search phrases, an online literature search was conducted (AI, Forensic, Skull, Dental, Imaging, Radiology) to identify papers about the advancement of AI in forensic dentistry in all kinds of radiographs, including two-dimensional (2D) and three-dimensional (3D) radiographs, cone beam computed tomography (CT) and CT. Most of the studies reported that automated methods of human identification based on 2D and 3D Dental and Skull radiographs using a convolutional neural network showed to assist in the fast and accurate identification by expertise evaluating a lot of images and quickly coming up with potential matches for identification. We advocate the application of AI techniques in the identification of individuals. However, there is a need to continue research with emphasis to validate models in skull identification. [ABSTRACT FROM AUTHOR]

Progress monitoring is an essential part of large construction projects. As manual progress monitoring is time-consuming, the need for automation emerges, especially as, nowadays, BIM for the design of buildings and laser scanning for capturing the as-built situation have become well adopted. However, to be able to compare the as-built model obtained by laser scanning to the BIM design, both models need to use the same reference system, which often is not the case. Transforming the coordinate system of the as-built model into the BIM model is a specialist process that is pre-requisite in automated construction progress monitoring. The research described in this paper is aimed at the automation of this so-called registration process and is based on the dominant planar geometry of most buildings with evident corner points in their structures. After extracting these corner points from both the as-built and the design model, a RANSAC-based pairwise assessment of the points is performed to identify potential matching points in both models using different discriminative geometric invariants. Next, the transformation for the potential matches is evaluated to find all the matching points. In the end, the most accurate transformation parameter is determined from the individual transformation parameters of all the matching corner points. The proposed method was tested and validated with a range of both simulated and real-life datasets. In all the case studies including the simulated and real-life datasets, the registration was successful and accurate. Furthermore, the method allows for the registration of the as-built models of incomplete buildings, which is essential for effective construction progress monitoring. As the method uses the standard IFC schema for data exchange with the BIM, there is no loss of geometrical information caused by data conversions and it supports the complete automation of the progress-monitoring process. [ABSTRACT FROM AUTHOR]

Copyright of Revista Pensamento Contemporâneo em Administração is the property of Universidade Federal Fluminense (Revista Pensamento Contemporaneo em Administracao) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The increasing volume of personal motorized vehicles (PMVs) in cities has become a serious issue leading to congestion, noise, air pollution and high land consumption. To ensure the sustainability of urban transportation, it is imperative to transition the current transportation paradigm toward a more sustainable state. Transitions within socio-technical systems often arise from niche innovation. Therefore, this paper pursues the technical optimization of such a niche innovation by applying a technical sustainability perspective on an innovative mobility and logistics concept within a case study. This case study is based on a centrally managed connected, automated, shared and electric (CASE) vehicle fleet which might replace PMV use in urban city centers of the future. The key technical system components of the envisioned mobility and logistics concept are analyzed and optimized with regard to economic, ecological and social sustainability dimensions to maximize the overall sustainability of the ecosystem. Specifically, this paper identifies key challenges and proposes possible solutions across the vehicle components as well as the orchestration of the vehicles’ operations within the envisioned mobility and logistics concept. Thereby, the case study gives an example of how different engineering disciplines can contribute to different sustainability dimensions, highlighting the interdependences. Finally, the discussion concludes that the early integration of sustainability considerations in the technical optimization efforts of innovative transportation systems can provide an important building block for the transition of the current transportation paradigm to a more sustainable state.

Volcanic activity is diverse. Therefore, a lot of volcanic ejecta need to be analyzed to properly assess the condition of a volcano. However, until now, rapid analysis in this regard has been insufficient. To accurately evaluate both the transition and the characteristics of eruptions, we constructed a platform to efficiently analyze volcanic ejecta incorporating a number of automatic processing functions into a data processing system composed of an electron beam microprobe analyzer (EPMA) and personal computers. A number of time-consuming tasks, such as crystal size distribution analysis and ash particle classification, were automated. Further, the analysis platform is equipped with a database function for collecting various analytical data as the basis for the future development of volcanology. Various quantitative values related to the composition and microtexture of the volcanic ejecta were collected and entered into the database. This paper introduces an outline of the system, collected data, and usage examples. [ABSTRACT FROM AUTHOR]

