Optimal BP Research Articles

Gender-specific differences in central blood pressure and optimal target blood pressure based on the prediction of cardiovascular events.

Hypertension (HBP) is a common disease among both men and women. Central blood pressure (CBP) is a method of evaluating aorta pressure that can assess the intrinsic BP of an individual patient that more closely correlates with cardiovascular disease (CVD) outcomes than peripheral BP parameters. We evaluated gender-specific differences in CBP and optimal target BP based on a composite outcome of CVD, heart failure (HF), and hypertensive complications in patients with HBP. Patients were enrolled from June 2011 to December 2015 and were followed through December 2019. CBP was measured using radial tonometry. The primary endpoint was a composite outcome. The median follow-up period for enrolled patients was 6.5 years. Out of a total of 2,115 patients with an average age of 57.9 ± 13.6 years, 266 patients (12.6%) had events of primary end points during the follow-up period. There was no difference in the lowest BP level between men and women in the incidence of CVD. Among the women (49.6%), 78.1% were postmenopausal. In a multivariable Cox proportional hazards model, CBP and systolic BP showed an increase in risk of 10 and 11%, respectively, with every 10 mmHg increase, and there was a similar trend of 12 and 13%, respectively, in postmenopausal women. However, PP showed an increase in risk of about 2% every 10 mmHg increase, but a tendency to increase risk by 19% in postmenopausal women. This study demonstrated that postmenopausal women will continue to show increased risk for CVD at BP higher than the optimal level. Conversely, there was no increase in CV risk due to menopause at BP values below the optimal level. Therefore, well-controlled BP is more important in postmenopausal women.

Are adherence to the Mediterranean diet and siesta individually or jointly associated with blood pressure in Spanish adolescents? Results from the EHDLA study.

BackgroundBoth dietary and sleep patterns can influence both blood pressure levels and heart rate, but these associations have been understudied in adolescents. Furthermore, it is not known whether diet and sleep could exert a synergistic effect with respect to the maintenance of optimal BP levels in this population.ObjectiveTo investigate the relationship of blood pressure levels with the combination of higher adherence to the Mediterranean diet and the habit of siesta (daytime napping) in Spanish adolescents.MethodsA cross-sectional study was conducted with data obtained through personal interviews and physical examination from a representative sample of 1,378 adolescents (12–17 years of age) from the Valle de Ricote (Region of Murcia, Spain) selected using a simple random sampling technique. Adherence to the Mediterranean diet was assessed with the Mediterranean Diet Quality Index in Children and Adolescents, and the frequency and duration of siesta were self-reported. Objective measurements of systolic and diastolic blood pressure were obtained under standardized conditions. Statistical procedures were performed with SPSS software (v.25) and included logistic and generalized regression models adjusted for sex, age, socioeconomic status, body mass index, total energy intake, nighttime sleep duration, and moderate-to-vigorous physical activity.ResultsOf the 698 adolescents finally studied (mean age 13.9 ± 1.5 years; 56.2% female), 37.1% (n = 259) had high adherence to the Mediterranean diet and 19.6% (n = 137) reported frequent siesta. In the completely adjusted models, compared to adolescents with low Mediterranean diet adherence and no or infrequent siesta, those with high Mediterranean diet adherence and frequent siesta were less likely to have high-normal blood pressure or hypertension (odds ratio = 0.47; 95% confidence interval: 0.26, 0.88) and showed slightly lower systolic blood pressure (ß-coef. = −2.60; 95% CI: −5.18, −0.02).ConclusionGreater adherence to the Mediterranean diet and frequent siesta have a synergistic effect on maintaining lower blood pressure levels in adolescence. These findings reinforce that adherence to both Mediterranean lifestyle behaviors early in life may be an important strategy to prevent hypertension throughout adulthood.

A fault diagnosis method of rolling element bearing based on improved PSO and BP neural network

Aiming at the inherent defects of BP neural network in the field of rolling bearing fault diagnosis, based on the optimization of particle swarm optimization algorithm, this paper uses a variety of optimization strategies to optimize the particle swarm optimization algorithm, and then uses the optimized particle swarm optimization algorithm to optimize the BP neural network. Therefore, a new fault diagnosis method (Dual Strategy Particle Swarm Optimization BP neural network, DSPSOBP) is proposed. DSPSOBP fault diagnosis method is mainly divided into two steps. The first step is EMD decomposition of vibration signal, and the second step is to classify rolling bearing faults by using BP neural network optimized by Double Strategy Particle Swarm Optimization algorithm. Experiments show that DSPSOBP has stronger advantages than BP neural network basic fault diagnosis model.

