Seasonal variation in bacterial contamination of water sources with antibiotic resistant faecal coliforms in relation to pollution

Water sample were collected from piped supplies, surface water and ground water sources in different locations of Lucknow city during summer, monsoon and winter season. Bacteriological quality of samples was determined by enumerating coliform isolated were subject to antibiotic susceptibility test with disc diffusion method. Maximum coliform and faecal coliform contamination were recorded during summer (67% and 75%) and monsoon (67% and 58.3%) while minimum during winter (50% and 50%). All the test isolates exhibited resistance (for nine antibiotics) was shown by river isolates. Antibiotic resistance index (ARI) ranged from 0.050 to 0.150 exhibiting the broad spectrum resistance for 3 to 9 out of 10 antibiotics tested. Occurrence of faecal pollution indicator organisms and multiple antibiotics resistance bacterial population in drinking water is alarming and a sign of potential health with therapeutic problems

Augmented Reality and AI: An Experimental Study of Worker Productivity Enhancement

The purpose of this experimental investigation is to determine how worker productivity may be enhanced by Augmented Reality (AR) and Artificial Intelligence (AI). Participants in the AR condition reported completing tasks 16% faster on average and receiving a high user satisfaction rating of 4.56 out of 5. Participants in the AI condition reported a 4.3 feedback rating and a 13% decrease in task completion time. Surprisingly, productivity increased by a remarkable 22% with an average score of 62 when AR and AI were coupled. These results demonstrate how AR and AI technologies may significantly increase worker productivity in real-world work environments, highlighting their importance for companies looking to maximize labor effectiveness and decision-making procedures

Leveraging Big Data Analytics for Urban Planning: A Study Using the Big Data Analytics Efficiency Test

Data from a variety of sample cities was evaluated as part of a research looking into the integration of big data analytics into urban planning. The goals were to evaluate the impact of data analytics infrastructure, data volume and processing time, urban development initiatives, and data analytics efficiency. The results showed significant differences in data analytics resources across cities, indicating different levels of investment and preparedness for data-driven decision making. It was clear that cities could handle large amounts of data efficiently thanks to their strong data processing skills. Data analytics have an impact on urban development initiatives, highlighting the revolutionary potential of data-driven urban planning. The outcomes of efficiency tests demonstrated how data analytics procedures are useful for improving urban services and for making well-informed judgments. This study offers important new insights into the mechanics of data-driven urban planning and how it can influence how cities develop in the future

IoT and AI Integration: An Experiment on Smart Manufacturing Efficiency in Industry 5.0

In line with the Industry 5.0 paradigm, this empirical research offers a data-driven investigation of the revolutionary effects of combining IoT and AI in smart manufacturing. The findings show a notable 1.52% gain in production efficiency, which is attributed to post-implementation temperature increases of 36.2°C and humidity decreases of 44.8%. A decrease in faults found (2) led to a 0.76% increase in quality scores (93.1) for quality control. With fewer maintenance hours (2.3) and downtime (52 minutes), maintenance practices were more effective. These results highlight the concrete advantages of integrating IoT and AI in smart manufacturing, putting it at the vanguard of Industry 5.0's revolutionary landscape and improving productivity, quality, and maintenance

Analysing Urban Social Networks for Civic Participation: Data-Intensive Insights from the Civic Participation Test

This research delves into the dynamics of civic engagement in urban settings. The sample of participants was broad, with ages ranging from 22 to 40 years, nearly equal gender distribution (52% female, 48% male), and a range of educational backgrounds, including postgraduates (35%), bachelor's degree holders (40%), and high school graduates (25%). Numerous chances for participation exist in urban environments, as seen by our examination of data on civic activities. These include voter registration campaigns, town hall meetings, and community clean-up initiatives. In addition, members' responsibilities in these activities varied: 35% attended events, 15% made financial contributions, and 45% actively volunteered. Our results highlight the significance of social networks in urban civic engagement by showing that individuals with a wide range of social connections were more likely to participate in civic activities. The study's findings highlight the complex nature of civic participation in cities and have applications for encouraging diversity and community building in urban environments

Data Analytics for Dynamic Urban Operations: A Test-Based Study on Data Analytics Efficiency

This paper explores the field of data analytics for dynamic urban operations and provides a systematic analysis of the importance and possible implications of this field. Our investigation indicates significant data volumes in an urban setting that is data-rich: 500 GB are generated by traffic sensors, 300 GB by environmental monitors, 150 GB by mobile apps, and 75 GB by emergency calls. A variety of analytics techniques, each with a different processing time, are built upon these data sources. These techniques include descriptive, predictive, prescriptive, and diagnostic analytics. The outcomes, which include 90% accuracy, an average processing time of 40 minutes, 80% resource utilization, and 4.2 user satisfaction ratings, highlight the benefits of data analytics. According to the comparison study, diagnostic analytics has a score of 7.8, indicating room for development, while prescriptive analytics leads with an efficiency score of 8.4. As urban stakeholders and academics work to improve urban systems and solve urban issues, the results give a thorough understanding of the effectiveness and application of data analytics in the context of dynamic urban operations

