Smart Choices Research Articles

Developing Advanced Navigation Algorithms for Autonomous Vehicles to Enhance Safety and Efficiency in Urban Environments Using Real-Time Sensor Data and Machine Learning

Autonomous vehicles (AVs) have the ability to completely change how people get around cities. They could make transportation safer and more efficient while also reducing pollution and traffic. But their broad utilize depends on the creation of progressed direction frameworks that can dependably discover their way around complicated urban settings. An inventive way to illuminate this issue is portrayed in this think about. It employments real-time sensor information and machine learning to form and utilize cutting-edge direction strategies that work well in cities. There are five primary steps within the study process. To begin with, exhaustive information collection strategies are utilized to urge valuable sensor information from numerous places, such as GPS, LiDAR, webcams, and more. Another, progressed machine learning models are made and instructed with this information so that AVs can make smart choices and find their way around. These models are made to require into consideration things just like the environment, activity, how individuals walk, and other changing factors to form beyond any doubt they work well in complicated urban circumstances. A number of distinctive measures are utilized to judge the suggested algorithms, such as their security, adequacy, steadfastness, and capacity to alter. Re-enactment circumstances are utilized to test the strategies in a run of urban settings, from swarmed city zones to calm roads within the rural areas. When compared to conventional strategies, the comes about appear enormous picks up in following execution. The equations make it more secure and more productive to utilize them in cities. The modern strategies are too compared to current direction frameworks to appear how much way better they are at being precise, adaptable, and fast to reply. The research also finds possible problems and restrictions, like the need for real-time processing and complicated computations, and suggests ways to fix them.

Assessment of reliability and availability of wireless sensor networks in industrial applications by considering permanent faults

SummaryWireless Sensor Networks (WSNs) are critical for communication within a mile radius and industrial applications. These networks are very prone to failure due to their enormous number of nodes and their unique hardware and software restrictions. To make sure network performance, a lot of study needs to be done to improve failure tolerance and stability. This study looks at how to judge the availability and dependability of WSNs that have long‐term issues. The suggested method checks how well a network works in various failure cases by using fault trees and Markov chain analysis. Such methods help us find and study possible failure scenarios and how they might impact the network's dependability in a planned way. The results show that WSNs have major flaws and give useful suggestions for making the systems work better. The findings show that using these evaluation methods may greatly enhance the ability to handle faults, lower the risk of damage, and allow developers of WSNs to make smart choices.

Importance of thoughts—how bias derails smart choices

Importance of thoughts—how bias derails smart choices

Multiview Multistatic vs. Multimonostatic Three-Dimensional Ground-Penetrating Radar Imaging: A Comparison

The availability of multichannel ground-penetrating radar systems capable of gathering multiview, multistatic, multifrequency data provides novel chances to improve subsurface imaging results. However, customized data processing techniques and smart choices of the measurement setup are needed to find a trade-off between image quality and acquisition time. In this paper, we adopt a Born Approximation-based full 3D approach, which can manage multiview-multistatic, multifrequency data and faces the imaging as a linear inverse scattering problem. The inverse problem is solved by exploiting the truncated singular value decomposition as a regularization scheme. The paper presents a theoretical study aimed at assessing how the reconstruction capabilities of the imaging approach depend on the adopted measurement configuration. In detail, the performance achievable in the standard case of multimonostatic, multifrequency data is compared with that provided by a multiview-multistatic, multifrequency configuration, where the data are gathered by considering a progressively increasing number of transmitting antennas. The comparison of the achievable imaging performance is carried out by exploiting the spectral content and the point spread function, which are general tools to foresee the achievable reconstruction capabilities. Reconstruction results related to a numerical experiment based on full-wave data are also provided.

