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Abstract: Road networks are the means of transporting, sharing and movement of goods and services from place to place in society. Road networks are also channels of communication in some parts of the world. Consequently, access to good road networks enhances the quality of life and work of people living in society. But the poor nature of design and development of road networks coupled with natural disasters such as heavy rainfall has brought about many unwanted potholes and scratches on the roads which are very dangerous to commuters and other road users as well as vehicles that utilise the roads. In addition, the lack of a proper road maintenance system has resulted in an ever increasing number of potholes which endanger efficient transportation and road safety. Meanwhile road maintenance works has largely depended on manual detection and reporting. The advent of computers, information technologies, network and communication technologies and sensors have brought about a new phenomenon known as “internet of things” also put “internet of everything” (IoT//IoE). The global network of things is the connection of the physical objects to virtual objects in the world. This enables physical world objects to be assigned unique internet protocol addresses with the purpose of sharing and exchanging data and information. The paper conducts a study into the use of internet of things to detect and report potholes on roads. The paper assembles an open hardware equipment and sensor to experiment the detection and reporting of potholes using IoT/IoE enabled devices. The paper presented the architectural design and system to detect, report and manage potholes and other road obstacles using IoT. The paper also presented some prospects and challenges in the implementation of internet of things (IoT).

Keywords: Pothole Detection, Pothole Reporting, Pothole Management System, Internet of Things/Everything

References: 

1. Al-Fuqaha et al., (2015), “Internet of Things: A Survey on Enabling Technologies, Protocols and Applications”, IEEE Communication Survey, Vol. 17, Issue 4
2. Erikson et al., (2008), “The Pothole Patrol: Using a Mobile Sensor Network for Road Surface Monitoring”, Proceedings of 6^th International Conference on Mobile Systems, Applications and Services, USA
3. Kulkarni et al., (2014), “Pothole Detection System Using Machine Learning on Android”, International Journal of Emerging Technology and Advanced Engineering, Vol 4, Issue 7
4. Mednis et al., (2011) “Real Time Pothole Detection Using Android Smartphones With Accelerometers”, International Conference on Distributed Computing in Sensor Systems and Workshops, Barcelona, Spain.
5. Pavan et al., (2014), “Real Time Pothole Tracking System Using Android Smart Phone”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol 3, Issue 5
6. Sudarshan et al., (2009), “Pothole Detection and Warning System Using Wireless Sensor Networks” Embedded Real time systems laboratory, India Institute of Technology, Bombay.
7. Yougtae Jo & Seungki Ryu, (2015), “Pothole Detection System Using a Black-box Camera”, Highway Research Institute, Korea Institute of Civil Engineering and Building Technology
8. Yu, B.X. & Yu, X. (2006), “Vibration Based System For Pavement Condition Evaluation”, Proceedings of 9^th International Conference on Applications of Advanced Technology in Transportation, USA
9. Zoysa et al., (2007), “A Public Transport System Based Sensor Network For Road Surface Condition Monitoring”, Proceedings of Workshop on Networked Systems for Developing Regions, Kyoto, Japan.
10. Zanella et al., (2014), “Internet of Things for Smart Cities”, IEEE Internet of Things Journal, Vol. 1, No.1

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Abstract: The attempt is made along the Cheyyar river basin, Kancheepuram district. The total sample collected along study area is 20. The physio-chemical parameters were tested. The pH are ranges from 6.5-8.1 and they are neutral. The other values such as EC, TDS, Turbidity, Total hardness are within the permissible limit comparing the standard specification. The chemical parameter such as calcium, magnesium value 75% within the permissible limit as specified in standard. The bicarbonate has high value, sodium, chloride and other parameters are as follows. The order of abundance of chemical concentration is Na+> Ca2+> Mg2+> K+ = HCO3−> Cl−> So4. The type of water present in the study area is Ca-Mg-So4 type, Ca-Mg-HCO3 type and Ca-Mg-Cl-So4 type. The Ground water qualities are compared with the Indian Standard and World Health Organization (WHO). The SAR value of the samples ranges from 0.825 to 7.829 (meq/L), Sodium percentage value ranges from 20.376 to 65.007(meq/L) .Then comparing SAR 65% is suitable and by sodium percentage 72.45% of water is suitable for the agriculture.

