Unsupervised Artificial Neural Network Research Articles

Assessing public opinion using self-organizing maps. Lessons from urban planning in Romania

Public consultation is an essential component of the planning process to achieve sustainable development. However, there is still a lack of knowledge related to public engagement in consultation processes on urban planning matters. Thus, the aim of this study was to understand public needs in terms of planning urban space by exploring the content of online comments made by the public. We used, as a case study, urban planning in Bucharest, Romania. We assessed 1879 online comments submitted between 2008 and 2020 through a municipality-set online platform, to 220 out of the 1148 zonal urban plans issued during that period. The self-organizing map (SOM) method, an unsupervised artificial neural network, was used to visualize and cluster the online comments based on their content. Using this method, 13 clusters were found to show the general landscape of topics in online comments. These revealed three main and frequently discussed issues among the public: urban green spaces (i.e., saving or developing new green spaces), protection of cultural heritage (i.e., preserving the cultural heritage of the city) and administrative issues (i.e., lack of confidence in the decision-making and permit granting process). In terms of dynamics over the analyzed time period, the topics evolved from esthetic and cultural concerns to issues relating to administrative shortcomings and lack of urban green spaces. Furthermore, we investigated the relationship between plan characteristics and comments and found that the objective of the plan and its year of approval have a positive influence on the presence and number of comments. These results are important to produce higher quality urban projects that are better aligned with the public needs. We further suggest the need to support a sustainable urban spatial development by incorporating the real public needs.

Tracking the Connection Between Brazilian Agricultural Diversity and Native Vegetation Change by a Machine Learning Approach

In Brazil, agribusiness has a considerable role in the country's GDP. Because of this, the State needs territorial planning to minimize the impacts on natural resources, especially in the Pantanal and Amazon biomes, where agribusiness has expanded. The lower the agricultural diversification, the lower the pattern of land use homogeneity, generally associated with agribusiness, especially when it occupies large areas with more technological productive units. This paper investigates the relationship between spatial diversification patterns and the dynamics of native vegetation in Brazil. We propose a feature engineering and clustering approach for 5570 Brazilian municipalities between 1999 and 2018. It was based on the unsupervised artificial neural network Self-Organizing Map (SOM) to divide the municipalities into homogeneous groups of agricultural products diversity trends. The results were compared with the change in vegetation area using data from the national land use-mapping project called Mapbiomas. The analysis allowed the identification of three different regimes of modification in native vegetation, particularly related to municipalities in Brazil's Midwest and North regions, indicating substantial changes in the Cerrado and Amazon biomes.

ELECTROFACIES DEFINITION AND ZONATION OF THE LOWER CRETACEOUS BARRA VELHA FORMATION CARBONATE RESERVOIR IN THE PRE‐SALT SEQUENCE OF THE SANTOS BASIN, SE BRAZIL

Lower Cretaceous carbonates in the pre‐salt succession in the Santos Basin, eastern Brazil, are highly heterogeneous in terms of their reservoir characteristics as a result of depositional and diagenetic factors. Electrofacies have widely been used for reservoir zonation and, when allied with computer‐based methods such as neural networks, may help with the study of such complex reservoir rocks and with the identification of high‐quality reservoir zones. In this work, an unsupervised artificial neural network known as a self‐organizing map (SOM) was used to carry out a zonation of the pre‐salt carbonates in the Aptian Barra Velha Formation, the main reservoir unit in the Santos Basin. Available data included gramma‐ray, neutron porosity, resistivity deep, sonic, density, photoelectric factor, total porosity and effective porosity profiles from 21 wells together with mineralogical models. Core descriptions and thin section images were used as additional data for the lithological characterization of the electrofacies and consequently for reservoir zonation. A total of four electrofacies were defined from the SOM application, and five reservoir zones were identified.The characterization of the reservoir zones also considered the structural locations of the wells based on the relative depth to top‐ Barra Velha Formation; well locations were classified as structurally high, intermediate or low. Based on the reservoir zone characteristics, the results could be correlated with zonations in previous studies. A general tendency was noted for there to be an increase of finer‐grained sediments in the formation in wells located in structural lows; packstone and mudstone facies were prevalent in these wells and were in general characterized as poor‐quality reservoir rocks. By contrast, the shrubstones and grainstones which were more frequent in structurally high wells comprised higher quality reservoir rocks.The basal reservoir zone showed wide lithological variation compared to the overlying reservoir zones. Grainstone‐dominated facies were identified in the middle of the formation, and the uppermost reservoir zones were characterized by an upward increase in shrubstones and reworked grainstones which in general pointed to better quality reservoirs.

