Wind power represents one of the most promising sources of renewable energy and improvements to wind turbine design and control can have a significant impact on energy sustainability. This proposal is about a new design for efficient VAWT. Typically, VAWT power output is generated from the difference between the forces on the forward and backward facing blades to the wind direction. That reduces their efficiency as compared to the Horizontal Axis Wind Turbine (HAWT). The current innovation, eliminates the forces on the backward facing blades using dynamic blades which improve their efficiency to be comparablewith the HAWT.
In addition, the turbine is fitted with aerodynamic brakes that safely stop the turbine at low and high wind speeds. This safety feature does not exist in any Vertical Axis Wind Turbine in the market. The innovation received the Accelerator to Commercialization award in 2014 from the state of Ohio and University of Cincinnati. Several small size prototypes were builtwhich validated the concept.
VAWTs are capable of catching wind from all directions which avoid the need for yaw mechanisms, rudders or downwind coning. The electric generators can be positioned near the ground and are easily accessible for maintenance. The new invention will revolutionize thewind turbines andwind farms technology by improving the VAWT efficiency and safety.
A formula has been developed that defines the relativity of time in a novel approach. In the present paper, this is particularized for cases of temporary dilation due to speed and gravity. Using the previous equation, that serves as basis of the theory proposed, an interpretation of the nature of Black Holes, their formation, growth, and dimension can be developed. Which ultimately leads to an alternative understanding of mass and energy.
A formula has been developed that defines the relativity of time in a novel approach. In the present paper, this is particularized for cases of temporary dilation due to speed and gravity. Using the previous equation, that serves as basis of the “Time Theory” proposed, an interpretation of the nature of black holes, their formation, growth, and dimension can be developed. Which ultimately leads to an alternative understanding of mass and energy.
A new formula has been developed that determines the passage of time. In the paper, this is particularized for cases of temporary dilation due to speed and gravity.
Additionally, using the previous equation, an interpretation of the nature of black holes, their formation, growth, and dimension can be developed.
Moreover, and based on all of the above, a different way of understanding mass and space is proposed. Which ultimately implies an alternative expression that relates mass and energy.
The development of complex and dependable systems like autonomous vehicles relies increasingly on the use of systems modeling language (SysML). In fact, SysML has become a de facto standard for systems engineering. With model-driven engineering, a SysML model serves as a reference for the early defect detection of the system under design: the earlier the errors are detected, the less is the cost of handling the errors. Mutation testing is a fault-based technique that has recently seen its applications to SysML behavioral models (e.g., state machine diagrams). Specifically, a system's state-transition design can be fed to a model checker where mutants are automatically generated and then killed against the desired design specifications (e.g., safety properties). In this paper, we present a novel approach based on process mining to improve the effectiveness and efficiency of the SysML mutation testing based on model checking. In our approach, the mutation operators are applied directly to the state machine diagram. These mutants are then fed as traces into a process mining tool and checked according to the event logs. Our initial results indicates that the process mining approach kills more mutants faster than the model checking method.
Artificial Intelligence (AI) is a cognitive science to enables human to explore many intelligent ways to model our sensing and reasoning processes. Industrial AI is a systematic discipline to enable engineers to systematically develop and deploy AI algorithms with repeating and consistent successes. In this paper, the key enablers for this transformative technology along with their significant advantages are discussed. In addition, this research explains Lighthouse Factories as an emerging status applying to the top manufacturers that have implemented Industrial AI in their manufacturing ecosystem and gained significant financial benefits. It is believed that this research will work as a guideline and roadmap for researchers and industries towards the real-world implementation of Industrial AI.
The NATO and the EU Peacebuilding Missions Dataset is created to use fuzzy seta Qualitative Comparative Analysis (fsQCA) analysis as a method of researching how NATO and the EU missions’ outcomes are influences by organizational assets and decision-making in both organizations. Outcome pertaining to these two sets of missions are intended to measure various aspects of organizational efficacy. There are two groups of variables – condition variables and outcome variables. In the next few sections, we will explain how these two groups of variables were generated, what existing sources and datasets were used and how mission indicators were generated. See attached research note for more detailed information.
