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The control of power converters is difficult due to their non-linear nature and, hence, the quest for smart and efficient controllers is continuous and ongoing. Fractional-order controllers have demonstrated superior performance in power electronic systems in recent years. However, it is a challenge to attain optimal parameters of the fractional-order controller for such types of systems. This article describes the optimal design of a fractional order PID (FOPID) controller for a buck converter using the cohort intelligence (CI) optimization approach. The CI is an artificial intelligence-based socio-inspired meta-heuristic algorithm, which has been inspired by the behavior of a group of candidates called a cohort. The FOPID controller parameters are designed for the minimization of various performance indices, with more emphasis on the integral squared error (ISE) performance index. The FOPID controller shows faster transient and dynamic response characteristics in comparison to the conventional PID controller. Comparison of the proposed method with different optimization techniques like the GA, PSO, ABC, and SA shows good results in lesser computational time. Hence the CI method can be effectively used for the optimal tuning of FOPID controllers, as it gives comparable results to other optimization algorithms at a much faster rate. Such controllers can be optimized for multiple objectives and used in the control of various power converters giving rise to more efficient systems catering to the Industry 4.0 standards.

Lean and flexible manufacturing is a matter of necessity for the automotive industries today. Rising consumer expectations, higher raw material and processing costs, and dynamic market conditions are driving the auto sector to become smarter and agile. This paper presents a machine learning-based soft sensor approach for identification and prediction of lean manufacturing (LM) levels of auto industries based on their performances over multifarious flexibilities such as volume flexibility, routing flexibility, product flexibility, labour flexibility, machine flexibility, and material handling. This study was based on a database of lean manufacturing and associated flexibilities collected from 46 auto component enterprises located in the Pune region of Maharashtra State, India. As many as 29 different machine learning models belonging to seven architectures were explored to develop lean manufacturing soft sensors. These soft sensors were trained to classify the auto firms into high, medium or low levels of lean manufacturing based on their manufacturing flexibilities. The seven machine learning architectures included Decision Trees, Discriminants, Naive Bayes, Support Vector Machine (SVM), K-nearest neighbour (KNN), Ensembles, and Neural Networks (NN). The performances of all models were compared on the basis of their respective training, validation, testing accuracies, and computation timespans. Primary results indicate that the neural network architectures provided the best lean manufacturing predictions, followed by Trees, SVM, Ensembles, KNN, Naive Bayes, and Discriminants. The trilayered neural network architecture attained the highest testing prediction accuracy of 80%. The fine, medium, and coarse trees attained the testing accuracy of 60%, as did the quadratic and cubic SVMs, the wide and narrow neural networks, and the ensemble RUSBoosted trees. Remaining models obtained inferior testing accuracies. The best performing model was further analysed by scatter plots of predicted LM classes versus flexibilities, validation and testing confusion matrices, receiver operating characteristics (ROC) curves, and the parallel coordinate plot for identifying manufacturing flexibility trends for the predicted LM levels. Thus, machine learning models can be used to create effective soft sensors that can predict the level of lean manufacturing of an enterprise based on the levels of its manufacturing flexibilities.

The proliferation of electric vehicle (EV) technology is an important step towards a more sustainable future. In the current work, two-layer feed-forward artificial neural-network-based machine learning is applied to design soft sensors to estimate the state of charge (SOC), state of energy (SOE), and power loss (PL) of a formula student electric vehicle (FSEV) battery-pack system. The proposed soft sensors were designed to predict the SOC, SOE, and PL of the EV battery pack on the basis of the input current profile. The input current profile was derived on the basis of the designed vehicle parameters, and formula Bharat track features and guidelines. All developed soft sensors were tested for mean squared error (MSE) and R-squared metrics of the dataset partitions; equations relating the derived and predicted outputs; error histograms of the training, validation, and testing datasets; training state indicators such as gradient, mu, and validation fails; validation performance over successive epochs; and predicted versus derived plots over one lap time. Moreover, the prediction accuracy of the proposed soft sensors was compared against linear or nonlinear regression models and parametric structure models used for system identification such as autoregressive with exogenous variables (ARX), autoregressive moving average with exogenous variables (ARMAX), output error (OE) and Box Jenkins (BJ). The testing dataset accuracy of the proposed FSEV SOC, SOE, PL soft sensors was 99.96%, 99.96%, and 99.99%, respectively. The proposed soft sensors attained higher prediction accuracy than that of the modelling structures mentioned above. FSEV results also indicated that the SOC and SOE dropped from 97% to 93.5% and 93.8%, respectively, during the running time of 118 s (one lap time). Thus, two-layer feed-forward neural-network-based soft sensors can be applied for the effective monitoring and prediction of SOC, SOE, and PL during the operation of EVs.

