Healthcare Engineering

The paper presents a recommendation model for developing new smart city and smart health projects. The objective is to provide recommendations to citizens about smart city and smart health startups to improve entrepreneurship and leadership. These recommendations may lead to the country’s advancement and the improvement of national income and reduce unemployment. This work focuses on designing and implementing an approach for processing and analyzing tweets inclosing data related to smart city and smart health startups and providing recommended projects as well as their required skills and competencies. This approach is based on tweets mining through a machine learning method, the Word2Vec algorithm, combined with a recommendation technique conducted via an ontology-based method. This approach allows discovering the relevant startup projects in the context of smart cities and makes links to the needed skills and competencies of users. A system was implemented to validate this approa...

There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic programming and machine learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize the learning algorithm. In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifiers. The performance of the proposed method was based on sensitivity, specificity, precision, accuracy, and the roc curves. The present study proves that genetic programming can automatically find the best model by combining feature preprocessing methods and classifier algorithms.

Short discharge time from hospitals increases both bed availability and patients’ and families’ satisfaction. In this study, the Six Sigma process improvement methodology was applied to reduce patients’ discharge time in a cancer treatment hospital. Data on the duration of all activities, from the physician signing the discharge form to the patient leaving the treatment room, were collected through patient shadowing. These data were analyzed using detailed process maps and cause-and-effect diagrams. Fragmented and unstandardized processes and procedures and a lack of communication among the stakeholders were among the leading causes of long discharge times. Categorizing patients by their needs enabled better design of the discharge processes. Discrete event simulation was utilized as a decision support tool to test the effect of the improvements under different scenarios. Simplified and standardized processes, improved communications, and system-wide management are among the propose...

This paper presents the research and development of an Internet of Things- (IoT-) based remote health monitoring system for asthmatic patients. Asthma is an inflammatory disease. Asthma causes the lungs to swell and get narrower, making it difficult to carry air in and out of the lungs. This situation makes breathing very difficult. Remote patient monitoring (RPM) is a method of collecting health-related data from patients who are in a remote location and electronically transmitting it to healthcare providers for evaluation and consultation. The aim of this study is to design a monitoring system that allows doctors to monitor asthmatic patients from a remote area. The proposed system will allow patients to measure oxygen saturation (SpO2), heart rate, body temperature, humidity, volatile gases, room temperature, and electrocardiogram (ECG) using various sensors, which will be displayed in an application. This data is then sent to the doctor to monitor the patient’s condition and sug...

In the present scenario, image fusion is utilized at a large level for various applications. But, the techniques and algorithms are cumbersome and time-consuming. So, aiming at the problems of low efficiency, long running time, missing image detail information, and poor image fusion, the image fusion algorithm at pixel level based on edge detection is proposed. The improved ROEWA (Ratio of Exponentially Weighted Averages) operator is used to detect the edge of the image. The variable precision fitting algorithm and edge curvature change are used to extract the feature line of the image edge and edge angle point of the feature to improve the stability of image fusion. According to the information and characteristics of the high-frequency region and low-frequency region, different image fusion rules are set. To cope with the high-frequency area, the local energy weighted fusion approach based on edge information is utilized. The low-frequency region is processed by merging the region ...

The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-te...

Inadequate design of emergency departments (EDs) is a major cause of crowding, increased length of stay, and higher mortality. The main reason behind this inadequacy is the lack of stakeholders’ involvement in the design process. This work reports and analyzes the results of a large survey of the requirements of ED stakeholders. It then compares these requirements with existing designs on the one hand and international standards on the other. Further, we propose a new hybrid design which combines the requirements of both the stakeholders and international standards using quality function deployment (QFD), also known as the House of Quality, method. The proposed method was used to assess two existing EDs located in two countries. The analysis of the survey responses showed certain discrepancies between stakeholder requirements and the existing designs such as the absence of an initial admission unit and insufficient space of the treatment unit. The results showed a strong correlation...

A maintenance program generated through the consideration of characteristics and failures of medical equipment is an important component of technology management. However, older technology devices and newer high-tech devices cannot be efficiently managed using the same strategies because of their different characteristics. This study aimed to generate a maintenance program comprising two different strategies to increase the efficiency of device management: preventive maintenance for older technology devices and predictive maintenance for newer high-tech devices. For preventive maintenance development, 589 older technology devices were subjected to performance verification and safety testing (PVST). For predictive maintenance development, the manufacturers’ recommendations were used for 134 high-tech devices. These strategies were evaluated in terms of device reliability. This study recommends the use of two different maintenance strategies for old and new devices at hospitals in dev...