Crystal Structure Directed Catalysis by Aluminum Metal-Organic Framework: Mechanistic Insight into the Role of Coordination of Al Sites and Entrance Size of Catalytic Pocket

© 2020 American Chemical Society. The use of metal-organic frameworks (MOFs) in the field of catalysis is growing exponentially due to their high surface area and distinctive active sites. In this work, we report a novel understanding of the active sites responsible for the catalytic activity of aluminum trimesate MOFs and their crystal/framework structure dependency. Here, we have studied the nature of the active sites of Al-MOFs with two different framework structures (MIL-100 and MIL-96). We found that the MOFs with MIL-100 framework structures were highly catalytically active, while the same Al-MOFs with MIL-96 framework structures exhibited poor catalytic activity. This behavior is explained based on the effect of coordinated water molecules on their Brønsted acidity as well as the effect of the coordination of Al sites, specifically hexacoordinated Al3+6c sites and pentacoordinated Al3+5c sites, on their Lewis acidity. The different entrance sizes of the catalytic pocket of MIL-96 and MIL-100 also played critical roles in their catalytic performance

Predictors of outcome in a contemporary cardiac sarcoidosis population: Role of brain natriuretic peptide, left ventricular function and myocardial inflammation

Aims Cardiac sarcoidosis (CS) is a potentially fatal condition that varies in its clinical presentation. Here, we describe baseline characteristics at presentation along with prognosis and predictors of outcome in a sizable and deeply phenotyped contemporary cohort of CS patients. Methods and results Consecutive CS patients seen at one institution were retrospectively enrolled after undergoing laboratory testing, electrocardiogram, echocardiography, cardiac magnetic resonance (CMR) imaging and $^{18}$F‐flourodeoxyglucose positron emission tomography (FDG‐PET) at baseline. The composite endpoint consisted of all‐cause mortality, aborted sudden cardiac death, major ventricular arrhythmic events, heart failure hospitalization and heart transplantation. A total of 319 CS patients were studied (67% male, 55.4 ± 12 years). During a median follow‐up of 2.2 years (range: 1 month–11 years), 8% of patients died, while 33% reached the composite endpoint. The annualized mortality rate was 2.7% and the 5‐ and 10‐year mortality rates were 6.2% and 7.5%, respectively. Multivariate analysis showed serum brain natriuretic peptide (BNP) levels (hazard ratio [HR] 2.41, 95% confidence interval [CI] 1.34–4.31, p = 0.003), CMR left ventricular ejection fraction (LVEF) (HR 0.96, 95% CI 0.94–0.98, p < 0.0001) and maximum standardized uptake value of FDG‐PET (HR 1.11, 95% CI 1.04–1.19, p = 0.001) to be independent predictors of outcome. These findings remained robust for different patient subgroups. Conclusion Cardiac sarcoidosis is associated with significant morbidity and mortality, particularly in those with cardiac involvement as the first manifestation. Higher BNP levels, lower LVEF and more active myocardial inflammation were independent predictors of outcomes

Comparison among Magnus/Floquet/Fer expansion schemes in solid-state NMR.

We here revisit expansion schemes used in nuclear magnetic resonance (NMR) for the calculation of effective Hamiltonians and propagators, namely, Magnus, Floquet, and Fer expansions. While all the expansion schemes are powerful methods there are subtle differences among them. To understand the differences, we performed explicit calculation for heteronuclear dipolar decoupling, cross-polarization, and rotary-resonance experiments in solid-state NMR. As the propagator from the Fer expansion takes the form of a product of sub-propagators, it enables us to appreciate effects of time-evolution under Hamiltonians with different orders separately. While 0th-order average Hamiltonian is the same for the three expansion schemes with the three cases examined, there is a case that the 2nd-order term for the Magnus/Floquet expansion is different from that obtained with the Fer expansion. The difference arises due to the separation of the 0th-order term in the Fer expansion. The separation enables us to appreciate time-evolution under the 0th-order average Hamiltonian, however, for that purpose, we use a so-called left-running Fer expansion. Comparison between the left-running Fer expansion and the Magnus expansion indicates that the sign of the odd orders in Magnus may better be reversed if one would like to consider its effect in order

Augmented Reality and AI: An Experimental Study of Worker Productivity Enhancement

The purpose of this experimental investigation is to determine how worker productivity may be enhanced by Augmented Reality (AR) and Artificial Intelligence (AI). Participants in the AR condition reported completing tasks 16% faster on average and receiving a high user satisfaction rating of 4.56 out of 5. Participants in the AI condition reported a 4.3 feedback rating and a 13% decrease in task completion time. Surprisingly, productivity increased by a remarkable 22% with an average score of 62 when AR and AI were coupled. These results demonstrate how AR and AI technologies may significantly increase worker productivity in real-world work environments, highlighting their importance for companies looking to maximize labor effectiveness and decision-making procedures