Metal matrix composites (MMC) are the materials of tomorrow. This paper presents an in-depth analysis of the MMC research articles published in Web of Science (WoS) during 2001–2020. The study firstly included year on year publications, publication types, sources, research directions as well as the most productive researchers, organizations and nations. Secondly, a detailed analysis of collaborations among various MMC researchers, organizations and countries has been presented. Thirdly, citations based linkages among the published articles, sources, researchers, institutions and places have been discussed relative to their respective collaborative link strengths. A co-occurrence analysis of MMC keywords was also conducted to highlight the most important keywords trending in this area. Finally, burst detection analyses of keywords and references were carried out to unearth sudden citation spikes of keywords and documents. Primary results indicate that research articles formed 80.54% of all MMC publications in WoS. The journal ‘Materials Science and Engineering A: Structural Materials, Properties, Microstructure and Processing’ published maximum MMC articles. Collaboration analysis results indicate that Zhang D, the Chinese Academy of Science and People’s Republic of China, attained topmost collaboration based total link strengths (TLS). Citations based analysis showed that Zhang D, the Shanghai Jiao Tong University (China), People’s Republic of China and the journal ‘Materials Science and Engineering A: Structural Materials, Properties, Microstructure and Processing’ received highest citation TLS values. Keyword ‘Graphene’ scored the highest citation burst strength (2018–2020). The future of MMC research lies in processing and characterization of novel nanocomposites with reinforcements such as graphene and boron carbide for various applications.

The reliable and effective automation of biomedical therapies is the need of the hour for medical professionals. A model predictive controller (MPC) has the ability to handle complex and dynamic systems involving multiple inputs/outputs, such as biomedical systems. This article firstly presents a literature review of MPCs followed by a survey of research reporting the MPC-enabled automation of some biomedical therapies. The review of MPCs includes their evolution, architectures, methodologies, advantages, limitations, categories and implementation software. The review of biomedical conditions (and the applications of MPC in some of the associated therapies) includes type 1 diabetes (including artificial pancreas), anaesthesia, fibromyalgia, HIV, oncolytic viral treatment (for cancer) and hyperthermia (for cancer). Closed-loop and hybrid cyber-physical healthcare systems involving MPC-led automated anaesthesia have been discussed in relatively greater detail. This study finds that much more research attention is required in the MPC-led automation of biomedical therapies to reduce the workload of medical personnel. In particular, many more investigations are required to explore the MPC-based automation of hyperthermia (cancer) and fibromyalgia therapies.

This paper analyzes the surface quality characteristics, such as arithmetic average roughness (Ra), maximum peak-to-valley height (Rt), and average peak-to-valley height (Rz), in hard turning of AISI 52100 steel using a (TiN/TiCN/Al2O3) coated carbide insert under a minimal cutting fluid environment (MCFA). MCFA, a sustainable high-velocity pulsed jet technique, reduces harmful effects on human health and the environment while improving machining performance. Taguchi’s L27 orthogonal array was used to conduct the experiments. The findings showed that surface roughness increases with feed rate, identified as the most influential parameter, while the depth of cut shows a negligible effect. The main effects plot of signal-to-noise (S/N) ratios for the combined response of Ra, Rt, and Rz revealed the optimal cutting conditions: cutting speed of 140 m/min, feed rate of 0.05 mm/rev, and depth of cut of 0.3 mm. Feed rate ranked highest in influence, followed by cutting speed and depth of cut. The lower values of surface roughness parameters were observed in the ranges of Ra ≈ 0.248–0.309 µm, Rt ≈ 2.013–2.186 µm, and Rz ≈ 1.566 µm at a feed rate of 0.05–0.07 mm/rev. MCFA-assisted hard turning reduces surface roughness by 35–40% compared to dry hard turning and 10% to 24% when compared to the MQL technique. Moreover, this study emphasizes the significant environmental benefits of MCFA, as it incorporates minimal eco-friendly cutting fluids that minimize ecological impact while enhancing surface finish.

