Selection Logic Research Articles

Fuzzy Logic in Selection of Maritime Search and Rescue Units

The article discusses methods of ships assessment when determining their suitability for search and rescue action (SAR) at sea. Selection of the most preferable ships is one of the action planning elements. Due to various construction and equipment the civilian ships can only perform rescue task to a certain degree. According to the Multi-Criteria Decision Analysis (MCDA), many parameters and data have to be compared in order to create a ranking of vessels ordered according to the coordinator’s preferences. When data are missing, incomplete or uncertain, a similar effect can be obtained using fuzzy logic. The author discussed the nature of the criteria, evaluation methods and presented a simulation of a ship study using fuzzy logic. The author developed fuzzy rules and presented the principle of operation of the controller. The article deals with the main principles of a decision support system (DSS) for the selection of ships in SAR operations.

How We Approach Difficult to Eradicate Helicobacter pylori

How We Approach Difficult to Eradicate Helicobacter pylori

Constrained Path Planning for Unmanned Aerial Vehicle in 3D Terrain Using Modified Multi-Objective Particle Swarm Optimization

This paper considered the constrained unmanned aerial vehicle (UAV) path planning problem as the multi-objective optimization problem, in which both costs and constraints are treated as the objective functions. A novel multi-objective particle swarm optimization algorithm based on the Gaussian distribution and the Q-Learning technique (GMOPSO-QL) is proposed and applied to determine the feasible and optimal path for UAV. In GMOPSO-QL, the Gaussian distribution based updating operator is adopted to generate new particles, and the exploration and exploitation modes are introduced to enhance population diversity and convergence speed, respectively. Moreover, the Q-Learning based mode selection logic is introduced to balance the global search with the local search in the evolution process. Simulation results indicate that our proposed GMOPSO-QL can deal with the constrained UAV path planning problem and is superior to existing optimization algorithms in terms of efficiency and robustness.

A Farewell to Firearms? The logic of weapon selection in terrorism: the case of jihadist attacks in Europe

ABSTRACT In the vast literature on terrorism the choice of weapons has received relatively limited attention, despite the importance and visibility of this topic. Building on the literature on innovation in terrorism, the article first proposes a multi-level analytical framework that helps study terrorist weapon selection. It then investigates the use of weapons in jihadist attacks in Europe from 2014, with the rise of the so-called Islamic State, until 2020, based on an original database. The empirical analysis shows that the two traditional types of weapon of modern terrorism, firearms and explosives, were largely replaced by more primitive tools like melee weapons. In fact, in recent years jihadist terrorists in Europe have become less technologically advanced. Based on the original analytical framework, the article examines the reasons of this evolution, paying special attention to the use of the most common type of weapon in the database, bladed weapons, and the most lethal type, firearms.

The transition of a socio-technical system to a smart grid: illustrated with the Indian electricity network

PurposeThis paper aims to explain transitions in a socio-technical system characterized by non-economic entities that influence economic activity, i.e. embeddedness and coalitions. The selected socio-technical system is an Indian electric network with an interventionist policy. Its embeddedness and coalitions drive the transition. The insights from such analysis expand socio-technical transition theory and provide valuable insights to practitioners in their policymaking.Design/methodology/approachThe authors need to observe the effects of non-economic institutions in their setting. Moreover, in India, the regional policies influence decision-making; therefore, selected two Indian states. The two Indian states, along with their non-economic entities, provided diverse analytic and heuristic views.FindingsThe findings show that coalitions, with their embeddedness in the absence of any mediating policy systems, act as external pressures and influence innovation and the socio-technical system’s transition trajectory. Their coalitions’ embeddedness follows a shaping, not selection logic. Thereby influence innovations in cumulating as stable designs. Such an approach provides benefits in the short-term but not in the long-term.Research limitations/implicationsThe study selected two states and examined two of the four trajectories. By considering other states, the authors can obtain more renewable energy investments and further insights into the transformational trajectory.Practical implicationsThe study highlights the coalition dynamics specific to the Indian electric power network and its transition trajectories. The non-economic entities influenced transition trajectories, innovation and policymaking of the socio-technical system.Originality/valueThe study expands the socio-technical transition theory by including embeddedness. The embeddedness brings a shaping logic instead of a selection logic.

