Applied Current Thermoacoustic Imaging (ACTAI) is a new imaging method which combines electromagnetic excitation with ultrasound imaging, and takes ultrasonic signal as medium and biological tissue conductivity as detection target. Taking the high contrast advantage of Electrical Impedance Tomography (EIT) and high resolution advantage of ultrasound imaging, ACTAI has broad application prospects in the field of biomedical imaging. Although ACTAI has high excitation efficiency and strong detectable Signal-to-Noise Ratio, yet while under low frequency electromagnetic excitation, it is still a big challenge to reconstruct a high-resolution image of target conductivity. This paper proposes a new method for reconstructing conductivity based on Generative Adversarial Network, and it consists of three main steps: firstly, use Wiener filtering deconvolution to restore the electrical signal output by the ultrasonic probe to a real acoustic signal. Then obtain the initial acoustic source image with filtered backprojection technology. Finally, match the conductivity image with the initial sound source image, which are used as training samples for generating the adversarial network to establish a deep learning model for conductivity reconstruction. After theoretical analysis and simulation research, it is found that by introducing machine learning, the new method can dig out the inverse problem solving model contained in the data, which further reconstruct a high-resolution conductivity image and has strong anti-interference characteristics. The new method provides a new way to solve the problem of conductivity reconstruction in Applied Current Thermoacoustic Imaging. It has a broad application prospect in the field of biomedical imaging. However, the low resolution of traditional Electrical Impedance Tomography has become a bottleneck restricting its further development. In order to improve the resolution of Electrical Impedance Tomography, the Multi-Physics Coupling Imaging technology based on the combination of electromagnetic field and sound field has become a research hotspot6, as it can improve the resolution while at the same time retaining the high contrast advantage of Electrical Impedance Tomography. Examples include Microwave Thermoacoustic Imaging8, Magneto-mediated Thermoacoustic Imaging9, and Applied Current Thermoacoustic Imaging11, etc.As a new type of functional imaging technology, Electrical Impedance TomographyMicrowave Thermoacoustic Imaging uses pulsed microwaves to irradiate biological tissues to produce transient thermal expansion, which in turn excites ultrasound signals. However, its penetration depth is limited by the working frequency of the excitation source, so it is difficult to penetrate deep into tissues and organs. In order to increase the penetration depth, Zheng Research Group of Nanyang Technological University in Singapore proposed a Magneto-mediated Thermoacoustic Imaging method. The principle is to apply a MHz-level alternating magnetic field to a conductive target by using the excitation coil to generate an induced electric field inside the target, and then stimulate the Joule heat to obtain thermoelastic ultrasonic signals. The excitation frequency of magneto-mediated thermoacoustic imaging is 20 MHz, and the penetration depth of electromagnetic wave to biological tissues can reach 15 cm. They detected magneto-mediated thermoacoustic signals using high-conductivity metal block samples and obtained thermoacoustic images of the samples. However, for targets with low conductivity, as the detection depth increases, the induced electric field gradually decreases, and the Signal-to-Noise Ratio of the ultrasonic signal continuously decreases, which increases the difficulty of detection. In 2016, Liu research team of the Institute of Electrical Engineering, Chinese Academy of Sciences proposed the Applied Current Thermoacoustic Imaging (ACTAI). Instead of using the above-mentioned induced electric field, they utilize the applied current signal with microsecond pulse width to generate an ultrasonic signal with a stronger Signal to Noise Ratio (SNR). Compared with the induced current, the excitation frequency of the applied current is easier to adjust and it has a higher penetration depth, which improves the energy conversion efficiency and detectable SNR.12 and so on. Although these algorithms can reflect the distribution characteristics of thermoacoustic sources, they cannot obtain the true conductivity distribution. The fundamental reason is that the Signal-to-Noise Ratio of the ultrasound signal is low, while the inverse problem of rebuilding the conductivity image from the ultrasound signal has a strong nonlinearity, which makes the reconstruction process greatly affected by noise, and the reconstruction results of conductivity cannot meet the requirements of functional imaging.As a new type of thermoacoustic imaging technology, ACTAI still has many problems in the high-resolution reconstruction of conductivity, which need to be further solved. In terms of image reconstruction, the current research mainly focuses on the reconstruction algorithm of sound source image, such as time inversion method, filtered back projection15 (GAN) can reconstruct image based on limited data, remove the view artifact16 and improve the image SNR17, improve the image similarity and get higher image quality. ACTAI, as a complex Multi-Physics Coupling Imaging mode, its inverse conductivity reconstruction problem precisely meets the above characteristics. Therefore, this paper proposes to apply the deep learning network to conductivity reconstruction in ACTAI in order to obtain the real conductivity distribution image.In recent years, inverse problem reconstruction algorithms based on deep learning have been widely studied in the field of medical imaging, such as magnetic resonance super-resolution imaging, low-dose CT imaging, and so on. From the existing research, we find that the deep learning method has such unique advantages as high degree of nonlinearity, fast solving speed, easy access to simulation training dataset, etc. in resolving large-scale nonlinear reconstruction problems. Generative Adversarial Network19 to obtain the original acoustic signal emitted by the measured sample. Then the filtered back projection (FBP) is used to reconstruct the sound source image, match the reconstructed sound source image with the conductivity image of the measured sample, and use it as a training sample to train a Generative Adversarial Network. Finally, the trained network is used to process the sound source image to obtain high-resolution conductivity images. This method comprehensively considers the characteristics of the physical model as well as the data-driven model, and constructs a deep neural network specifically for the ACTAI inverse problem, which can effectively improve the imaging resolution and imaging speed.The implementation of the new method consists of the following steps: first, the electrical signal measured by the ultrasonic probe is preprocessed by Wiener filter deconvolutionIn ACTAI, the duration of the pulse current is much shorter than the heat conduction time inside the sample, so only the thermoacoustic signal caused by the adiabatic expansion of biological tissues needs to be considered. The thermoacoustic pressure Refer to , \documeentclass1pt{minimaRefer to , \documeentclass1pt{minimabelow in :Figure 1Refer to , * repreThe original acoustic signal at the ultrasound probe can be restored by the Wiener filter deconvolution method, namely:Refer to , \documeAccording to the above original sound signal below in :7\documeRefer to , \documeThe inverse problem of acoustic field is to reconstruct the thermoacoustic source from the collected thermoacoustic signals, while the inverse problem of electromagnetic field is to reconstruct the conductivity of the measured sample from the thermoacoustic source. Assuming that the target body is an acoustically homogeneous medium with electrical conductivity of below in :8\documeThe relationship between the thermal sound source term and the conductivity of the target body can be expressed as follows in :10\documRefer to , the scaA two-dimensional ACTAI area is established for the imaging tomographic plane, which is discretized into rectangular cells with M rows and N columns, and converted into M × N-dimensional column vectors. Let n = M × N, and assume that the conductivity inside the j-th rectangular area is uniform, where j = 1,2,…,n. According to the ACTAI forward problem model, the thermal function in the imaging area can be calculated as The least square method is adopted to find the optimal solution, and the optimization problem is established as follows:Then the problem of reconstructing the conductivity of the target body The conductivity reconstruction algorithm based on least squares solves the nonlinear problem through approximate linearization. However there are problems of low image reconstruction accuracy and poor anti-noise performance. In addition, multiple iterations are often required in the calculation process, and the calculation cost is very high. Therefore, it is necessary to explore a more efficient, accurate and stable conductivity reconstruction method.The Generative Adversarial Network draws on the idea of a two-player game, and is composed of two independent units: a Generator Network (GN) and a Discriminator Network (DN). As shown in Fig. 20 structure based on the residual network (ResNet). Firstly, the addition of the ResNet network avoids the degradation phenomenon that occurs with the increase in the number of network layers. Secondly, we also introduced a new convolutional layer and activation function Relu22 in the front end of ResNet23, and the back end uses the Dropout layer to prevent over-fitting. The U-shaped symmetric structure and jump connection mode of the generating network can further improve the problem of gradient disappearance in the training process of the deep learning network. All convolutional and deconvolutional layers used in this paper have a convolution kernel size of 3 × 3, and the sliding step size is 2. The overall structure of the number of feature maps and the size of feature maps is shown in Fig. The Generator Network of GAN is demonstrated in Fig. The Discriminator Network of GAN is demonstrated in Fig. The loss function of a standard GAN network is:In addition to the loss function of standard GAN network, the loss function of pix2pix also introduces the loss function of Therefore, the final GAN network's loss function is:After the GAN training, the parameters of the Generator Network are fixed and saved for the reconstruction of the conductivity image. We can then use the simulation data to test the above-mentioned GAN network. The specific process is as follows: first, for each new conductivity sample, calculate the sound field distribution in the COMSOL software according to the multi-physics coupling positive problem model, and then the measurement signal is obtained after convolution probe characteristics to solve the