The effective development of novel therapies in mouse models of neurologic disorders relies on behavioral assessments that provide accurate read-outs of neuronal dysfunction and/or degeneration. We designed an automated behavioral testing system (PiPaw), which integrates an operant lever-pulling task directly into the mouse home cage. This task is accessible to grouphoused mice 24 h per day, enabling high-throughput longitudinal analysis of forelimb motor learning. Moreover, this design eliminates the need for exposure to novel environments and minimizes experimenter interaction, significantly reducing two of the largest stressors associated with animal behavior. Male mice improved their performance of this task over 1 week of testing by reducing intertrial variability of reward-related kinematic parameters (pull amplitude or peak velocity). In addition, mice displayed short-term improvements in reward rate, and a concomitant decrease in movement variability, over the course of brief bouts of task engagement. We used this system to assess motor learning in mouse models of the inherited neurodegenerative disorder, Huntington disease (HD). Despite having no baseline differences in task performance, male Q175-FDN HD mice were unable to modulate the variability of their movements to increase reward on either short or long timescales. Task training was associated with a decrease in the amplitude of spontaneous excitatory activity recorded from striatal medium spiny neurons in the hemisphere contralateral to the trained forelimb in WT mice; however, no such changes were observed in Q175-FDN mice. This behavioral screening platform should prove useful for preclinical drug trials toward improved treatments in HD and other neurologic disorders. [ABSTRACT FROM AUTHOR]

The development of peptide-based pharmaceutics is a hot topic in the pharmaceutical industry and in basic research. However, from the research and development perspective there is an unmet need for new, alternative, solid-phase peptide synthesizers that are highly efficient, automated, robust, able to synthetize peptides in parallel, inexpensive (to obtain and operate), have potential to be scaled up, and even comply with the principles of green chemistry. Moreover, a peptide synthesizer of this type could also fill the gap in university research, and therefore speed the advancement of peptide-based pharmaceutical options. This paper presents a Tecan add-on peptide synthesizer (TaPSy), which has operational flexibility (coupling time: 15–30 min), can handle all manual synthesis methods, and is economical (solvent use: 34.5 mL/cycle, while handling 0.49 mmol scale/reactor, even with ≤3 equivalents of activated amino acid derivatives). Moreover, it can carry out parallel synthesis of up to 12 different peptides (0.49 mmol scale in each). TaPSy uses no heating or high pressure, while it is still resistant to external influences (operating conditions: atmospheric pressure, room temperature 20–40 ˚C, including high [>70%] relative humidity). The system's solvent can also be switched from DMF to a green and biorenewable solvent, γ-valerolactone (GVL), without further adjustment. The designed TaPSy system can produce peptides with high purity (>70%), even with the green GVL solvent alternative. In this paper we demonstrate the optimization path of a newly developed peptide synthesizer in the context of coupling reagents, reaction time and reagent equivalents applying for a synthesis of a model peptide. We compare the results by analytical characteristics (purity of raw material, crude yield, yield) and calculated overall cost of the syntheses of one mg of crude peptide using a specified set of reaction conditions.

Impedance spectroscopy represents a basic operating principle for biomedical sensors, bioimpedance spectroscopy, electrochemical analyses and for characterization of functional biomaterials. For automated long-term investigations, an impedance analyzer for multi-channel testing of up to eight passive two-pole networks is presented in this paper. Its operating system is application-specific adapted to the required test functionalities and measuring ranges. Measurements are based on a commercially available integrated impedance converter circuit. Our current analyzer setup is capable of measuring impedance values from 50 kΩ up to 10 MΩ with automated range selection for most accurate results. The impedance under test is excited with a single frequency of 1 kHz. An impedance accuracy of 1.5 % was determined in reference measurements. The presented impedance analyzer is a low cost system ready for use particularly in long-term characterization of dielectric networks, such as material properties, with multiple samples. [ABSTRACT FROM AUTHOR]

