Efficiency of plant growth-promoting rhizobacteria (PGPR) for the enhancement of rice growth

Plant growth-promoting rhizobacteria (PGPR) are beneficial bacteria that colonize plant roots and enhance plant growth by a wide variety of mechanisms. The use of PGPR is steadily increasing in agriculture and offers an attractive way to replacechemical fertilizers, pesticides, and supplements. Here, we have isolated and characterized the PGPR from the rhizosphere soil of rice field for the enhancement of growth of rice. Rhizosphere soils were collected from different areas of Mymensingh in Bangladesh. Ten isolates of bacteria, designated as PGB1, PGB2, PGB3, PGB4, PGB5, PGT1, PGT2, PGT3, PGG1 and PGG2, were successfully isolated and characterized. Subsequently, to investigate the effects of PGPR isolates on the growth of rice, a pot culture experiment was conducted. Prior to seeds grown in plastic pots, seeds were treated with PGPR isolates and seedlings were harvested after 21 days of inoculation. Isolates PGB4, PGT1, PGT2, PGT3, PGG1 and PGG2 induced the production of indole acetic acid (IAA), whereas only PGT3 isolate was able to solubilize phosphorus. Most of isolates resulted in a significant increase in plant height, root length, and dry matter production of shoot and root of rice seedlings. Furthermore, PGPR isolates remarkably increased seed germination of rice. Among the ten isolates, PGB4 and PGG2 were found almost equally better in all aspects such as dry matter production, plant height and root length of rice, and IAA production. Isolate PGT3 was also found to be promising in IAA production having an additional property of phosphate solubilization. The present study, therefore, suggests that the use of PGPR isolates PGB4, PGG2 and PGT3 as inoculant biofertilizers might be beneficial for rice cultivation as they enhanced growth of rice, and induced IAA production and phosphorus solubilization

Molecular detection of Vibrio cholerae and Vibrio parahaemolyticus from healthy broilers and backyard chickens for the first time in Bangladesh- A preliminary study

Many of the Vibrio spp. are major public health concerns globally. Vibrio cholerae and Vibrio parahaemolyticus are the etiology of pandemic and epidemic diarrhea and foodborne illness, respectively. Poultry has the potential to harbor pathogenic Vibrio spp., which can have a detrimental impact on public health if they are transmitted to humans. We, therefore, screened 54 cloacal swab samples from healthy chickens (broiler=27, backyard= 27) to detect V. cholerae and V. parahaemolyticus. Vibrio spp. were isolated and identified by culturing, biochemical tests, PCR, and antibiogram profiles were determined by disk diffusion method. By PCR, 29.63% (16/54; 95% CI: 19.14-42.83%) samples were positive for Vibrio spp., where backyard chickens had a significantly higher (p< 0.05) occurrence (44.44%; 27.59-62.69%) than broilers (14.82%; 95% CI: 5.92-32.48%). V. parahaemolyticus was found in 22.22% (6/27; 95% CI: 10.61-40.76%) of backyard chicken samples, which was significantly dominant (p< 0.05) than in broilers (0/27, 0%, 95% CI: 0.00-12.46%). In addition, V. cholerae was positive in 7.41% (2/27; 95% CI: 1.32-23.37%) of broiler, and 14.82% (4/27; 95% CI: 5.92-32.48%) of backyard chicken samples. The toxR gene was found in all V. cholerae isolates, suggesting the presence of other virulence genes, whereas no isolates of V. parahaemolyticus contained the tdh gene. Isolated Vibrio spp. had high to moderate resistance to tetracycline, azithromycin, erythromycin, and streptomycin. The occurrence of antibiotic-resistant V. cholerae and V. parahaemolyticus in broiler and backyard chickens is of public health concern because of the possibility of food chain contaminatio

Data Management in Multicountry Consortium Studies: The Enterics For Global Health (EFGH) Shigella Surveillance Study Example