positive problem; secondly, the original sound signal is obtained by wiener filtering deconvolution and the sound source distribution is reconstructed through filtered back projection; finally use the reconstructed sound source as the input of the generator in the GAN network, the output of which is the conductivity image to be solved.The simulation model shown in Fig. mentclass2pt{minimformulas  and 10)\documentAssuming that the volume expansion coefficient of the tissue is If 72-channel ultrasound probes are placed around the target, then the acoustic signal they receive contains the frequency response characteristics of each probe. Figure Input the sound source in Fig. Structural Similarity (SSIM) and Peak Signal to Noise Ratio (PSNR) evaluation indicators are introduced to perform quantitative evaluation on the reconstructed image quality of conductivity, which is expressed in the following formula:Refer to , \documeWhen solving the PSNR value, it is necessary to first calculate the Mean Squared Error (MSE) of each target image, as indicated in :16\documThen calculate the Peak Signal to Noise Ratio of the reconstructed image according to the MSE, as shown in :17\documIn the above formula, mentclass2pt{minim24. By giving an initial conductivity value, the solution of conductivity is transformed into the solution of the objective function of conductivity. On the premise of meeting the calculation accuracy, the optimal solution is the conductivity. The conductivity reconstruction quality in Fig. For the network trained by digital conductivity samples, we continue to use digital conductivity samples for testing. Although the test samples have never appeared in the training set, they are still lack of persuasion. Therefore, we use the sample which is quite different from the digital conductivity to test. In Fig. It can be found that compared with the traditional method, the conductivity image reconstructed by GAN has less artifacts and higher resolution. In addition, GAN takes less time to reconstruct the conductivity image, which meets the real-time requirements of medical imaging.We also tested the generalization ability of the GAN network: after training the network with handwritten digital samples, other samples that are significantly different from the training samples  are used for imaging tests. Firstly, establish the phantom model of conductivity distribution as shown in Fig. Secondly, place the ultrasound probe with a dominant frequency of 1 MHz at 0.02 m from the center of the phantom. When then probe position is at  m, the time curve of the received acoustic signal is shown in Fig. Thirdly, Fig. The next step is to take the sound source image of Fig. Finally, in order to verify the anti-noise performance of the GAN network, white noise of different intensities is introduced into the thermal sound source image. Figure In order to ensure the feasibility of the method, we use cooled sodium chloride gel solution to prepare the target, and the concentration of sodium chloride determines the conductivity of the target. We conducted experiments on a mimic composed of two different electrical conductivities, and the measured model is shown in Fig. The sources were reconstructed using filtered back projection method. The reconstruction results are shown in Fig. After obtaining the preliminary sound source reconstruction results, input them into the built GAN network for conductivity imaging. The results are shown in Fig. Although the network we built is specifically trained on MNIST handwritten digit samples, satisfactory reconstruction results can still be obtained for other types of test samples. This indicates that in the Applied Current Thermoacoustic Imaging system, the GAN network has learned the mapping relationship between the input thermoacoustic source and the output conductivity. In other words, it can not only complete the image matching between input and output, but also has the ability to learn nonlinear problems.To conclude, this study focuses on Applied Current Thermoacoustic Imaging, mainly exploring the reconstruction method of inverse problem based on GAN network. Firstly, a physical model of electromagnetic and acoustic fields of Applied Current Thermoacoustic Imaging is established. Then the electric field in the model is calculated by finite element method, and the acoustic signal detected by the ultrasonic probe is simulated by the coupling relationship between electric field and acoustic field, and the thermoacoustic source distribution is reconstructed. In response to the problem of conductivity reconstruction, the research proposes an imaging method based on Generative Adversarial Network, and finally, with conductivity-based handwritten digit set training, it finally verifies that the network can learn the non-linear mapping relationship between the sound source and the conductivity, and thus realizes the deep learning reconstruction process of conductivity. Theoretical analysis and simulation results show that the method proposed in this paper can quickly and accurately reconstruct the conductivity image from the sound source image under the condition of low Signal to Noise Ratio, which verifies the applicability of the GAN network in the ACTAI problem.


Although the traditional bilateral surgical approach to treat hiatal hernia (HH) with gastroesophageal reflux disease (GERD) can provide local protection of the vagus nerve, the integrity of the entire vagus nerve cannot be evaluated. Therefore, we developed and described the total left-side surgical approach (TLSA), which theoretically reduces injury to the vagus nerve, and described the detailed surgical procedure.Initially, we performed a cadaver study to explore the characteristics of the vagus nerve. Then, we prospectively evaluated the TLSA in 5 patients with HH and GERD between June 2020 and September 2020. Demographic characteristics, surgical parameters, perioperative outcomes, and follow-up findings were analyzed.The TLSA was successfully used in five patients (40–64 years old), and no major complications were noted. The median total operative time was 114 min, median blood loss was 50 mL, and median postoperative hospital stay was 3.8 days. Gastrointestinal function recovered within 4 days of surgery in all the patients. The 6-month follow-up gastroscopy examination showed well-established gastroesophageal flap valves. Compared with the baseline results, the 6-month follow-up results showed lower values for the total GerdQ score (12.4 vs. 6.2) and the total esophageal acid exposure time (3.48% vs. 0.38%). Based on the European Organization for Research and Treatment of Cancer quality of life questionnaire-stomach module 52 results, the incidence of dysphagia and flatulence decreased over time after the TLSA.The TLSA provides a clear and broad surgical field, less trauma, and rapid recovery; moreover, it is technically simple. Although our results suggest that the TLSA provides safety and short-term efficacy and is feasible for patients with HH and GERD, long-term results from a larger clinical trial are needed to validate these findings.Trial registration ChiCTR2000034028, registration date is June 21, 2020. The study was registered prospectively Hiatal hernia (HH) is a condition that involves herniation of abdominal contents into the mediastinum via the diaphragmatic hiatus and is characterized by a dilated esophageal hiatus . HH is cTherefore, we developed a new surgical approach that we called the “total left-side surgical approach” (TLSA) for laparoscopic HH repair with fundoplication. The potential advantages of the TLSA are that it is technically simple and preserves the lesser omentum and the hepatogastric ligament to a great extent based on the anatomical characteristics of the vagus nerve. This technique may also help improve the postoperative quality of life of patients with HH and GERD. This report describes our findings regarding the anatomical characteristics of the vagus nerve from a cadaver study, which we used to develop the TLSA. Further, we describe the detailed surgical procedure and discuss the preliminary results regarding the safety and short-term effectiveness of the TLSA in five patients who underwent surgical treatment for HH and GERD.Chest and abdomen specimens were collected from deceased donors. Formalin-fixed cadaveric specimens were provided by Capital Medical University. This study was approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University (2019-P2-060-02). The preliminary findings were used to evaluate the anatomical characteristics of the vagus nerve and the feasibility of the TLSA before it was used in patients.http://www.chictr.org.cn/index.aspx, ChiCTR2000034028) and was approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University (L-2020-019). The clinical trial was launched in July 2020 and is scheduled to end in December 2023. During the research period, patients will be recruited from the Beijing Friendship Hospital for treatment, and they must provide written informed consent before undergoing surgery. We present the following article in accordance with the CONSORT reporting checklist.We have initiated a prospective trial of laparoscopic TLSA for treating HH. The trial protocol is registered at the Chinese Clinical Trial Registry website (The inclusion criteria are listed as follows which we described in previously work . (1) PatPatients will be excluded from the study which we described in previously work . (1) PatThe present report covers the characteristics and short-term outcomes of five patients with HH and GERD who underwent surgical procedures via the TLSA at the General Surgery Department of Beijing Friendship Hospital affiliated to the Capital Medical University between June 2020 and September 2020. The patients were followed up for ≥ 6 months after the surgery via outpatient visits, inpatient visits, telephone contact, or mailed correspondence. The patient’s demographic characteristics, surgical parameters, perioperative outcomes, and follow-up findings were analyzed.Based on the cadaver study’s results, we developed a new surgical approach for performing laparoscopic esophageal HH repair and fundoplication via the TLSA. The degree of fundoplication was determined based on the esophageal manometry and pH monitoring results. If the HH size was > 5 cm or if the diaphragm appeared weak on both sides of the defect, a surgical non-absorbable mesh was used. Surgery was performed with the patient in the supine position after successful general anesthesia induction and tracheal intubation. The surgeon stood on the patient’s left side throughout the surgery, and the first assistant stood on the patient’s right side. A second assistant stood between the patient’s legs to manipulate the laparoscope Fig. .The port placements are detailed shown in Fig. The operative technique is shown in Fig. The left and right branches of the vagus nerve are distributed in the anterior and posterior walls of the esophagus below the bronchial bifurcation. The left branch of the vagus nerve travels in the right