Stochastic Optimization of Distribution Networks: Application of Probabilistic Graphical Models in Electrical Grid Management

In electricity grid management, optimizing distribution networks is a must for making sure that the grid is reliable, efficient, and resilient. Stochastic optimization methods have become very useful for dealing with the unknowns that come up in grid operations because of things like adding green energy, changing demand, and broken equipment. We present a new way to improve distribution networks when there is doubt in this study. It uses probabilistic graphical models (PGMs). Using PGMs lets us describe the complicated connections between loads, producers, and grid infrastructure, as well as the relationships between these parts of the distribution network. By recording these relationships, we can accurately show how unclear the grid is and make smart choices to make it work better. In particular, we use Bayesian networks (BNs) and Markov random fields (MRFs) to describe how the different factors in the network are likely to be related to each other. We show how well our method works by using it on a real-life delivery network problem. We look at a case study of a distribution network that has a lot of green energy sources and changing load levels. We use PGMs to build a statistical model of the distribution network by combining past data, weather forecasts, and real-time measures. Then, we create a stochastic optimization problem to find the best way to reduce the predicted operational cost while still meeting different operational restrictions, like voltage limits, power balance, and equipment limitations. We use advanced optimization algorithms, like stochastic gradient descent and genetic algorithms, to quickly solve the optimization problem that was given. We show that our method works and can be scaled up for handling distribution networks when there is doubt by doing a lot of computer tests and risk analyses. The suggested method can make delivery networks much more reliable, cost-effective, and resilient than traditional linear optimization methods, as shown by our results. Overall, this study shows that probabilistic graphical models can be a very useful tool for managing the electricity grid and finding the best ways to use distribution networks in the face of randomness. Including unknowns in the modeling process helps we make stronger and more dependable choices that will help current distribution systems work well.

Analysis and Statistical Assessment of Liquefaction Potential Using SPT-N Approach in Bhandara Region Maharashtra: Implications for Infrastructure Development

The Bhandara area in Maharashtra, India, has a lot of earthquakes, so its liquefaction potential needs to be studied in detail to help with building infrastructure. This research uses the Standard Penetration Test (SPT-N) method to check how easily the dirt in the area can become liquefied. When SPT-N numbers are added to soil qualities and seismic factors, they are used to figure out how likely it is that the ground will liquefy when it is loaded with earthquake energy. Different types of dirt and different levels are more or less likely to liquefy, according to the results. The makeup of the soil, the level of the groundwater table, and the history of earthquakes are some of the most important things that affect the liquefaction potential. The study finds places where there is a high risk of liquefaction that are also expected to have big building projects. These results make it clear how important it is to include liquefaction mitigating measures when designing and building roads, houses, and other important infrastructure to make sure it is safe and strong. The study also gives a plan for future earthquake risk ratings in the area, showing how important it is to keep an eye on and update records of soil data and earthquakes. Using the SPT-N method along with current geotechnical and earthquake analysis methods, this study gives a full picture of the area's liquefaction risk. The results have big effects on the growth of infrastructure because they help engineers and managers make smart choices about how to lower risks, improve design, and make infrastructure in the Bhandara region last longer and be safer. In the larger field of geotechnical engineering and crisis preparation, this study adds to it by showing how important specific studies are for building strong infrastructure in areas that are prone to earthquakes.

ByteBuddy - Enhanced Storage Device Verification Tool

In today's digital world, it's crucial to know if storage devices are real or fake. ByteBuddy helps with this. It checks how much data a device can hold and how fast it can transfer that data. It does this by filling the device with random data, checking it, and measuring its speed. This helps us know if the device's claimed capacity is real or not. ByteBuddy is important because it helps people make smart choices when buying storage devices. ByteBuddy is not just a tool; it's a game-changer for consumers and data safety. In this paper, we'll dive into ByteBuddy's details to understand how it works, why it's helpful, and what it means for consumers. By learning about ByteBuddy, people can better protect their digital stuff and make wiser buying decisions. Let's explore ByteBuddy, where simple tests lead to big discoveries about storage devices.

Health Literacy: A Two-Way Street

Health information spreads quickly these days, but not all of it is true. That is why it is important to build your health literacy—your ability to find and understand evidence-based health information. When you learn the basics of health topics, it is easier to tell fact from fiction. Understanding science also helps you trust experts like health professionals and scientists. You can boost your health literacy by staying informed, learning how science works, and double-checking new information with knowledgeable and trusted sources. Health experts have responsibilities, too. They should try to explain things clearly using simple words, make themselves available to answer questions, and share their work openly. The more the public understands science, the more they will trust good health advice. If you can learn from reliable sources and make smart choices to the best of your ability, you can help to keep yourself and your community healthy. Working together to increase health literacy can improve lives. Keep reading, asking questions, and learning—a healthy future starts with you!