Influence of coalitions and embeddedness on public innovation: illustrated with the Indian agriculture

Socio-technical systems respond to external pressures by going through transitions. When the socio-technical system involves various coalitions or groups, i.e. the non-economic entities, theories with a broad focus can better explain the transitions. In this paper, the author selected evolutionary economics, neo-institutionalism, and economic sociology to explain the coalition dynamics. The selected socio-technical system, Indian agriculture, relies on public innovation and interacts closely with society. We need to observe these dynamics in the situations where they occur; therefore, a case study method is appropriate. As part of case selection, selected two Indian states to provide the required analytic and heuristic perspectives. The findings indicated that the government’s interventionist approach had no mediating policy systems. With its coalitions and embeddedness influenced the innovation selection logic, system’s transition, and pathways. It provided short-term benefits in social conscience schemes despite innovations cumulating as stable designs. Besides, the system remains in a constant state of a struggle in coming to terms with external pressures.

Highly sensitive broadband binary photoresponse in gateless epitaxial graphene on 4H–SiC

Due to weak light-matter interaction, standard chemical vapor deposition (CVD)/exfoliated single-layer graphene-based photodetectors show low photoresponsivity (on the order of mA/W). However, epitaxial graphene (EG) offers a more viable approach for obtaining devices with good photoresponsivity. EG on 4H–SiC also hosts an interfacial buffer layer (IBL), which is the source of electron carriers applicable to quantum optoelectronic devices. We utilize these properties to demonstrate a gate-free, planar EG/4H–SiC-based device that enables us to observe the positive photoresponse for (405–532) nm and negative photoresponse for (632–980) nm laser excitation. The broadband binary photoresponse mainly originates from the energy band alignment of the IBL/EG interface and the highly sensitive work function of the EG. We find that the photoresponsivity of the device is > 10 A/W under 405 nm of power density 7.96 mW/cm2 at 1 V applied bias, which is three orders of magnitude greater than the obtained values of CVD/exfoliated graphene and higher than the required value for practical applications. These results path the way for selective light-triggered logic devices based on EG and can open a new window for broadband photodetection.

Bridging the model-to-code abstraction gap with fuzzy logic in model-based regression test selection

Regression test selection (RTS) approaches reduce the cost of regression testing of evolving software systems. Existing RTS approaches based on UML models use behavioral diagrams or a combination of structural and behavioral diagrams. However, in practice, behavioral diagrams are incomplete or not used. In previous work, we proposed a fuzzy logic based RTS approach called FLiRTS that uses UML sequence and activity diagrams. In this work, we introduce FLiRTS 2, which drops the need for behavioral diagrams and relies on system models that only use UML class diagrams, which are the most widely used UML diagrams in practice. FLiRTS 2 addresses the unavailability of behavioral diagrams by classifying test cases using fuzzy logic after analyzing the information commonly provided in class diagrams. We evaluated FLiRTS 2 on UML class diagrams extracted from 3331 revisions of 13 open-source software systems, and compared the results with those of code-based dynamic (Ekstazi) and static (STARTS) RTS approaches. The average test suite reduction using FLiRTS 2 was 82.06%. The average safety violations of FLiRTS 2 with respect to Ekstazi and STARTS were 18.88% and 16.53%, respectively. FLiRTS 2 selected on average about 82% of the test cases that were selected by Ekstazi and STARTS. The average precision violations of FLiRTS 2 with respect to Ekstazi and STARTS were 13.27% and 9.01%, respectively. The average mutation score of the full test suites was 18.90%; the standard deviation of the reduced test suites from the average deviation of the mutation score for each subject was 1.78% for FLiRTS 2, 1.11% for Ekstazi, and 1.43% for STARTS. Our experiment demonstrated that the performance of FLiRTS 2 is close to the state-of-art tools for code-based RTS but requires less information and performs the selection in less time.

Neural Network Training Acceleration With RRAM-Based Hybrid Synapses.

Hardware neural network (HNN) based on analog synapse array excels in accelerating parallel computations. To implement an energy-efficient HNN with high accuracy, high-precision synaptic devices and fully-parallel array operations are essential. However, existing resistive memory (RRAM) devices can represent only a finite number of conductance states. Recently, there have been attempts to compensate device nonidealities using multiple devices per weight. While there is a benefit, it is difficult to apply the existing parallel updating scheme to the synaptic units, which significantly increases updating process’s cost in terms of computation speed, energy, and complexity. Here, we propose an RRAM-based hybrid synaptic unit consisting of a “big” synapse and a “small” synapse, and a related training method. Unlike previous attempts, array-wise fully-parallel learning is possible with our proposed architecture with a simple array selection logic. To experimentally verify the hybrid synapse, we exploit Mo/TiOx RRAM, which shows promising synaptic properties and areal dependency of conductance precision. By realizing the intrinsic gain via proportionally scaled device area, we show that the big and small synapse can be implemented at the device-level without modifications to the operational scheme. Through neural network simulations, we confirm that RRAM-based hybrid synapse with the proposed learning method achieves maximum accuracy of 97 %, comparable to floating-point implementation (97.92%) of the software even with only 50 conductance states in each device. Our results promise training efficiency and inference accuracy by using existing RRAM devices.