Objectives Liver volumetry has emerged as an important tool in clinical practice. Liver volume is assessed primarily via organ segmentation of computed tomography (CT) and magnetic resonance imaging (MRI) images. The goal of this paper is to provide an accessible overview of liver segmentation targeted at radiologists and other healthcare professionals. Methods Using images from CT and MRI, this paper reviews the indications for liver segmentation, technical approaches used in segmentation software and the developing roles of liver segmentation in clinical practice. Results Liver segmentation for volumetric assessment is indicated prior to major hepatectomy, portal vein embolisation, associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) and transplant. Segmentation software can be categorised according to amount of user input involved: manual, semi-automated and fully automated. Manual segmentation is considered the "gold standard" in clinical practice and research, but is tedious and time-consuming. Increasingly automated segmentation approaches are more robust, but may suffer from certain segmentation pitfalls. Emerging applications of segmentation include surgical planning and integration with MRI-based biomarkers. Conclusions Liver segmentation has multiple clinical applications and is expanding in scope. Clinicians can employ semi-automated or fully automated segmentation options to more efficiently integrate volumetry into clinical practice. [ABSTRACT FROM AUTHOR]

The growing need for better monitoring and control of road construction projects, together with rapid technological progress, have led to a number of interesting developments, which are reviewed below. The present paper describes the development of a real-time monitoring model capable of measuring productivity and progress automatically. The model, which uses global positioning system for on-site automated data collection, was tested and validated on site. The results of the field experiments have indicated that the expected accuracy level of the model can be assessed as ±4–5% for unstructured activities and even higher for more structured ones, such as asphalt spreading. The paper concludes that it is possible to automatically measure the performance of earthmoving operations. Based on the results, it also highlights the needs for further research. [ABSTRACT FROM AUTHOR]

In the field of automated technology research and development, trajectory tracking plays a crucial role in the energy consumption of the vehicle’s power battery. Reducing the deviation between the actual trajectory and the reference trajectory is the focus of trajectory tracking research. This paper proposes the use of the model predictive control (MPC) method to reduce the deviation of lateral and longitudinal position between the actual driving trajectory and the reference trajectory. First, the driving conditions of the vehicle are reflected by establishing the vehicle dynamics model. Then, the MPC trajectory tracking controller is built by designing the objective function with constraints; Finally, the feasibility of this approach was verified by a joint Carsim-Simulink simulation. The simulation results show that the MPC controller designed in this paper can track the trajectory better, and reduce the lateral and longitudinal position deviation. To a certain extent, the battery energy consumption is reduced and the accuracy of the tracking trajectory and the safety of vehicle driving are improved.

Introduction: Endodontic treatment success rate relies majorly on apical and coronal sealing. In order to achieve a good hermetic seal the removal of smear layer is unavoidable. Hence this study was conducted to assess the various root canal irrigating materials. Materials and Methodology: Freshly extracted human mandibular premolar teeth were selected and were randomly divided into two groups. Inclusion criteria include those teeth that have less than 10 degrees of angulation. The study samples include 30 freshly extracted teeth that were basically divided into two groups. Group - 1 comprised of manual syringe needle irrigation, Group - 2 consisted of automated root canal irrigation. Canals were effectively dried with paper points after which the roots were split longitudinally and examined. Results: The debris score and smear layer score was observed in both the study groups namely manual needle and automated irrigation device. Interestingly a low mean score in debris as well as smear layer was observed in both the groups. (p>0.05) Moreover no statistically significant difference was prompt in the debris and the smear layer score at the apical, middle or coronal region. Conclusion: To conclude, it has been effectively observed that the manual syringe irrigation and automated irrigation devices clearly showed identical results in removing the smear an debris layer with no significant differences were elicited between the two groups. [ABSTRACT FROM AUTHOR]