Condition Sets: Description
By and large conditions sets that have been generated measure organizational assets for these NATO and EU missions, as well as patterns in their decision-making process. Two critical organizational assets used for both sets of missions are their annual operational budget and their annual deployed personnel. The dataset contains two control variables measuring operational legitimacy – number of contributing nations and number of UN resolutions passed in relevance to the situation in the area of deployment for the duration of the EU and NATO Mission.
Operational Duration – duration of the operation (in months). For ongoing missions, we have used December 31, 2018 as the end date. All data reflect occurrences no later than December 31, 2018.
Type of Operation – based on their mandate, operations are classified as civilian (coded as 0), military (coded as 1) and hybrid (i.e. with military and civilian components, coded as 0.5).
Annual Operational Budget – total annual mission budget in USD. Sources include SIPRI yearbook and peace operations database. In cases of missing data from the SIPRI yearbook, mission factsheets and original data from the mission have been used. This latter technique applies for the following missions: AMUK, AVSEC, BAM1, BAM2, CAP1, CAP2, MAM1, NAVF1, NAVF2, TMC1, EUAMI. If data is reported in EUR, average exchange rate for the duration of the mission has been used to convert the cost. Data has been adjusted to reflect operational budget over a 12-month period.
Average Annual Mission Personnel – it reflects the average total number of personnel/ staff supporting the NATO or EU peacebuilding mission per annum. Sources have been collected from SIPRI yearbook based on reportings for actual deployments on the ground. In cases when no data has been reported I the SIPRI yearbook/ peace operations dataset, mission factsheets and original data from the mission have been used. The data has been averaged and adjusted for a 12-month period.
Days to Launch – describes the number of days needed from the time a decision has been made by the IO top decision-making body (the European Council and NAC) to launch the mission to the time that the mission is officially declared “operational.” If no declaration that the mission is “fully operational” exists, landmark indicators that the mission is fully operational include: ceremony on the ground marking the beginning of the mission, the appointment of mission commander or first recoded operational presence involving activity on the ground. Sources include official EU and NATO documents announcing the decision to create the peacebuilding operation as well as official documents, press releases and reports in reliable media outlets (including New Agencies) documenting an event that would indicate the mission is “fully operational.”
Number of Contributing Nations –highest reported number of contributing nations for the duration of the NATO and the EU peacebuilding operation.
UN Security Council Resolutions – total number of UN Security Council (UNSG) resolutions relevant for the area of conflict adopted for the duration of the NATO and the EU mission. In cases when UNSC resolutions are relevant for multiple NATO and EU peacebuilding missions those have been reported to all relevant missions.
Outcome Sets: Description
Outcome sets include various indicators created to measure operational efficacy. They include annual events contributing toward peace, conflict and the mission’s functioning, annual fatalities and annual deaths among mission personnel, as well as annual difference in fatalities. A more detailed description of these indicators is included below:
Annual Peace Events – this is an annual indicator based on chronologically recorded events by the SIPRI yearbook that have contributed for the peace process in the conflict area where NATO and EU mission have been deployed. Examples of peace events include steps taken to contribute to the peace process (e.g. creation of buffer zone, cession of hostilities, meeting intended to cease fire or set up the peace process, political events related to or contributing toward the peace process and successful conclusion of a peace agreement. It may also include a decision of an international body (e.g. UN Security Council, UN General Assembly or UN Secretary General, as well as a decision made by the NATO and the EU D-M bodies that contributes toward the peace process in the areas where the mission operates. For ongoing missions is December 31, 2017 the last date when annual peace events are recoded.
Annual Conflict Events -- this is an annual indicator based on chronologically recorded events by the SIPRI yearbook that have increased the conflict and the conflict potential in the area where NATO and EU mission have been deployed. Instances include resumption of hostilities among warring parties, occurrence of attacks, clashes, eruption of violence, the killing of civilians, military and peacemaking personnel and other violence-related events that contribute toward instability in the mission’s area. For ongoing missions is December 31, 2017 the last date when annual conflict events are recoded.
Annual Mission-related Events -- this is an annual indicator based on chronologically recorded events by the SIPRI yearbook that measures events related to functioning of the mission – the decision to launch, the actual launch, implementation, transfer of authority and/ or mandate, transformation and termination of the mission. It also includes events that reflect decisions made by the contributing nations or sponsoring IOs intended to impact mission’s performances (e.g. decisions related to funding, control and command, transformation of mission mandate and rules and other similar events). For ongoing missions is December 31, 2017 the last date when annual mission-related events are recoded.