The number of people living with dementia is growing, leading to increasing pressure upon care providers. The mechanisms to reduce symptoms of dementia can take many forms and have the aim of improving the wellbeing and quality of life of the person living with dementia and those who care for them. Besides the person who has dementia, the condition has a profound impact upon their loved ones and carers. One therapeutic approach is the use of music, an area recognised as having potential benefit, but requiring further research. The present paper reports upon a mixed methods cohort study that examines the use of a musical mobile app as a way to promote song-task association in people living with dementia. The study took place in care home environments in the UK. A total of fourteen participants (N = 14) were recruited. Quantitative measurements were taken on a daily basis prior to, and during, use of the mobile app over several weeks. Metrics came from the complete Self-Assessment Man...

In today’s competitive environment, one of the new tools in the field of information technology is business or organizational dashboards that are a backup in the process of strategic management of organizations. The purpose of the current research is to provide a framework to design the healthcare dashboard through technical architecture with fulfilling the decision-makers’ requirements. In this study, a common qualitative research method, metasynthesis, is applied, including a seven-step set of research questions, conducting systematic literature search and selection of suitable papers, data extraction, analysis and findings of the qualitative composition, quality control, and presentation of findings. During this process, 102 articles were found by saturation of information resources and then 12 articles were selected for extracting data using acceptance and rejection criteria. A critical evaluation method was used to evaluate the quality of selected articles. After investigating ...

More than 1 billion people suffer from chronic respiratory diseases worldwide, accounting for more than 4 million deaths annually. Inhaled corticosteroid is a popular medication for treating chronic respiratory diseases. Its side effects include decreased bone mineral density and osteoporosis. The aims of this study are to investigate the association of inhaled corticosteroids and fracture and to design a clinical support system for fracture prediction. The data of patients aged 20 years and older, who had visited healthcare centers and been prescribed with inhaled corticosteroids within 2002-2010, were retrieved from the National Health Insurance Research Database (NHIRD). After excluding patients diagnosed with hip fracture or vertebrate fractures before using inhaled corticosteroid, a total of 11645 patients receiving inhaled corticosteroid therapy were included for this study. Among them, 1134 (9.7%) were diagnosed with hip fracture or vertebrate fracture. The statistical result...

In the field of medicine, shared decision-making (SDM) is an important issue primarily aimed at resolving the problem of information asymmetry between clinicians and patients in the selection of treatment options and follow-up nursing plans. Most previous studies on this topic have focused on key elements and the development and implementation of SDM scales. This study used the decision-making trial and evaluation laboratory (DEMATEL) method to establish a network of influence relationships among factors that are keys to the success of the SDM process. Survey data were obtained from a well-known brain hospital in China. The key factors of success included tailor information, flexibility approach, check understanding patient, document (discussion about) decision, present evidence, make or explicitly defer decision, and patient values and preferences. We determined that clinicians should provide a series of treatment options and follow-up care plans based on a patientʼs conditions and...

In this study, a fuzzy AHP-VIKOR method is presented to help decision makers (DMs), especially physicians, evaluate and rank intervention strategies for influenza. Selecting the best intervention strategy is a sophisticated multiple criteria decision-making (MCDM) problem with potentially competing criteria. Two fuzzy MCDM methods, fuzzy analytic hierarchy process (F-AHP) and fuzzy VIsekriterijumska optimizacija i KOmpromisno Resenje (F-VIKOR), are integrated to evaluate and rank influenza intervention strategies. In fuzzy AHP-VIKOR, F-AHP is used to determine the fuzzy criteria weights and F-VIKOR is implemented to rank the strategies with respect to the presented criteria. A case study is given where a professor of infectious diseases and clinical microbiology, an internal medicine physician, an ENT physician, a family physician, and a cardiologist in Turkey act as DMs in the process.

Healthcare information systems can reduce the expenses of treatment, foresee episodes of pestilences, help stay away from preventable illnesses, and improve personal life satisfaction. As of late, considerable volumes of heterogeneous and differing medicinal services data are being produced from different sources covering clinic records of patients, lab results, and wearable devices, making it hard for conventional data processing to handle and manage this amount of data. Confronted with the difficulties and challenges facing the process of managing healthcare big data such as volume, velocity, and variety, healthcare information systems need to use new methods and techniques for managing and processing such data to extract useful information and knowledge. In the recent few years, a large number of organizations and companies have shown enthusiasm for using semantic web technologies with healthcare big data to convert data into knowledge and intelligence. In this paper, we review t...