Zooplankton community of Bakreswar reservoir: Assessment and visualization of distribution pattern using self-organizing maps

Self-organizing maps, otherwise known as Kohonen-maps, are one form of unsupervised artificial neural networks that can produce two-dimensional plots from multidimensional data. This tool is especially useful in community pattern analyses and has been previously used in spatial pattern analysis with different perspectives. The present study aims to find zooplankton's community pattern in the Bakreswar reservoir ecosystem. Bakreswar reservoir is a freshwater ecosystem in the Birbhum district of West Bengal, India. The reservoir is primarily used to supply freshwater to the Bakreswar thermal power plant. However, the local villages around the reservoir depend on it for drinking water and fishing sustenance. The data used in this study was collected over two years from three different stations. Thus, in addition to describing the spatial pattern of community distribution of zooplankton groups, the temporal variation was also studied. It is observed in the study that the four major groups of zooplankton – Copepoda, Cladocera, Ostracoda, and Rotifera – react differently to the different environmental attributes. Primarily directed by the physical environmental factors, the effect of the chemical factors on the patterning is also evident from the study. Copepods are the dominant group in the system, closely followed by cladocerans and rotifers. But this observation changes at different stations and throughout the study period. The temperature profiles of the reservoir primarily direct the occurrence of ostracods and rotifers, whereas cladocerans and copepods are inclined more towards a chemical factor directive. Rotifers are dominant in the monsoon, whereas the post-monsoon and winter seasons show an increased presence of copepods and cladocerans. The overall observation that the reservoir's water quality is good, and the trophic structure is healthy is in accordance with previous studies as well.

Modeling intraspecific variation in habitat utilization of the Indo-Pacific humpback dolphin using self-organizing map

Coastal cetaceans are recognized as ecologically important species and have been the target for environmental monitoring programs and conservation strategies. Although supervised models have increasingly been used to better monitor complexity relationships between cetaceans and their habitats, the development of unsupervised learning techniques that have significant advantages in visualizing, grouping and reducing the dimensionality of data has been overlooked. Here, using the unsupervised artificial neural network of self-organizing map (SOM), we examined the intraspecific variation of habitat utilization among three geographically neighboring populations (waters southwest of Hainan Island abbreviated as WS Hainan, Sanniang Bay, and Zhanjiang waters) of the Indo-Pacific humpback dolphin (Sousa chinensis) in the northern South China Sea. The results showed that the population inhabiting in WS Hainan occupied a largest area with scattered patterns comparing to the other two in Sanniang Bay and Zhanjiang waters. The SOM analysis further showed that the examined populations in different waters have distinct habitat characteristics. S. chinensis in WS Hainan was sighted in deeper water with higher salinity, whereas the population in Sanniang Bay inhabited in estuary with lower pH and salinity. More complicated distributed patterns and environmental heterogeneity were observed in Zhanjiang waters, where the dolphins distributed contractively in a small area at the entrance of Zhanjiang Port and dispersed in another large area in Leizhou Bay. Correspondently, part of the sightings in Zhanjiang waters had similar habitat with those in WS Hainan, while others were reported in more inshore, deeper and muddied waters. Based on these findings, we hypothesized that S. chinensis can be divided into two ecotypes: estuarine and non-estuarine. Here, we confirmed that the SOM can well identify the habitat differentiation in S. chinensis, and therefore suggested it is a powerful modeling tool for cetacean monitoring program. Our findings contribute to a specific habitat conservation strategy that one integrated protected area would be better for S. chinensis in Sanniang Bay and Zhanjiang waters, while several small protected areas with connecting corridors were more suitable for population in WS Hainan.

MicroRNA expression is associated with auditory dysfunction in workers exposed to ototoxic solvents and noise.