anterior wall of the esophagus, where it forms the anterior trunk of the vagus nerve, and the right branch of the vagus nerve travels in the right posterior wall of the esophagus, where it forms the posterior trunk of the vagus nerve. Both branches pass through the esophageal hiatus of the diaphragm and give off additional nerve branches.The anterior vagal trunk is located between the muscular layer and the peritoneum of the anterior esophageal wall of the abdominal segment. A single trunk is a common trunk and travels from the upper left side to the lower right aspect of the anterior esophageal wall. It closely adheres to the muscular layer of the esophagus and can be easily damaged if it is not carefully identified during surgery. At the level of the cardia, the anterior vagal trunk divides into hepatic branches and into the anterior gastric branch. The hepatic branches mainly accompany the proper hepatic artery and travel to the hepatic portal system to join the hepatic plexus, which is distributed throughout the liver and the biliary tract, where it helps regulate the secretion activities of the hepatobiliary system. After dividing into the hepatic branch, the anterior vagal trunk continues to travel down the lesser curvature of the stomach to form the anterior gastric branch, which is often close to the lesser curvature of the stomach (< 1 cm). This branch travels down to the gastric angle and forms the anterior “crow’s claw” branch, which travels between the two peritoneal layers of the lesser omentum Fig. .The posterior vagal trunk is generally thicker than the anterior vagal trunk, and it travels through loose tissues outside the muscular layer of the right posterior wall of the abdominal esophagus. The posterior vagal trunk is simple to identify and dissect in the gastropancreatic fold, where it gives off the celiac branch below the cardia, which is closely related to the left gastric artery. The celiac branch travels diagonally to the lower right aspect of the abdominal ganglion, where it participates in the formation of the abdominal nerve plexus. After the celiac branch is removed, the posterior vagal trunk continues to travel inferiorly along the posterior wall of the stomach’s lesser curvature to form the posterior gastric branch, which adheres to the lesser curvature and continues to form the posterior “crow’s claw” branch at the gastric angle, where it further divides into 3 or 4 branches that are distributed to the posterior wall of the pylorus. The posterior vagal trunk and its branches are located in the triangular area created by the right crus of the diaphragm, the lateral margin of the stomach’s lesser curvature, and the left gastric artery Fig. .In our study, the demographic characteristics and operative results are summarized in Table The patients attended a comprehensive follow-up visit after 6 months. Compared with the preoperative results, the follow-up endoscopic examination results showed that the Hill grade of the gastroesophageal flap valve had improved from grades 2–4 to grade 1 and that 2 patients experienced complete resolution of their esophagitis. Moreover, the mean total GerdQ score improved from 12.4 to 6.2 and the total esophageal acid exposure time improved from 3.48 to 0.38 %, which supports the short-term effectiveness of anti-reflux procedures performed with the TLSA  have led to an increased use of minimally invasive techniques for treating HH. As a result, the primary treatment of HH now involves laparoscopic repair with fundoplication via the TBSA , 19. TheHere, we performed a cadaver study to evaluate the anatomical characteristics of the vagus nerve and used the results to develop the TLSA, which is currently being tested in a clinical trial involving patients with HH and GERD. Through the present study, we found that the TLSA can theoretically preserve the physiological function of the vagus nerve and the organs it innervates, which is supported by the fact that none of our five patients experienced any postoperative adverse effects or complications. Moreover, compared with the TBSA, the TLSA permits full dissociation of the gastrosplenic ligament at the stomach’s greater curvature, which allows the surgeon to stretch the stomach to the right and, thus, obtain a broader surgical field. The increased working area may permit better outcomes in terms of laparoscopic hiatus reconstruction, mesh placement, suturing, and fixation, especially in bariatric and morbidly obese patients. Finally, the TLSA may also help shorten the operation time, reduce surgical trauma, and improve short-term therapeutic effects and postoperative quality of life.Both TBSA and TLSA can be used in function-preserving procedures, however the TLSA is a modification of the TBSA. The significant difference between the two is that the TLSA can reduce the involvement of the lesser omentum, so that the vagus trunk and its branches that are distributed in the lesser omentum can be protected to the maximum extent possible, thereby decreasing the probability of injury to the vagus nerve.At present, neurophysiological monitors are widely used in the management of thyroid and orthopedic diseases with good efficacy; however, their use in the treatment of HH is still controversial. In their study involving mice, Berthoud et al. used neurophysiological monitors to discover that the hepatic branch of the vagus nerve is mainly distributed in the distal stomach and the celiac branch is distributed in the duodenum . Based oThis study has several strengths. First, it is a prospective study; therefore, the efficacy and safety endpoints to be studied were clearly and completely recorded, guaranteeing high quality and objectivity of the data. Second, the successful treatment and progress of patients in this study will encourage treatment of patients with HH and GERD using a novel approach. Lastly, the TLSA procedure was developed based on the results of the cadaver study, which has laid a theoretical foundation for the implementation of the TLSA.This study has some limitation. Our findings are limited due to its single-center design, small sample size, and a short follow-up period. In addition, this is an observational study. Although the safety and efficacy of the TLSA can be demonstrated to some extent, the study lacks a comparison with the clinical data of procedures performed using the TBSA. Therefore, a multi-centered prospective trial which we conducted currently that includes long-term follow-up periods and a large sample to compare the outcomes of the TLSA and TBSA to validate our preliminary findings of good safety and efficacy .The TLSA provides a broad and clear surgical field, less trauma, and rapid recovery; moreover, it is a technically simple surgery, which may make it especially suitable for obese patients, and patients with substantial comorbidities. The findings of this study will allow for a safer and simpler treatment of patients with HH and GERD, without compromising the structure and function of the vagus nerve to a certain extent.


A Bayesian Belief Network, validated using past observational data, is applied to conceptualize the ecological response of Lake Maninjau, a tropical lake ecosystem in Indonesia, to tilapia cage farms operating on the lake and to quantify its impacts to assist decision making. The model captures ecosystem services trade-offs between cage farming and native fish loss. It is used to appraise options for lake management related to the minimization of the impacts of the cage farms. The constructed model overcomes difficulties with limited data availability to illustrate the complex physical and biogeochemical interactions contributing to triggering mass fish kills due to upwelling and the loss in the production of native fish related to the operation of cage farming. The model highlights existing information gaps in the research related to the management of the farms in the study area, which is applicable to other tropical lakes in general. Model results suggest that internal phosphorous loading (IPL) should be recognized as one of the primary targets of the deep eutrophic tropical lake restoration efforts. Theoretical and practical contributions of the model and model expansions are discussed. Short- and longer-term actions to contribute to a more sustainable management are recommended and include epilimnion aeration and sediment capping. Inland water cage culture farms (IWCCF) operate at a range of spatial scales, from traditional households to corporate modern farms. IWCCF is rapidly growing around the globe as an answer to the increasing demand for aquatic products . In AsiaDespite their benefits, there are increasing concerns about the negative impacts of IWCCF on the aquatic environment. One of the widely identified effects is eutrophication , 7, 8. EMFK events have been long reported from various lakes, rivers, and reservoirs in Indonesia and many other countries see –17) and  and 17])Gobiopterus sp. This species is the only finfish species reported by the fishers to diminish during and after major upwelling events; thus, it is chosen as the indicator of IWCCF impacts on the native biota.In this study, we apply BBN to generate information on the ecological response to IWCCF that can underpin appraisals of IWCCF management scenarios of Lake Maninjau, Indonesia. We specifically aim to answer the research question: to what extent can BBN be used to conceptualize the lake’s complex ecological system related to IWCCF management and quantify its impacts to assist decision making? We surmise that BBN is particularly suited to investigate the impacts of IWCCF on the occurrence of MFK incidents due to upwelling processes in locations where data is scarce. The developed model can then help improve our understanding of both the economy-environment trade-offs of IWCCF expansion and the effects of IWCCF and upwelling processes on a native fish species, This study differs from previous BBN applications , 20 becaBeyond the applied perspective, the paper makes several contributions to the academic literature. First, we develop an integrated model to predict the probability of MFK incidents in a complex tropical lake system. To the best of our knowledge, this is the first such modelling effort. In line with this,  identifiThe paper is organised as follows: first, a brief overview of the history of IWCCF in Lake Maninjau and of the lake’s current ecological condition. An introduction of the BBN approach follows this. The third section will present the methodology applied to build and validate the model. In the fourth section, the developed model is shown and the results of several IWCCF management scenarios are presented. A concluding section then follows a discussion of model performance and scenario results.2, and its catchment area is approximately 132.6 km2 with a full water volume of 10 million m3 2+) creatAs for many lakes in Indonesia , there is limited (time series) data available for Lake Maninjau concerning important biogeochemical indicators and general lake condition. The Research Centre for Limnology, Indonesian