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Smart cities must adopt innovative technologies and strategies to boost existing sustainability solutions in order to fight climate change and reduce greenhouse gas emissions. We provide an overview of the existing work on the application of blockchain technologies in conjunction with other digital technologies such as IoT for incentivizing individuals and organizations to engage in more sustainable behaviors. We focus on three main areas in which these digital technologies can encourage actions aimed at reducing environmental impact: low-carbon transportation, energy efficiency, and waste diversion. Some notable examples are The Plastic Bank, ECO-Coin and SolarCoin. By analyzing case studies in which monetary and nonmonetary incentives have successfully demonstrated behavior change, we seek to understand the key elements for implementing blockchain-based solutions. We also identify key directions for future research in this area.

In this paper, we determine the r-dynamic chromatic number of the fan graph Fm,n and determine sharp bounds of this graph invariant for four related families of graphs: The middle graph M(Fm,n), the total graph T(Fm,n), the central graph C(Fm,n) and the line graph L(Fm,n). In addition, we determine the r-dynamic chromatic number of each one of these four families of graphs in case of being m = 1.

In this paper, we determine the r-dynamic chromatic number of the fan graph Fm,n and determine sharp bounds of this graph invariant for four related families of graphs: The middle graph M(Fm,n), the total graph T(Fm,n), the central graph C(Fm,n) and the line graph L(Fm,n). In addition, we determine the r-dynamic chromatic number of each one of these four families of graphs in case of being m = 1.

In this paper, we determine the r-dynamic chromatic number of the fan graph Fm,n and determine sharp bounds of this graph invariant for four related families of graphs: The middle graph M(Fm,n), the total graph T(Fm,n), the central graph C(Fm,n) and the line graph L(Fm,n). In addition, we determine the r-dynamic chromatic number of each one of these four families of graphs in case of being m = 1.

In this paper, we determine the r-dynamic chromatic number of the fan graph Fm,n and determine sharp bounds of this graph invariant for four related families of graphs: The middle graph M(Fm,n), the total graph T(Fm,n), the central graph C(Fm,n) and the line graph L(Fm,n). In addition, we determine the r-dynamic chromatic number of each one of these four families of graphs in case of being m = 1.

In this paper, we determine the r-dynamic chromatic number of the fan graph Fm,n and determine sharp bounds of this graph invariant for four related families of graphs: The middle graph M(Fm,n), the total graph T(Fm,n), the central graph C(Fm,n) and the line graph L(Fm,n). In addition, we determine the r-dynamic chromatic number of each one of these four families of graphs in case of being m = 1.

In this paper, we determine the r-dynamic chromatic number of the fan graph Fm,n and determine sharp bounds of this graph invariant for four related families of graphs: The middle graph M(Fm,n), the total graph T(Fm,n), the central graph C(Fm,n) and the line graph L(Fm,n). In addition, we determine the r-dynamic chromatic number of each one of these four families of graphs in case of being m = 1.

Making Unmanned Aerial Vehicles (UAV) fully autonomous faces many challenges, some of which are connected to the inherent limitations of their on-board resources, such as energy supply, sensing capabilities, wireless characteristics, and computational power. The sensing, communication, and computation Internet of Things (IoT) infrastructure surrounding the UAVs can mitigate such limitations. However, external traffic dynamics, signal propagation, and other poignant characteristics of the IoT infrastructure make it an extremely dynamic and incoherent environment, especially in urban scenarios, thus challenging the use of IoT resources for mission-critical UAV applications. Herein, the concept of information autonomy is introduced to extend autonomy to encompass how information-related tasks are handled in this challenging scenario. In this paper, we motivate the need for 'Information Autonomy' based on our observations from real-world experiments and present a self-adaptive framework for edge-assisted UAV applications. Through our preliminary evaluation, we show that our 'Information Autonomy' framework is capable of handling uncertainties autonomously during run-time.

Consensus, or state machine replication, is critical for the deployment of distributed battlefield systems. Battlefield networks operate in environments with unpredictable wireless connectivity which lead to sparse networks and frequent partitioning, and this makes deploying centralized architectures where nodes require a connection to a remote server unsuitable. The Extended Virtual Synchrony (EVS) model provides membership views which enables a network to reach consensus even after experiencing a series of partitions and mergers. If a node wants to propose state transitions that require nodes that are not currently in its membership view, then the node needs to wait until it reconnects with those nodes. The time the node has to wait to reconnect to the other nodes introduces consensus delays in the network. In this work, we evaluate consensus latency by focusing on these queued state transition proposals due to both network partition characteristics and distributed application/mission design. The key findings of our results show that consensus delay is least affected by network partitioning when the network splits at a rate equal to or less than 1/4 the rate in which partitions merge. Our evaluation results provide application and mission designers guidelines on the tradeoffs between several network characteristics and desired consensus latency properties.

Objectives: 1) To Formulate a normative data of variables affecting the eruption of third molar. 2) To help format a research data to compare variable changes occurring during formative and eruption stage (11-16 years)of third molar. Materials and Methods: The sample comprised of Orthodontic patients at CMCH from 2012-2019, within an age group of 11years -16 years, which was approved by the ethical committee. 40 Male and 40 Female Patient’s Orthopentogram with complete set of teeth were considered in the study. The measurements analysed were: 1) Retromolar space:is distal contact of PMM2(Permanent mandibular second molar) and junction of occlusal plane to the anterior border of the mandible. 2) Gonial Angle: angle between the tangent from inferior border of the mandible and posterior border of the mandible. 3) Mesiodistal width of second molar : greatest distance between mesial and distal portion of the crown. 4) Mesiodistal width of third molar: greatest distance between mesial and distal extension of the crown. 5) Angulation of first and second Molar : the angle formed between the long axis of PMM1 (Permanent mandibular first molar) and PMM2 (Angulation m1m2). 6) Angulation of second molar to base of Mandible: the angle formed between the long long axis of PMM2 and the tangent drawn to the inferior border of the mandible (Angulation m2bm). 7) Nolla’s classification of third Molar. Trophy Dicom; carestream LS 8000c machine programmed with Kodak software was used to record the digital OPG with standard protocol and head position. The 7 Variables were analysed with the Kodak software and the measurements were recorded on excel worksheet. Results: The Data was used for comparison of all variables to explore gender specific differences, only the mesiodistal width was significantly different between the gender. A descriptive table was formulated with a range of measurements for all variables. A correlation between Retromolar space and all other variables was analyse

