Search

Recurrent neural networks have achieved outstanding recognition performance for handwriting identification despite the enormous variety observed across diverse handwriting structures, and poor-quality scanned documents. Initially, we proposed a BiLSTM baseline model with a sequential architecture well-suited for modeling text lines due to its capacity to learn probability distributions over character or word sequences. However, employing such recurrent paradigms prevents parallelization and suffers from vanishing gradients for long sequences during training. To alleviate these limitations, we propose four significant contributions to this work. First, we devised an end-to-end model composed of a split-attention CNN-backbone that serves as a feature extraction method and a self-attention Transformer encoder-decoder that serves as a transcriber method to recognize handwriting manuscripts. The multi-head self-attention layers in an encoder-decoder transformer-based enhance the model’s ability to tackle handwriting recognition and learn the linguistic dependencies of character sequences. Second, we conduct various studies on transfer learning (TL) from large datasets to a small database, determining which model layers require fine-tuning. Third, we attained an efficient paradigm by combining different strategies of TL with data augmentation (DA). Finally, since the robustness of the proposed model is lexicon-free and can recognize sentences not presented in the training phase, the model is only trained on a few labeled examples with no extra cost of generating and training on synthetic datasets. We recorded comparable and outperformed Character and Word Error Rates CER/WER on four benchmark datasets to the most recent models (SOTA). The code and models will be uploaded on Github.

The seed metering mechanism is the heart of the seed drill. The seed drill is a machine that places the seeds upto a predetermined desired depth. This ensures that seeds will be distributed evenly. The main aim of the paper is to develop a new seed-metering mechanism. By developing the new mechanism, the goal is to reduce the inefficiency of the current mechanism in terms of seed crushing rate and maintenance time. Also, there was a focus on developing a versatile seed drill to adjust row-row and seed-seed distance which is a major factor related to the net productivity of the crop.

A study was held at N. E. Borlaug Crop Research Centre during the kharif season of 2018-19, 2019-2020 and 2020-21. The seeds of various basmati and non-basmati lines were irradiated with ϒ rays at NBRI, Lucknow in 2018-19. The seeds were grown in the same year, the observed viability was less than 99.7 % (M1). The seeds were very meagre and so they were collected and grown in the kharif season of 2019-20. In this generation a screening test for examining BLB resistance was conducted. Resistant mutants were identified in the M2 generation. Similarly, the seeds obtained from M2 were grown as the M3 generation and again screening was done for the BLB. Some mutants were identified which revealed resistance and could be used in the future breeding program for the alleviation of disease susceptibility and catering resistance.

The use of microelectromechanical systems (MEMS) in electronics, automotive, consumer and medical sector is increasing rapidly. It is expected that by 2024 the Global MEMS and sensor market will reach the value of 93 billion dollars. MEMS inertial measurement sensors (accelerometer and gyroscopes) are used in low end consumer electronics such as smartphones to high end products such as drones used in military applications. MEMS IMU (Inertial Measurement Unit) are often deployed in dynamic environment such as in machining process for condition monitoring, vehicles for identifying driving behaviors, wearable technology for fall detection and many others. Consequently, Gaussian noise is always present in the output signal of such sensors. Denoising the output signal is critical for feature extraction and data analysis. Recently hybrid approaches for noise removal have gained much research attention. The aim of this paper is to present a hybrid noise removal algorithm for MEMS IMU sensors. In this technique, the signal to noise ratio has been improved and remove glitches by minimizing the hybrid noise from the original output signal of the MEMS IMU sensor.

Gimballed sensor system is a precision electromechanical assembly designed primarily to isolate the optical system from disturbances induced by the operating environment. This paper gives an insight to the design and development of gimballed sensor system for Line of Sight (LOS) stabilization of an electro-optical tracking and pointing system. Initially kinematic equations are formulated to establish a relationship between LOS angle and applied torque. This relationship is used to obtain nested loop transfer function model. First, the parameters of the proposed assembly are determined through experimentation & rigorous analysis process, and then conventional control design methodology is adopted for controller configuration design for current and rate loop. The frequency response analysis of the designed LOS stabilization model with conventional controller is done in simulation and the obtained results are verified experimentally against angular disturbances in real time scenario. Further, Based on prior qualitative information about system dynamics and linguistic performance criteria, a fuzzy logic controller of mamdani type with simplified rule set is developed with an objective to improve the disturbance attenuation and command response performance of designed system irrespective of angular disturbances due to platform vibrations, model uncertainties and mass imbalance in gimbal assembly. Both the Fuzzy logic simulation model and conventional model are tested based on critical performance characteristics such as stability of the loop, responsiveness of the loop and insensitivity to disturbances. Finally, the comparative analysis suggests that, although both the control configuration satisfies the required accuracy, Fuzzy logic control certainly improvised the performance of the gimballed sensor system and hence can be very effective for more precise pointing, tracking and stabilization application.