Background: Rigorous data management systems and planning are essential to successful research projects, especially for large, multicountry consortium studies involving partnerships across multiple institutions. Here we describe the development and implementation of data management systems and procedures for the Enterics For Global Health (EFGH) Shigella surveillance study—a 7-country diarrhea surveillance study that will conduct facility-based surveillance concurrent with population-based enumeration and a health care utilization survey to estimate the incidence of Shigella­-associated diarrhea in children 6 to 35 months old. Methods: The goals of EFGH data management are to utilize the knowledge and experience of consortium members to collect high-quality data and ensure equity in access and decision-making. During the planning phase before study initiation, a working group of representatives from each EFGH country site, the coordination team, and other partners met regularly to develop the data management systems for the study. Results: This resulted in the Data Management Plan, which included selecting REDCap and SurveyCTO as the primary database systems. Consequently, we laid out procedures for data processing and storage, study monitoring and reporting, data quality control and assurance activities, and data access. The data management system and associated real-time visualizations allow for rapid data cleaning activities and progress monitoring and will enable quicker time to analysis. Conclusions: Experiences from this study will contribute toward enriching the sparse landscape of data management methods publications and serve as a case study for future studies seeking to collect and manage data consistently and rigorously while maintaining equitable access to and control of data

Smartphone Based Context Flow Recognition For Outdoor Parking System

Outdoor parking system is one of the most crucial needs for smart cities to find the occupancy of parking in outdoor environments such as roadsides, university campus, and so on. Currently, there are many camera-based and external sensors-based parking systems available. The camera-based parking systems rely on camera set-up which is sophisticated, while sensors-based parking systems require installation of sensors at the parking spots or vehicles. Due to such complication, the deployment and maintenance costs of the existing parking systems are very high. Besides, the need for additional hardware and networks increases the cost and complexity which makes it difficult to use in outdoor environments. The objective of this research is to design a method to automatically detect the flow of a driver’s context for outdoor parking or unparking actions by taking advantage of the rapid deployment of smartphones. The proposed method has three major components, which are (1) signal pre-processing, (2) context recognition, and (3) context flow recognition. The input signals received from the user’s phone are preprocessed to prepare the raw input for further processing. After that, the context recognition component recognises the contexts of drivers. Lastly, context flow recognition detects the flows of activities to conclude whether the driver is parking or leaving the parking place. The driver’s activit ies flow like walking → idle→ driving, walking → driving tells whether the driver is leaving the parking space or parking his/her vehicle

An Approach to Recognize Handwritten Digits Using Machine Learning Classifiers

Handwritten digit recognition is one of the most important issues in the area of pattern recognition researches. There are many uses of handwritten digit recognition such as Bank check processing, sorting postal mail form, phone number data entry are common applications of automatic digit recognition. The sentiment of the problem deceits within the capability to develop an efficient algorithm that can recognize handwritten digits. Usually, these digits are normally found from scanning documents with digital devices. Typically storing handwritten digits such as phone number, bank account number, postal numbers and so is extremely troublesome with human intervention. An efficient handwritten digit recognition can eradicate this hazard. To eliminate the difficulties of recognizing handwritten digits, this paper proposes an approach using machine learning algorithms. The objective of this research is to present a reliable and effective approach to recognize handwritten digits. Several supervised machine learning classifiers were employed for the recognition and their accuracy are compared and discussed. The highest 97.07% accuracy is found by the Random Forest classifier

A Study on the Aspects of Quality of Big Data on Online Business and Recent Tools and Trends Towards Cleaning Dirty Data

The reliability, efficiency, and accuracy of e-business depend on the quality of data that is associated with a buyer, seller, brokers, e-business portals, admins, managers, decision-makers and so on. However, maintaining the quality of data in e-business is very challenging. It is because e-business data typically comes from different communication channels and sources. Integrating and managing the data quality of different sources is generally much troublesome than dealing with traditional business data. Even though there are several data cleaning methods and tools exist those methods and tools have some constraints. None of them directly working, particularly on e-business data that motivates to do research to highlight the aspects of big data quality related to e-business. Therefore, this research demonstrates the problems related to data quality related to online business, discusses the existing literature of data quality, the current tools and techniques that are being used for data quality and provides a research finding highlighting the weaknesses of current tools to address the problem of online business

A Comprehensive Study on the Emerging Effect of Artificial Intelligence in Agriculture Automation