Using Genetic Algorithm and Particle Swarm Optimization BP Neural Network Algorithm to Improve Marine Oil Spill Prediction

Using Genetic Algorithm and Particle Swarm Optimization BP Neural Network Algorithm to Improve Marine Oil Spill Prediction

Application of PSO-BPNN-PID Controller in Nutrient Solution EC Precise Control System: Applied Research.

In this paper, we present a nutrient solution control system, designing a nutrient solution electrical conductivity (EC) sensing system composed of multiple long-range radio (LoRa) slave nodes, narrow-band Internet of Things (NB-IoT) master nodes, and a host computer, building a nutrient solution EC control model and using the particle swarm optimization (PSO) algorithm to optimize the initial weights of a back-propagation neural network (BPNN). In addition, the optimized best weights are put into the BPNN to adjust the proportional–integral–derivative (PID) control parameters Kp, Ki, and Kd so that the system performance index can be optimized. Under the same initial conditions, we input EC = 2 mS/cm and use the particle swarm optimization BP neural network PID (PSO-BPNN-PID) to control the EC target value of the nutrient solution. The optimized scale factors were Kp = 81, Ki = 0.095, and Kd = 0.044; the steady state time was about 43 s, the overshoot was about 0.14%, and the EC value was stable at 1.9997 mS/cm–2.0027 mS/cm. Compared with the BP neural network PID (BPNN-PID) and the traditional PID control approach, the results show that PSO-BPNN-PID had a faster response speed and higher accuracy. Furthermore, we input 1 mS/cm, 1.5 mS/cm, 2 mS/cm, and 2.5 mS/cm, respectively, and simulated and verified the PSO-BPNN-PID system model. The results showed that the fluctuation range of EC was 0.003 mS/cm~0.119 mS/cm, the steady-state time was 40 s~60 s, and the overshoot was 0.3%~0.14%, which can meet the requirements of the rapid and accurate integration of water and fertilizer in agricultural production.

Rapid and accurate detection of starch content in mixed sorghum by hyperspectral imaging combined with data fusion technology

AbstractSorghum is a type of brewing material. The starch content of different kinds of mixed sorghum affect the quality and yield of liquor. Therefore, an accurate and efficient detection of its starch content is of great significance to obtain high‐quality and high‐yield of liquor. Based on the data of single visible light (Vis), near‐infrared (NIR), and Vis and NIR fusion, this study develops a genetic algorithm optimization BP neural network (GA‐BPNN) model and a particle swarm optimization support vector machine model. It is deduced that the model based on the data from the Vis and NIR fusion has the highest performance in predicting the starch content of mixed sorghum. For the prediction of amylose, the GA‐BPNN model developed after using the Pearson correlation coefficient to select the characteristic wavelength for fusion, has the highest performance (RMSEP = .0298 and = .9948). For the prediction of amylopectin, the GA‐BPNN model developed after fusion of spectral features extracted by principal component analysis, has the highest performance (RMSEP = .0213 and = .9985). In conclusion, the hyperspectral imaging combined with data fusion can rapidly and accurately detect the starch content of mixed sorghum.Practical ApplicationsAs the single raw material of Maotai‐flavor liquor and the main raw material of Luzhou‐flavor liquor, the amylopectin content of sorghum directly affects the quality of liquor. Its amylose content also affects the yield of liquor. Because the different varieties of sorghum have different amylose and amylopectin content, liquor manufacturers often use several mixed varieties of sorghum as a brewing material. In order to ensure the quality and yield of liquor, it is particularly crucial to detect the amylose and amylopectin content variables of blended sorghum. The traditional sorghum starch content determination methods, such as the chemical detection methods and near infrared spectroscopy, have the disadvantages of destructiveness, low efficiency, and low detection accuracy. In the process of liquor brewing, the traditional detection methods cannot guide the timely adjustment of brewing process parameters. The hyperspectral imaging technology is widely used in food material detection, due to its fast and nondestructive advantages. The data fusion technology can fuse data from different sources of the same object to be detected, in order to obtain more comprehensive data for the development of accurate prediction models. This study uses the hyperspectral imaging combined with data fusion in order to quickly and accurately predicts the starch content (amylose, amylopectin) of mixed sorghum, which has a guiding significance for the timely adjustment of process parameters in the brewing process of liquor. In addition, it provides an accurate method for the component detection of other grains.