Due to the nonlinear nature of the dynamics of a robot manipulator, controlling the robot meticulously is a challenging issue for control engineers. The key purpose of this paper is to provide an a...

The aim of this paper is to analyze and evaluate the sensitivity and non-linearity of graphene piezoresistive MEMS force sensor by optimizing its geometrical parameters. In this work, important fac...

The study aimed to assess the hand-arm vibration (HAV), noise exposure, and shift in hearing threshold (SHT) due to the prolonged use of hand tools used in three different handicraft occupations. D...

Wave height is a critical consideration in the planning and execution of maritime projects. Wave height forecasting methods include numerical and machine learning (ML) techniques. The traditional process involves using numerical wave prediction models, which are very successful but are highly complex as they require adequate information on nonlinear wind–wave and wave–wave interactions, such as the wave energy-balance equation. In contrast, ML techniques can predict wave height without prior knowledge of the above-mentioned complex interactions. This research aims to predict wave height using micro-electromechanical systems (MEMS), internet of things (IoTs), and ML-based approaches. A floating buoy is developed using a MEMS inertial measurement unit and an IoT microcontroller. An experiment is conducted in which the developed buoy is subjected to different wave heights in real time. The changes in three-axis acceleration and three-axis gyroscope signals are acquired by a computer via IoT. These signals are analyzed using ML-based classification models to accurately predict wave height. The obtained validation accuracy of the ML models K-NN (K-nearest neighbor), support vector machine, and the bagged tree is 0.9906, 0.9368, and 0.9887 respectively, which indicates that MEMS and IoT can be used to accurately classify and predict wave heights in real-time.

Purpose This paper aims to propose a dynamically balanced mechanism for cleaning unit used in agricultural thresher machine using a dynamically equivalent system of point masses. Design/methodology/approach The cleaning unit works on crank-rocker Grashof mechanism. This mechanism can be balanced by optimizing the inertial properties of each link. These properties are defined by the dynamic equivalent system of point masses. Parameters of these point masses define the shaking forces and moments. Hence, the multi-objective optimization problem with minimization of shaking forces and shaking moments is formulated by considering the point mass parameters as the design variables. The formulated optimization problem is solved using a posteriori approach-based algorithm i.e. the non-dominated sorting Jaya algorithm (NSJAYA) and a priori approach-based algorithms i.e. Jaya algorithm and genetic algorithm (GA) under suitable design constraints. Findings The mass, center of mass and inertias of each link are calculated using optimum design variables. These optimum parameters improve the dynamic performance of the cleaning unit. The optimal Pareto set for the balancing problem is measured and outlined in this paper. The designer can choose any solution from the set and balance any real planar mechanism. Originality/value The efficiency of the proposed approach is tested through the existing cleaning mechanism of the thresher machine. It is found that the NSJAYA is computationally more efficient than the GA and Jaya algorithm. ADAMS software is used for the simulation of the mechanism.

This paper deals with the optimum two-plane balancing of the threshing drum using discrete parameters such as masses and corresponding angular locations on balance planes. To improve the dynamic pe...

Purpose The purpose of this paper is to propose the dynamically balanced mechanism for cleaning unit used in the agricultural thresher machine using the system of point masses. Design/methodology/approach The cleaning unit works on crank-rocker Grashof mechanism. To balance the mechanism, the shaking forces and shaking moments are minimized by optimizing the mass distribution of links using the dynamically equivalent system of point masses. The point mass parameters are taken as the design variables. Then, the optimization problem is solved using Jaya algorithm and genetic algorithm (GA) under suitable design constraints. Findings The mass, center of mass and inertias of each link are calculated using optimum design variables. These optimum parameters improve the dynamic performance of the cleaning unit. Originality/value The proposed methodology is tested through the standard four-bar mechanism taken from literature and also applied to the existing cleaning mechanism of the thresher machine. It is observed that the Jaya algorithm is computationally more efficient than the GA. The dynamic analysis of the proposed mechanism is simulated using ADAMS software.

The aim of the study was to investigate the occupational use of low-cost intervention for carpet trimming occupation. This study quantifies the effect of hand-arm vibration and noise exposure to loss in hearing threshold among trimming workers. Ten male participants (right handed) involved in carpet trimming volunteered to participate in this interventional experiment. The participants were queried about the usability of prototype handle based on their subjective perception evaluated through the use of scoresheet/questionnaire. Results demonstrated that the handle intervention reduced the total value of root mean square frequency-weighted vibrations by more than 40% when compared with the conventional handle. The workers were exposed to noise levels ranging from 87.2 to 91.8 dB(A) (mean 89.55 dB(A)). They exhibit mild-to-moderate hearing impairment in the frequency range of 3000–6000 Hz. Based on this preliminary work, we have found that prototype handle was effective in curtailing hand-arm vibration. The prototype handle showed a positive effect on the usability ratings. More practical applications should be explored to prevail the combined effect of hand-arm vibration and noise.

Purpose This paper aims to present the optimum two-plane discrete balancing procedure for rigid rotor. The discrete two-plane balancing in which rotor is balanced to minimize the residual effects or the reactions on the bearing supports using discrete parameters such as masses and their angular positions on two balancing planes. Design/methodology/approach Therefore as a multi-objective optimization problem is formulated by considering reaction forces on the bearing supports as a multi objective functions and discrete parameters on each balancing plane as design variables. These multi-objective functions are converted into a single-objective function using appropriate weighting factors. Further, this optimization problem is solved using discrete optimization algorithm, based on Jaya algorithm. Findings The performance of the discrete Jaya algorithm is compared to genetic algorithm (GA) algorithm. It is found that Jaya algorithm is computationally more efficient than GA algorithm. A number of masses per plane are used to balance the rotor. A comparison of reaction forces using number of masses per plane is investigated. Originality/value The effectiveness of the proposed methodology is tested by the balancing problem of rotor available in the literature. The influence of a number of balance masses on bearing forces and objective function are discussed. ADAMS software is used for validation of a developed balancing approach.

Mechanization of agriculture plays an important role for improved crop productivity. In harvesting, machines reaper is used to harvest wheat crops. In the existing machines, there is a need to improve the crop conveying mechanism which can also uplift the slant crops occurred due to wind and nature occurring problems. Thus, in this paper, an improved mechanism is proposed which can contribute to increase the efficiency of the machine. Kinematic analysis is also performed to validate and compare the experimental to design data. Torque required to run the crank is also identified in this work through dynamic analysis. Finally, a prototype was developed through 3D printing.

Pro-inflammatory and amyloidogenic S100A9 protein is central to the amyloid-neuroinflammatory cascade in neurodegenerative diseases. Polyoxometalates (POMs) constitute a diverse group of nanomaterials, which showed potency in amyloid inhibition. Here, we have demonstrated that two selected nanosized niobium POMs, Nb10 and TiNb9, can act as potent inhibitors of S100A9 amyloid assembly. Kinetics analysis based on ThT fluorescence experiments showed that addition of either Nb10 or TiNb9 reduces the S100A9 amyloid formation rate and amyloid quantity. Atomic force microscopy imaging demonstrated the complete absence of long S100A9 amyloid fibrils at increasing concentrations of either POM and the presence of only round-shaped and slightly elongated aggregates. Molecular dynamics simulation revealed that both Nb10 and TiNb9 bind to native S100A9 homo-dimer by forming ionic interactions with the positively charged Lys residue-rich patches on the protein surface. The acrylamide quenching of intrinsic fluorescence showed that POM binding does not perturb the Trp 88 environment. The far and near UV circular dichroism revealed no large-scale perturbation of S100A9 secondary and tertiary structures upon POM binding. These indicate that POM binding involves only local conformational changes in the binding sites. By using intrinsic and 8-anilino-1-naphthalene sulfonate fluorescence titration experiments, we found that POMs bind to S100A9 with a Kd of ca. 2.5 μM. We suggest that the region, including Lys 50 to Lys 54 and characterized by high amyloid propensity, could be the key sequences involved in S1009 amyloid self-assembly. The inhibition and complete hindering of S100A9 amyloid pathways may be used in the therapeutic applications targeting the amyloid-neuroinflammatory cascade in neurodegenerative diseases.

The amyloid cascade is central for the neurodegeneration disease pathology, including Alzheimer’s and Parkinson’s, and remains the focus of much current research. S100A9 protein drives the amyloid-neuroinflammatory cascade in these diseases. DOPA and cyclen-based compounds were used as amyloid modifiers and inhibitors previously, and DOPA is also used as a precursor of dopamine in Parkinson’s treatment. Here, by using fluorescence titration experiments we showed that five selected ligands: DOPA-D-H-DOPA, DOPA-H-H-DOPA, DOPA-D-H, DOPA-cyclen, and H-E-cyclen, bind to S100A9 with apparent Kd in the sub-micromolar range. Ligand docking and molecular dynamic simulation showed that all compounds bind to S100A9 in more than one binding site and with different ligand mobility and H-bonds involved in each site, which all together is consistent with the apparent binding determined in fluorescence experiments. By using amyloid kinetic analysis, monitored by thioflavin-T fluorescence, and AFM imaging, we found that S100A9 co-aggregation with these compounds does not hinder amyloid formation but leads to morphological changes in the amyloid fibrils, manifested in fibril thickening. Thicker fibrils were not observed upon fibrillation of S100A9 alone and may influence the amyloid tissue propagation and modulate S100A9 amyloid assembly as part of the amyloid-neuroinflammatory cascade in neurodegenerative diseases.

Background: At the onset of COVID-19, lockdowns and movement restrictions were put in place to control the pandemic, which halted in-person, non-COVID-19 public health and information sharing activities and field visits. To reach beneficiaries during this time, Project Samvad, funded by USAID India and led by Digital Green, implemented a supplementary intervention to share health and nutrition-related advisories among the communities using digital media tools like WhatsApp and interactive voice response (IVR). Digital Green repurposed its videos on health and nutrition in order to share them via digital means. Digital Green evaluated the feasibility of the adapted approaches using digital media tools and capacity building of community-level frontline workers to enhance their knowledge and skills through virtual training programs. Methods: A telephonic survey was conducted among community and frontline workers. 299 community members from three Indian states, i.e., Bihar, Jharkhand, and Odisha, where WhatsApp and IVR advisories were sent, and 224 frontline workers were interviewed in five states Bihar, Jharkhand, Chhattisgarh, and Uttarakhand using a quantitative questionnaire developed in ODK. Result: Our analysis indicates that the reach of active phone numbers is high (76% of frontline workers and 73% of community members). The exposure to WhatsApp videos is good; 79% of frontline workers and 49% of community members watched videos on WhatsApp. However, only 33% of community members received IVR messages. Conclusion: By increasing the size and reach of the phone database, improving frontline workers’ training quality, and increase digital literacy among the community, the outreach of digital media tools can be enhanced to improve the health and nutrition outcomes.

In last few decades, fast steering mirrors (FSMs) are predominantly used in steering and control of line of sight (LOS) in many applications which includes surveillance, tracking, pointing, photography, ground, space communication, and astronomy research. This chapter presents the dynamic modeling and control configuration design of FSM in azimuth direction. The ultimate objective of control configuration design is to obtain the faster input command response and isolate the line of sight movement from any kind of internal or external disturbances. Initially, a simplified model of FSM with suitable parametric values and adjustable gains is designed and that establishes the relationship between angular moment of LOS and applied torque. Then, a pre-existing proportional-integral-derivative (PID) controller configuration is developed, whose gains are further optimized using genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. Integral time absolute error is considered as an objective function for both the algorithm. However, both GA and PSO tuned PID controller improves the performance of FSM system. But PSO tuned configuration certainly outperforms the existing controller in terms of desired characteristics.

Optical tracking and pointing system are nowadays employed in vast variety of civil and defence applications. Laser beam steering and pointing have always been a challenging task due to complex dynamics of the stabilization platform, mass imbalance, platform vibrations and other environmental factors. This paper presents a systematic approach to modelling and control of fast steering mirror (FSM), used for precise pointing and tracking applications. Initially, a transfer function model is developed to establish the relationship between line of sight (LOS) angle and applied torque. The objective of the control configuration design is to keep the line of sight of the sensor fixed with respect to inertial frame, irrespective of angular disturbances. Grey wolf optimization (GWO)-based PID controller is designed for rate loop configuration of fast steering mirror. The concept of GWO follows the hunting behaviour of grey wolves as search agents. Alpha, beta, delta and omega group of wolves are used for simulating the social hierarchy. The integral of time multiplied absolute error (ITAE) is used as an objective function for proportional integral derivative (PID) controller parameter evaluation using GWO. Although the design of PID controller satisfies the required pointing accuracy of the electro-optical FSM system, but the design of proposed GWO-based PID controller with ITAE as an objective function completely outperforms the existing controller in terms of dynamic input command response and insensitivity to the disturbances.

This chapter presents the balancing procedure of the mechanism using a nondominated sorting Jaya algorithm (NSJAYA). Planar mechanisms can be balanced by optimizing each moving link’s inertial properties. These properties are derived by the point-mass system. Thus, the multi-objective optimization problem (HOOP) is posed to minimize the unbalance force and moment in which the point mass parameters are treated as the design variables. The formulated optimization problem is solved by a posteriori approach-based algorithm as a NSJAYA under suitable design constraints. This algorithm uses the concept of crowding distance and a nondominated sorting approach to find a Pareto set of optimal solutions. The proposed method is tested through a four-bar planar standard mechanism taken from literature. It is established that the NSJAYA is computationally more efficient than NSGA-II. The designer can choose any solution from the set and balance any real planar mechanisms.

This paper reviews the various methods developed for balancing of the planar mechanisms and synthesizing the link shapes. The methods discussed in this paper are used for complete force balance, complete force and moment balance, partial force and moment balance as well as for the link shape synthesis of different planar mechanisms. The concepts, applications, and limitations of various methods are discussed and reviewed from the available literature in the area of mechanism balancing. The better understanding of available methods will definitely help the researchers working in this area in analyzing the current practices and in developing the new methods.

Deposition of fibrillar amyloid β (Aβ) in senile plaques is a pathological signature of Alzheimer’s disease. However, senile plaques also contain many other components, including a range of different proteins. Although the composition of the plaques can be analyzed in post-mortem tissue, knowledge of the molecular details of these multiprotein inclusions and their assembly processes is limited, which impedes the progress in deciphering the biochemical mechanisms associated with Aβ pathology. We describe here a bottom-up approach to monitor how proteins from human cerebrospinal fluid associate with Aβ amyloid fibrils to form plaque particles. The method combines flow cytometry and mass spectrometry proteomics and allowed us to identify and quantify 128 components of the captured multiprotein aggregates. The results provide insights into the functional characteristics of the sequestered proteins and reveal distinct interactome responses for the two investigated Aβ variants, Aβ(1–40) and Aβ(1–42). Furthermore, the quantitative data is used to build models of the structural organization of the multiprotein aggregates, which suggests that Aβ is not the primary binding target for all the proteins; secondary interactions account for the majority of the assembled components. The study elucidates how different proteins are recruited into senile plaques and establishes a new model system for exploring the pathological mechanisms of Alzheimer’s disease from a molecular perspective.

A classical solution where the (scalar) field value moves by an ${\cal O}(1)$ range in Planck units is believed to signal the breakdown of Effective Field Theory (EFT). One heuristic argument for this is that such a field will have enough energy to be inside its own Schwarzschild radius, and will result in collapse. In this paper, we consider an inverse problem: what kind of field ranges arise during the gravitational collapse of a classical field? Despite the fact that collapse has been studied for almost a hundred years, most of the discussion is phrased in terms of fluid stress tensors, and not fields. An exception is the scalar collapse made famous by Choptuik. We re-consider Choptuik-like systems, but with the emphasis now on the evolution of the scalar. We give strong evidence that generic spherically symmetric collapse of a massless scalar field leads to super-Planckian field movement. But we also note that in every such supercritical collapse scenario, the large field range is hidden behind an apparent horizon. We also discuss how the familiar perfect fluid models for collapse like Oppenheimer-Snyder and Vaidya should be viewed in light of our results., v3: references and minor improvements

An ergonomic assessment of static muscular strength like hand grip-and pinch strength in handicraft occupation was made. In total, 170 female participants were selected for the case-control study, out of which 120 handicraft and 50 office workers were examined as exposed and control groups. Their maximum static handgrip- and pinch strength were measured using hand and pinch dynamometers. We analyze our findings to indicate static muscular strength varies significantly due to repetitive use of hand tools. The decrement in pinch grip strength was evident due to long cycle repetitive pinching movements of distal phalanx during hand knotting and pearl drilling. Based on our results we propose the need for ergonomically designed hand tool interventions that may reduce the accumulation of loss in static muscle strength.

Sesame (Sesamum indicum L.) is one of the oldest cultivated plants in the world and is also known as ‘queen’ of oilseed crops. The production of sesame is not consistent compared to other oilseed crops as it is susceptible to waterlogging and low temperature conditions and constant devastating effects of various diseases. Phytophthora blight caused by Phytophthora nicotianae is an economically important oomycete pathogen affecting sesame plant growth and yield to great extent. This disease is predominant in all sesame growing areas. Mutant varieties resistant to Phytophthora blight in sesame have been developed in the Republic of Korea and Sri Lanka. In the present study, 416 gamma-rays induced M3 mutants were raised at Palampur, 164 mutants at Kangra and 314 at Akrot in India. The mutants were screened for Phytophthora blight resistance both under natural field as well as laboratory conditions through ‘detached leaf technique’ at Palampur. Out of these, 186 and 62 M4 mutants were screened again under natural field conditions at Palampur and Kangra, respectively during rainy season of 2016. Based on the screening for 2 years through ‘detached leaf technique’ as well as under natural field conditions, twelve mutants having moderate to high resistance against Phytophthora blight could be isolated. One mutant P97-1 (300 Gy) exhibited highly resistant reaction against Phytophthora blight during both seasons. The isolated mutant can further be utilized in hybridization programme for the introgression of Phytophthora blight resistance in sesame.

This paper presents the optimal design procedure of a flywheel using a cubic B-spline curve. The flywheel plays a vital role in storing kinetic energy in modern machines. The kinetic energy evaluates the performance of the flywheel. In order to improve the kinetic energy of a flywheel, a shape optimization model of the flywheel, with maximization of kinetic energy, is formulated using a cubic spline curve under the constraints of the mass of flywheel, and the maximum value of Von Mises stresses at all points along the radial direction. The Von Mises stresses at all points are determined using the two-point boundary value differential equation. The control points of the cubic B-spline curve are taken as design variables. Then the formulated problem is solved using particle swarm algorithm (PSO), genetic algorithm (GA), and Jaya algorithm. The proposed approach is applied to the flywheel of an agricultural thresher machine. It is found that the Jaya algorithm gives better results compared to the other algorithms. The optimized shape of the flywheel is simulated using MSC ADAMS software.

Mixed-variable optimization problems consist of the continuous, integer, and discrete variables generally used in various engineering optimization problems. These variables increase the computational cost and complexity of optimization problems due to the handling of variables. Moreover, there are few optimization algorithms that give a globally optimal solution for non-differential and non-convex objective functions. Initially, the Jaya algorithm has been developed for continuous variable optimization problems. In this paper, the Jaya algorithm is further extended for solving mixed-variable optimization problems. In the proposed algorithm, continuous variables remain in the continuous domain while continuous domains of discrete and integer variables are converted into discrete and integer domains applying bound constraint of the middle point of corresponding two consecutive values of discrete and integer variables. The effectiveness of the proposed algorithm is evaluated through examples of mixed-variable optimization problems taken from previous research works, and optimum solutions are validated with other mixed-variable optimization algorithms. The proposed algorithm is also applied to two-plane balancing of the unbalanced rigid threshing rotor, using the number of balance masses on plane 1 and plane 2. It is found that the proposed algorithm is computationally more efficient and easier to use than other mixed optimization techniques.

Tillage operations play an important role in soil manipulation processes. This paper aim to design a vibratory mechanism for agricultural tillage operation which would provide the desired motion and path to the cutting tool in the soil. A four-bar mechanism is designed through the principles of mechanism synthesis technique. The dimensions obtained are modelled and validated through MATLAB and MSC Adams to examine whether the tillage tool follows the required trajectory or not. Velocity and acceleration analysis are also studied for the designed mechanism. The study confirmed that the tillage tool followed the required sinusoidal path and completed the objective of vibratory tillage operation.

Purpose: The assistive knee exoskeleton device is used for supporting the surrounding ligaments, tendons, and muscles of the injured knee joint. Various knee exoskeletons have been discussed; howev...

While extracting land cover from remote sensing images, each pixel in the image is allocated to one of the possible class. In reality different land covers within a pixel can be found due to continuum of variation in landscape and intrinsic mixed nature of most classes. Mixed pixels may not be appropriately processed by traditional image classifiers, which assume that pixels are pure. The existence of mixed pixels led to the development of several approaches for soft (often termed fuzzy in the remote sensing literature) classification in which each pixel is allocated to all classes in varying proportions. However, while the proportions of each land cover within each pixel may be predicted, the spatial location of each land cover within each pixel is not. Thus, it is important to develop and implement a classifier that can work as soft classifiers for landslide identification. This work is an attempt to document and identify landslide areas by five spectral indices using temporal multi-spectral images from IRS-P6 LISS-IV images. To improve the spectral properties of spectral indices for specific class identification (in this case landslide) a Class Based Sensor Independent (CBSI) technique proposed. The result indicates that CBSI based Transformed Normalized Difference Vegetation Index (TNDVI) temporal indices data gives better results for landslide identification with minimum entropy and membership range.

This paper proposes a novel hybrid teaching–learning particle swarm optimization (HTLPSO) algorithm, which merges two established nature-inspired algorithms, namely, optimization based on teaching–learning (TLBO) and particle swarm optimization (PSO). The HTLPSO merges the best half of population obtained after the teacher phase in TLBO with the best half of the population obtained after PSO. The population so obtained is used subsequently in learner phase of TLBO. To validate the proposed algorithm, five constrained benchmark functions are considered to prove its robustness and efficiency. The proposed algorithm is applied to synthesize four-bar linkage for prescribed path. It is found that the HTLPSO performs better than other single nature-inspired algorithms for path synthesis problem in mechanism theory. Hence, HTLPSO may prove to be an important tool for mechanism design to follow the prescribed path.

Technological advances in prosthetics have attracted the curiosity of researchers in monitoring design and developments of the sockets to sustain maximum pressure without any soft tissue damage, skin breakdown, and painful sores. Numerous studies have been reported in the area of pressure measurement at the limb/socket interface, though, the relation between amputee's physiological parameters and the pressure developed at the limb/socket interface is still not studied. Therefore, the purpose of this work is to investigate the effects of patient-specific physiological parameters viz. height, weight, and stump length on the pressure development at the transtibial prosthetic limb/socket interface. Initially, the pressure values at the limb/socket interface were clinically measured during stance and walking conditions for different patients using strain gauges placed at critical locations of the stump. The measured maximum pressure data related to patient's physiological parameters was used to develop an artificial neural network (ANN) model. The effects of physiological parameters on the pressure development at the limb/socket interface were examined using the ANN model. The analyzed results indicated that the weight and stump length significantly affects the maximum pressure values. The outcomes of this work could be an important platform for the design and development of patient-specific prosthetic socket which can endure the maximum pressure conditions at stance and ambulation conditions.

The octahedron seven point mass model of equimomental point masses and optimization technique “teaching–learning-based optimization” is presented to minimize constraint forces and moments at joints of an industrial manipulator. The octahedron point mass model configuration presented offers positive values for equimomental point masses to facilitate link shape formation. Equations are derived to compute point masses and their locations for the rigid links of an industrial manipulator. The flow chart of the teaching–learning-based optimization applicable in solving the manipulators problem is presented and used. The constraint moments at heavily loaded joints are reduced significantly. Moreover, the maximum values of driving torques are also reduced at joints. It is observed that teaching–learning-based optimization gives better results with less computational effort vis-à-vis genetic algorithm for the manipulator optimization problem formulated. The teaching–learning-based optimization algorithm introduced first time for manipulator balancing as optimization solver.

This paper proposes the synthesis of a human knee exoskeleton using the reduced number of necessary and sufficient constraints. The proposed human knee exoskeleton consists of a defect-free crank-rocker mechanism capable of accurately moving its coupler point along the prescribed trajectory. For synthesizing the crank-rocker mechanism based on proposed reduced number of constraints, an optimization problem is formulated to synthesize the mechanism. An algorithm based on teaching-learning-based-optimization (TLBO) is presented to solve this highly nonlinear optimization problem. The optimization minimizes the error between generated and prescribed trajectory and simultaneously avoids any defect in the synthesized mechanism. The penalty method is used to manage all the constraints. Besides a realistic nontrivial example of human knee flexion/extension, a straight line trajectory is also considered to demonstrate the effectiveness of the refinement scheme in the optimal syntheses of planar crank-rocker linkage free from all defects. The optimization problem is solved using well-established nature-inspired algorithms such as genetic algorithm (GA), particle swarm algorithm (PSO), and teaching-learning-based-optimization (TLBO) algorithm. It is found that TLBO is computationally more efficient than other algorithms used here. Additionally, the proposed human knee exoskeleton model is experimentally validated for one gait cycle.

The interaction of α‐synuclein (αS) with membranes is thought to be critical in the etiology of Parkinson's disease. Besides oligomeric αS aggregates that possibly form membrane pores, the aggregation of αS into amyloid fibrils has been reported to disrupt membranes. The mechanism by which aggregation affects the integrity of membranes is, however, unknown. Here, we show that whereas mature αS fibrils only weakly adhere to POPC/POPG giant unilamellar vesicles (GUVs), fibrillization of αS on the membrane results in large‐scale membrane remodeling. Fibrils that grow on the vesicle surface stiffen the membrane and make the initially spherical membrane become polyhedral. Additionally, membrane‐attached fibrils extract lipids. The lipid extraction and membrane remodeling of growing fibrils can consume the complete bilayer surface and results in loss of vesicle content. These observations suggest that there are several mechanisms by which growing fibrils can disrupt membrane function.

The set of measured data points acquired from the Coordinate Measuring Machine (CMM) need to be processed and analyzed for evaluating the form errors inside the manufactured components. This paper presents a modified algorithm of particle swarm optimization (MPSO) for assessing the form error from the set of coordinate measured data points. In the classical algorithm of the particle swarm optimization (PSO), the value of the candidate solution is updated from its existing value without actually comparing the value obtained in the consecutive iterations for fitness. This behaviour attributes to a lack of exploitation ability in the defined search space. The proposed algorithm generates new swarm position and fitness solution for the objective function through an improved and modified search equation based on a proposed heuristic step. In this step, the swarm searches around the best solution of the previous iteration for improving the swarm exploitation capability. The particle swarm uses greedy selection procedure to choose the best candidate solution. A non-linear minimum zone objective function is formulated mathematically for different types of form errors and then optimized using proposed MPSO. Five benchmark functions are used to prove the effectiveness of the modified algorithm, which is verified by comparing its solution and convergence with those obtained from the established algorithms namely PSO and genetic algorithm (GA). Finally, the result of the proposed algorithm for form error evaluation is compared with previous work and other established nature-inspired algorithms. The results demonstrate that the proposed MPSO algorithm is more efficient and accurate than the other conventional heuristic optimization algorithms. Furthermore, it is well suited for form error evaluation using CMM acquired data.

Flexible Graphene piezoresistive MEMS force sensors have been attracting wide attention for applications in bio-medical robots, robotic flies, minimal invasive surgeries and soft robotics for micromanipulation because of their flexibility, high sensitivity and temperature compatibility. The key objective of this research is to analyze the effect of temperature on sensitivity on the designed and simulated MEMS Force sensor through finite element analysis. In this work, flexibility is introduced in MEMS force sensor by utilizing polydimethylsiloxane (PDMS) as substrate material and graphene as piezoresistors. The designed model of force sensor detects the applied force in micro-Newton range by utilizing piezoresistive sensing mechanism connected in Wheatstone bridge configuration. At 0°C,40°C and 80°C the reported sensitivity are 2.24 mV/μN,2.26mV/μN and 2.28mV/μN respectively. So, it can be concluded that as the temperature increases in graphene piezorsesitive based force sensor sensitivity enhances by 0.89% which makes it reliable for harsh conditions

The pattern of changing hydro-geochemical characteristics of water in Himalayan rivers is primarily controlled by sediment load from natural sources in higher altitudes and anthropogenic sources such as the burning of fossil fuels for domestic use, vehicular emissions, and wind transported industrial pollutants in the downstream region. The assessment of water quality is critical for the comparison of natural and anthropogenic sources in the downstream areas due to the dependence of the population on the glacial meltwater for freshwater supply. In the present study, we investigate the physical and ionic characteristics of glacial meltwater from Naradu Glacier catchment concerning the dominant weathering process. The freshwater samples were collected during the ablation period of 2016 and 2017 from specified locations. The physical parameters (pH, electrical conductivity, and temperature) were measured in the field while the analyses for concentrations of major cations (Ca2+, Mg2+, K+, Na+) and major anions (Cl−, SO42−, HCO3−, NO3−) were done in the laboratory. The anions (HCO3− > SO42− > Cl− > NO3−) and cations (Ca2+ > Mg2+ > Na+ > K+) concentrations were observed to have similar trends for both of the ablation period. The statistical analysis shows the predominance of geological weathering processes in the catchment as the controlling factor for the variation in concentration of different ionic species. The catchment was found to be rich in rocks with carbonate mineral making the Ca2+ and HCO3− the most dominant ions in the glacial meltwater.

Tillage system design is one of the important areas of interest for farming community. Oscillatory tillage is one such area which reduces the draft consumption and plays a crucial role to farmers during soil manipulation process. The paper deals to design a vibratory mechanism to provide a continuous motion to the tillage tool for following a particular path adopted from the literature through proper synthesis theory and procedure. A four bar mechanism is designed through proper synthesis procedure to identify the dimensions. Analytical and optimal synthesis method is followed during the design process. Optimization algorithms such as hybrid teaching–learning particle swarm optimization based algorithm (HTLPSO), teaching–learning based algorithm, and particle swarm optimization is used to find the values of the design variables. MATLAB is the software used for the synthesis and analysis process. It is observed in the study that designed four mechanism follows the required path for vibratory tillage operation. The results attained through optimization algorithm in HTLPSO performed better for the required path than other nature-inspired algorithms. Also the developed vibratory cultivator performed better in the field trials.

The paper presents to design a vibratory mechanism for agricultural tillage operation. Agriculture tillage plays an important role for crop cultivation. There are different unit farm operations. Tillage is the mechanical manipulation of soil to obtain suitable soil condition for seed germination and growth. There are two basic soil manipulation processes such as primary and secondary. Draft consumption plays a vital role while performing the operations. Vibratory tillage concept was introduced since 1955 which results in low draft consumption. Tools oscillate in a particular mode of oscillation with certain amplitude and frequency along with the implement forward motion. The objective of the paper is to design an optimal four-bar mechanism for vibratory agricultural tillage operation to trace the desired experimental trajectory by the tillage tool in the field.