This study is part of a project on early hearing dysfunction induced by combined exposure to volatile organic compounds (VOCs) and noise in occupational settings. In a previous study, 56 microRNAs were found differentially expressed in exposed workers compared to controls. Here, we analyze the statistical association of microRNA expression with audiometric hearing level (HL) and distortion product otoacoustic emission (DPOAE) level in that subset of differentially expressed microRNAs. The highest negative correlations were found; for HL, with miR-195-5p and miR-122-5p, and, for DPOAEs, with miR-92b-5p and miR-206. The homozygous (mut) and heterozygous (het) variants of the gene hOGG1 were found disadvantaged with respect to the wild-type (wt), as regards the risk of hearing impairment due to exposure to VOCs. An unsupervised artificial neural network (auto contractive map) was also used to detect and show, using graph analysis, the hidden connections between the explored variables. These findings may contribute to the formulation of mechanistic hypotheses about hearing damage due to co-exposure to noise and ototoxic solvents.

3D facies and reservoir property prediction of deepwater turbidite sands; case study of an offshore Niger delta field

The distribution of lithofacies and key reservoir properties such as porosity, net to gross and water saturation is essential to describing reservoir heterogeneity predicted within a clastic deep water reservoir unit in the study area. A systematic analysis of core and gamma-ray wireline logs was used to identify the reservoir unit in this study. The identified reservoir unit was mapped on 3D seismic data, and four lithological and fluid sensitive seismic attributes extracted for the reservoir were fed as inputs into an unsupervised artificial neural network (UANN) analysis to define the depositional pattern of the reservoir unit. Afterwards, a 3D sand facies trend model was built using the facies depositional pattern map and upscaled properties from well data. The generated 3D sand probability model from trend modelling was subsequently used to constrain geostatistical facies modelling of the reservoir unit. The systematic stratal analysis from core, well data and the integration of seismic attributes using UANN reveal the reservoir unit as a system of turbidite lobe sands and turbidite channel levees sands. The 3D Geostatistical facies distribution reveals spatial and vertical facies heterogeneity. It indicates high sand amalgamations and static hydraulic connectivity of turbidite lobe sands in the distal part of the study area. Further geostatistical distribution of reservoir properties conditioned to facies indicated good to excellent reservoir quality within the turbidite lobe sands, moderate to low reservoir quality within turbidite channel sands and poor reservoir quality at the turbidite channel margins and the deep-water shale facies. The 3D facies and reservoir property models built in this study would ensure optimisation of the reservoir unit, enable better estimation of hydrocarbon volumes and serve as input data for flow simulation.

Why is your company not robotic? The technology and human capital needed by firms to become robotic

The impact of companies’ adoption of robotics is increasingly interesting. This study aims to elucidate how the adoption of these technologies will affect companies and society. Companies that use these technologies expect to gain a competitive advantage, but robotization implies risks that must be managed by companies and governments. This research focuses on one of the most sensitive elements of this transformation process—the workforce.First, we analyze the characteristics of the workforce and the degree of adoption of robotics using a sample of 4,640 firms with 26 years of observation. We develop a predictive model using a supervised artificial neural network multilayer perceptron (ANN-MLP) to evaluate a company’s readiness to make this transformation according to its workforce’s characteristics.Second, we focus on the characterization and segmentation of the companies for which the ANN-MLP is unable to correctly predict the degree of adoption of robotics. This classification failure means that there are unidentified factors that determine why a company has a workforce composition and structure that do not correspond to its expected degree of robotization. For this analysis, we investigate the main business indicators of these companies and cluster them using an unsupervised artificial neural network, specifically the Kohonen self-organizing map.Our findings will enable companies to understand the importance of transforming to robotics at the right moment, considering factors such as the optimum structure and composition of the workforce. The combination of technology and human capital is the key to boosting the efficiency of the transformation process toward robotics. At this point, a recommendation model to determine whether the company has sufficient maturity to make the transition is crucial for decision makers.