Institute of Sciences (RCL-IIOS), has collected data on selected limnological aspects over the years. However, the data was sporadically collected due to financial constraints and is therefore patchy . BecauseRasbora maninjau and Gobiopterus sp. are the most economically important. Importantly, ).2 value. Then, using this cut-off point, a 2x2 cross table of predicted MFK and observed MFK was produced. Finally, the error rate is calculated by dividing the incorrect prediction with total observation (n = 100). The true skill statistic (TSS) was also calculated at the obtained cut-off point. This value of TSS was obtained using the equation suggested in ).Gobiopterus disappearance, although the losses due to MFK can be mitigated (Scenario 1a vs 1b). Meanwhile, keeping 6,000 IWCCF offers similar results as the business-as-usual scenario (Scenario 1a vs 1c). More importantly, combating the IPL is expected to improve the lake condition and may reduce the probability of MFK and Gobiopterus disappearance considerably (Scenario 1a vs 1d). Overall, the suggested best short-term management option is by conducting aeration alone or combine it with a reduction of IPL (Scenario 1e and 1f).The results show that in the short-term, removing IWCCF will not have a significant impact on Gobiopterus disappearance as long as the assumptions above are fulfilled (Scenario 2b). Further, limiting the number of IWCCF to a moderate amount  may cut probability of MFK and Gobiopterus disappearance by more than half, if it is complemented with a reduction of IPL and better feeding management (Scenario 2a vs 2d). Without performing these efforts, a less significant improvement can result from reducing the cages to the 6,000 units (Scenario 2a vs 2c).For the longer-term scenarios, removing all IWCCF may become the best option to avoid MFK events and The constructed BBN model can provide a relatively accurate prediction of MFK when tested with only two explanatory variables (wind speed and wind direction). This finding indicates that the developed model can perform well with limited data availability. The BBN’s ability to incorporate both data learning and expert knowledge is the principal benefit in supporting this feature. The model, therefore, can be useful to assist decision-making in the study area.Further, the model results show that weather conditions, especially wind speed, can be used as a good predictor of the outputs. Therefore, in the current lake condition, forecasting wind speed is crucial to minimize financial loss during MFK by harvesting the fish from cages before stormy events. However, some loss is inevitable in such a strategy, because not all fish will have reached the most profitable size. Interestingly, some local IWCCF farmers have adopted this adaptation strategy in recent years . They haLa Niña which likely triggers more upwelling in the study area. Thus, it gives us an insight that developing the model to take into account the climate change impacts in the future is necessary aligning with the work of [Our previous study  has corr work of .2S mimic the current condition of the lake . This action commonly practiced in pond aquaculture by infusing oxygen in the water column is aimed at reducing the probability of fish death (see [Gobiopterus disappearance events (Scenario 1f). The aeration can be placed in the IWCCF area. In the future, development of spatial BBN is necessary to advise which areas should be prioritized to be aerated.In the short-term, conducting aeration may significantly reduce the probability of MFK and ath see , 105). I. IGobiopath (see ). FurtheThe significance of spatial BBN has been demonstrated by –108. TheGobiopterus disappearance more unlikely in the longer-term, other considerations such as poverty alleviation and significant potential to create social conflict  sho shoGobioict .The model can assist decision making by highlighting the importance of wind speed forecasts to reduce the loss of MFK. Other important factors that should be observed are related to lake stability . Further, it reflects that reducing external phosphorus loading (in this case by reducing the number of cages used for fish farming on the lake) may not give expected results within a short time and also in the longer-term, if IPL is not controlled. Thus, the findings underline the necessity of actions to combat IPL in deep tropical lake systems.Gobiopterus disappearance. In the longer-term, maintaining a moderate amount of the cages along with efforts to control IPL may become an alternative solution to balance the lake’s ecological condition and to conserve economic benefits. Together, this may also help alleviating social conflicts in the study area and elsewhere.In the short-term, combining control of internal phosphate released with aeration is considered the best option to reduce the probabilities of MFK and The use of BBN does not undermine the importance of availability of adequate data to create a mechanistic model. Rather, it should be complemented by such model to provide more comprehensive decision making. Further, the identified data and knowledge gaps are relevant to improving lake management in the tropical system in general.S1 File(DOCX)Click here for additional data file.


The accuracy of the pitch angle deviation directly affects the guidance accuracy of the laser seeker. During the guidance process, the abnormal pitch angle deviation data will be produced when the seeker is affected by interference sources. In this paper, a new abnormal data detection method based on Smooth Multi-Kernel Polarization Support Vector Data Description (SMP-SVDD) is proposed. In the proposed method, the polarization value is used to determine the weight of the multi-kernel combination coefficient to obtain the multi-kernel polarization function, in which the particle swarm optimization is used to find the optimal kernels for higher detection accuracy. Besides, by using smoothing mechanism, the constrained quadratic programming problem is translated to be smooth and differentiable. Then, this problem can be solved by the conjugate gradient method, which could reduce the computational complexity. In experimental section, abundant simulation experiments were designed and the experimental results verify that the proposed SMP-SVDD method could achieve higher detection accuracy and low computational cost compared with different detection methods in different guidance stages. These interference factors will produce abnormal data about the pitch angle deviation in laser guidance data. High-frequency jamming can force the jamming signal into the time wave gate of seeker by generating a high-frequency laser signal, thus flooding the real signal and generating jamming data6. Jamming sends out a signal that is consistent with the indication signal by measuring the parameter information, such as the wavelength, frequency, and azimuth of the indicating laser, and enters the wave gate of the seeker to generate jamming data7. During the flight of the missile, the function of laser seeker is to measure the angle information between missile and target and the accuracy of the angle will directly affect the final hit accuracy of the missile8. When the laser seeker is disturbed, the abnormal seeker angle measurement data can reduce the guidance accuracy of the missile9. Eliminating the abnormal data from the angle measurement information of the seeker is of great significance to improve the guidance accuracy and anti-jamming ability of the missile10.In the actual guidance process, the laser seeker faces various interference factors, such as laser high repetition frequency interference12, discriminant analysis methods13, clustering methods14, support vector machine methods15, cascade model-aware generative (CMAG)16, Tradaboost methods17, generalized least squares methods18, MLP(multi-layer perceptron)19, probability methods20, local outlier factor21, and so on. Yuen et al.20 adopted a probability method for outlier detection and quantified the outlier probability of data points, considering not only the optimal values of parameters and residuals, but also the uncertainty of data. However, this method needs to give a threshold probability to judge whether the data is abnormal or not. Liu et al.21 proposed an outlier detection method based on local information, which combines the traditional local outlier detection method LOF with the outlier factor of uncertain information. Paola et al.22 proposed an adaptive distributed Bayesian method to detect outliers in data collected by wireless sensors and also considered the external constraints of these target data. However, this method needs a probability density distribution model with uncertain data, which is difficult for seeker guidance angle data. Li et al.23 proposed an outlier detection method based on structural scores to process high-dimensional data, which can reflect the characteristics of high-dimensional data. However, because outliers are judged by calculating the included angle of vectors and sorting the structure, this method may have a higher false detection rate for outliers with a small Euclidean distance from normal data. Yuan et al.24 introduced fuzzy rough set (FRSs) to deal with the problem of anomaly detection and classification of mixed attribute data, generalized the outlier detection model by FRS, and constructed a generalized outlier detection model based on fuzzy rough granules. However, this method has high time and space complexity and needs further optimization. Abid et al.25 adopted a density-based method to detect clusters with arbitrary shapes and outliers. However, the method based on density clustering is not suitable for data with uneven density of sample set and large cluster spacing. Support vector machine (SVM) has been introduced to solve the outlier detection problem because of its advantages in binary classification. The support vector data description SVDD is a single classification method of support vector machine, which does not need any distribution assumptions for target data, can map the original data to high-dimensional feature space, establish the smallest hypersphere containing the given data, and can detect outliers26. However, SVDD algorithm has high complexity, and it is difficult to select kernel functions and kernel parameters27.The elimination of interference can be regarded as the problem of outlier detection, where normal data and abnormal data can be classified to achieve the purpose of anti-interference. The abnormal interference data of laser seeker guidance can be processed by detection and classification methods. The abnormal interference data of laser seeker guidance can be processed by detection and classification. At present, there are many abnormal data detection methods, such as convolutional neural networks (CNN)In the actual guidance process, the pitch angle deviation data of the laser seeker in different guidance stages varies greatly nonlinearly, which makes it difficult to assume distribution. Besides, due to the limited hardware resources of the missile