Utilization of Artificial Intelligence Technology in Assisting House Chores

AI is accomplished by the combination of a large amount of input, repetitive analysis, and intelligent algorithms. This enables the program to automatically learn from the trends or features found in the results. Artificial intelligence is also being used in a variety of areas, one of which is schooling. It may also be used to assist households with domestic chores. Technology was developed to improve the life of a large number of citizens. Western technology is heavily reliant on computer technology as a result of a scarcity of human capital. That is why they developed a large number of robotic machines. Technology is extremely beneficial in terms of performance, efficacy, and also cost; utilizing technology is significantly less expensive. Any of the most prominent AI developments are reimagining the consumer electronics market, such as the smarthome. AI has enabled the easy control of household appliances.

Computational observation : Challenges and opportunities of automated observation within algorithmically curated media environments using a browser plug-in

A lot of modern media use is guided by algorithmic curation, a phenomenon that is in desperate need of empirical observation, but for which adequate methodological tools are largely missing. To fill this gap, computational observation offers a novel approach—the unobtrusive and automated collection of information encountered within algorithmically curated media environments by means of a browser plug-in. In contrast to prior methodological approaches, browser plug-ins allow for reliable capture and repetitive analysis of both content and context at the point of the actual user encounter. After discussing the technological, ethical, and practical considerations relevant to this automated solution, we present our open-source browser plug-in as an element in an adequate multi-method design, along with potential links to panel surveys and content analysis. Ultimately, we present a proof-of-concept study in the realm of news exposure on Facebook; we successfully deployed the plug-in to Chrome and Firefox, and we combined the computational observation with a two-wave panel survey. Although this study suffered from severe recruitment difficulties, the results indicate that the methodological setup is reliable and ready to implement for data collection within a variety of studies on media use and media effects.

Computational observation: Challenges and opportunities of automated observation within algorithmically curated media environments using a browser plug-in

A lot of modern media use is guided by algorithmic curation, a phenomenon that is in desperate need of empirical observation, but for which adequate methodological tools are largely missing. To fill this gap, computational observation offers a novel approach—the unobtrusive and automated collection of information encountered within algorithmically curated media environments by means of a browser plug-in. In contrast to prior methodological approaches, browser plug-ins allow for reliable capture and repetitive analysis of both content and context at the point of the actual user encounter. After discussing the technological, ethical, and practical considerations relevant to this automated solution, we present our open-source browser plug-in as an element in an adequate multi-method design, along with potential links to panel surveys and content analysis. Ultimately, we present a proof-of-concept study in the realm of news exposure on Facebook; we successfully deployed the plug-in to Chrome and Firefox, and we combined the computational observation with a two-wave panel survey. Although this study suffered from severe recruitment difficulties, the results indicate that the methodological setup is reliable and ready to implement for data collection within a variety of studies on media use and media effects.

Identification of Cryptosporidium Species and Genotypes in Scottish Raw and Drinking Waters during a One-Year Monitoring Period

ABSTRACT We analyzed 1,042 Cryptosporidium oocyst-positive slides (456 from raw waters and 586 from drinking waters) of which 55.7% contained 1 or 2 oocysts, to determine species/genotypes present in Scottish waters. Two nested PCR-restriction fragment length polymorphism (RFLP) assays targeting different loci (1 and 2) of the hypervariable region of the 18S rRNA gene were used for species identification, and 62.4% of samples were amplified with at least one of the PCR assays. More samples (577 slides; 48.7% from raw water and 51.3% from drinking water) were amplified at locus 1 than at locus 2 (419 slides; 50.1% from raw water and 49.9% from drinking water). PCR at loci 1 and 2 amplified 45.4% and 31.7% of samples containing 1 or 2 oocysts, respectively. We detected both human-infectious and non-human-infectious species/genotype oocysts in Scottish raw and drinking waters. C ryptosporidium andersoni, C ryptosporidium parvum, and the Cryptosporidium cervine genotype (now C ryptosporidium ubiquitum) were most commonly detected in both raw and drinking waters, with C. ubiquitum being most common in drinking waters (12.5%) followed by C. parvum (4.2%) and C. andersoni (4.0%). Numerous samples (16.6% total; 18.9% from drinking water) contained mixtures of two or more species/genotypes, and we describe strategies for unraveling their identity. Repetitive analysis for discriminating mixtures proved useful, but both template concentration and PCR assay influenced outcomes. Five novel Cryptosporidium spp. (SW1 to SW5) were identified by RFLP/sequencing, and Cryptosporidium sp. SW1 was the fourth most common contaminant of Scottish drinking water (3%).

Rational Approximants in Structural Design Reanalysis

The design of mechanical structures often involves the analysis of several candidate designs before a final selection is made. To avoid the high cost of repetitive analysis, rapid reanalysis methods based on power series expansions have been proposed. While these methods can be effective for small design changes, for larger changes poor convergence or divergence can occur. In this paper a reanalysis method based on rational approximants is presented. The method exploits the superior convergence behavior of rational approximants to gain a substantial improvement in convergence and accuracy. The method is applied to reanalysis problems involving linear equations and eigenproblems and is illustrated through representative examples.

FPT: An Integrated Fundamental Parameters Program for Broadband EDXRF Analysis without a Set of Similar Standards

The x-ray fluorescence (XRF) method is well known for its capability to perform fast and accurate quantitative analysis for all elements with atomic numbers greater than ten. Energy dispersive x-ray fluorescence (EDXRF) adds to this capability the benefit of quick qualitative analysis, due to its simultaneous sensitivity to all the elements. The method has the potential for rapid and complete chemical analysis of any sample which arrives on the analytical chemist's doorstep. Although the method has been a productive tool for fast and accurate repetitive analysis of similar samples, its applicability to unique unknowns has been rather limited. The limitation arises from the usual need to calibrate the instrument's response with a set of 6 to 12 standards, whose compositions must be similar to the unknown sample. Anyone who has struggled to develop and maintain such a suite of accurately certified standards knows that a great deal of effort and expense is involved. This effort is well justified when the analyst expects to analyze the same type of material frequently over an extended tine period. However, for a unique sample analysis, the task of developing a suite of similar standards simply makes the analysis impractical. What is needed is a method that requires minimal standards, or uses no standards at all.