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Abstract Dry gas pipelines can encounter various operational, technical, and environmental issues, such as corrosion, leaks, spills, restrictions, and cyber threats. To address these difficulties, proactive maintenance and management and a new technological strategy are needed to increase safety, reliability, and efficiency. A novel neural network model for forecasting the life of a dry gas pipeline system and detecting the metal loss dimension class that is exposed to a harsh environment is presented in this study to handle the missing data. The proposed strategy blends the strength of deep learning techniques with industry-specific expertise. The main advantage of this study is to predict the pipeline life with a significant advantage of predicting the dimension classification of metal loss simultaneously employing a Bayesian regularization-based neural network framework when there are missing inputs in the datasets. The proposed intelligent model, trained on four pipeline datasets of a dry gas pipeline system, can predict the health condition of pipelines with high accuracy, even if there are missing parameters in the dataset. The proposed model using neural network technology generated satisfactory results in terms of numerical performance, with MSE and R2 values closer to 0 and 1, respectively. A few cases with missing input data are carried out, and the missing data is forecasted for each case. Then, a model is developed to predict the life condition of pipelines with the predicted missing input variables. The findings reveal that the model has the potential for real-world applications in the oil and gas sector for estimating the health condition of pipelines, even if there are missing input parameters. Additionally, multi-model comparative analysis and sensitivity analysis are incorporated, offering an extensive comprehension of multi-model prediction abilities and beneficial insights into the impact of various input variables on model outputs, thereby improving the interpretability and reliability of our results. The proposed framework could help business plans by lowering the chance of severe accidents and environmental harm with better safety and reliability.

This paper presents a technique of harmonic minimization from output voltage waveform of a reduced switch Multilevel Inverter (MLI) through an efficient bio inspired metaheuristic algorithm called Black widow optimization (BWO). The proposed reduced switch 13- level MLI scheme uses a single Photovoltaic (PV) source which can be suitable for grid integration. The proposed BWO algorithm minimizes the Total Harmonic Distortion (THD) of output voltage with low operational time compared to other existing nature based algorithms considering large searching area. The weighted THD (WTHD) of the output voltage is also minimized in order to reduce the effect of lower order harmonics from the output voltage in a greater extent. The convergence rate and level of accuracy of BWO algorithm is compared with two different bio inspired algorithms for justification. The MLI operation is carried out with fundamental frequency, minimizing the switching stress while lowering the power loss compared to PWM or Sinusoidal PWM switching schemes. A single PV panel with multi winding flyback converter is used for medium voltage application through reduced switch MLI, serving the purpose of both isolation and reduction of energy sources. Simulation and experimental analysis are carried out with online control technique using a three phase 13-level reduced MLI to validate the proposed concept on a practical system.

The present study focuses on mapping and identification of the pigment concentration and dominant invasive species in Mudumalai Tiger Reserve (MTR), Tamil Nadu, India using optical (Sentinel 2A) and hyperspectral (Hyperion) earth observation satellite images. The Hyperion satellite data-based study exhibited dominance of dense vegetation (40.64%) and moderate vegetation (27.81%), in contrast to low vegetation (15.71%) in MTR. While vegetation indices based analysis demonstrated more accurate results of forest cover through Modified Red Edge Normalised Difference Vegetation Index (MRENDVI) compared to other vegetation indices. The dominance of moderate Normalised Difference Vegetation Index (NDVI) values (0.55-0.6) were observed in MTR with moderate carotenoid concentration (<0.3) and low anthocyanin concentration (<0.2). The study highlights the prominence of Lantana camara, Parthenium hysterophorus, Prosopis juliflora, and Chromolaena odorata in the Mudumalai Tiger Reserve. The correlation of species and foliar pigment concentration provides a better understanding of the species distribution in tropical deciduous forests in southern India. The study highlights better results in Sentinel 2A images compared to Hyperion images in LULC classification due to higher spatial resolution, while Hyperion images provide more improved classification and diversity for species characterisation due to high spectral agility. The study necessitates concurrent monitoring of invasion in forestrich regions through the adoption of potential forest conservation and management plans. [ABSTRACT FROM AUTHOR]

Quadcopter drones have become increasingly popular because of their versatility and usefulness in various applications, such as surveillance, delivery, and search and rescue operations. Weather conditions and obstacles can undoubtedly pose challenges for drone flights, sometimes causing the loss of one or two propellers. This is a significant challenge as the loss of one or more propellers leads to a sudden loss of control, potentially resulting in a crash, which must be addressed through advanced control strategies. Therefore, this article develops and implements a fractional-order control algorithm to enhance quadrotor drones’ safety and resilience during propeller failure scenarios. The research encompasses the complexities of quadrotor dynamics, fractional-order control theory, and existing methodologies for ensuring safe drone landings. The study emphasizes case validation on experimental results, where four distinct cases were tested using PID and Fractional-order PID (FOPID) controllers. These cases involve various simulated failure conditions to assess the performance and adaptability of the developed control algorithms. The results show the proposed FOPID control’s superior robustness and adaptability compared to traditional PID controllers. These offer significant advancements in navigating dynamic environments and managing disruptive elements introduced during propeller failure simulations in drone control technology.