A new control system of a hand gesture-controlled wheelchair (EWC) is proposed. This smart control device is suitable for a large number of patients who cannot manipulate a standard joystick wheelchair. The movement control system uses a camera fixed on the wheelchair. The patient’s hand movements are recognized using a visual recognition algorithm and artificial intelligence software; the derived corresponding signals are thus used to control the EWC in real time. One of the main features of this control technique is that it allows the patient to drive the wheelchair with a variable speed similar to that of a standard joystick. The designed device “hand gesture-controlled wheelchair” is performed at low cost and has been tested on real patients and exhibits good results. Before testing the proposed control device, we have created a three-dimensional environment simulator to test its performances with extreme security. These tests were performed on real patients with diverse hand pa...

Fusional vergence is a disjunctive movement of the eyes that is made in order to obtain single vision. The aim of the study was to provide a quantitative and objective approach for analyzing the fusional convergence response using eye tracking (ET) technology and automatic data analysis provided by the intuitive SacLab toolbox previously developed by our group. We evaluated the proposed approach in a population of 26 subjects with normal binocular vision, who were tested with base-out prisms (magnitudes 4Δ, 6Δ, and 10Δ) in order to elicit fusional convergence response. Eye movements were recorded using the Viewpoint ET and analyzed using SacLab. Parameters describing both the vergence and the version components of the fusional response (convergence duration, CD; peak convergence velocity, PCV; number of intrusive saccades, NS; and mean saccadic amplitude, MSA) were automatically calculated and provided to clinicians for an objective evaluation. Results showed that the number of subj...

Tissue engineering the aortic heart valve is a challenging endeavor because of the particular hemodynamic and biologic conditions present in the native aortic heart valve. The backbone of an ideal valve substitute should be a scaffold that is strong enough to withstand billions of repetitive bending, flexing and stretching cycles, while also being slowly degradable to allow for remodeling. In this review we highlight three overlooked aspects that might influence the long term durability of tissue engineered valves: replication of the native valve trilayered histoarchitecture, duplication of the three-dimensional shape of the valve and cell integration efforts focused on getting the right number and type of cells to the right place within the valve structure and driving them towards homeostatic maintenance of the valve matrix. We propose that the trilayered structure in the native aortic valve that includes a middle spongiosa layer cushioning the motions of the two external fibrous l...

Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, including tuberculosis. We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning model using a publicly accessible tuberculosis dataset. This study was able to reliably detect tuberculosis (TB) from chest X-ray images by utilizing image preprocessing, data augmentation, and deep learning classification techniques. Four distinct deep CNNs (Xception, InceptionV3, InceptionResNetV2, and MobileNetV2) were trained, validated, and evaluated for the classification of tuberculosis and nontuberculosis cases using transfer learning from their pretrained starting weights. With an F1-score of 99 percent, InceptionResNetV2 had the highest accuracy. This research is more accurate than earlier published work. Additionally, it outperforms all other models in terms of reliability. The suggested approach, with its state-of-the...

The purpose of this study was to examine the potential of soft-shelled rugby headgear to reduce linear impact accelerations. A hybrid III head form instrumented with a 3-axis accelerometer was used to assess headgear performance on a drop test rig. Six headgear units were examined in this study: Canterbury Clothing Company (CCC) Ventilator, Kukri, 2nd Skull, N-Pro, and two Gamebreaker headgear units of different sizes (headgears 1–6, respectively). Drop heights were 238, 300, 610, and 912 mm with 5 orientations at each height (forehead, front boss, rear, rear boss, and side). Impact severity was quantified using peak linear acceleration (PLA) and head injury criterion (HIC). All headgear was tested in comparison to a no headgear condition (for all heights). Compared to the no headgear condition, all headgear significantly reduced PLA and HIC at 238 mm (16.2–45.3% PLA and 29.2–62.7% HIC reduction; P < 0.0005, ηp2 = 0.987–0.991). Headgear impact attenuation lowered significantly as...

Low-dose Computed Tomography (LDCT) has gained a great deal of attention in clinical procedures due to its ability to reduce the patient’s risk of exposure to the X-ray radiation. However, reducing the X-ray dose increases the quantum noise and artifacts in the acquired LDCT images. As a result, it produces visually low-quality LDCT images that adversely affect the disease diagnosing and treatment planning in clinical procedures. Deep Learning (DL) has recently become the cutting-edge technology of LDCT denoising due to its high performance and data-driven execution compared to conventional denoising approaches. Although the DL-based models perform fairly well in LDCT noise reduction, some noise components are still retained in denoised LDCT images. One reason for this noise retention is the direct transmission of feature maps through the skip connections of contraction and extraction path-based DL modes. Therefore, in this study, we propose a Generative Adversarial Network with Inc...