and the complexity of the algorithm, the above methods cannot meet the requirements of abnormal data detection of the laser seeker. Therefore, this paper proposes a smooth multi-kernel polarization support vector data description (SMP-SVDD) method to classify and detect the pitch angle deviation data. Compared with single-kernel kernel function, multi-kernel function can adapt to data with different nonlinear characteristics and improve the detection accuracy of the algorithm. However, because the SVDD algorithm needs to solve quadratic programming problems, the complexity of the algorithm is high, and multi-kernel will also increase the complexity of the algorithm to a certain extent, thus these factors will increase the resource consumption of the onboard system. Therefore, the proposed method also introduces the smoothing function to reduce the complexity of the algorithm, by transforming the constrained quadratic programming problem into an unconstrained differentiable optimization problem which can be solved by conjugate gradient method. However, because the nonlinear characteristics of data in different stages are quite different, this method adopts a multi-stage method to construct the detection model, and adopts the particle swarm optimization method to determine the optimal kernel function and kernel parameters in each stage. Experiments show that this method is effective in dealing with outliers of the seeker pitch angle deviation data.The rest of this paper is organized as follows. In the second part, the theoretical calculation and analysis of smooth multi-kernel polarization support vector data description algorithm are given, including classical SVDD algorithm, multi-core polarization SVDD algorithm, smooth multi-kernel polarization SVDD algorithm, optimal selection of kernel parameters and algorithm complexity analysis. In the third part, through simulation experiments, we verify the detection performance of the proposed method both on detection accuracy and computational cost. Finally, a conclusion of this work is given.26. The goal of SVDD is to find a minimum radius to distinguish outliers form normal data.The basic idea of the support vector data description is to map the normal data to the high-dimensional feature space, construct a minimum hypersphere to describe the data, contain all the normal data, and eliminate the outliers from the outliersTake the pitch angle deviation data of the laser seeker as the training sample SVDD is the non-linear transformation 27:The center By constructing a Lagrange function, the original problem can be transformed into the following problem:By solving the linear constrained quadratic optimization problem mentioned above, The distance from the test data sample The pitch angle deviation data of the laser seeker will show different nonlinear characteristics in different stages. Therefore, when using the SVDD model, compared with a single-kernel function, multi-kernel function has a stronger classification ability and better flexibility for data in different guidance stages. However, in the process of multi-kernel combination, numerous combination weight parameters will be artificially introduced, which will make it difficult to find the best parameters, and it is easy to have a dimension disaster and local extremum problems when searching for the best parameters.29. If there is a clear correspondence between the nuclear data points and the labeled values, the classification process will become easier. Suppose that the training data set is y is the labeled data, Polarization can reflect the similarity between a kernel function and an ideal kernel matrix. The same kind of data is close to each other, while different kinds of data are far away from each other, and the combination relationship between different kernels can be determinedThe greater the contribution rate of the kernel function to the correct classification of the sample, the greater the corresponding In this work, we chose the following basic kernel functions: Gaussian kernel function, Laplace kernel function, and exponential kernel function. We can combine the following polynuclear polarization functions as follows:Among them, Among them, Because MP-SVDD is still an optimization problem in the form of quadratic programming, it cannot be directly converted into an unconstrained differentiable function for optimization. This leads to high algorithm complexity in the process of seeker angle data training, and the training time will increase geometrically with the increase of data. Inspired by the smoothing function, the MP-SVDD model is smoothed and transformed into a differentiable unconstrained optimization problem, and the conjugate gradient method is used to find the optimal solution.30.The smooth function can be obtained by integrating the sigmoid functionLet The partial derivative of the R and Compared with the constrained quadratic programming problem, the conjugate gradient method mentioned above avoids the complicated operations, such as solving linear matrix equations, by which the complexity of the algorithm can be reduced.The pitch angle deviation data of the seeker has different nonlinear characteristics at different stages, and the classification accuracy of pitch angle deviation data is different with different kernel function parameters and different linear combinations. Therefore, the particle swarm optimization algorithm is adopted in this paper, and different kernel function parameters are adopted for different guidance stages to obtain the optimal multi-kernel function and penalty factor.32, neural network training34, and so on. The PSO method defines a fitness function according to the objective function, and every particle is updated by speed and position in the iterative a process of optimization. Every particle will determine a local optimal solution ipbest, and the optimal solution found by the whole population is called global optimal solution gbest. The PSO algorithm adaptively updates the speed and position information of particles based on the good past experience.Particle Swarm Optimization (PSO) is a heuristic evolutionary computation technique, which initializes a group of particles and iterates to find the optimal solution. Particle Swarm Optimization is widely used in target optimizationThe process of PSO algorithm to optimize the SMP-SVDD model is shown in Algorithm 2. Among them, mentclass2pt{minimN data in the whole guidance phase, the time complexity of the classical SVDD algorithm27 is Assuming that there are However, the characteristics of pitch angle deviation data are quite different in each guidance stage. If the data of the whole guidance stage is trained at one time, it will not only be difficult to ensure the accuracy of data detection, but the computational complexity will also increase geometrically because of the increase in data volume in the whole process. If the entire guidance process is divided into n guidance stages according to the characteristics of different stages, the data volume of each stage is In this paper, the accuracy rate, recall rate (TPR), false positive rate (FPR), true negative rate (TNR), and false negative rate (FNR) are used to evaluate the detection performance of the model. The higher the accuracy and recall rate, the better the performance of the model. The statistical result of sample classification is shown in Table The calculation formulas of evaluation indexes are as follows:In this section, the experiments simulate the laser guided missile attacking the ground target. The whole trajectory simulation range is 8 km. When the seeker is 5 km away from the target, it starts to guide. When it is 3 to 5 km away from the target, it is the initial guidance stage, wherein the seeker is in the state of searching for the target and tracking it, the intermediate guidance process is 1.5 to 3 km away from the target, and the final guidance stage is 0 to 1.5 km away. Trajectory simulation is conducted under the conditions of no interference and laser decoy interference (4 to 2.5 km with interference), and the pitch angle deviation data set during laser seeker guidance is obtained. The specific conditions of the data set are shown in Table According to the experimental dataset obtained from ballistic simulation, we used SVDD, SA-SVDD, and SMP-SVDD to detect the outliers of pitch angle deviation data in the whole guidance process. Through setting optimization parameters, the particle population size is 60, the maximum number of iterations is 1000, the range of penalty factor is [0 1], the range of kernel parameters of Gaussian kernel function is [0.1 10], the range of kernel parameters of Laplace kernel function is [0.1 10], and the range of kernel parameters of exponential kernel function is [0.1 10]. The comparison results can be obtained through optimization, as shown in Table 3When compared to SVDD and SA-SVDD, the SMP-SVDD model used in this paper has higher accuracy in data classification and detection, and the highest detection accuracy is obtained when the According to the data in different stages, the SMP-SVDD model is used for detection, and particle swarm optimization is used to find the optimal parameters of different polarization kernel functions in different guidance stages, as shown in Table According to the results above, when using the SMP-SVDD model to detect outlier data points in the initial stage of guidance, the According to the guidance angle data of laser seeker in the whole guidance stage and different guidance stages, under the hardware and software environment described in this section, the time of single sample training of SVDD, SA-SVDD and SMP-SVDD is compared to verify the time complexity of different algorithms.As shown in Table In this paper, a SMP-SVDD method is proposed to detect the abnormal data of seeker interference and the particle swarm optimization algorithm is used to get the best kernel parameters. (1) Compared with SVDD and SA-SVDD, SMP-SVDD has better detection accuracy and higher detection accuracy. (2) The smoothing function is introduced to transform the constrained quadratic programming problem into a differentiable unconstrained problem and the conjugate gradient solution can reduce the complexity of the algorithm. Compared with SA-SVDD, the detection accuracy is improved and the calculation efficiency is slightly reduced, but the difference is not large. (3) Various polarization multi-kernel functions can be used in different guidance stages. Compared with using a polarization multi-kernel function in the whole guidance stage, this processing mode has better detection and classification performance and it improves the overall data training efficiency. The improvement in the detection performance of the seeker's interference anomaly data meant that seekers will have higher intelligent processing abilities and anti-interference performance. In the future, we will conduct further in-depth research on the detection and recognition of interference data in view of the improvement of the seeker's anti-interference performance.