Design and Application of a Financial Distress Early Warning Model Based on Data Reasoning and Pattern Recognition

Since the 1990s, emerging market financial crises have occurred frequently, causing huge damage to the real economy, and if we cannot find effective means of early warning and prevention of financial crises, the entire international economy and society will bear the high costs of crisis management. Difference nonparametric test and Spearman nonparametric correlation analysis were carried out with cash flow financial data, and 14 financial indicators with strong discriminant ability were selected from 28 financial indicators as the input variables of the model. Due to the limitations of traditional statistical methods, a BP neural network financial distress early warning model is established. Finally, a particle swarm optimization BP neural network financial early warning model is established for the shortcomings of the BP network. These 14 indicators can have strong information timeliness. The prediction accuracy rates of the two early warning models for the test samples are 80% and 85%, respectively. The empirical results show that the two models have good prediction effects. The prediction effect of the swarm optimization BP neural network model is better than that of the BP neural network model. Therefore, the particle swarm optimization BP neural network model proposed in this paper is suitable for solving the problem of discrimination and prediction of the financial distress of enterprises. The company’s financial distress early warning has good application prospects and application value. Therefore, it has very important research significance for the early warning of the corporate financial crisis.

A Comparative Study of Six Hybrid Prediction Models for Uniaxial Compressive Strength of Rock Based on Swarm Intelligence Optimization Algorithms

Uniaxial compressive strength (UCS) is a significant parameter in mining engineering and rock engineering. The laboratory rock test is time-consuming and economically costly. Therefore, developing a reliable and accurate UCS prediction model through easily obtained rock parameters is a good way. In this paper, we set five input parameters and compare six hybrid models based on BP neural network and six swarm intelligence optimization algorithms–bird swarm algorithm (BSA), grey wolf optimization (GWO), whale optimization algorithm (WOA), seagull optimization algorithm (SOA), lion swarm optimization (LSO), firefly algorithm (FA) with the accuracy of two single models without optimization–BP neural network and random forest algorithm. Finally, the above eight models were evaluated and compared by root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and a10 index to obtain the most suitable prediction model. It is indicated that the best prediction model is the FA-BP model, with a RMSE value of 4.883, a MAPE value of 0.063, and a R2 of 0.985, and an a10 index of 0.967. Furthermore, the normalized mutual information sensitivity analysis shows that point load strength is the most effective parameters on the UCS, respectively.

TELMISARTAN/AMLODIPINE SINGLE-PILL COMBINATION THERAPY TO ACHIEVE BLOOD PRESSURE GOAL VALUES IN HYPERTENSIVE PATIENTS IN GPS’ PRACTICE – AN OBSERVATIONAL STUDY

Objective: Current ESC/ESH Guidelines suggests a simple therapeutic strategy to increase treatment effectiveness: the application of single pill combination (SPC) of a RAS inhibitor with a calcium channel antagonist or a diuretic as a first step. Our primary goal was to assess the percentage of patients reaching optimal BP goal (130/80 mmHg) after 12 weeks of SPC therapy with telmisartan/amlodipine according to office BP measurements in hypertensive patients treated by general practitioners (GPs) in regular clinical practice. We also examined the percentage of patients achieving primary target BP (<140/90 mmHg). Design and method: After given informed consent, 729 patients with primary hypertension from 120 GP’s offices participated in this multicentre, observational, non-interventional, prospective study. Results: The average age of the patients was 62 ± 12 years, 357 patients (49.8%) of the population was male. The average office BP at baseline was 150 ± 17/88 ± 10 mmHg, and decreased to 130 ± 8.1/78 ± 6.1 mmHg at 12 week (p < 0.05). Pulse rate has decreased as well (from 80 ± 9.8/min to 75 ± 6.3/min). At baseline, 10.1 % of the patients were at optimal systolic BP goal (<130 mmHg), 15.8% at optimal diastolic BP goal (<80 mmHg) and only 5.5% simultaneously at systolic and diastolic BP goal (<130/80 mmHg). By the end of the observational period the percentage of patients reaching these optimal BP goal values increased to 51.6%, 54.1% and 33.0%, respectively. At baseline 19.2% of patients had BP less than 140/90 mmHg, which increased to 87.1% by the end of the study. Dose adjustments during the study were made in only 3 % of patients (n = 22). Patients tolerated SPC telmisartan/amlodipine well, 95% of the patients continued medication after study conclusion. Conclusions: The introduction of a SPC therapy of telmisartan/amlodipine increases the success rate of treatment during a relative short period. High proportion of patients were able to lower within 3 months their BP levels under 140/90 mmHg, however, only one third of the patients reached optimal target BP. Main possible reasons for this was the low rate of dose adjustment during the study. The study was sponsored by KRKA Hungary Ltd.

Leakage Diagnosis of Air Conditioning Water System Networks Based on an Improved BP Neural Network Algorithm

Compared with traditional pipe networks, the complexity of air conditioning water systems (ACWSs) and the alternation of cooling and heating are more likely to cause pipe network leakage. Pipe leakage failure seriously affects the reliability of the air conditioning system, and can cause energy waste or reduce human comfort. In this study, a two-stage leakage fault diagnosis (LFD) method based on an Adam optimization BP neural network algorithm, which locates leakage faults based on the change values of monitoring data from flow meters and pressure sensors in air conditioning water systems, is proposed. In the proposed LFD method, firstly, the ACWS network’s hydraulic model is built on the Dymola platform. At the same time, a cuckoo algorithm is used to identify the pipe network’s characteristics to modify the model, and the experimental results show that the relative error between the model-simulated value and the actual values is no more than 1.5%. Secondly, all possible leakage conditions in the network are simulated by the model, and the dataset is formed according to the change rate of the observed data, and is then used to train the LFD model. The proposed LFD method is verified in a practical project, where the average accuracy of the first-stage LFD model in locating the leaking pipe is 86.96%; The average R2 of the second-stage LFD model is 0.9028, and the average error between the predicted location and its exact location with the second-stage LFD model is 6.3% of the total length of the leaking pipe. The results show that the proposed method provides a feasible and convenient solution for timely and accurate detection of pipe network leakage faults in air conditioning water systems.

Harmonic detection technology based on ant colony optimization BP neural network

At present, ip-iq algorithm based on instantaneous reactive power theory is the most widely used in the field of harmonic detection.However, the ip-iq algorithm based on instantaneous reactive power has coordinate transformation and inverse transformation, which leads to cumbersome calculation.the existence of low-pass filter leads to delay and poor dynamic performance of the algorithm.The harmonic detection based on BP neural network has the advantages of simple calculation, fast detection speed, strong adaptive ability and good dynamic performance.However, the initial weights and thresholds are random, so it is easy for the network to fall into local optimal solution during training, resulting in low detection accuracy. This paper presents a harmonic detection method based on ant colony algorithm optimized BP neural network. ACO-BP, BP and ip-iq algorithms are compared by Simulink simulation. The results show that ACO-BP harmonic detection algorithm is feasible and superior

Fault Diagnosis of Automobile Engine Based on Improved BP Neutral Network

With the continuous enrichment of automobile functions and the complexity of automobile structure, the difficulty of automobile fault diagnosis is constantly increasing. The study of fault diagnosis methods with high real-time performance, accuracy, and predictability is of great significance to improve automobile safety performance and ensure driving safety. Since the convergence speed of the traditional BP neural network algorithm is slow and the accuracy is insufficient in the process of automobile engine fault diagnosis, this paper improves convergence speed of the algorithm by introducing the momentum term, and the weights and thresholds of the neural network are optimized by using GA selection, crossover, and genetic characteristics, to propose a genetic algorithm (GA) optimization BP neural network fault diagnosis method. The average absolute error of the traditional BP neural network algorithm is 0.5976, while the average absolute error of the improved BP neural network algorithm in this paper is 0.1027. The comparative simulation results show that the proposed improved algorithm is better than the traditional BP neural network algorithm in diagnosis precision, accuracy, and other key indicators.

Cardiovascular health in pediatric heart transplant patients

BackgroundIdeal “cardiovascular health” (CVH)–optimal diet, exercise, nonsmoking, BMI, BP, lipids, and glucose—is associated with healthy longevity in adults. Pediatric heart transplant (HT) patients may be at risk for suboptimal CVH.MethodsSingle-center retrospective study of HT patients 2003–2014 who survived 1 year post-transplant. Five CVH metrics were collected at listing, 1, 3 and 5 years post-transplant (diet and exercise were unavailable). CVH was scored by summing individual metrics: ideal = 2, intermediate = 1, and poor = 0 points; total scores of 8–10 points were considered high (favorable). CVH was compared between HT patients and the US pediatric population (GP) utilizing NHANES 2007–2016. Logistic regression was performed to examine the association of CVH 1 year post-transplant with a composite adverse outcome (death, re-listing, coronary vasculopathy, or chronic kidney disease) 3 years post-transplant.ResultsWe included 110 HT patients (median age at HT: 6 years [range 0.1–21]) and 19,081 NHANES participants. CVH scores among HT patients were generally high at listing (75%), 1 (74%), 3 (87%) and 5 (76%) years post-transplant and similar to GP, but some metrics (e.g., glucose) were worse among HT patients. Among HT patients, CVH was poorer with older age and non-Caucasian race/ethnicity. Per 1-point higher CVH score, the demographic-adjusted OR for adverse outcomes was 0.95 (95% CI, 0.7–1.4).ConclusionsHT patients had generally favorable CVH, but some metrics were unfavorable and CVH varied by age and race/ethnicity. No significant association was detected between CVH and adverse outcomes in this small sample, but study in a larger sample is warranted.

Age- and Hypertension-Related Changes in NOS/NO/sGC-Derived Vasoactive Control of Rat Thoracic Aortae.

This study was aimed at examining the role of the NOS/NO/sGC signaling pathway in the vasoactive control of the thoracic aorta (TA) from the early to late ontogenetic stages (7 weeks, 20 weeks, and 52 weeks old) of normotensive Wistar-Kyoto (WKY) rats and spontaneously hypertensive rats (SHRs). Systolic blood pressure (SBP) and heart rate (HR) were significantly increased in SHRs compared to age-matched WKYs, which was associated with left heart ventricle hypertrophy in all age groups of rats. The plasma urea level was increased in 20-week-old and 52-week-old SHRs compared with WKYs without increasing creatinine and uric acid. The total cholesterol levels were lower in 20-week-old and 52-week-old SHRs than in WKYs, but triglycerides were higher in 7-week-old SHRs. The fructosamine level was increased in 52-week-old SHRs compared with age-matched WKYs and unchanged in other age groups. Superoxide production was increased only in 7-week-old SHRs compared to age-matched WKYs. The endothelium-dependent relaxation (EDR) of the TA deteriorated in both rat strains during aging; however, endothelial dysfunction already occurred in 20-week-old SHRs and was even more enhanced in 52-week-old rats. Our results also demonstrated increased activity of NOS in 52-week-old WKYs. Moreover, 7-week-old and 52-week-old WKY rats displayed an enhanced residual EDR after L-NMMA (NOS inhibitor) incubation compared with 20-week-old rats. Our results showed that in 7-week-old SHRs, the residual EDR after L-NMMA incubation was increased compared to that in other age groups. The activity of NOS in the TA was comparable in 7-week-old and 20-week-old SHRs, but it was reduced in 52-week-old SHRs compared to younger SHRs and 52-week-old WKYs. Thus, it seems that, in contrast to SHRs, the NOS/NO system in WKYs is probably able to respond to age-related pathologies to maintain endothelial functions and thus optimal BP levels even in later periods of life.

677 UTILITY OF 24 HOUR AMBULATORY BLOOD PRESSURE MONITORING (ABPM) IN PATIENTS WITH ORTHOSTATIC HYPOTENSION (OH) AT SYNCOPE CLINIC

Abstract Introduction OH is a disabling condition resulting from a sustained reduction in blood pressure (>20 systolic or 10 diastolic) within 3 minutes of standing. It is a common cause of syncope. Patients with concurrent hypertension experiencing syncope present a complex management dilemma where a balance must be established between symptom burden and risk of cardiovascular disease. Current guidance on ABPM use in syncope is limited. European society of cardiology syncope guidelines suggest ABPM in patients with ‘autonomic failure’ to assess nocturnal hypertension or drug-induced hypotension. Could this be improved with further explicit criteria on which patients to assess and how to act on results? The objective of this study is to review the use of 24 hour ABPM in OH within a tertiary referral syncope clinic. Method A retrospective analysis was performed electronically for patients with a final diagnosis of OH seen in a syncope clinic between March 2017 and May 2019. Data was collected on comorbidities, medication history, physical mobility, clinic blood pressure, ABPM Results: (if performed) and medication changes. Comparisons were made between patients who had ABPM and those who did not. Statistics were calculated using Fisher’s Exact Test (2 tailed). Results 119 patients had a final diagnosis of OH in the study period. 45 had ABPM, 74 did not. The ABPM group had a significantly higher proportion of diagnosed hypertension (51.1% vs 23% (p = 0.0025)). A similar proportion of patients in both groups had medication changed however the ABPM group were significantly more likely to have antihypertensive therapy added (19.4% vs 1.8% (p = 0.0053)). Conclusion Using 24 hour ABPM in OH patients can aid clinical decision making in the sub-group with hypertension. This can guide the need for alteration/addition of antihypertensive therapy to balance optimum BP control with symptom burden.

Progressive Muscle Relaxation (PMR) Intervention with Slow Deep Breathing Exercise (SDBE) on Blood Pressure of Hypertension Patients

This study aims to determine the difference between Intervention Effectiveness Progressive Muscle Relaxation (PMR) and Slow Deep Breathing Exercise (SDBE) in TD patients with hypertension. This type of research is an experimental design with a randomized pre-test and post-test three-group design without a control group. The results showed that there was a decrease in systolic and diastolic BP after the intervention in all groups. There was no significant difference in effectiveness between the three groups after the intervention; the optimal time to decrease systolic BP in the PMR and SDBE groups occurred on day 3. On the other hand, the diastolic BP does not show time. Optimal BP reduction. The conclusion of the three relaxation techniques effectively reduces blood pressure in patients with hypertension. Adequate time to lower blood pressure occurs on the third day.
 
 Keywords: Hypertension, Progressive Muscle Relaxation (PMR), Slow Deep Breathing Exercise (SDBE), Blood Pressure

Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with Levy flight

The battery state of charge (SoC) of lithium batteries for electric vehicles is highly non-linear and time-varying. The convergence speed is slow and the accuracy is low when using ordinary neural network models for SoC estimation. The particle swarm optimization (PSO) algorithm based on Levy's flight strategy (LPSO) is proposed to optimize the weights and thresholds of BP neural network, which would improve the prediction accuracy of SoC. According to the mechanism of lithium battery charging and discharging, voltage, current and temperature are selected as input vectors and SoC is selected as output vector. The comparison of the model before and after optimization is carried out by using NASA lithium battery charging and discharging data, which shows this method has better generalization ability and high prediction accuracy. It has practical application significance for SoC estimation.

Harris hawks optimization algorithm and BP neural network for ultra-wideband indoor positioning.

Traditional back propagation neural networks (BPNNs) for ultrawideband (UWB) indoor localization can effectively improve localization accuracy, although there is high likelihood of becoming trapped in nearby minima. To solve this problem, the random weights and thresholds of the BPNN are optimized using the Harris Hawks optimization algorithm (HHO) to obtain the optimal global solution to enhance the UWB indoor positioning accuracy and NLOS resistance. The results show that the predicted trajectory of the HHO and BPNN hybrid algorithm (HHO-BP) matches the actual position in the two-dimensional localization scenario with four base stations; the optimized average positioning error is effectively reduced in both indoor LOS and NLOS environments. In the LOS environment, the total mean error of the traditional BPNN algorithm is 6.52 cm, which is 26.99% better than the UWB measurement error; in the NLOS environment, the total mean error of the conventional BPNN is 14.82 cm, which is 50.08% better than the UWB measurement error. The HHO-BP algorithm is further optimized on this basis, and the total mean error in the LOS environment is 4.50 cm, which is 22.57% better than the conventional BPNN algorithm; in the NLOS environment, the total mean error is 9.56 cm, which is 17.54% better than the conventional BPNN algorithm. The experimental findings suggest that the approach has greater calibration accuracy and stability than BPNN, making it a viable choice for scenarios requiring high positional precision.