Clostridioides difficile detection in community settings is time-intensive, resulting in delays in diagnosing and quarantining infected individuals. However, with the advent of semi-automated devices and improved algorithms in recent decades, the ability to discern CDI infection from asymptomatic carriage has significantly improved. This, in turn, has led to efficiently regulated monitoring systems, further reducing endemic risk, with recent concerns regarding a possible surge in hospital-acquired Clostridioides difficile infections post-COVID failing to materialize. This review highlights established and emerging technologies used to detect community-acquired Clostridioides difficile in research and clinical settings. [ABSTRACT FROM AUTHOR]

Background/Objectives: Radiography is an essential and low-cost diagnostic method in pulmonary medicine that is used for the early detection and monitoring of lung diseases. An adequate and consistent image quality (IQ) is crucial to ensure accurate diagnosis and effective patient management. This pilot study evaluates the feasibility and effectiveness of the International Atomic Energy Agency (IAEA)'s remote and automated quality control (QC) methodology, which has been tested in multiple imaging centers. Methods: The data, collected between April and December 2022, included 47 longitudinal data sets from 22 digital radiographic units. Participants submitted metadata on the radiography setup, exposure parameters, and imaging modes. The database comprised 968 exposures, each representing multiple image quality parameters and metadata of image acquisition parameters. Python scripts were developed to collate, analyze, and visualize image quality data. Results: The pilot survey identified several critical issues affecting the future implementation of the IAEA method, as follows: (1) difficulty in accessing raw images due to manufacturer restrictions, (2) variability in IQ parameters even among identical X-ray systems and image acquisitions, (3) inconsistencies in phantom construction affecting IQ values, (4) vendor-dependent DICOM tag reporting, and (5) large variability in SNR values compared to other IQ metrics, making SNR less reliable for image quality assessment. Conclusions: Cross-comparisons among radiography systems must be taken with cautious because of the dependence on phantom construction and acquisition mode variations. Awareness of these factors will generate reliable and standardized quality control programs, which are crucial for accurate and fair evaluations, especially in high-frequency chest imaging. [ABSTRACT FROM AUTHOR]

The work proposed here is an automated pre and post-processor integrated to PanAir that is is a high-order aerodynamic panel method-based software for flow analysis developed in 70s but still in active use especially for preliminary aircraft design. With the integrated environment proposed in this work, manipulation of input and output data to and from PanAir is bypassed successfully that is otherwise requires manual manipulations and use of third party software. The integrated environment is validated over a Cessna 210 aircraft with a modified NLF (1)-0414 airfoil. The flow around the aircraft is analyzed using PanAir together with the integrated environment and results show that pre and post processing times reduced and ease in PanAir use is increased significantly. [ABSTRACT FROM AUTHOR]

This study aimed to develop a web-based course scheduling system for College of Computer Studies under FEU institute of Technology, Manila Philippines. The developed system make used of Greedy Algorithm for managing schedules on courses and assigning to faculty. The method of this research was research and development (R&D). The researcher also discusses the scheduling process which is attributed to the Scheduling Module of the system that follows the principle of the Greedy Algorithm. The system has been tested and evaluated using criteria for which includes functionality, usability, reliability, portability and supportability (FURPS). Results and recommendation has been discuss on the last part of this paper. [ABSTRACT FROM AUTHOR]

Ball end magnetorheological finishing (BEMRF) is a novel method for nanofinishing of ferromagnetic as well as nonmagnetic materials. With increasing demands of precision finished products in various markets, the quality of surface finish and production rate are of prime importance. For products having complex geometries, this need intensifies as the manual finishing methods are incapable of producing uniform finish throughout the complex geometry of the surface. This paper describes the finishing of 3D surfaces using five-axis CNC BEMRF process in a fully automated manner such that a uniform finish is obtained throughout the workpiece. This eliminates the labor-intensive efforts and the subjective errors involved in manual finishing operations and increases the production rate of the finished products. A customized five-axis CNC controller is developed that enables the sequential and precise control of motion as well as process parameters in the BEMRF machine. The magnetizing current which governs the forces responsible for finishing in BEMRF process is controlled in-process through CNC part program using a specially defined "E" code for magnetizing current. A dedicated graphical user interface is also developed in Visual C# for the control of BEMRF process, enabling the user to make use of BEMRF machine without going into the complexities of the system. Using the customized five-axis CNC controller, automated finishing of a freeform mild steel workpiece is done. The finishing results obtained by the automated five-axis BEMRF machine shows uniformly finished surface profile (up to order of nanometer) on the mild steel test specimen. [ABSTRACT FROM AUTHOR]