This work deals with the parametric optimization of the position and form of a conformal cooling used in the injection molding industry. Based on a literature survey, an optimization routine concerning the parameter optimization of cooling system designs was developed and implemented with the help of the software package Moldflow. The main objective of the optimization is to reduce the cooling time; the second is to obtain an optimized homogeneous temperature distribution over the complete tool surface. To enable a comparison of the new close-contour solution with a classical manufacturing process, an optimized cooling system simulation, based on a conventional manufacturing solution, was established. It can be shown that the optimized close-contour cooling design offers significant advantages that cannot be exploited using classical manufacturing. Finally, the additive manufacturing of a prototype in the framework of powder bed fusion is documented as a proof of concept. Injection molding is the most common fully automatized process for manufacturing thermoplastic parts with complex geometries . Its ecoThe aim of this paper is to document the development of a new close-contour cooling system for a metallic injection molding tool using dedicated structural optimization and the numerical modeling of the flow and heat transfer and the injection molding process, itself. Based on a literature survey, a parameter optimization of cooling system designs was performed with the help of the Moldflow software package . This optimization was used to establish a close-contour cooling system in a reference mold core. Finally, the manufacturing of a prototype is documented as a proof of concept before this paper closes with its summary and conclusions.Conventional cooling systems in injection molding tools usually consist of straight drilled or milled holes connected to the cooling circuit, where the mold cores that are used for forming three-dimensional parts are especially challenging. In such cases, straight holes are drilled where the cooling medium  reaches temperatures up to 300 °C . The strIn a close-contour cooling system, the location and the pathways of the cooling channels follow the shape of the molded part closely, whereas in a conventional cooling system the channel locations are dictated by the available means of manufacturing. Additive manufacturing, in the form of the so-called Powder Bed Fusion (PBF) process, is based on the layer-wise manufacturing of a structure by adding material rather than subtracting it as it is done in “classical” manufacturing ,7,8 Fig. Thus, PPark and Dang  used a cIt is worth mentioning that while all cited investigations proved the significant potential of close-contour cooling systems in injection molding tools for improved processing time and part quality, none performed a systematic mathematical programming optimization; rather, a heuristic design was manufactured and employed in numerical and experimental investigations. It is thus of importance to research the potential that can be reached when systematic optimizations are performed, which is the purpose of this paper.The literature describes a good number of methods that had been successfully employed to optimize tool cooling, of which the majority of investigations were geared towards finding an optimal cooling system without a pre-existing design. The optimization approaches can be divided into deterministic methods, which take specific temperature distributions into account, and heuristic methods by which a close-contour cooling is developed based on simplified strategies without taking local temperature distributions into account.Agazzi et al.  tackled Due to the high computational effort involved when using deterministic methods for generating temperature fields in the mold part and tooling, according to optimal close-contour cooling channel systems, a relatively large number of heuristic methods have been employed that rely on engineering intuition and systematic parametric studies to determine a channel distribution without resorting to mathematical programming . In the In summary, it can be stated that up to this day no optimization routine exists that can establish an optimized distribution of cooling channels without necessitating user interaction and interpretation of results. Furthermore, none of the discussed optimization routines accounted for the actual manufacturing process of the injection molding tool, which also holds for the additive PBF process. Consequently, any design that is the result of numerical simulation and optimization requires a certain amount of post-processing.www.freecadweb.org, v0.18, 2019), and ANSA , which has specific Python scripts to ensure compatibility among the different software systems. Then, the model was imported into the injection molding simulation software and the corresponding results are generated. The simulations were performed using the Autodesk Moldflow software package. Finally, the most relevant ones were extracted and sent back to the optimization software. The post-processing step was performed using Autodesk Moldflow and specific Python scripts for software compatibility purposes.The optimization methodology used in this investigation is described briefly. For each calculation, the optimization software provides information about selected parameters for the creation and meshing of cooling channels in the pre-processing step. For the actual optimizations, the program LS-Opt  was employed, while the model pre-processing was performed using the open source CAD software FreeCAD . The process parameters were corresponded to test runs performed at BASF SE and are given in The current cooling system of the mold core is presented in Due to the high amount of calculation time involved in injection molding simulations, optimizations are performed on a meta model. To determine the investigation points of the meta model, a so-called space-filling algorithm was used. This method maximized the minimum distance between two arbitrary investigation points within the design space by means of heuristic optimization  so that the maximum criterion was fulfilled ,20,21. TThe aims and boundary conditions considered by subsequent optimizations are listed in The optimization of a conventional cooling system was investigated for comparison purposes with a close-contour cooling. To do so, the optimization was performed for an arbitrary number of straight cooling channels. The CAD model of the existing mold core, the initial cooling system and the cooling channel axes, as required for boundary element method analysis, are given in lbaffle of the pass partition plates, the number nbaffle of equidistant channels, the orientation angle äbaffle of the channels, and the radius rbase of the base cooling channel. Restrictions for the design variables are outlined in The following optimizations are based on the design variables given in dinner describes the distance of the two inner control points (1) and (2) from the inner hollow cylinder of the mold part, while douter is the distance of control from the outer hollow cylinder––points (4) and (5). Together with the distance dz, the cooling channel in the rib region is fully parametrized, and it is assumed that the cooling channels are always oriented parallel to the ribs. A minimum distance of 1.5 mm between the cooling channels and rib surfaces, corresponding to the remaining wall thickness, was taken into account at all times.To assess the optimization potential of a close-contour cooling system, an initial parametrized design was established  Figure  and gradr1 and outer radius r2  equipped with a build volume of 250 × 250 × 325 mm and a 400 W Yb fiber laser, and the prototype was printed with AlSi10Mg. First, aluminum is commonly used for prototype manufacturing in injection molding, so a proof of concept manufactured with AlSi10Mg stands for the additive counterpart of a prototype made by conventional injection molding. Second, the additive manufacturing process is established at the Institute for Lightweight Construction and Design for this specific aluminum alloy. The 3D model was created using Autodesk Inventor  and ANSA  software. Positioning of the part and supporting structures were performed by Materialise Magics  software, while parameter assignment required EOS Print  software. The part was manufactured upright in the center of the build platform, which enabled the lowest possible build height for time efficiency (34 h) and the minimum amount of support structures to involve as little rework as possible. To avoid geometrical inaccuracies induced by eigenstresses and inhomogeneous temperature gradients, solid supports based on extruded surfaces were used coupled with a build platform temperature of 180 °C. Moreover, all external surfaces were manufactured with an oversize of 0.5 mm to enable post-processing of all relevant surfaces. This consisted of turning all external surfaces with rotational symmetry, starting with the outer surface, and a precise CNC milling of the rib area by means of tool extension to achieve low external surface roughness. In the framework of this proof of concept, the inlets were not post-processed. This paper documented the numerical parameter optimization of a close-contour cooling system of an thermoplastic injection molding tool, based on numerical simulations, to optimize the overall cooling phase and thus reduce the cycle time and increase the part quality. An in-depth literature study revealed the driving parameters behind the performance of cooling channels as well as the main challenges when such optimized tools are manufactured using PBF. The investigations showed that optimizing cooling systems and transforming them from conventional designs to close-contour configurations contains significant potential for reducing cycle times, increasing productivity and improving part quality due to uniform temperature distributions and economic advantages. Lastly a prototype was manufactured, thus successfully enabling a proof of concept of the optimized tool.Future investigations should be concerned with an optimized design taking into account post-cooling shrinkage. Furthermore, in-service tests on a steel tool should be performed to find the optimal porosity manufacturing parameters. Other interesting fields of research could be a comparison of close-contour cooling with other means of improving injection molding tools, such as implemented high conductivity inserts and, or the assessment of the potential and limits of additively manufactured tools compared to conventional ones and their impact on a cost-estimation model.


Background: Innovative methodologies to redesign care delivery are being applied to increase value in health care, including the creation of enhanced recovery pathways (ERPs) for surgical patients. However, there is a lack of standardized methods to evaluate ERP implementation costs.Objectives: This Recommendations Statement aims to introduce a standardized framework to guide the economic evaluation of ERP care-design initiatives, using the Time-Driven Activity-Based Costing (TDABC) methodology.Methods: We provide recommendations on using the proposed framework to support the decision-making processes that incorporate ERPs. Since ERPs are usually composed of activities distributed throughout the patient care pathway, the framework can demonstrate how the TDABC may be a valuable method to evaluate the incremental costs of protocol implementation. Our recommendations are based on the review of available literature and expert opinions of the members of the TDABC in Healthcare Consortium.Results: The ERP framework, composed of 11 steps, was created describing how the techniques and methods can be applied to evaluate the economic impact of an ERP and guide health-care leaders to optimize the decision-making process of incorporating ERPs into health-care settings. Finally, six recommendations are introduced to demonstrate that using the suggested framework could increase value in ERP care-design initiatives by reducing variability in care delivery, educating multidisciplinary teams about value in health, and increasing transparency when managing surgical pathways.Conclusions: Our proposed standardized framework can guide decisions and support measuring improvements in value achieved by incorporating the perioperative redesign protocols. Health-care systems continuously register a significant waste of scarce resources.Evidence demonstrates that due to their impact on patient clinical outcomes , ERP initiatives have contributed to decreased overall costs of care and fewer complications.There are several existing regional and global initiatives to standardize outcomes evaluation by defining standard sets of measures for several health-care fields, such as the International Consortium for Health Outcomes Measurement (ICHOM), the Meetbaar Beter – Netherlands, the consolidated databases from Medicare, Medicaid and the Navy in the United States, and the National Health Service in the United Kingdom. The ERP guidelines usually include measures for health status achieved and recovery process that may be monitored for each patient.Advanced cost accounting methods such as Time-Driven Activity-Based CostingBeyond cost-saving opportunities with surgical redesign initiatives and ERP adoption, there is a potential to generate significant economic returnsOne of the primary goals of the ERP care-design initiatives has been to center the delivery of care on the patient and improve the health-care value delivered.The objective of this Recommendations