An Advanced Stochastic Numerical Approach for Host-Vector-Predator Nonlinear Model

A novel design of the computational intelligent framework is presented to solve a class of host-vector-predator nonlinear model governed with set of ordinary differential equations. The host-vector-predator nonlinear model depends upon five groups or classes, host plant susceptible and infected populations, vectors population of susceptible and infected individuals and the predator population. An unsupervised artificial neural network is designed using the computational framework of local and global search competencies of interior-point algorithm and genetic algorithms. For solving the host-vector-predator nonlinear model, a merit function is constructed using the differential model and its associated boundary conditions. The optimization of this merit function is performed using the computational strength of designed integrated heuristics based on interior point method and genetic algorithms. For the comparison, the obtained numerical solutions of networks models optimized with efficacy of global search of genetic algorithm and local search with interior point method have been compared with the Adams numerical solver based results or outcomes. Moreover, the statistical analysis will be performed to check the reliability, robustness, viability, correctness and competency of the designed integrated heuristics of unsupervised networks trained with genetic algorithm aid with interior point algorithm for solving the biological based host-vector-predator nonlinear model for sundry scenarios of paramount interest.

Mapping organizational complexity: a network-based approach to behavior through machine learning tools

The current uncertain, dynamic scenario calls for a systemic perspective when referring to organizational complexity and behavior. Our research contributes to the analysis of organizational complexity through multidimensional behavioral mapping. Our method uses machine learning tools to detect the interconnections between the different behaviors of a person in his/her operating context. First, the research project dealt with prototyping a model to read the organizational behavior, the related detection tool, and a data analysis methodology. It used machine learning tools and ended with a data visualization phase. We set our model to read the organizational behavior by comparing the literature benchmark theories with our field experience. The model was organized around 4 areas and 16 behaviors. These were the basis for singling out the indicators and the questionnaire items. The data analysis methodology aimed at detecting the interconnections between behaviors. We designed it by joining univariate analysis with a multivariate technique based on the application of machine learning tools. This led to a high-resolution network map through three specific steps: (a) creating a multidimensional topology based on a Kohonen Map (a type of unsupervised learning artificial neural network) to geometrically represent behavioral relationships; (b) implementing k-means clustering for identifying which areas of the map have behavior similarity or affinity factors; and (c) locating people and the various identified clusters within the map. The research highlighted the validity of machine learning tools to detect the multidimensionality of organizational behavior. Therefore, we could delineate the networking of the observed elements and visualize an otherwise unattainable complexity through multimedia and interactive reporting. Application in the field of research consisted of the design and development of a prototype integrated with our LMS platform via a plugin. Field experimentation confirmed the effectiveness of the method for creating professional growth and development paths. Furthermore, this experimentation allowed us to obtain significant data by applying our model to several sectors, namely pharmaceutical, TLC, banking, automotive, machinery, and services.

Predictive geologic mapping from geophysical data using self-organizing maps: A case study from Baie Verte, Newfoundland, Canada

Self-organizing maps (SOMs) are a type of unsupervised artificial neural networks clustering tool. SOMs are used to cluster large multivariate data sets. They can identify patterns and trends in the geophysical maps of an area and generate proxy geology maps, known as remote predictive mapping. We have applied SOMs to magnetic, radiometric, and gravity data sets compiled from multiple modern and legacy data sources over the Baie Verte Peninsula, Newfoundland, Canada. The regional and local geologic maps available for this area and knowledge from numerous geologic studies has enabled the accuracy of SOM-based predictive mapping to be assessed. Proxy geology maps generated by primary clustering directly from the SOMs and secondary clustering using a k-means approach reproduced many geologic units identified by previous traditional geologic mapping. Of the combinations of data sets tested, the combination of magnetic data, primary radiometric data and their ratios, and Bouguer gravity data gave the best results. We found that using reduced-to-the-pole residual intensity or using the analytic signal as the magnetic data were equally useful. The SOM process was unaffected by gaps in the coverage of some of the data sets. The SOM results could be used as input into k-means clustering because this method requires no gaps in the data. The subsequent k-means clustering resulted in more meaningful proxy geology maps than were created by the SOM alone. In regions where the geology is poorly known, these proxy maps can be useful in targeting where traditional, on-the-ground geologic mapping would be most beneficial, which can be especially useful in parts of the world where access is difficult and expensive.