Smart Homes, Smart Choices: Using Big Data to Boost Energy Efficiency and Environmental Sustainability

The major contributor to home energy consumption is home appliances, comprising about two-thirds of the total energy used. Analyzing electricity usage at the appliance level is crucial due to varying energy consumption levels among different appliances. This analysis allows individuals to either upgrade or minimize the use of inefficient appliances. To prioritize energy conservation, reducing the usage of energy-inefficient household appliances is imperative. Smart meters in a smart home scenario record electricity consumption data. The collected and monitored data from smart meters integrate with Big Data, playing a substantial role in this domain. Our proposed model optimizes electricity usage by identifying and categorizing household energy-inefficient appliances with a novel transition detection algorithm and appliance detection model. Furthermore, Utilizing Appliance-Level power consumption data from the UK-DALE dataset and One vs Rest clustering techniques, the proposed model effectively identifies devices with higher energy consumption. Additionally, the result analysis of the proposed model highlights the advantages of transitioning to energy-efficient devices, reducing both power consumption and financial wastage.

Design of a Meaningful Framework for Time Series Forecasting in Smart Buildings

This paper aims to provide discernment toward establishing a general framework, dedicated to data analysis and forecasting in smart buildings. It constitutes an industrial return of experience from an industrialist specializing in IoT supported by the academic world. With the necessary improvement of energy efficiency, discernment is paramount for facility managers to optimize daily operations and prioritize renovation work in the building sector. With the scale of buildings and the complexity of Heating, Ventilation, and Air Conditioning (HVAC) systems, the use of artificial intelligence is deemed the cheapest tool, holding the highest potential, even if it requires IoT sensors and a deluge of data to establish genuine models. However, the wide variety of buildings, users, and data hinders the development of industrial solutions, as specific studies often lack relevance to analyze other buildings, possibly with different types of data monitored. The relevance of the modeling can also disappear over time, as buildings are dynamic systems evolving with their use. In this paper, we propose to study the forecasting ability of the widely used Long Short-Term Memory (LSTM) network algorithm, which is well-designed for time series modeling, across an instrumented building. In this way, we considered the consistency of the performances for several issues as we compared to the cases with no prediction, which is lacking in the literature. The insight provided let us examine the quality of AI models and the quality of data needed in forecasting tasks. Finally, we deduced that efficient models and smart choices about data allow meaningful insight into developing time series modeling frameworks for smart buildings. For reproducibility concerns, we also provide our raw data, which came from one “real” smart building, as well as significant information regarding this building. In summary, our research aims to develop a methodology for exploring, analyzing, and modeling data from the smart buildings sector. Based on our experiment on forecasting temperature sensor measurements, we found that a bigger AI model (1) does not always imply a longer time in training and (2) can have little impact on accuracy and (3) using more features is tied to data processing order. We also observed that providing more data is irrelevant without a deep understanding of the problem physics.

Smart Choices: Artificial Intelligence in Embryo Selection

Smart Choices: Artificial Intelligence in Embryo Selection

Practical prototype for energy management system in smart microgrid considering uncertainties and energy theft

The conventional electrical grid faces significant issues, which this paper aims to address one of most of them using a proposed prototype of a smart microgrid energy management system. In addition to relying too heavily on fossil fuels, electricity theft is another great issue. The proposed energy management system can simultaneously detect electricity theft and implement demand response tactics by employing time-of-use pricing principles and comparing real electricity consumption with grid data. The system uses the Al-Biruni earth radius (BER) optimization algorithm to make smart choices about how to distribute the load, intending to reduce energy consumption and costs without sacrificing comfort. As a bonus, it considers limitations imposed by battery charging/discharging and decentralized power generation. Incorporating sensors and SCADA-based monitoring, the system provides accurate measurement and management of energy usage through load monitoring and control. An intuitive mobile app also helps consumers connect, allowing for more active participation and better control over energy use. Extensive field testing of the prototype shows that by moving loads from peak period to another off-peak period, electricity expenditures can be reduced by up to 48.45%. The energy theft value was calculated to be 1199 W, proving that the system's theft detection model was effective.

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime.

Genetic resources and breeding approaches for improvement of amaranth (Amaranthus spp.) and quinoa (Chenopodium quinoa).

Nowadays, the human population is more concerned about their diet and very specific in choosing their food sources to ensure a healthy lifestyle and avoid diseases. So people are shifting to more smart nutritious food choices other than regular cereals and staple foods they have been eating for a long time. Pseudocereals, especially, amaranth and quinoa, are important alternatives to traditional cereals due to comparatively higher nutrition, essential minerals, amino acids, and zero gluten. Both Amaranchaceae crops are low-input demanding and hardy plants tolerant to stress, drought, and salinity conditions. Thus, these crops may benefit developing countries that follow subsistence agriculture and have limited farming resources. However, these are underutilized orphan crops, and the efforts to improve them by reducing their saponin content remain ignored for a long time. Furthermore, these crops have very rich variability, but the progress of their genetic gain for getting high-yielding genotypes is slow. Realizing problems in traditional cereals and opting for crop diversification to tackle climate change, research should be focused on the genetic improvement for low saponin, nutritionally rich, tolerant to biotic and abiotic stresses, location-specific photoperiod, and high yielding varietal development of amaranth and quinoa to expand their commercial cultivation. The latest technologies that can accelerate the breeding to improve yield and quality in these crops are much behind and slower than the already established major crops of the world. We could learn from past mistakes and utilize the latest trends such as CRISPR/Cas, TILLING, and RNA interference (RNAi) technology to improve these pseudocereals genetically. Hence, the study reviewed important nutrition quality traits, morphological descriptors, their breeding behavior, available genetic resources, and breeding approaches for these crops to shed light on future breeding strategies to develop superior genotypes.

Smart Choices and the Selection Monad

Describing systems in terms of choices and their resulting costs and rewards offers the promise of freeing algorithm designers and programmers from specifying how those choices should be made; in implementations, the choices can be realized by optimization techniques and, increasingly, by machine-learning methods. We study this approach from a programming-language perspective. We define two small languages that support decision-making abstractions: one with choices and rewards, and the other additionally with probabilities. We give both operational and denotational semantics. In the case of the second language we consider three denotational semantics, with varying degrees of correlation between possible program values and expected rewards. The operational semantics combine the usual semantics of standard constructs with optimization over spaces of possible execution strategies. The denotational semantics, which are compositional, rely on the selection monad, to handle choice, augmented with an auxiliary monad to handle other effects, such as rewards or probability. We establish adequacy theorems that the two semantics coincide in all cases. We also prove full abstraction at base types, with varying notions of observation in the probabilistic case corresponding to the various degrees of correlation. We present axioms for choice combined with rewards and probability, establishing completeness at base types for the case of rewards without probability.

Fault-tolerant practical tracking control for uncertain robotic system with general reference trajectory and output constraint

This paper is devoted to the fault-tolerant tracking control for a class of uncertain robotic systems under time-varying output constraints. Notably, both actuator fault and the disturbances are present while all the dynamic matrices are not necessarily to be parameterized by unknown parameters or have known nominal parts, and moreover, the reference trajectories as well as the output constraints functions are not necessarily twice continuously differentiable without any time derivatives of them being available for feedback. These remarkable characteristics greatly relax the corresponding assumptions of the related literature and in turn to bring the ineffectiveness of the traditional schemes on this topic. For this, a powerful adaptive control methodology is established by incorporating adaptive dynamic compensation technique into the backstepping framework based on Barrier Lyapunov functions. Then, an adaptive state feedback controller with the smart choices of adaptive law and virtual controls is designed which guarantees that all the states of the closed-loop system are bounded and the system output practically tracks the reference trajectory while not violates the output constraints.