Statement is twofold: first, to describe techniques and methods for applying each TDABC framework step; and second, to create a list of practice recommendations for how one could use the framework to support ERP development and associated decision-making processes.Figure 1 shows the methodological sequence of the consensus process, which is comprised of three phases.The proposed standardized framework and the list of recommendations are based on the consensus group members’ current literature review and expert opinion. For the first phase, we searched Medline/Pubmed for applied case studies that evaluated the economic impact of ERP implementation. To guide the information extraction from those studies, we used the variable extractions of the following items: surgery type, patients’ clinical outcomes, the method used to evaluate costs, reduction in complication rates, LOS reduction, the report of cost savings, and, if available, information about how the ERP contributed to increasing health-care value. Descriptive analysis with a significant focus on the cost methods and economical and value assessments was used to report literature review results.Consequently, in the second phase, members from the TDABC in Healthcare Consortium conducted a critical analysis of how TDABC can be implemented as a better cost method to guide the financial analysis of ERP implementations. One of the primary goals was to introduce a step-by-step standard framework to support the decision-making process before and during ERP pathway implementation. Our proposed framework is followed by a list of recommendations of how to apply each step.In the third phase, a group of experts with doctoral-level education or significant experience in the surgical field’s health economics was invited for a focus group discussion. The session was moderated by two of the senior authors (APE and RDU) to discuss the steps of the framework and their applicability to health-care leaders. The moderators shared the article draft and the framework with the group of experts. During the focus group workshop, each member was asked about their agreement with each step. If a member identified a disagreement between one step, the moderators asked for the focus group member(s) to report an example from their real-world experience. After discussion designed to understand the example better, the group achieved a consensus. At the end of the focus group session, all the participants were invited to share their opinions about how the framework contributes to guiding ERP incorporation decisions and should be used in the health-care setting. These two final questions were essential before beginning work on the list of recommendations.After the session, APE and RDU included the group’s recommendations for the framework and finalized the recommendations list designed to guide ERP design and implementation decisions. A first version of the list of recommendations was circulated among the consensus group members to review and validate each recommendation.Finally, APE and RDU shared the participants’ final recommendations list, and the group reached a consensus regarding the importance of each item until the last Recommendations Statement was composed.Financial benefits associated with ERP implementation have been demonstrated in several procedures involving different surgical complexities and potential complications or adverse outcomes.Furthermore, another study focused on gynecologic procedures that applied a more rigorous cost analysis method to evaluate data on 1191 patients, comparing the costs of ERP and non-ERP patients—including labor, medication, materials, and hospital structure—based on data monitored and provided by the hospital finance department. This study also demonstrated a 7.8% decrease in LOS and 8.4% in costs per patient.One recently published studyMany health-care organizations have successfully implemented ERP protocols and conducted surgical care redesign initiatives often based on professional society recommendations, such as those of the Enhanced Recovery After Surgery Society (https://erassociety.org/) and the American Society for Enhanced Recovery (http://www.aserhq.org/web/). At Dell Medical School at the University of Texas at Austin, a group of researchers applied the re-engineering and design thinking methods in an internal program, “Preoperative Assessment and Global Optimization.”The assumption that clinical and associated financial outcomes will accrue with minimal investmentFigure 2).Understanding the financial implications of ERP requires an understanding of the investments needed for successful implementation and the detailing of cost savings that can accrue when an ERP has been fully incorporated into clinical practice.This framework guides the analysis that should be performed before the decision to implement an ERP is taken, suggests the economic impact that the ERP implementation can achieve, and serves as a basis for administrators and clinicians to discuss the redesign opportunity in detail. By detailing the care pathways in a TDABC study, the multidisciplinary teams can also learn in detail the sequence of activities related to patient care and all resources needed to perform those activities.The consensus group developed a detailed list of recommendations to be followed in each step during the implementation process. We outline 11 key steps below:For the redesign project to fit given value-based health-care goals, it is suggested that the care pathway selection that will have a redesign evaluation consider patient volume at the institution that will be subjected to the care pathway and the clinical literature available indicating that it is possible to achieve better health outcomes by designing a given ERP.Once the focus of a redesign surgical pathway is defined, the first activity should examine published protocols in the existing literature. If the hospital already has other ERP protocols implemented and a specific protocol for this surgery that a professional society has endorsed, the ERP protocol can be considered the desired clinical practice. However, if the hospital has not yet implemented ERP protocols and there is no specific protocol for the surgery care pathway under evaluation, a literature review to identify best practices that can support the ERP design may be considered.Design thinking and the Business Process Model and NotationAll the required investments needed to operationalize the perioperative care pathway should be identified and budgeted. The technology may be described as medical equipment, telemedicine platforms, additional data cloud storage, software, or app development, if necessary. A detailed classification of the job descriptions can be developed for all the clinical and non-clinical staff for hire. The remuneration can be estimated according to the job description if it is necessary to hire new professionals. An education program to engage the team on the redesigned processes can be organized according to the estimated schedule for starting the redesigned perioperative care pathway. It is also essential to calculate the expected timing to structure the redesigned care pathway and, if necessary, a team to structure it (including clinician and non-clinician hours).For the designed ERP, all the additional resources necessary to perform each activity should be identified. The resources can be divided into technology, professional services, supplies, and hospital infrastructure. For example, technology may represent telemedicine services or innovative equipment necessary along the care pathway; professional services include all possible employees who dedicate additional time to the patients under ERP protocols. Changing postoperative patient allocation should also be considered, as in intensive care unit or high-dependency unit allocation for high-risk surgical patients or a hospital ward dedicated to hip surgery patients. The sum of additional resources identified represents the incremental operational cost of the ERP.The methodological sequence of steps to apply the TDABC in health care should be followed.Once the cost-per-activity for the two care pathways is calculated, the cost results can be compared and the differences observed for each activity and the care episode may be considered potential incremental cost, or cost-savings.Previous literature associated with the ERP protocol being implemented is the primary reference for the anticipated impact on patient LOS and health-care services consumption. However, after implementing the ERP, the mean LOS and readmission rates with the real-world data registered at the institution should be reviewed.After the total investment, incremental costs, and potential cost-savings are calculated, it is necessary to preview the volume of patients expected to undergo the procedure over a period of time and assume a weighted average cost of capital according to the institutional capital cost. A discounted cash flow can then be structured, and the NPV calculated.A hypothetical example can elucidate this process. An investment need of US$30 000 was estimated to implement a perioperative care pathway; the hospital established a team of anesthesiologists and surgeons to support the program, composed of physicians from the hospital who had their current practices redesigned, and these resulted in an incremental service cost of US$9675. A mean decrease of 2 days of the hospital LOS per patient was observed. For this example, a Medicare hospitalization charge per day was considered. Therefore, assuming a weighted average cost of capital of 5% per year in hospital practices (0.41% per month) and an average of 10 patients undergoing the care pathway per month, the protocol implementation could represent an NPV of US$80 910 in 6 months.The economic analysis could also include other cost-saving or cost-increase variables, if identified later, such as a decrease in medication consumption or additional costs related to complication rates or other outcomes such as unplanned intensive care unit admissions, surgical re-interventions, or postoperative death.These two final steps are optional, but are recommended for hospital leaders who wish to incorporate opportunity costs associated with decreasing patient LOS into the economic analysis. In this scenario, hospital leaders would estimate the different surgeries that could be done within the exact structure of the surgical department, per period of analysis, and add their financial results as financial gains to the discounted cash flow. If the leaders identify that there is no pent-up demand for any procedure, another way to include the opportunity cost associated with the decrease in LOS is to evaluate a project to reduce the operating room capacity and make it less idle.Complementary outcomes to be evaluated can be chosen according to the previous discussion with peers and stakeholders. For example, if the institution is organized to collect and manage patient-reported outcomes or patient-reported experience, given some evidence of their improvement with ERP, these must be considered in new pragmatic studies.The NPV may be recalculated to include the extra financial gain from the additional procedures that can be performed within the same operating room structure. Finally, improvement in patient experience scores reported by patient-reported outcomes and patient-reported experience measures may also be evaluated, and radar charts with value measures can be used to express the gains.Implementing a perioperative care pathway as a quality improvement project may pose numerous operational challenges. After the planning process, to successfully implement any change some important principles should be followed: (i) get support from all stakeholders; (ii) form an implementation team with the hospital quality improvement leaders, the recovery room and ward nurse leaders, and the perioperative anesthetists and surgical teams; (iii) adopt an established quality improvement model such as one based on “plan-do-check-act” cycles across the phases of the perioperative pathway; (iv) engage surgical teams in adopting the postoperative bundle within their assistance; and (v) audit perioperative practices to examine the new protocol compliance and keep perioperative caregivers updated.The framework described in this Recommendations Statements—building on the suggestions presented by Najjar et al.how things are being done is just as important as what it is being done. In emerging economies where the administrative and patient outcomes data availability is limited, the redesign of surgical pathways can be facilitated by following the proposed step-by-step process.With millions of surgeries performed annually in the United States, an estimated 51% of the total Medicare expenses are consumed by surgical care, and these expenditures continue to rise.Once the perioperative redesign protocols begin to include robust economic analyses, it will be possible to measure the resulting value expansion opportunities. Given how the impact of the implementation of protocols on functional and patient-reported outcomes is already known and available in the literature,One of the LEAN management techniques recommended by the value-based health-care implementation literature as a data-driven strategy to increase health-care efficiency is the Define Measure Analyze Improve and Control (DMAIC) method.Figure 3 suggests how the framework introduced in this article is related to the DMAIC continuous improvement cycle.The standardized framework to guide the decision-making process of ERP incorporation