Agriculture is one of the oldest and most important professions in the world. It plays a vital role in the economic sector. The impact and application of artificial intelligence (AI) have been prominent and evident in the agriculture sector. The world population is increasing which will require more food and agricultural products. AI can help us to produce the additional requirement of agricultural products. Agriculture faces many challenges like crop disease, lack of irrigation, water management, the effect on the environment, low output, and improper soil treatment. It can be solved by the applications of AI. The use of AI in soil management, weeding, crop monitoring and disease management can solve the problems of farmers. The application of AI in agriculture is producing more with less manpower, land, and time. AI in agriculture can foster smart farming practices to limit the loss of farmers and give them high returns. This research is conducted to review several key aspects of AI in the field of agriculture. Besides, it has highlighted the anticipation and future scopes and challenges of AI in the agricultural sectors

IoT Based Indoor Object Location Tracking Solution

Internet of Things (IoT) is enhancing the pleasant of present-day life. IoT-based objects tracking is a crying need for a smart indoor environment. In the age of smart cities, there are many applications in which indoor localization can be used for monitoring and tracking objects inside smart buildings. This research study is based on the development of a robust real-time system capable of localizing and tracking objects accurately. Global Positioning Systems (GPS) are typically used for outdoor localization because of their ease of implementation and accuracy of up to five meters. Because of the limited space and the many obstacles in indoor environments, GPS is not an appropriate option for overcoming those obstacles. Thus, tracking objects in an indoor environment is a major challenge, both in terms of accuracy and efficiency. The main objective of this research is to design and develop an IoT-based effective solution for tracking the location of objects indoors using the fingerprinting technique. There are some existing applications for tracking objects in indoor localization. Those existing indoor location tracking technologies' reported pitfalls are expensive infrastructure, high connectivity, and less accuracy. Therefore, we have come up with this proposed algorithm to solve those problems. The proposed approach has the potential to estimate the position and track objects very accurately indoors. The proposed algorithm is applied in two different indoor location simulations. The proposed method has been implemented and experiments have been conducted. Experiment results demonstrate that the proposed approach works very well with wi-fi/LTE collected data

Smartphone-Based Context Flow Recognition for Outdoor Parking System with Machine Learning Approaches

Outdoor parking systems are one of the most crucial needs in a smart city to find vacant parking spaces in outdoor environments, such as roadsides, university campuses, and so on. In a typical outdoor parking system, the detection of a vehicle entering and leaving the parking zone is a major step. At present, there are numerous external sensor-based and camera-based parking systems available to detect the entrance and leaving of vehicles. Camera-based parking systems rely on sophisticated camera set-ups, while sensor-based parking systems require the installation of sensors at the parking spots or vehicles&rsquo; sides. Due to such complication, the deployment and maintenance costs of the existing parking systems are very high. Furthermore, the need for additional hardware and network capacity increases the cost and complexity, which makes it difficult to use for large deployment. This paper proposes an approach for outdoor parking utilizing only smartphone integrated sensors that do not require manpower support nor additional sensor installation. The proposed algorithm first receives sensor signals from the driver&rsquo;s phone, performs pre-processing to recognize the context of drivers, which is followed by context flow recognition. The final result is obtained from context flow recognition which provides the output of whether the driver is parking or unparking. The proposed approach is validated with a set of comprehensive experiments. The performance of the proposed method is favorable as it uses only the smartphone&rsquo;s internal sensors to recognize whether the cars are entering or leaving the parking area

Smartphone-Based Drivers Context Recognition

Various embedded sensors such as accelerometer and gyroscope have opened a new horizon in the scientific studies. One of the most prevailing areas of research is context recognition which can be adopted for smartphone-based parking, road condition detection and sports. To the best of our knowledge, the existing context recognition research covers human’s basic contexts such as walking, jogging and are position dependent that require tightening sensors in fixed position of the body. Furthermore, none of the work has seen to be more specific to detect the contexts of driver. Therefore, to be more specific, in this study, we have constructed a position-independent approach to recognize driver’s contexts that occurs while a driver parks car or leaves from parking place. The support vector machine, random forest and decision tree are employed and the accuracies of 83.38, 93.71 and 98.41% are obtained, respectively