Multimedia Technology Based Interactive Translation Learning for Students

Multimedia technology incorporates into the educational arena to translate traditional educational material into interactive digital mode. This has permitted teachers into the learning environment to design and integrate interactive multimedia learning. Many problems towards giving more attention to students facing teacher's multimedia levels are uneven, and their integration significance, bringing enormous curriculum learning strategies, is not entirely apparent. This research introduces multimedia network interpretation teaching using machine learning (MNIT-ML)in multimedia education design to enhance and strengthen the traditional teaching process and promote various modern approaches to transmit knowledge towards students.Allocation of learning resources (ALR) framework is mainly to enlarge the use and utilization of materials for learning activities from impressed resources into recordings, video content, motion graphics, and other forms of resources. The purpose of the Allocation of Learning Resources (ALR) framework is to aid teachers in making sound decisions about how to distribute available educational materials. Its goal is to guide teachers in making smart choices about how to use limited resources like time, money, and technology in order to improve students' educational achievements. The ALR framework was picked because it offers a methodical strategy for selecting choices that is founded in educational research and best practices. Resource allocation is a key aspect in influencing student outcomes, and efficient allocation can help guarantee that all students have access to the tools they need to succeed. There are a number of alternative frameworks that might direct studies on the distribution of educational resources. The Technological Acceptance Model (TAM) is a common tool for analysing what factors lead to widespread implementation of educational technology. The goal of other frameworks like the Universal Design for Learning (UDL) framework is to help educators create lessons and methods that are inclusive of students of all backgrounds and abilities. The final decision on which framework to use will be determined by the nature of the research issues and the setting in which they are being investigated. In order to make educated decisions, it is crucial to pick a framework that is appropriate for the research issues at hand and that gives a systematic approach based on established educational best practices. The digital promoting resources for teaching (DPRT) method creates a real environment for students to learn, focusing on enhancing training to make students feel the standard language translation skills. The simulation analysis is performed based on security 94.6%, the performance of 95.9%, and privacy, proving the proposed framework's reliability overall ratio of 93.4%.

Effective environmental education for sustainable development beyond the Plastic Age in South Korea

The skyrocketing single-plastic demand during the coronavirus disease 2019 (COVID-19) pandemic proved the limitation of top-down environmental policies in accomplishing sustainable consumption and waste management. Nevertheless, ‘smart consumers’ can make smart choices for sustainable development. However, a review of the South Korean environmental education curriculum reveals that the current structure heavily leans towards the elementary level and does not enlighten the public on ‘what’ and ‘how’ to live sustainably. This study uses a quick survey to suggest that the effectiveness of environmental education may be enhanced if: (1) guidance on how to live with the natural environment in harmony for collective cooperation towards resilient and sustainable community building is incorporated with a clear purpose, (2) environment-friendly culture-building activities are enforced to institutionalise a new paradigm of sustainability to replace today’s consumerism, and (3) contents are updated regularly to include Earth and environmental science knowledge in the state of art.

Attitudes to adolescent pregnancy among families in the Dominican Republic and El Salvador: insights from a longitudinal study

Over the past few decades growing attention has focused on the perceived challenge of adolescent pregnancy and the need for girls to make ‘smart choices’. This has generated considerable debate particularly because of the failure of many programmes and interventions to consider the structural constraints faced by young women in accessing sexual and reproductive health and rights (SRHR). Yet limited attention has been given to the views and experiences of girls’ parents and caregivers, many of whom were often adolescent parents themselves. We use data from the Real Choices, Real Lives longitudinal study conducted by Plan International to consider how the experiences of girls’ families shape their attitudes to teenage pregnancy in the Dominican Republic and El Salvador. Many families believe girls need to practise abstinence and avoid men and boys but given the lack of provision for SRHR faced by young women this response is not unexpected.

How ‘smart’ are we with smart technology: comparison of water ATMs in Nairobi and Delhi

Abstract New forms of decentralized water infrastructure with digital projects driven by private investment are being introduced in low-income urban neighborhoods in the global south with the aim of increasing the coverage of affordable and safe water. These ‘smart’ water-vending machines are gaining popularity to meet water demand in growing cities as they are low-cost, transparent, revenue generating and replicable. However, field evidence from Nairobi and Delhi shows that similar technologies implemented to solve similar problems in different geographical and social settings can have completely different outcomes. Smart technologies can offer better outcomes when the implementing agencies and policy makers make smart choices about implementation, setting aside utopian hype.