can help those involved in care redesign follow the DMAIC cycle. We understand that the latter framework can be used before the ERP incorporation by supporting the definition of the project and measuring its potential impact. Once the decision is made to incorporate the ERP design project, the economic steps (4 to 10) listed in the framework can be continuously monitored to assess the real economic impact and identify improvement opportunities by analyzing the observed results. The framework introduced in this Recommendations Statement might provide health-care administrators with the ability to generate information for value equation variablesBased on the expert consensus framework introduced here and the previous recommendations for using TDABC to evaluate and identify value increase opportunities in surgical settings,Use the ERP framework to guide the ERP design decisions. By applying the framework, it is possible to estimate the economic impact of the surgical pathway redesign.Educate health-care professionals about the designed ERP. Applying the framework, including the TDABC methodology, requires a multidisciplinary team approach, helping clinicians learn about finance and the administrators and financial analysts to learn about clinical care pathways.Support the continuous improvement cycle. Applying the proposed framework with the DMAIC cycle in mind and involving multidisciplinary groups can help identify opportunities to make the care cycle more efficient.Minimize unnecessary variability and move toward care standardization. By applying the TDABC methodology and detailing care cycles that identify the resources necessary to provide high-quality care to patients, standards of care are defined.Utilize data to monitor the pathway financial results continuously. Once the framework is applied to support the redesign incorporation decision, it must be frequently updated in a structured manner to provide real-world data to administrators and guide economic choices that increase the value delivered through the surgical pathways.Utilize the framework to bring internal and external transparency to the management of surgical pathways, especially in middle-income countries where databases that collect outcomes are beginning to be utilized. Share the results with payers and internal teams, highlighting the benchmarks identified and encourage them to achieve better health outcomes and economic impacts.While comprehensive, the framework has a few limitations. First, we did not develop a new systematic review to evaluate all studies that developed economic methods to evaluate ERP implementations. We used recent review articles published to sustain our justifications and economic methods selected to build the framework,


High-resolution satellite images (HRSIs) obtained from onboard satellite linear array cameras suffer from geometric disturbance in the presence of attitude jitter. Therefore, detection and compensation of satellite attitude jitter are crucial to reduce the geopositioning error and to improve the geometric accuracy of HRSIs. In this work, a generative adversarial network (GAN) architecture is proposed to automatically learn and correct the deformed scene features from a single remote sensing image. In the proposed GAN, a convolutional neural network (CNN) is designed to discriminate the inputs, and another CNN is used to generate so-called fake inputs. To explore the usefulness and effectiveness of a GAN for jitter detection, the proposed GANs are trained on part of the PatternNet dataset and tested on three popular remote sensing datasets, along with a deformed Yaogan-26 satellite image. Several experiments show that the proposed model provides competitive results. The proposed GAN reveals the enormous potential of GAN-based methods for the analysis of attitude jitter from remote sensing images. In the application of high-resolution (HR) optical satellites, attitude jitter is a key factor affecting the accuracy of geopositioning and 3D mapping. Satellite jitter is commonly induced by the satellite’s thermal change, attitude control operation, dynamic structure and other factors ,2,3. JitThe linear array pushbroom camera has been used in high resolution remote sensing as a mature sensor device. Line scan imaging refers to forming a line image, or even a two-dimensional image. During each scan, the same scan line is imaged through the center projection, such as a linear CCD pushbroom camera . TherefoThe oscillations of a spacecraft around its rotation axis consequently deform the geometric performance of HR satellite imagery. Remote sensing satellites, such as Terra, ALOS, MOMS-2P, and QuickBird, suffer from satellite jitter. research , the infGenerally, three traditional methods are used to detect spacecraft jitter and restore deformed remote sensing images. The first is to use high-performance attitude sensors to obtain the attitude information with high time and angular resolution . The secIn recent years, the study of feature extraction is particularly important. Fan, Mengbao, et al. proposed the phase of spectral PEC response to serve as an original feature in pulsed eddy current (PEC) research and presented a strategy to determine frequency and select superior feature . FurtherAlthough considerable progress has been made, deep-learning-based models often encounter a serious problem known as overfitting when the training data are limited. Unfortunately, because data preparation is time-consuming and expensive, training data are commonly limited in the remote sensing community, which substantially restricts the generalization of models. Thus, effective training strategies for deep models are required to address the issue of overfitting. In a deep convolutional GAN model, the discriminator network of the GAN can be regarded as a regularization method that can force the generator network to learn essential features and mitigate overfitting to a great extent. Because of the advantages of deep convolutional GAN, GAN has shown great feasibility in a variety of remote sensing applications, such as image classification , image pIn this work, the application of GANs for deformed image compensation is original introduced to explore for attitude correction. We have conducted a comprehensive and in-depth study on attitude jitter analysis and the restoration of deformed images based on the generative adversarial network, by using completely different ideas and methods to design a new network architecture that can automatically detect jitter and restore deformed remote sensing images end-to-end through one network architecture. A GAN-based image jitter compensation network (RestoreGAN) for remote sensing images is proposed. RestoreGAN takes a single deformed image as input and outputs a restored image along with jitter curves. The experimental results for three datasets and a Yaogan-26 deformed image illustrate the superiority of RestoreGAN compared with other methods under the condition of limited training samples. The rest of this work is organized as follows. Aiming at detection and compensation of satellite attitude jitter, in this paper, a generative adversarial network architecture is original introduced to automatically learn and correct the deformed scene features from a single remote sensing image suffer from geometric disturbance in the presence of attitude jitter.We proposed a new GAN-based image jitter compensation network (RestoreGAN) for remote sensing images. Compared with the previous architecture, two convolution blocks with large kernels are first applied, which has been proven helpful to learn the HR features and improve network capability. Then, one stride convolution block and two residual blocks with batch ormalization are introduced. The discriminator network of the GAN can be regarded as a regularization method that can force the generator network to learn essential features and mitigate overfitting to a great extent.We constructed a comprehensive and in-depth study on the analysis of attitude jitter from remote sensing images based on generative adversarial network. The experimental results on three public datasets indicate that the proposed framework achieves the highest DM and best performance on most of the restored images. The image retrieval results demonstrate the necessity and effectiveness of the proposed method in image retrieval tasks.A jitter detection and compensation model is established to compensate for the image distortion caused by satellite jitter. According to previous work , jitter t. Furthermore, p denotes the attitude jitter direction in the body coordinate reference system, as shown in the Here, Here, the first term on the right-hand side of the equation is the dominant frequency sinusoidal function. The second and third terms are the high-frequency and low-frequency parts of the jitter displacement, respectively. In the satellite platform, the dominant frequency is based on the expected attitude accuracy of the AOCS control system, which is expected to have the largest influence on attitude inaccuracies . The higIn the geometric compensation, the relationship between the satellite attitude jitter and the pixel displacement of the images is demonstrated below:Equation . MoreoveThen, the image displacement formula with random parameters is used in the high-resolution remote sensing images and the distorted images are generated. In addition, original and distorted image pairs are prepared for training and evaluating our models.E is the expectation operator. The basic GAN structure encounters several problems, such as difficult convergence, mode collapse, and vanishing gradient, as described in [L is the set of 1-Lipschitz functions. By introducing Lipschitz functions, WGAN provides a gradient to push GAN was first proposed by Goodfellow et al.  in 2014.ribed in ; thereforibed in  was propminGmaxD=Given the deformed remote sensing image As shown in x. The adversarial loss is calculated as follows:N is the number of image batches.The format of the critical function from WGAN is utilized to achieve stable, high-quality results, and to reduce the instability of the GAN training, feature matching loss  is impleTo optimize the generator towards learning contextual information of the correct images, content loss is introduced to penalize the generator by measuring the distance between the correct images and the generated images. According to , L1-typeIn this task, jitter loss is the Euler distance between the generated jitter vectors and the true jitter vectors. Jitter loss is introduced to help the generator find the correct direction to reduce the loss and thereby accelerate the training phase. Jitter loss is defined as follows:A to The architecture of the discriminator in RestoreGAN is the same as that of the discriminator from DCGAN  and is iAfter applying the flatten layer and reshape layer, the proposed generator outputs the two directional jitter vectors with the same length as the image height. The generated jitter is then utilized in the loss function to train RestoreGAN.In practice, when applying pretrained models to deformed remote sensing images, image patches of the same size as the model input should be selected to detect jitter and compensate the images. Considering that different scenes present different degrees of deformation and that the pixels of the same imaging lines suffer the same jitter, some image samples with obvious deformation are retrieved from the raw image to detect jitter. Because the onboard camera scans scenes in the horizontal direction, vertical features are more sensitive to jitter deformation than are horizontal features. W and H are the width and height of the images. Image patches with high IDM are selected as inputs of the proposed method, and the raw image is then corrected by image interpolation with combined jitter.Thus, the image deformation metric  are the averaged values over all the queries.The detailed definition of the metrics can be found in [To further verify the validity of the proposed method, a pretrained ResNet50 deep learning model from  is estabfound in . The imaIn this paper, the usefulness and effectiveness of GAN for HR remote sensing image restoration is explored. Based on improved GAN, the proposed method has been trained and evaluated on distorted HR remote sensing image datasets with simulated jitter vectors. The experimental results on three public datasets indicate that the proposed network architecture achieves the highest DM and best performance on most of the restored images. The image retrieval results demonstrate the necessity and effectiveness of our method in image retrieval tasks. Furthermore, the Yaogan-26 image compensation results illustrate that the deformed images can be corrected without any auxiliary sensor data, which are usually hard to obtain. In conclusion, the proposed RestoreGAN reveals the huge potential of GAN-based methods for the analysis of attitude jitter in remote sensing images. To increase the validity of this work, several aspects remain for further improvement. The first is that we expect to use fewer samples to learn better features, which is meaningful in remote sensing images. Secondly, it is normally a reasonable assumption for satellite HR images that the attitude jitter in the yaw direction is much less noticeable than that in the other two directions. However, we should also pay attention to the correction of the yaw direction jitter in remote sensing images. Moreover, the present methodology can be independently implemented with conventional methodologies, which will be a future research direction.