The gas sweetening process is essential for removing harmful acid gases, such as hydrogen sulfide (H2S) and carbon dioxide (CO2), from natural gas before delivery to end-users. Consequently, chemical absorption-based acid gas removal units (AGRUs) are widely implemented due to their high efficiency and reliability. The most common solvent used in AGRU is monodiethanolamine (MDEA), often mixed with piperazine (PZ) as an additive to accelerate acid gas capture. The absorption performance, however, is significantly influenced by the solvent mixture composition. Despite this, solvent composition is often determined through trial and error in experiments or simulations, with limited studies focusing on predictive methods for optimizing solvent mixtures. Therefore, this paper aims to develop a predictive technique for determining optimal solvent compositions under varying sour gas conditions. An ensemble algorithm, Extreme Gradient Boosting (XGBoost), is selected to develop two predictive models. The first model predicts H2S and CO2 concentrations, while the second model predicts the MDEA and PZ compositions. The results demonstrate that XGBoost outperforms other algorithms in both models. It achieves R2 values above 0.99 in most scenarios, and the lowest RMSE and MAE values of less than 1, indicating robust and consistent predictions. The predicted acid gas concentrations and solvent compositions were further analyzed to study the effects of solvent composition on acid gas absorption across different scenarios. The proposed models offer valuable insights for optimizing solvent compositions to enhance AGRU performance in industrial applications.

It was on the 10 SP th sp of November, 1979 that I entered the portals of St Joseph Leprosy Hospital (SJLH), a unit of Fr Muller's Charitable Institutions (FMCI), located in my hometown Mangalore, Karnataka, on being appointed as a junior doctor. St Joseph Leprosy Hospital had a bed strength of 140 when I joined, and Dr Jerome Pinto was managing the medical aspects with a dedicated team of nursing and paramedical staff. I was a novice in the field of leprosy as I was just out of the medical college and I gradually started learning under Dr Pinto, but it lasted only for a few months as he left for pursuing Master's degree. [Extracted from the article]

Byline: I. Dubey, A. Jain, M. Chauhan, R. Kumar, S. Agarwal, B. Kishore, M. Vishnoi, D. Paliwal, A. John, N. Kumar, A. Sharma, A. Pandit BACKGROUND: In India, carcinoma breast [...]

The first direct evidence for a decline in HIV incidence rates in female sex workers (FSWs) and male patients with sexually transmitted infection (STIs) over time from HIV analysis from clinics in Pune, India is reported.

This paper presents a novel design approach for series-fed microstrip patch antenna arrays. The design is based on the principle of gain modification of a patch antenna array with an amplitude distribution, to generate a desired beam pattern. The proposed antenna uses the feature that the patch width can be varied to obtain amplitude tapering. The widths are calculated using Fourier's method of array synthesis for shaped beam pattern generation. A small rectangular slot is removed from the radiating patch for dual band operation. The ground plane is made defected with 'U' slots, to increase the bandwidth. The structure is simulated in HFSS and fabricated on FR-4 Epoxy material to test the validation practically. Compared to the existing designs, the proposed structure does not necessitate the use of any complicated power dividers or phase shifting circuits. It exhibits two resonant frequencies, at 3.6 GHz and 6.8 GHz. At 3.6 GHz, the array produces a sector pattern gain of magnitude 14.7 dB, with low level side lobes at -21 dB. At 6.8 GHz, it exhibits a sector pattern of gain of 14.5 dB, with a low side lobe level of -19 dB. The impedance bandwidths of the structure are 11.1% (2.6 to 4.3 GHz) and 10.8% (6.3 to 7.8 GHz), respectively. The simulated results and prototype measured values have a great similarity. [ABSTRACT FROM AUTHOR]

Abstract Preoperative knowledge of expected postoperative pain can help guide perioperative pain management and focus interventions on patients with the greatest risk of acute pain. However, current methods for predicting postoperative pain require patient and clinician input or laborious manual chart review and often do not achieve sufficient performance. We use routinely collected electronic health record data from a multicenter dataset of 234,274 adult non-cardiac surgical patients to develop a machine learning method which predicts maximum pain scores on the day of surgery and four subsequent days and validate this method in a prospective cohort. Our method, POPS, is fully automated and relies only on data available prior to surgery, allowing application in all patients scheduled for or considering surgery. Here we report that POPS achieves state-of-the-art performance and outperforms clinician predictions on all postoperative days when predicting maximum pain on the 0–10 NRS in prospective validation, though with degraded calibration. POPS is interpretable, identifying comorbidities that significantly contribute to postoperative pain based on patient-specific context, which can assist clinicians in mitigating cases of acute pain.

Abstract Wireless technology is becoming increasingly critical in industrial environments in recent years, and the popular wireless standards are WirelessHART, ZigBee, WLAN and ISA100.11a, commonly used in closed-loop systems. However, wireless networks in closed-loop control experience packet loss or drops, system delay and data threats, leading to process instability and catastrophic system failure. To prevent such issues, it is necessary to implement dead-time compensation control. Traditional techniques like model predictive and predictive PI controllers are frequently employed. However, these methods’ performance is sluggish in wireless networks, with processes having long dead times and set-point variations, potentially affecting network and process performance. Therefore, this paper proposes a fractional calculus-based predictive PI compensator for wired and wireless networks in the process control industries. The proposed technique has been simulated and evaluated on industrial process models, including pressure, flow, and temperature, where measurement and control are carried out wirelessly. The wireless network’s performance has been evaluated based on packet loss, reduced throughput, and increased system latency. The proposed compensator outperformed traditional methods, demonstrating superior set-point tracking, disturbance rejection, and delay compensation characteristics in the performance evaluations of the first, second, and third-order systems. Overall, the findings indicate that the proposed compensator enhances wireless networks’ performance in the process control industry and improves system stability and reliability by reducing almost half of the overshoot and settling an average of 8.3927% faster than the conventional techniques in most of the systems.