Natural disasters all too often place human lives and property at risk. Recovery efforts following a disaster can be slow and painstaking work, and potentially put responders in harm's way. A system which helps identify defects in critical building elements (e.g., concrete columns) before responders must enter a structure could save lives. In this paper we propose such a system, centered around an image-based three-dimensional (3D) reconstruction method and a new 3D crack detection algorithm. The image-based method is capable of detecting and analyzing surface damages in 3D. We also demonstrate how a robotic platform could be used to gather the set of images from which the reconstruction is created, further reducing the risk to responders. In this regard, image-based 3D reconstructions represent a convenient method of creating 3D models because most robotic platforms can carry a lightweight camera payload. Additionally, the proposed 3D crack detection algorithm also provides the advantage of being able to operate on 3D mesh models regardless of their data collection source. Our experimental results showed that the 3D crack detection algorithm performed well on several sample building elements, successfully identifying cracks, reconstructing 3D profiles, and measuring geometrical characteristics on damaged elements and not finding any cracks on intact ones. The operation and perceived benefits of the proposed method in a post-disaster situation are also discussed in detail. [ABSTRACT FROM AUTHOR]

Objective: To summarize significant contributions to sensor, signal, and imaging informatics published in 2016. Methods: We conducted an extensive search using PubMed® and Web of Science® to identify the scientific contributions published in 2016 that addressed sensors, signals, and imaging in medical informatics. The three section editors selected 15 candidate best papers by consensus. Each candidate article was reviewed by the section editors and at least two other external reviewers. The final selection of the six best papers was conducted by the editorial board of the Yearbook. Results: The selected papers of 2016 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information. Conclusion: The growing volume of signal and imaging data provides exciting new challenges and opportunities for research in medical informatics. Evolving technologies provide faster and more effective approaches for pattern recognition and diagnostic evaluation. The papers selected here offer a small glimpse of the high-quality scientific work published in 2016 in the domain of sensor, signal, and imaging informatics.

A highly challenging problem that has been plaguing regulators is the inaccurate collection and sharing of safety risks for underground construction. In order to establish a mechanism that could achieve timely and accurate recognition at the pre-construction stage, Building information model (BIM) is adopted as the risk recognition platform. This paper provides the automated safety risk recognition process based on BIM which is generally composed of three parts. The first part is to build the risk database. By means of knowledge structuralizing, questionnaires, depth interviews and group decision-making, explicit and tacit knowledge source are acquired. The safety risk knowledge source is divided into three categories. SQL database is used to express the safety risk knowledge, and all safety risks are stored in the BIM-cloud. The second part is to analyse the relation between engineering information and safety risks. Risk-related engineering information is extracted from BIM models. Backus–Naur form is used to describe the syntax of languages used in computing. A mapping table of engineering information and safety risks is established. The third part concerns the automated safety risk recognition mechanism in the BIM platform. The safety risk recognition mechanism is expressed as “If e, then h (CF (h, e), λ)”. The confidence level as the link is adopted to reveal the mechanism. Finally, a case about flowing sand risk at the foundation pit bottom is conducted. The reasoning process and recognition results are demonstrated. The paper concludes by summarizing the main scientific contribution and giving direction to future research in this field. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) is a noninvasive neuroimaging technique widely recognized for epilepsy and tumor mapping. MEG clinical reporting requires a multidisciplinary team, including expert input regarding each dipole's anatomic localization. Here, we introduce a novel tool, the "Magnetoencephalography Atlas Viewer" (MAV), which streamlines this anatomical analysis. The MAV normalizes the patient's Magnetic Resonance Imaging (MRI) to the Montreal Neurological Institute (MNI) space, reverse-normalizes MNI atlases to the native MRI, identifies MEG dipole files, and matches dipoles' coordinates to their spatial location in atlas files. It offers a user-friendly and interactive graphical user interface (GUI) for displaying individual dipoles, groups, coordinates, anatomical labels, and a tri-planar MRI view of the patient with dipole overlays. It evaluated over 273 dipoles obtained in clinical epilepsy subjects. Consensus-based ground truth was established by three neuroradiologists, with a minimum agreement threshold of two. The concordance between the ground truth and MAV labeling ranged from 79% to 84%, depending on the normalization method. Higher concordance rates were observed in subjects with minimal or no structural abnormalities on the MRI, ranging from 80% to 90%. The MAV provides a straightforward MEG dipole anatomic localization method, allowing a nonspecialist to prepopulate a report, thereby facilitating and reducing the time of clinical reporting. [ABSTRACT FROM AUTHOR]