Comparison of Invasive Apple Snail (Pomacea canaliculata) Behaviors in Different Water Temperature Gradients

Pomacea canaliculata (known as invasive apple snail) is a freshwater snail native to South America that was introduced into many countries (including Asia and North America) as a food source or for organic farming systems. However, it has invaded freshwater ecosystems and become a serious agricultural pest in paddy fields. Water temperature is an important factor determining behavior and successful establishment in new areas. We examined the behavioral responses of P. canaliculata with water temperature changes from 25 °C to 30 °C, 20 °C, and 15 °C by quantifying changes in nine behaviors. At the acclimated temperature (25 °C), the mobility of P. canaliculata was low during the day, but high at night. Clinging behavior increased as the water temperature decreased from 25 °C to 20 °C or 15 °C. Conversely, ventilation and food consumption increased when the water temperature increased from 25 °C to 30 °C. A self-organizing map (an unsupervised artificial neural network) was used to classify the behavioral patterns into seven clusters at different water temperatures. These results suggest that the activity levels or certain behaviors of P. canaliculata vary with the water temperature conditions. Understanding the thermal biology of P. canaliculata may be crucial for managing this invasive snail.

Integrated mineralogical and geochemical study of the Rapel fluvial system, central Chile: An application of multidimensional analysis to river sedimentation

The mineralogical and chemical composition of the <180 μm fraction of 90 stream sediments collected along the Rapel, Cachapoal and Tinguiririca rivers were studied in order to assess the influence of natural and anthropogenic factors. In order to distinguish major and secondary chemical patterns the data was analyzed using an unsupervised artificial neural network technique. The studied rivers present different chemical signatures, which are associated with geological formations and anthropogenic factors such as mining, agro-industry and urbanization. The upper-middle reaches of the Cachapoal River are characterized by elevated Cu–Mo–As–Sb concentrations that reflect input of mining-derived material. This signature, which is further amplified in the middle reaches of this river due to the influence of the city of Rancagua, masks other signatures such as organic matter-related high B concentrations. The sediments of the Tinguiririca River are strongly depleted in heavy metals, but present high P2O5 concentrations, which reflects the extensive use of phosphates in its floodplain. At the upper reaches of the Rapel River the transition from granitic to sedimentary-dominated bedrock coincides with a marked decrease in P2O5–La–Ce–Y abundances. Similarly, the lower reaches of this river coincide with a marked CaO–MgO increase, which reflects an increase in the availability of CaO-rich detritus associated to a lithological transition. Enrichment of strongly correlated Fe2O3–TiO2–MnO–P2O5–Pb–V–Ni–Cr–Y–Ce–La in the middle-lower reaches of this river is interpreted as reflecting heavy mineral accumulation in point-bar deposits. This is further supported by the mineralogy of the associated samples. Most of the suspended load of the Cachapoal and Tinguiririca rivers settles in the Rapel Lake. Thus the quantity of metals and P2O5 contained in the sediments of this lake is considerable and, together with the very high Cu–Mo–As–Sb concentration of the sediments of the Cachapoal River constitute a potential hazard to the catchment environment.

Interplay of lipid and surfactant: Impact on nanoparticle structure

Liquid lipid nanoparticles (LLN) are oil-in-water nanoemulsions of great interest in the delivery of hydrophobic drug molecules. They consist of a surfactant shell and a liquid lipid core. The small size of LLNs makes them difficult to study, yet a detailed understanding of their internal structure is vital in developing stable drug delivery vehicles (DDVs). Here, we implement machine learning techniques alongside small angle neutron scattering experiments and molecular dynamics simulations to provide critical insight into the conformations and distributions of the lipid and surfactant throughout the LLN. We simulate the assembly of a single LLN composed of the lipid, triolein (GTO), and the surfactant, Brij O10. Our work shows that the addition of surfactant is pivotal in the formation of a disordered lipid core; the even coverage of Brij O10 across the LLN shields the GTO from water and so the lipids adopt conformations that reduce crystallisation. We demonstrate the superior ability of unsupervised artificial neural networks in characterising the internal structure of DDVs, when compared to more conventional geometric methods. We have identified, clustered, classified and averaged the dominant conformations of lipid and surfactant molecules within the LLN, providing a multi-scale picture of the internal structure of LLNs.