Objectives: 1) To Formulate a normative data of variables affecting the eruption of third molar. 2) To help format a research data to compare variable changes occurring during formative and eruption stage (11-16 years)of third molar. Materials and Methods: The sample comprised of Orthodontic patients at CMCH from 2012-2019, within an age group of 11years -16 years, which was approved by the ethical committee. 40 Male and 40 Female Patient’s Orthopentogram with complete set of teeth were considered in the study. The measurements analysed were: 1) Retromolar space:is distal contact of PMM2(Permanent mandibular second molar) and junction of occlusal plane to the anterior border of the mandible. 2) Gonial Angle: angle between the tangent from inferior border of the mandible and posterior border of the mandible. 3) Mesiodistal width of second molar : greatest distance between mesial and distal portion of the crown. 4) Mesiodistal width of third molar: greatest distance between mesial and distal extension of the crown. 5) Angulation of first and second Molar : the angle formed between the long axis of PMM1 (Permanent mandibular first molar) and PMM2 (Angulation m1m2). 6) Angulation of second molar to base of Mandible: the angle formed between the long long axis of PMM2 and the tangent drawn to the inferior border of the mandible (Angulation m2bm). 7) Nolla’s classification of third Molar. Trophy Dicom; carestream LS 8000c machine programmed with Kodak software was used to record the digital OPG with standard protocol and head position. The 7 Variables were analysed with the Kodak software and the measurements were recorded on excel worksheet. Results: The Data was used for comparison of all variables to explore gender specific differences, only the mesiodistal width was significantly different between the gender. A descriptive table was formulated with a range of measurements for all variables. A correlation between Retromolar space and all other variables was analyse

Objectives: 1) To Formulate a normative data of variables affecting the eruption of third molar. 2) To help format a research data to compare variable changes occurring during formative and eruption stage (11-16 years)of third molar. Materials and Methods: The sample comprised of Orthodontic patients at CMCH from 2012-2019, within an age group of 11years -16 years, which was approved by the ethical committee. 40 Male and 40 Female Patient's Orthopentogram with complete set of teeth were considered in the study. The measurements analysed were: 1) Retromolar space:is distal contact of PMM2(Permanent mandibular second molar) and junction of occlusal plane to the anterior border of the mandible. 2) Gonial Angle: angle between the tangent from inferior border of the mandible and posterior border of the mandible. 3) Mesiodistal width of second molar : greatest distance between mesial and distal portion of the crown. 4) Mesiodistal width of third molar: greatest distance between mesial and distal extension of the crown. 5) Angulation of first and second Molar : the angle formed between the long axis of PMM1 (Permanent mandibular first molar) and PMM2 (Angulation m1m2). 6) Angulation of second molar to base of Mandible: the angle formed between the long long axis of PMM2 and the tangent drawn to the inferior border of the mandible (Angulation m2bm). 7) Nolla's classification of third Molar. Trophy Dicom; carestream LS 8000c machine programmed with Kodak software was used to record the digital OPG with standard protocol and head position. The 7 Variables were analysed with the Kodak software and the measurements were recorded on excel worksheet. Results: The Data was used for comparison of all variables to explore gender specific differences, only the mesiodistal width was significantly different between the gender. A descriptive table was formulated with a range of measurements for all variables. A correlation between Retromolar space and all other variables was analysed ,t

Objectives: 1) To Formulate a normative data of variables affecting the eruption of third molar. 2) To help format a research data to compare variable changes occurring during formative and eruption stage (11-16 years)of third molar. Materials and Methods: The sample comprised of Orthodontic patients at CMCH from 2012-2019, within an age group of 11years -16 years, which was approved by the ethical committee. 40 Male and 40 Female Patient's Orthopentogram with complete set of teeth were considered in the study. The measurements analysed were: 1) Retromolar space:is distal contact of PMM2(Permanent mandibular second molar) and junction of occlusal plane to the anterior border of the mandible. 2) Gonial Angle: angle between the tangent from inferior border of the mandible and posterior border of the mandible. 3) Mesiodistal width of second molar : greatest distance between mesial and distal portion of the crown. 4) Mesiodistal width of third molar: greatest distance between mesial and distal extension of the crown. 5) Angulation of first and second Molar : the angle formed between the long axis of PMM1 (Permanent mandibular first molar) and PMM2 (Angulation m1m2). 6) Angulation of second molar to base of Mandible: the angle formed between the long long axis of PMM2 and the tangent drawn to the inferior border of the mandible (Angulation m2bm). 7) Nolla's classification of third Molar. Trophy Dicom; carestream LS 8000c machine programmed with Kodak software was used to record the digital OPG with standard protocol and head position. The 7 Variables were analysed with the Kodak software and the measurements were recorded on excel worksheet. Results: The Data was used for comparison of all variables to explore gender specific differences, only the mesiodistal width was significantly different between the gender. A descriptive table was formulated with a range of measurements for all variables. A correlation between Retromolar space and all other variables was analysed ,t