Traditional clinical follow-up after breast cancer is inefficient at detecting relapse and is poorly suited to detecting and ameliorating psychological problems. There is interest in developing more effective and efficient methods of follow-up. We report a prospective cohort study of the acceptability and feasibility of remote, automated telephone follow-up after breast cancer. Women with a history of breast cancer were approached at their annual follow-up visit. For participants, the follow-up questionnaire was administered on paper at baseline. In place of a clinic visit following year, the women completed the same questionnaire using an automated telephone system. All patients were given mammograms. A semi-structured interview was then conducted to assess the acceptability. The potential impact on clinic usage was assessed. In all, 110 of 121 women (91%) agreed to participate. Seventy-five patients (71%) completed follow-up using the new automated system 1 year later. Seventy-one of the 75 patients found the system easy to use. Forty-nine of the 75 (65.33%) liked the system and were happy to use it as their sole method of follow-up. A further 12% were happy to use it as part of their follow-up. In only 10.66% of participants were concerns raised which led to clinic attendance. Automated questionnaire-based telephone follow-up is acceptable to women and has the potential to reduce attendance at clinic. Further studies to validate this method further are planned. [ABSTRACT FROM AUTHOR]

A crucial problem that involves feature-based image registration algorithms is how to reliably establish the correspondence between the features detected in the sensed image and those detected in the reference image. Generally, most existing methods only use spatial relations or feature similarity, or a simple combination of them, to solve this problem, and all have some limitations. In this paper, a new feature-matching strategy is developed. It is realized by introducing a function whose independent variable is the match matrix, which describes the correspondence of the features, to combine spatial relations and organically feature similarity, and its global maximum is assumed to be reached if the sensed image is completely aligned with the reference image. Thus, the feature correspondence can be estimated by finding the maximum of the function. Two approaches are devised to solve the optimization problem. One is based on the branch-and-bound strategy to yield a global optimal solution, and the other uses an iterative algorithm that combines graduated assignment and variable metric methods to search for a local optimal solution with low computational complexity. The proposed method can work without the limitations of feature type, similarity criterion, and transform model, and its performance is evaluated using a variety of real images. Compared with some existing methods, it is fast and robust, and has the highest accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes an automated grid-based alignment system utilizing lasers and an array of light-detecting photodiodes. The intent is to create an inexpensive and scalable alignment system for pick-and-place of industrial objects (titer plates) using a SCARA robot manipulator. The system utilizes the robot trajectory, the transformation matrix, and geometric and trigonometric principles to determine the movements to align the robot with a grid-based array of photodiodes. The alignment system consists of a sending unit utilizing lasers, a receiving module consisting of photodiodes, a data acquisition unit, a computer-based control system, and the robot manipulator. The control system computes the robot movements needed to position the lasers based on laser positions detected by the photodiodes. With a spacing of 10mm between sensors, the system shows a resolution of 0.5mm along both the X and Y-axes, with a maximum single-point X-position detection error of 2.9mm and a maximum single-point Y-position detection error of 2.0mm. After the alignment, the maximum positional errors for both X and Y-position is 1.4mm. With a grid spacing of 10mm from center-to-center between each photodiode, the system still shows a high relative resolution. The results, along with system improvements, show potential for the system being a viable and relatively simple solution for 2D alignment. [ABSTRACT FROM AUTHOR]