Computer-Assisted Orthopaedic Surgery (CAOS) defines a set of techniques that use computers and other devices for planning, guiding, and performing surgical interventions. The important components of CAOS are accurate geometrical models of human bones and plate implants, which can be used in preoperational planning or for surgical guiding during an intervention. Software framework which is introduced in this study is based on the Model-View-Controller (MVC) architectural pattern, and it uses 3D models of bones and plate implants developed by the application of the Method of Anatomical Features (MAF). The presented framework may be used for preoperative planning processes and for the production of personalized plate implants. The main idea of the research was to develop a novel integrated software framework which will provide improved personalized healthcare to the patient, and at the same time, provide the surgeon with more control over the patient’s treatment and recovery.

The pluralism that characterized the development of psychiatric services around the world created a variety of policies, care models and building types, and fostered experimental approaches. Increased complexities of care, institutional remnants, stigma, and the limited diagnostic and interventional accuracy of psychiatric treatments resulted in institutional behaviors surviving, even in newly built facilities. This was raised by research on awarded psychiatric buildings. The locus of the research comprised two acute psychiatric wards in London. Each was evaluated using the SCP model, a tool specifically developed for the evaluation of mental health facilities, identifying the relation between policy, care regime, and patient-focused environment. Data were derived from plans, visits, and staff and patient interviews. Findings were juxtaposed to those of an earlier study using the same methodology. Also, a syntactic analysis was conducted, to identify the social logic of ward layouts...

Alzheimer’s disease (AD) is an irreversible illness of the brain impacting the functional and daily activities of elderly population worldwide. Neuroimaging sensory systems such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) measure the pathological changes in the brain associated with this disorder especially in its early stages. Deep learning (DL) architectures such as Convolutional Neural Networks (CNNs) are successfully used in recognition, classification, segmentation, detection, and other domains for data interpretation. Data augmentation schemes work alongside DL techniques and may impact the final task performance positively or negatively. In this work, we have studied and compared the impact of three data augmentation techniques on the final performances of CNN architectures in the 3D domain for the early diagnosis of AD. We have studied both binary and multiclass classification problems using MRI and PET neuroimaging modalities. We have found th...

In today’s scenario, sepsis is impacting millions of patients in the intensive care unit due to the fact that the mortality rate is increased exponentially and has become a major challenge in the field of healthcare. Such peoples require determinant care which increases the cost of the treatment by using a large number of resources because of the nonavailability of the resources. The treatment of sepsis is available in the early state, but treatment is not started at the right time, and then it converts to the advanced level of sepsis and increases the fatalities. Thus, an intensive analysis is required to detect and identify sepsis at the early stage. There are some models available that work based on the manual score and based on only the biomark features, but these are not fully automated. Some machine learning-based models are also available, which can reduce the mortality rate, but accuracy is not up to date. This paper proposes a machine learning model for early detecting and ...

In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs. The effort aims mainly to reduce storage costs and cloud computing associated with neural networks as the complexity of the computations increases with increasing numbers of hidden layers. This study modifies federated teaching to function with distributed assignment resource settings as a distributed deep learning model. It improv...

Introduction The purpose of this systematic review was to compare the accuracy of the three-dimensional images among different scanners, scanning techniques, and substrates. Materials and methods. Electronic databases (PubMed and Elsevier) were searched until March 2020. The systematic search was performed to identify the most precise method of obtaining a 3D image of the dentition. Results Thirteen articles out of 221, considering the accuracy of 3D images, were selected. The main factors that are considered to have an influence on the precision are substrate type in the oral cavity, experience of the scanner's operator, direct vs. indirect scanning, and the reproducibility of the procedure. Conclusion Substrate type does have an impact on the overall accuracy of intraoral scans where dentin has the most and enamel the least accurately recorded dental structure. Experience of the operator has an influence on the accuracy, where more experienced operators and smaller scan sizes ...

