Nonlinear model predictive control strategy based on soft computing approaches and real time implementation on a coupled-tank system

In order to effectively implement a good model based control strategy, the combination of different linear models working at various operating regions are mostly utilised since a single model that can operate in that fashion is always a difficult task to develop. This work presents the use of soft computing approaches such as evolutional algorithm called simulated annealing (SA), a genetic algorithm (GA) and an artificial neural network (ANN) to design both a robust single nonlinear dynamic ANN model derived from an experimental data driven system identification approach and a nonlinear model predictive control (NMPC) strategy. SA is employed to give an initial weight for the training of the ANN model structure while a gradient descent based Levenberg–Marquardt Algorithm (LMA) approach is used to optimise the ANN weights. The designed NMPC strategy is optimised using a stochastic GA optimisation method and is tested first in simulation and then implemented in real time practical experiment on a highly nonlinear single input single output (SISO) coupled tank system (CTS). An excellent control performance is reported over the conventional proportional-integral-derivative (PID) controller and results show the effectiveness of the approach under disturbances. The nonlinear neural network model proved very reliable in different operating regions. The SISO system can be upgraded to multi-input multi-output (MIMO) system while the whole NMPC approach can easily be adapted to other industrial processes

Multi-objective optimization for time-based preventive maintenance within the transport network: a review

Preventive maintenance in transportation is essential not only to safeguard billions in business and infrastructure investment, but also to guarantee safety, reliability and efficacy within the network. Government, industry and society have been increasingly recognising the importance of keeping transport units condition well-preserved. The challenge, however, is to achieve optimal performance of the existing transport systems within acceptable costs, effective workforce use and minimum disruption. Those are generally conflicting objectives. Multi-objective optimisation approaches have served as powerful tools to assist stakeholders to properly deploy preventive maintenance in industry. In this study, we review the research conducted in the application of multi-objective optimisation for preventive maintenance in transport-related activities. We focus on time-based preventive maintenance for production, infrastructure, rail and energy providers. In our review, we are interested in aspects such as the types of problems addressed, the existing objectives, the approaches to solutions, and how the outcomes obtained support decision

Spatio-temporal patterns act as computational mechanisms governing emergent behavior in robotic swarms

Our goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their self-coordinating emergent behavior, has proven ineffective, largely due to the swarm's inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micro-macro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm's emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

PEMANFAATAN BOOKLET KESEHATAN REPRODUKSI REMAJA PUTRI DI SMP 1 NEGERI I NDONA KABUPATEN ENDE

This community service aims to empower young women to prevent early reproductive health problems. The methods used in this community service activity are screening, lecture, discussion, simulation and practice. While the stages of problem solving are field observation, problem identification, solution offerings, activity design, implementation, evaluation and monitoring and integration. The results of these activities are age over 14 years (50.0%), menstrual periods up to 4 days (35.5%), changing pads two to 3 times per day (78.9%), height 140 to 150 cm (50.00 %), body weight of 30 to 45 kg (81.6%), the color of milk white mucus (81.6%), the amount of mucus that comes out is small (63.2%) and itching in the genital area is felt occasionally (68.4%). The implementation of community service is expected to produce an outcome in the form of the results of activities in an accredited journal with ISSN

Chlamydomonas DYX1C1/PF23 is essential for axonemal assembly and proper morphology of inner dynein arms

Cytoplasmic assembly of ciliary dyneins, a process known as preassembly, requires numerous non-dynein proteins, but the identities and functions of these proteins are not fully elucidated. Here, we show that the classical Chlamydomonas motility mutant pf23 is defective in the Chlamydomonas homolog of DYX1C1. The pf23 mutant has a 494 bp deletion in the DYX1C1 gene and expresses a shorter DYX1C1 protein in the cytoplasm. Structural analyses, using cryo-ET, reveal that pf23 axonemes lack most of the inner dynein arms. Spectral counting confirms that DYX1C1 is essential for the assembly of the majority of ciliary inner dynein arms (IDA) as well as a fraction of the outer dynein arms (ODA). A C-terminal truncation of DYX1C1 shows a reduction in a subset of these ciliary IDAs. Sucrose gradients of cytoplasmic extracts show that preassembled ciliary dyneins are reduced compared to wild-type, which suggests an important role in dynein complex stability. The role of PF23/DYX1C1 remains unknown, but we suggest that DYX1C1 could provide a scaffold for macromolecular assembly

A deep learning approach for human activities recognition from multimodal sensing devices

Research in the recognition of human activities of daily living has significantly improved using deep learning techniques. Traditional human activity recognition techniques often use handcrafted features from heuristic processes from single sensing modality. The development of deep learning techniques has addressed most of these problems by the automatic feature extraction from multimodal sensing devices to recognise activities accurately. In this paper, we propose a deep learning multi-channel architecture using a combination of convolutional neural network (CNN) and Bidirectional long short-term memory (BLSTM). The advantage of this model is that the CNN layers perform direct mapping and abstract representation of raw sensor inputs for feature extraction at different resolutions. The BLSTM layer takes full advantage of the forward and backward sequences to improve the extracted features for activity recognition significantly. We evaluate the proposed model on two publicly available datasets. The experimental results show that the proposed model performed considerably better than our baseline models and other models using the same datasets. It also demonstrates the suitability of the proposed model on multimodal sensing devices for enhanced human activity recognition

Chlamydomonas DYX1C1/PF23 is essential for axonemal assembly and proper morphology of inner dynein arms

Cytoplasmic assembly of ciliary dyneins, a process known as preassembly, requires numerous non-dynein proteins, but the identities and functions of these proteins are not fully elucidated. Here, we show that the classical Chlamydomonas motility mutant pf23 is defective in the Chlamydomonas homolog of DYX1C1. The pf23 mutant has a 494 bp deletion in the DYX1C1 gene and expresses a shorter DYX1C1 protein in the cytoplasm. Structural analyses, using cryo-ET, reveal that pf23 axonemes lack most of the inner dynein arms. Spectral counting confirms that DYX1C1 is essential for the assembly of the majority of ciliary inner dynein arms (IDA) as well as a fraction of the outer dynein arms (ODA). A C-terminal truncation of DYX1C1 shows a reduction in a subset of these ciliary IDAs. Sucrose gradients of cytoplasmic extracts show that preassembled ciliary dyneins are reduced compared to wild-type, which suggests an important role in dynein complex stability. The role of PF23/DYX1C1 remains unknown, but we suggest that DYX1C1 could provide a scaffold for macromolecular assembly

Persistent hypertension up to one year postpartum among women with hypertensive disorders in pregnancy in a low-resource setting:A prospective cohort study

BACKGROUND: Hypertensive disorders in pregnancy (HDPs) are associated with lifelong cardiovascular disease risk. Persistent postpartum hypertension in HDPs could suggest progression to chronic hypertension. This phenomenon has not been well examined in low- and middle-income countries (LIMCs), and most previous follow-ups typically last for maximally six weeks postpartum. We assessed the prevalence of persistent hypertension up to one year in women with HDPs in a low resource setting and determined associated risk factors. METHODOLOGY: A prospective cohort study of women conducted at eight tertiary health care facilities in seven states of Nigeria. Four hundred and ten women with any HDP were enrolled within 24 hours of delivery and followed up at intervals until one year postpartum. Descriptive statistics were performed to express the participants’ characteristics. Univariable and multivariable logistic regressions were conducted to identify associated risk factors. RESULTS: Of the 410 women enrolled, 278 were followed up to one year after delivery (follow-up rate 68%). Among women diagnosed with gestational hypertension and pre-eclampsia/eclampsia, 22.3% (95% CI; 8.3–36.3) and 62.1% (95% CI; 52.5–71.9), respectively, had persistent hypertension at six months and this remained similar at one year 22.3% (95% CI; 5.6–54.4) and 61.2% (95% CI; 40.6–77.8). Maternal age and body mass index were significant risk factors for persistent hypertension at one year [aORs = 1.07/year (95% CI; 1.02–1.13) and 1.06/kg/m(2) (95% CI; 1.01–1.10)], respectively. CONCLUSION: This study showed a substantial prevalence of persistent hypertension beyond puerperium. Health systems in LMICs need to be organized to anticipate and maintain postpartum monitoring until blood pressure is normalized, or women referred or discharged to family physicians as appropriate. In particular, attention should be given to women who are obese, and or of higher maternal age

Metabolic syndrome following hypertensive disorders in pregnancy in a low-resource setting:A cohort study

Objectives: Hypertensive disorders in pregnancy (HDPs) are associated with risk of future metabolic syndrome. Despite the huge burden of HDPs in sub-Saharan Africa, this association has not been adequately studied in this population. Study design: This was a prospective cohort study on pregnant women recruited between August 2017 - April 2018 and followed up to one year after their deliveries and evaluated for presence of metabolic syndrome at delivery, nine weeks, six months and one year. Main outcome measures: Prevalence of metabolic syndrome Results: A total of 488 pregnant women were included: 410 and 78 with HDPs and normotensive, respectively. None of the normotensive had metabolic syndrome until one year (1.7% = 1 out of 59 observations), while among those with HDPs were 17.4% (71 of 407), 8.7% (23 of 263), 4.7% (11 of 232) and 6.1% (17 of 278), at delivery, nine weeks, six months and one year postpartum, respectively. High BMI and blood pressure were the drivers of metabolic syndrome in this population. The incidence rate in HDPs versus normotensive at one year were, respectively, 57.5/1000 persons’ year (95%CI; 35.8 – 92.6) and 16.9/1000 persons’ years (95%CI; 2.4-118.3), with incidence rate ratio of 3.4/1000 person's years. Only parity significantly predicted the presence of metabolic syndrome at one year [(aOR= 3.26/delivery (95%CI; 1.21-8.79)]. Conclusion: HDPs were associated with a higher incidence of metabolic syndrome up to one year postpartum. Women with HDPs should be routinely screened for metabolic syndrome within the first year postpartum to reduce cardiometabolic risks.</p