An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network

The solution of robot inverse kinematics has a direct impact on the control accuracy of the robot. Conventional inverse kinematics solution methods, such as numerical solution, algebraic solution, and geometric solution, have insufficient solution speed and solution accuracy, and the solution process is complicated. Due to the mapping ability of the neural network, the use of neural networks to solve robot inverse kinematics problems has attracted widespread attention. However, it has slow convergence speed and low accuracy. This paper proposes the FOA optimized BP neural network algorithm to solve inverse kinematics. It overcomes the shortcomings of low convergence accuracy, slow convergence speed, and easy to fall into local minima when using BP neural network to solve inverse kinematics. The experimental results show that using the trained FOA optimized BP neural network to solve the inverse kinematics, the maximum error range of the output joint angle is [−0.04686, 0.1271]. The output error of the FOA optimized BP neural network algorithm is smaller than that of the ordinary BP neural network algorithm and the PSO optimized BP neural network algorithm. Using the FOA optimized BP neural network algorithm to solve the robot kinematics can improve the control accuracy of the robot.

Severe Cushing Syndrome Due to Ectopic ACTH Secretion by Pheochromocytoma

Introduction: Ectopic ACTH production is a very unusual cause of Cushing Syndrome (CS). When it occurs, lung cancer is the main cause. Very rarely, this ectopic source of ACTH can arise from a Pheochromocytoma (Pheo). A recent literature review identified less than 100 cases described. We present a case of 28 years old woman who was referred for adrenalectomy for CS with notorious adrenal mass. However, during the investigation, ectopic ACTH due a Pheo was identified. Case Report: A 28-year-old woman required emergency care for ecchymosis and asymptomatic hypertension (BP: 230x130mmHg). Hyperpigmentation of the skin was evident on physical examination. Severe hypokalemia (K: 2.5mEq/liter) was detected. She denied taking any medication and was unaware of any previous illness. She always had normal BP measurements as well as laboratory tests. No family history of adrenal disease or secondary hypertension causes. During hospitalization, the hypothesis of CS was made and confirmed after: cortisol after 1mg dexamethasone: 44.5mcg/dl (<1.8mcg/dl) and 24h urinary free cortisol: 6228 mcg/dl (28-213mcg/dl). Concomitantly, a CT scan of the abdomen exhibited a left adrenal mass (3.1x2.8x3.5cm) of uncertain etiology and ACTH: 352pg/ml (<46pg/ml). Additionally, the patient presented hyperglycemia during hospitalization. After confirmation of the ACTH dependent CS, pituitary MRI was performed with normal results and a chest CT scan ruled out lung masses. As there was still no etiological confirmation and due to clinical deterioration, it was decided to start Ketoconazole 200mg/day, rising until 600mg and spironolactone with doses up to 250mg/day with a significant improvement in hypokalemia, decreased cortisol levels and optimal BP control. Due to the extremely high levels of ACTH and indeterminate adrenal mass, the hypothesis of ACTH ectopic due Pheo was postulated. Patient underwent abdomen MRI with left adrenal mass with hypersignal at T2 and urinary metanephrines levels: 6132mcg/24h (<289mcg/24h), urinary normetanephrines: 1808mcg/24h (<732mcg/24h). Once the diagnosis was elucidated, she received preoperative preparation with alpha blocker (Doxazosin) and underwent adrenalectomy without complications. After discharge, she received prednisone 10mg/day. The patient presented normalization of BP levels, as well as glycemic control with a slight improvement in skin hyperpigmentation. The pathology department confirmed Pheo and an ACTH expression was observed in immunohistochemistry. A genetic panel for Pheo is running with no results so far. Conclusion: Despite an extremely rare cause of CS, the ectopic production of ACTH by a Pheo has extremely high mortality rates, especially when not diagnosed or managed correctly. The clinicians must always remain alert and suspect this syndrome when the patient has a confirmed ACTH dependent CS associated with adrenal masses.