In this paper, force sensor signals are classified using a pattern recognition neural network (PRNN). The signals are classified to show if there is a collision or not. In our previous work, the joints positions of a 2-DOF robot were used to estimate the external force sensor signal, which was attached at the robot end-effector, and the external joint torques of this robot based on a multilayer feedforward NN (MLFFNN). In the current work, the estimated force sensor signal and the external joints’ torques from the previous work are used as the inputs to the proposed designed PRNN, and its output is whether a collision is found or not. The designed PRNN is trained using a scaled conjugate gradient backpropagation algorithm and tested and validated using different data from the training one. The results prove that the PRNN is effective in classifying the force signals. Its effectiveness for classifying the collision cases is 92.8%, and for the non-collisions cases is 99.4%. Therefore, the overall efficiency is 99.2%. The same methodology and work are repeated using a PRNN trained using another algorithm, which is the Levenberg–Marquardt (PRNN-LM). The results using this structure prove that the PRNN-LM is also effective in classifying the force signals, and its overall effectiveness is 99.3%, which is slightly higher than the first PRNN. Finally, a comparison of the effectiveness of the proposed PRNN and PRNN-LM with other previous different classifiers is included. This comparison shows the effectiveness of the proposed PRNN and PRNN-LM.

Postpartum psychosis is a condition characterised by rapid onset of psychotic symptoms several weeks after childbirth. Outside of its timing and descriptions of psychotic features, minimal research exists due to its relative rarity (1 to 2 per 1000 births in the USA), with greater emphasis on postpartum sadness and depression. With the existing literature, cultural differences and language barriers previously have not been taken into consideration as there are no documented cases of postpartum psychosis in a non–English-speaking patient. Correctly differentiating postpartum psychosis from other postpartum psychiatric disorders requires adeptly evaluating for the presence of psychotic symptoms with in-depth history taking. Here, we present a case of postpartum psychosis in a Japanese-speaking only patient, with an associated clinical course and culturally appropriate approach to treatment. A review of postpartum psychosis and language/cultural considerations are also discussed, with attention on the Japanese concept of ‘Satogaeri bunben’.

This paper presents a study on forces generated due to different mechanisms in turning low reinforcement Al/B4C composites. Forces due to shearing, ploughing and particle fracture/debonding mechanisms have been determined based on experimental data and latest models from literature. These mechanisms form the basis of the different kinds of forces generated during machining of metal matrix composites. This study analyses force behaviour due to the above-mentioned mechanisms under varying machining conditions and material compositions. Primary findings establish the relative dominance of shearing mechanism over ploughing and particle fracture under all conditions. Secondly, ploughing and particle fracture forces’ estimates respond in tune with the increasing particle reinforcement fractions in the metal matrix compositions. However, shearing forces do not necessarily follow such trends. It is found that chip thickness increments with rising feed rates are better indicators of augmentation in composite reinforcement levels. Thirdly, cutting forces and material flow strengths may exhibit contradictory trends under exactly same machining conditions. Fourthly, flow stresses are found to be more strain rate sensitive for low reinforcement composites. Lower reinforcement composites are relatively less ductile at low cutting speeds and more ductile with varying feeds and depths of cut. These results establish better machinability of metal matrix composites having lesser particulate inclusions at higher cutting speeds and feed rates. Composites reinforced with higher percentage of boron carbide particles do not necessarily generate higher shearing forces, although increased tool wear can certainly be expected due to the higher ploughing and particle fracture/debonding forces.

This paper demonstrates how person-focused, prevention-based, risk/needs-related, team-delivered, minimum intervention oral care (MIOC) principles and approaches can be integrated into the dental profession for the delivery of environmentally sustainable, optimal care to high-needs and high caries-risk/susceptibility patients. It highlights the potential for NHS remuneration for prevention-based, phased, personalised care pathways/plans (PCPs) within a reformed NHS dental contract system. It emphasises the importance of comprehensive and longitudinal patient risk/susceptibility assessments, prevention and stabilisation of the oral environment before considering more complex, definitive restorative work. This paper forms the first of several components of a suite of educational/information materials needed to instil confidence and implementation protocols within primary care clinical oral health care teams delivering MIOC through phased PCPs, especially when managing patients with high needs and/or disease susceptibility. Key points The development of a team-delivered, prevention-based, person-focused, susceptibility/needs-related phased care approach to modern caries management using the minimum intervention oral care delivery framework in primary care is proposed. Using personalised care plans within phased courses of treatment, aligned to periodontal management guidelines, caries prevention in primary care can be incentivised. The potential for NHS remuneration for prevention-based, phased, personalised care pathways/plans (PCPs) within a reformed NHS dental contract system is outlined.