Keywords: Bicarbonate, Calcium, Chloride, Electrical Conductivity, EDTA (Ethylene Diamine Tetra Acetic acid), Magnesium, NTU (Nephelometric Turbidity Unit), pH, Potassium, SAR (Sodium Adsorption Ratio), Sodium, Sulphate, Total Hardness, Turbidity, Total Dissolved Solids, WHO (World Health Organization).

References: 

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Abstract: The investigation into the study is required to Obtain disconnects were additionally screened for their probiotic properties, for example, protection from low (pH 2.0, 2.5, 3.0), protection from bile salt (0.3%, 0.5%, 1.0%), antimicrobial test, anti-toxin protection test, haemolytic test and starch aging test. On plates the highest antifungal activity was observed with isolates HPoY8B9, HPoY9B10, HCoY2B6 which was found to be 71%, 65%, 60%. Isolates HPoY8B9, HPoY9B10, HCoY2B6 showed good antifungal activity and emulsification index percentage and hence were selected for further studies. Isolates HPoY8B9 showed highest emulsification index of 92% and 93% with coconut oil and engine oil as hydrocarbon source respectively. Whereas isolate HPoY9B10 showed 90%, 85% and isolate HCoY2B6 showed 90%, 91% of emulsification index with coconut oil and engine oil respectively after an incubation period of 5 days. The highest biosurfactant yield from isolates HPoY8B9, HPoY9B10, HCoY2B6was found to be 2.0, 1.8, 1.2 gm/L respectively after 5 days of incubation. Crude biosurfactant extracted from these three isolates was subjected to TLC for the identification of sugars and lipids present. All the Three isolates were then screened for their probiotic properties and were observed to have the capacity to endure against stomach environment(pH 2.0, 2.5, 3.0), they could make due against 0.3%, 0.5%, 1.0% centralization of bile salts. Isolates HPoY8B9, HPoY9B10, HCoY2B6 indicated protection against all the five anti-infection agents tried and were even observed to be non-haemolytic. Of the three isolates tried segregate HPoY8B9 acquired from coconut indicated great antifungal, biosurfactant and probiotics properties and consequently was sent for 18S rRNA sequencing and results uncovered it Saccharomyces cerevisiae HPoY8B9. Molecular Identification of Probiotic Strains Methods utilized for discovery of probiotics in human gastrointestinal tract are ID of province morphology, maturation designs, serotyping or some mix of these. In spite of the fact that these conventional techniques have constraints they are utilized for recognizable proof. With the creating innovation about the sub-atomic writing it is getting more solid to distinguish and separate bacterial strains. Classical microbiological systems are extremely critical for choice, list and biochemical portrayal (maturation profiles, salt-pH temperature resiliences) yet it isn't proficient to order a culture systematically. Atomic portrayal strategies are effective even between firmly related species. There are number of option ordered grouping strategies surely understood incorporating hybridization with species-particular tests and age of profile PCR candidates by species-particular preliminaries. Polymerase chain response based techniques (PCR-RFLP, REP-PCR, PCR ribotyping and RAPD) are predominantly utilized as atomic apparatuses. Examination between these techniques, the most intense and precise one is sequencing.

Keywords: (0.3%, 0.5%, 1.0%), (pH 2.0, 2.5, 3.0), The highest biosurfactant yield from isolates HPoY8B9, HPoY9B10, HCoY2B6was found to be 2.0, HPoY8B9, HPoY9B10, HCoY2B6 HPoY8B9. (PCR-RFLP, REP-PCR, PCR ribotyping and RAPD).

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