A coordinated multi-year ocean exploration campaign on the Blake Plateau offshore of the southeastern U.S. has mapped what appears to be the most expansive cold-water coral (CWC) mound province thus far discovered. Nearly continuous CWC mound features span an area up to 500 km long and 110 km wide, with a core area of high-density mounds up to 254 km long by 42 km wide. This study synthesized bathymetric data from 31 multibeam sonar mapping surveys and generated a standardized geomorphic classification of the region in order to delineate and quantify CWC mound habitats and compare mound morphologies among subregions of the coral province. Based on the multibeam bathymetry, a total of 83,908 individual peak features were delineated, providing the first estimate of the overall number of potential CWC mounds mapped in the region to date. Five geomorphic landform classes were mapped and quantified: peaks (411 km2), valleys (3598 km2), ridges (3642 km2), slopes (23,082 km2), and flats (102,848 km2). The complex geomorphology of eight subregions was described qualitatively with geomorphic "fingerprints" (spatial patterns) and quantitatively by measurements of mound density and vertical relief. This study demonstrated the value of applying an objective automated terrain segmentation and classification approach to geomorphic characterization of a highly complex CWC mound province. Manual delineation of these features in a consistent repeatable way with a comparable level of detail would not have been possible. [ABSTRACT FROM AUTHOR]

Background: In this paper we describe a novel method to achieve high yield bacterial expression of a small protein domain with considerable therapeutic potential; Domain I of Beta-2-glycoprotein I (β2GPI). β2GPI is intrinsic to the pathological progression of the Antiphospholipid Syndrome (APS). Patients develop autoantibodies targeting an epitope located on the N-terminal Domain I of β2GPI rendering this domain of interest as a possible therapeutic. Results: This new method of production of Domain I of β2GPI has increased the production yield by ~20 fold compared to previous methods in E.coli. This largely scalable, partially automated method produces 50-75 mg of pure, folded, active Domain I of β2GPI per litre of expression media. Conclusion: The application of this method may enable production of Domain I on sufficient scale to allow its use as a therapeutic. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an automated framework that can model covert communication algorithms. Our proposed framework, Automated Covert Channel Modeling (ACCM), can provide a reliable and valid implementation method for fully functional covert communication techniques that can be used to study various concepts, characteristics and features of covert communication. ACCM exploits the shared features of covert channel algorithms to define common blocks and automatically converts tasks and events of the common blocks to executable codes. In order to verify the accuracy of our proposed ACCM framework, we implement two different covert channel algorithms that are referenced repeatedly in literature. We demonstrate that the characteristics obtained from implementing covert communication using the ACCM framework match the theoretical concepts of network communication and the expected results. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Locomotion scoring is time consuming and is not commonly completed on farms. Farmers also underestimate their herds' lameness prevalence, a knowledge gap that impedes lameness management. Automation of lameness detection could address this knowledge gap and facilitate improved lameness management. The literature pertinent to adding lameness detection to accelerometers is reviewed in this paper. Options for lameness detection systems are examined including the choice of sensor, raw data collected, variables extracted, and statistical classification methods used. Two categories of variables derived from accelerometer-based systems are examined. These categories are behavior measures such as lying and measures of gait. For example, one measure of gait is the time a leg is swinging during a gait cycle. Some behavior-focused studies have reported accuracy levels of greater than 80%. Cow gait measures have been investigated to a lesser extent than behavior. However, classification accuracies as high as 91% using gait measures have been reported with hardware likely to be practical for commercial farms. The need for even higher accuracy and potential barriers to adoption are discussed. Significant progress is still required to realize a system with sufficient specificity and sensitivity. Lameness detection systems using 1 accelerometer per cow and a resolution lower than 100 Hz with gait measurement functions are suggested to balance cost and data requirements. However, gait measurement using accelerometers is rather underdeveloped. Therefore, a high priority should be given to the development of novel gait measures and testing their ability to differentiate lame from nonlame cows. [ABSTRACT FROM AUTHOR]