Continuous blood pressure (BP) monitoring has a significant meaning for the prevention and early diagnosis of cardiovascular disease. However, under different calibration methods, it is difficult to determine which model is better for estimating BP. This study was firstly designed to reveal a better BP estimation model by evaluating and optimizing different BP models under a justified and uniform criterion, i.e., the advanced point-to-point pairing method (PTP). Here, the physical trial in this study caused the BP increase largely. In addition, the PPG and ECG signals were collected while the cuff bps were measured for each subject. The validation was conducted on four popular vascular elasticity (VE) models (MK-EE, L-MK, MK-BH, and dMK-BH) and one representative elastic tube (ET) model, i.e., M-M. The results revealed that the VE models except for L-MK outperformed the ET model. The linear L-MK as a VE model had the largest estimated error, and the nonlinear M-M model had a weaker ...

For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients’ lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud com...

The Internet of Health Thing (IoHT) has various applications in healthcare. Modern IoHTintegrates health-related things like sensors and remotely observed medical devices for the assessment and managment of a patient's record to provide smarter and efficient health diagnostics to the patient. In this paper, we proposed an IoT with a cloud-based clinical decision support system for prediction and observation of disease with its severity level with the integration of 5G services and block-chain technologies. A block-chain is a system for storing and sharing information that is secure because of its transparency. Block-chain has many applications in healthcare and can improve mobile health applications, monitoring devices, sharing and storing of the electronic media records, clinical trial data, and insurance information storage. The proposed framework will collect the data of patients through medical devices that will be attached to the patient, and these data will be stored in a ...

Fetal heart rate (FHR) detection can be monitored using either direct fetal scalp electrode recording (invasive) or by indirect noninvasive technique. Weeks before delivery, the invasive method poses a risk factor to the fetus, while the latter provides accurate fetal ECG (FECG) information which can help diagnose fetal’s well-being. Our technique employs variable order linear phase sharp transition (LPST) FIR band-pass filter which shows improved stopband attenuation at higher filter orders. The fetal frequency fiduciary edges form the band edges of the filter characterized by varying amounts of overlap of maternal ECG (MECG) spectrum. The one with the minimum maternal spectrum overlap was found to be optimum with no power line interference and maximum fetal heart beats being detected. The improved filtering is reflected in the enhancement of the performance of the fetal QRS detector (FQRS). The improvement has also occurred in fetal heart rate obtained using our algorithm which is...

Because of the availability of more than an actor and a wireless component among e-health applications, providing more security and safety is expected. Moreover, ensuring data confidentiality within different services becomes a key requirement. In this paper, we propose to collect data from health and fitness smart devices deployed in connection with the proposed IoT blockchain platform. The use of these devices helps us in extracting an amount of highly valuable heath data that are filtered, analyzed, and stored in electronic health records (EHRs). Different actors of the platform, coaches, patients, and doctors, collaborate to provide an on-time diagnosis and treatment for various diseases in an easy and cost-effective way. Our main purpose is to provide a distributed, secure, and authorized access to these sensitive data using the Ethereum blockchain technology. We have designed an integrated low-powered IoT blockchain platform for a healthcare application to store and review EHR...

The Internet of Health Thing (IoHT) has various applications in healthcare. Modern IoHTintegrates health-related things like sensors and remotely observed medical devices for the assessment and managment of a patient's record to provide smarter and efficient health diagnostics to the patient. In this paper, we proposed an IoT with a cloud-based clinical decision support system for prediction and observation of disease with its severity level with the integration of 5G services and block-chain technologies. A block-chain is a system for storing and sharing information that is secure because of its transparency. Block-chain has many applications in healthcare and can improve mobile health applications, monitoring devices, sharing and storing of the electronic media records, clinical trial data, and insurance information storage. The proposed framework will collect the data of patients through medical devices that will be attached to the patient, and these data will be stored in a ...

This study examines the alterations in scalp recorded cortical activity due to surgical incision in anaesthetized cardiac patients using electroencephalogram (EEG) patterns. The primary aim was to compare the changes in electrocortical activity after surgical incision. The secondary aim was to compare the changes in time, frequency, and wavelet domain parameters after loss of consciousness (LoC) and after intubation. Real-time EEG data were recorded from 19 patients undergoing cardiac surgery and signals were quantified with time, frequency, and wavelet domain parameters. An increase in hjorth activity, hjorth complexity, rms value, total band power, relative delta band power, standard deviation and maxima of approximation coefficients (a5), minima of detail coefficients (d5, d4, and d3) and decrease in hjorth mobility; approximate entropy; relative theta, alpha, and beta band power; specentropy; median, spectral edge, and mean frequency; mean of detail coefficients (d4); standard d...

Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals’ visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshoppe...