Patterns of trait associations in various wheat populations under different growth environments

Five wheat populations were investigated for two years to explore the pattern of trait associations and their contribution to grain yield. The correlation pattern between two traits and their association with grain yield was similar in CIMMYT and Pakistani germplasm. Indian germplasm had different pattern of trait association from those of CIMMYT and Pakistani germplasm. The number of kernels per plant, number of spikes per plant, spike length and spike dry weight were the major yield contributing traits in CIMMYT, Pakistani and ICARDA genotypes. In Indian and miscellaneous genotypes, the number of kernels per plant and number of spikes per plant were the only traits with a positive effect on grain yield. Furthermore, three traits, the number of kernels per plant, the number of spikes per plant and the spike dry weight appeared to have positive effect on grain yield and other major yield traits. Spike density had a negative effect on grain yield in CIMMYT germplasm in dry season. Chlorophyll contents showed no correlation with grain yield in all populations.Key words: Pakistani, CIMMYT, genotypes, wheat, ICARDA, populations

Review on electrical impedance tomography: Artificial intelligence methods and its applications

© 2019 by the authors. Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper, a comprehensive review of efforts and advancements undertaken and achieved in recent work to improve this technology and the role of artificial intelligence to solve this non-linear, ill-posed problem are presented. In addition, a review of EIT clinical based applications has also been presented

Advanced Particle Swarm Optimization Algorithm with Improved Velocity Update Strategy

© 2018 IEEE. In this paper, advanced particle swarm optimization Algorithm (APSO) with improved velocity updated strategy is presented. The algorithm incorporates an improved velocity update equation so that the particles will reach the optimum point quickly and convergence is much faster than the standard PSO (SPSO) and other improved PSOs in the literature. Five benchmark functions have been selected to evaluate the efficiency of the proposed algorithm. The simulation results demonstrate that the proposed technique has remarkably improved in terms of convergence and solution quality

A hybrid advanced PSO-neural network system

© 2019 IEEE. In this paper, a combination of Advanced Particle Swarm Optimization (APSO) and Neural Network are presented to compensate the drawbacks of both the techniques and utilize the strong attributes to form a hybrid system called Hybrid Advance Particle Swarm Optimization-Neural Network System (HAPSONNS). APSO is used for the training of the neural network. In the initial phases of the search, PSO has swift convergence for global optimum, but later it suffers from slow convergence around the global optimum position. On the contrary, the gradient method attains prior to convergence around the global optimum point, therefore, attaining better accuracy in terms of convergence. This paper elucidates the usage of APSO applied to feedforward neural network to improve the classification accuracy of the network and also decreases the network training time

A modified particle swarm optimization algorithm used for feature selection of UCI biomedical data sets

© 2019 IEEE. In biomedical and health care applications, classification examination is extensively used to help diagnose health problems, decision making and enhance standards of patient care. Feature selection is a significant data pre-processing method in classification problems. Training of the data is achieved by using a subset dataset from the UCI biomedical database. If the training dataset comprises inappropriate features, classification analysis resulted in inaccurate and incomprehensible results. In data mining, feature subset selection is data pre-processing phase that is of enormous importance. In this paper, for selecting a minimum number of features K-Nearest Neighbour (KNN) classifier is presented with a modified particle swarm optimization (MPSO) to obtain good classification precision. The proposed method is applied to three UCI medical data sets and is compared with other feature selection approach available in the literature. Results demonstrate that the feature subset recognized by the presented MPSO with KNN neighbor classifiers give better results and accuracy as compared to the other techniques

ВЛИЯНИЕ КАРТИНЫ МИРА НА КУЛЬТУРНУЮ ИДЕНТИЧНОСТЬ

This article is devoted to the study of the concept of cultural identity. It examines in detail the study of this concept in psychology, philosophy, as well as in other socio-humanitarian sciences. The study of this term is considered in more detail in the works of E. Erickson, Freud, J. Mead, I. Hoffmann, representatives of symbolic interactionism, as well as Russian psychology.It is shown how the cultural picture of the human world has changed over the centuries, starting from Antiquity, the Middle Ages, the Renaissance, and ending in the 16th-17th centuries.Separately, such a concept as "alien" is considered, which forms a person's idea of his own identity. This article also highlights the difference between social and personal iden-tity.In addition, this article shows how changing a person's attitude towards their own body and appearance, as well as concern for health, influenced a person's identity.Данная статья посвящена исследованию понятия культурной идентичности. В ней подробно рассматривается изучение данного понятия в психологии, философии, а также в других социогуманитарных науках. Более детально рассматривается изу-чение данного термина в трудах Э.Эриксона, Фрейда, Дж. Мида, И. Гофмана, пред-ставителей символического интеракционизма, а также отечественной психологии.Показано, как менялось культурная картина мира человека на протяжении веков, начиная с Античности, Средних веков, эпохи Возрождения, и заканчивая XVI-XVII вв.Отдельно рассматривается такое понятие, как «чужой», формирующее у человека представление о собственной идентичности. Также в данной статье отмечена раз-ница между социальной и личностной идентичностью.Помимо этого, в данной статье показано, как изменение отношения человека к соб-ственному телу и внешнему виду, а также забота о здоровье влияли на идентич-ность человека.Ключевые слова

A novel hybrid gravitational search particle swarm optimization algorithm

Particle Swarm Optimization (PSO) algorithm is a member of the swarm computational family and widely used for solving nonlinear optimization problems. But, it tends to suffer from premature stagnation, trapped in the local minimum and loses exploration capability as the iteration progresses. On the contrary, Gravitational Search Algorithm (GSA) is proficient for searching global optimum, however, its drawback is its slow searching speed in the final phase. To overcome these problems in this paper a novel Hybrid Gravitational Search Particle Swarm Optimization Algorithm (HGSPSO) is presented. The key concept behind the proposed method is to merge the local search ability of GSA with the capability for social thinking (gbest) of PSO. To examine the effectiveness of these methods in solving the abovementioned issues of slow convergence rate and trapping in local minima five standard and some modern CEC benchmark functions are used to ensure the efficacy of the presented method. Additionally, a DNA sequence problem is also solved to confirm the proficiency of the proposed method. Different parameters such as Hairpin, Continuity, H-measure, and Similarity are employed as objective functions. A hierarchal approach was used to solve this multi-objective problem where a single objective function is first obtained through a weighted sum method and the results were then empirically validated. The proposed algorithm has demonstrated an extraordinary performance per solution stability and convergence

OPERATIONS THROUGHPUT AS A DETERMINANT OF GOLDEN-HOUR IN MASS-GATHERING MEDICINE

BACKGROUNDGolden-hour, a time-tested concept for trauma-care, involves a systems approach encompassing healthcare, logistics, geographical, environmental and temporal variables. Golden-hour paradigm in mass-gathering-medicine such as the Hajj-pilgrimage entwines along healthcare availability, accessibility, efficiency and interoperability; expanding from the patient-centric to public-health centric approach. The realm of mass-gathering-medicine invokes an opportunity for incorporating operations-throughput as a determinant of golden-hour for overall capacity-building and interoperability.METHODSGolden-hour was evaluated during the Indian-Medical-Mission operations for Hajj-2016; which established, operated and coordinated a strategic network of round-the-clock medical operations. Throughput was evaluated as deliverables/time, against established Standard-Operating-Procedures for various clinical, investigation, drug-dispensing and patient-transfer algorithms. Patient encounter-time, waiting-time, turnaround-time were assessed throughout echeloned healthcare under a patient-centric healthcare-delivery model. Dynamic evaluation was carried out to cater for variation and heterogeneity.RESULTSMassive surge of 3,94,013 patients comprising 2,25,103 males (57.1%) and 1,68,910 females (42.9%) overwhelmed the throughput capacities of outpatient attendance, pharmacy, laboratory, imaging, ambulance, referrals and documentation. There was delay in attendance, suspicion, diagnosis and isolation of patients with communicable infections. The situational-analysis of operations-throughput highlights wasted turnaround-time due to mobilization of medical-team, diverting critical healthcare resources away from emergency situations.CONCLUSIONTime being a crucial factor in the complexity of medical-care, operations-throughput remains an important determinant towards interoperability of bottlenecks, thereby being a determinant of golden-hour in mass-gathering-medicine. Early transportation of patient to definitive-care reduces treatment initiation-time, notwithstanding logistics of communication, evacuation, terrain and weather being deterministic in outcome. Golden-hour needs to be emphasized under a population-based approach targeting the clientele towards administering first-aid and reaching out to hospital within the golden-hour