Real-Time Outlier Detection Applied to a Doppler Velocity Log Sensor Based on Hybrid Autoencoder and Recurrent Neural Network

This article presents a real-time outlier detection deep-learning (OD-DL)-based method using a hybridized artificial neural network (ANN) approach. We propose an unsupervised ANN scheme that runs in parallel, a denoising autoencoder (DAE) and a recurrent neural network (RNN). The DAE aims to reconstruct relevant/normal input data, whereas it seeks to ignore outliers; the RNN, with a recursive structure, is used to predict time-series data. As measurements arrive, two tasks are performed: 1) the outlier decision, which is based on a reconstruction error and an energy score criteria from the output difference between the DAE and the RNN; and 2) the training procedure for both DAE and RNN. The proposed OD-DL scheme is specifically targeted to address the outlier problem of the data generated by a Doppler velocity log (DVL) sensor installed on board of an autonomous underwater vehicle (AUV) to enhance the AUV navigation system performance. In particular, the DVL data enter into the OD-DL scheme whose output is fed into an AUV navigation system that runs an error-state Kalman filter that integrates the corrected DVL data with the measurements of an inertial measurement unit and a depth meter. The experimental results show that the AUV navigation system with the OD-DL method outperforms in terms of a more accurate estimated position when compared with the case that there is no outlier detection and with the case of a navigation system using a conventional outlier detection method, or other simpler deep-learning methods.

Genetic diversity of weedy rice (Oryza sativa f. spontanea) populations in Sri Lanka: An application of Self Organizing Map (SOM)

Abstract. Weerakoon SR, Somaratne S. 2021. Genetic diversity of weedy rice (Oryza sativa f. spontanea) populations in Sri Lanka: An application of Self Organizing Map (SOM). Asian J Agric 5: 35-43. Weedy rice (WR) (Oryza sativa f. spontanea) has become a major threat in rice cultivation. Discrimination of WR from cultivated rice is difficult since agro-morphology of WR and cultivated rice are overlapping. Molecular markers are useful and can be an informative tool for estimating genetic diversity and relationships in closely related WR eco-types. Self-Organizing Maps (SOM) is an interesting and promising classification tool employing an innovative and data-driven classification method based on unsupervised artificial neural networks. The present study focused on exploring the potential use of SOM to classify WR populations of different eco-climatic zones in Sri Lanka using agro-morphological and molecular data. Separate SOMs for each set of variables, agro-morphological and molecular data were developed. The best SOM was chosen based on the error performance. Findings of SOM analyses showed that certain morphological characters (seedling height, leaf blade width, leaf blade length, culm strength, panicle shattering, seed coat color and leaf angle) and certain molecular characters detected from SSR primers (RM 11, RM 21, RM 14, and RM 280) are important in separation of different WR eco-types satisfactorily. SOM clustering of cultivated, wild, and WR eco-types indicated specific patterns of grouping with respect to climatic conditions of the country. WR eco-types in dry zone and wet zone of the country are closely related to Oryza nivara and O. rufipogon respectively.

Performance Enhancement of Roof-Mounted Photovoltaic System: Artificial Neural Network Optimization of Ground Coverage Ratio

Buildings in hot climate areas are responsible for high energy consumption due to high cooling load requirements which lead to high greenhouse gas emissions. In order to curtail the stress on the national grid and reduce the atmospheric emissions, it is of prime importance that buildings produce their own onsite electrical energy using renewable energy resources. Photovoltaic (PV) technology is the most favorable option to produce onsite electricity in buildings. Installation of PV modules on the roof of the buildings in hot climate areas has a twofold advantage of acting as a shading device for the roof to reduce the cooling energy requirement of the building while producing electricity. A high ground coverage ratio provides more shading, but it decreases the efficiency of the PV system because of self-shading of the PV modules. The aim of this paper was to determine the optimal value of the ground coverage ratio which gives maximum overall performance of the roof-mounted PV system by considering roof surface shading and self-shading of the parallel PV modules. An unsupervised artificial neural network approach was implemented for Net levelized cost of energy (Net-LCOE) optimization. The gradient decent learning rule was used to optimize the network connection weights and the optimal ground coverage ratio was obtained. The proposed optimized roof-mounted PV system was shown to have many distinct performance advantages over a typical ground-mounted PV configuration such as 2.9% better capacity factor, 15.9% more energy yield, 40% high performance ratio, 14.4% less LCOE, and 18.6% shorter payback period. The research work validates that a roof-mounted PV system in a hot climate area is a very useful option to meet the energy demand of buildings.

Planning Adaptive Brachistochrone and Circular Arc Hip Trajectory for a Toe-Foot Bipedal Robot going Downstairs

A novel efficient downstairs trajectory is proposed for a 9 link biped robot model with toe-foot. Trajectory of hip joint plays a significant role towards stability of the robot reason being the center of mass which lies approximately close to the hip position. Brachistochrone is the fastest descent trajectory for a particle moving only under the influence of gravity. In case of downstairs movement of robot, methodology for brachistochrone and circular arc trajectories for hip joint is developed. Here, an adaptive trajectory planning algorithm is implemented along with unsupervised artificial neural network (UANN) based inverse kinematics solution, so that biped robots of varying link lengths, masses can climb down on varying staircase dimensions. Zero Moment Point (ZMP) based COG trajectory is considered and its stability is ensured. Cycloidal trajectory is considered for ankle of the swing leg. Parameters of both cycloid and brachistochrone depends on dimensions of staircase steps. Hence this paper can be broadly divided into 4 steps 1) Developing ZMP based brachistochrone and circular arc trajectory for hip 2) Cycloidal trajectory planning for ankle by taking proper collision constraints 3) Solving Inverse kinematics using UANN 4) Comparison between both proposed hip trajectories. The proposed algorithms have been implemented using MATLAB®.

Classification of the social distance during the COVID-19 pandemic from electricity consumption using artificial intelligence.

SummaryAccurately quantifying the social distancing (SD) practice of a population is essential for governments and health agencies to better plan and adapt restrictions during a pandemic crisis. In such a scenario, the reduction of social mobility also has a significant impact on electricity consumption, since people are encouraged to stay at home and many commercial and industrial activities are reduced or even halted. This paper proposes a methodology to qualify the SD of a medium‐sized city, located in the northwest of the state of Rio Grande do Sul (RS), Brazil, using data of electricity consumption measured by the municipality's energy utility. The methodology consists of combining a data set, and an average consumption profile of Sundays is obtained using data from 4‐months, it is then defined as a high SD profile due to the typical lower social activities on Sundays. An supervised and an unsupervised artificial neural network (ANN) are trained with this profile and used to analyze electricity consumption of this city during the COVID‐19 pandemic. Low, moderate, and high SD ranges are also created, and the daily population behavior is evaluated by the ANNs. The results are strongly correlated and discussed with government restrictions imposed during the analyzed period and indicate that the ANNs can correctly classify the intensity of SD practiced by people. The unsupervised ANN is used more easily and in different scenarios, so it can be indicated for use by public administration for purposes of assess the effectiveness of SD policies based on the guidelines established during the COVID‐19 pandemic.

Communication improvement reduces BPSD: a music therapy study based on artificial neural networks.

Literature showed the effects of music therapy on behavioral disturbances, cognitive functions, and on quality of life in people with dementia. Especially, relational active music therapy approach is oriented to reduce behavioral disturbances increasing communication, especially non-verbal communication. This study aimed at exploring the connection between the baseline characteristics of responders and the positive outcome of the intervention, but also the close relationship between the behavioral disturbances and the core of the therapeutic intervention (the relationship/communication improvement). Linear correlation index between input variables and the presence of a critical improvement of behavioral symptoms according Neuropsychiatric Inventory and a semantic connectivity map were used to determine, respectively, variables predictive of the response and complex connections between clinical variables and the relational nature of active music therapy intervention. The dataset was composed of 27 variables and 70 patients with a moderate-severe stage of dementia and behavioral disturbances. The main predictive factor is the Barthel Index, followed by NPI and some of its sub-items (mainly, Disinhibition, Depression, Hallucinations, Irritability, Aberrant Motor Activity, and Agitation). Moreover, the semantic map underlines how the improvement in communication/relationship is directly linked to "responder" variable. "Responder" variable is also connected to "age," "Mini Mental State Examination," and sex ("female"). The study confirms the appropriateness of active music therapy in the reduction of behavioral disturbances and also highlights how unsupervised artificial neural networks models can support clinical practice in defining predictive factors and exploring the correlation between characteristics of therapeutic-rehabilitative interventions and related outcomes.