Average Annual Fatalities – this indicator reports how many average annual civilian deaths have been recorded for the duration of the mission. The data is drawn from the Armed Conflict Dataset (ACD) managed by the London-based International Institute for Strategic Studies ( https://acd.iiss.org/member/datatools.aspx).
Average Annual Mission Casualties – average annual number of deaths among peacebuilding personnel as reported in SIPRI yearbook/ peace operations database for the duration of the mission. Authors have used discretion to determine the accuracy in cases when there is discrepancy of reported data.
Fatalities Annual Difference – an indicator of differenced annual data of civilian casualties on the ground for the duration of the mission. The indicator is calculated as follows: Differenced Fatalities = Ʃ (CasualtiesY1-Y2 … Casualties Yn-Y(n-1))/ Duration of the mission (in years). It is intended to capture improvement of situation on the ground as a result of presence of the peacebuilding effort.
Condition Sets: Calibration and Rationale
Annual Operational Budget – mission budget reflects resources USD 5 million or less indicate fully out while USD 100 million or more would indicate fully in. A budget of USD 30 should be the watershed borderline of “nether in, not out.” [5-100 million]
Average Annual Mission Personnel – this indicator draws distinction between larger well-resourced missions and smaller missions with limited assets. By and large, missions with 20 personnel or less are fully out, while those with 20,000 or more are fully in. The borderline (net hither in, not out) is 130 people.
Days to Launch – the speed with which the decision is taken indicates how decision-making operated in the case of this mission. D-M that took 5 days or less should be fully out (in, change direction) while D-M 150 days or more should be fully in (out, change direction). 30 days (1 month) should be the neither in, nor out border.
Number of Contributing Nations –control indicator that demotes how high number of contributing nations contribute toward greater legitimacy (30 or more countries marks fully in), while 5 or fewer nations marks fully out. The “nether fully in, nor fully out” is at 15 nations.
UN Security Council Resolutions – total number of UNSC resolutions can vary, fully out is at 0 resolutions while fully in at 50 or more. Since moist of the missions are shorter, Nether fully in, not fully out would be at 8 UNSC resolutions. [Inductive]
Operational Duration – 1 year (12 months) denotes fully out (i.e. short-term mission) while 10 year 120 months denotes fully in; nether in not out would be for missions lasting 5 years (60 months). In other words, a decade is too long, a year is to short, five years is in the middle.
Outcome Variables: Calibration and Rationale
Annual Peace Events – this variable measures the occurrence of peace-related events – 0 events per annum is fully out; 10 events per annum is fully in. 1 event is nether in not out.
Annual Conflict Events -- this variable measures the occurrence of conflict-related events – 0 events per annum is fully out; 10 events per annum is fully in. 1 event is nether in not out.
Annual Mission-related Events -- this variable measures the occurrence of peace-related events – 0 events per annum is fully out; 10 events per annum is fully in. 0.5 event is nether in not out.
Average Annual Fatalities – this set measures average number of annual fatalities for the duration of the mission. Cases with 0 fatalities are fully out; cases with 10,000 fatalities are fully in. 1,000 fatalities represent “nether in, not out” value.
Fatalities Annual Difference – this is an indicator that measures the average year-to-year difference in number of fatalities for the duration of the conflict. -50 casualties is fully out (i.e. average growth of casualties by 50 per annum) as this indicator reflects low mission efficacy. 500 is fully in. This number indicates high efficacy; it denotes an average annual decline of casualties by 500 people. If the average number of casualties remains unchanged, then 0 denotes nether in, nor out.
Average Annual Mission Casualties – this indicator measures average number of annual casualties for the duration of the mission. 0 casualties is fully out; 500 casualties is fully in. 0.5 is nether in, nor out.
Shortly after the comparative analysis of Codding et al. was published, I prepared a comment on the article that I submitted for publication. In response to feedback from the editors, I eventually revised the manuscript substantially. That revised version has now been published. In this paper, I share the original submission of the comment, which focuses on important considerations for future studies of risk-‐ sensitive foraging. Meanwhile, Codding and his colleagues have published a response to my comment. They exhibit some confusion about my position, which they describe as “paradoxical.” In a reply to their response, I have therefore added some clarifying remarks at the end of this paper