A novel model-based multivariable framework for aircraft gas turbine engine limit protection control

Control technologies are innovated to satisfy increasingly complicated control demands of gas turbine engines. In terms of limit protection control, a novel model-based multivariable limit protection control method, which is achieved by adaptive command reconstruction and multiple-control loop selection and switch logic, is proposed in this paper to address the problem of balancing smaller thrust loss and safe operations by comparing with widely-used Min-Max logic. Five different combination modes of control loops, which represent the online control loop of last time instant and that of current time instant, is analyzed. Different command reconstructions are designed for these modes, which is based on static gain conversion of amplitude beyond limits by using an onboard model. The double-prediction based control loop selection and switch logic is developed to choose a control loop appropriately by comparing converted amplitude beyond limits regardless of one or more parameters tending to exceed limits. The proposed method is implemented in a twin-spool turbofan engine to achieve limit protection with direct thrust control, and the loss of thrust is improved by about 30% in comparison with the loss of thrust caused by Min-Max logic when limit protection control is activated, which demonstrates the effectiveness of the proposed method.

Dynamic zone selection for busbar protection Using graph theory and path analysis

This paper presents a detailed description of a dynamic zone selection approach for busbar protection using path analysis and graph theory. The main benefit is that the dynamic zone selection logic equations for busbar protection applications are automatically obtained by an offline software-based tool. The method accounts for normal busbar-switching sequences, in-service transfer, bypassed current transformers, breaker substitution, paralleled busbars, and a check zone. The equations can be set up and executed in existing protective relays. The method was tested in a busbar arrangement for different switching scenarios and fault conditions, and the results have proven its effectiveness.

Anti-unwinding sliding mode attitude control via two modified Rodrigues parameter sets for spacecraft

In this paper, the attitude maneuver control without unwinding for rigid spacecraft is addressed based on the modified Rodrigues parameters (MRPs). First of all, an attitude control system model for rigid spacecraft is constructed by designing a selection logic for the MRP set and its shadow set. To guarantee the asymptotic stability in the large and unwinding-free performance of the closed-loop system on the sliding phase, a sliding function is presented. Then, a sliding mode control law is developed to ensure that the system states are driven to the sliding surface. Moreover, a dynamic parameter is designed in the proposed control scheme, such that the unwinding phenomenon is avoided when the system states are outside the sliding surface. Further, a boundary layer is introduced for the constructed controller to avoid the chattering phenomenon. It is also proven that the convergence property and anti-unwinding performance are ensured when the system states are within the boundary layer. Numerical simulations are performed to demonstrate the unwinding-free performance of the closed-loop system under the proposed control scheme.

Candidate Selection, Personalization and Different Logics of Centralization in New Southern European Populism: The Cases of Podemos and the M5S

AbstractIn this article, we focus on the candidate selection processes of Podemos in Spain and the Movimento 5 Stelle (M5S – Five Star Movement) in Italy, processes which are related to intra-party democracy. With a mixed-methods approach (including novel data from the expert survey PoPES, 31 semi-structured interviews with party representatives and militants, and analysis of party documents, statutes and leader speeches) and a comparative perspective, we explore candidate selection in the context of the broader organizational party structures over time. We find that: (1) Podemos functions like a typical centralized party, whereas M5S is a deviant case with strong and centralized control over party organization but decentralized mechanisms for candidate selection; (2) both these logics of centralization of power undermine the parties’ democratic credentials but have different consequences in terms of cohesion (expulsions/departures in M5S and splintering in Podemos); and (3) in both cases, the parties’ organizational culture and symbolic tools legitimate their top-down features.

Safely Returning America to Work Part II: Industry-Specific Guidance.

Safely Returning America to Work Part II: Industry-Specific Guidance.

A study of the dual-target corporate environmental behavior (DTCEB) of heavily polluting enterprises under different environment regulations: Green innovation vs. pollutant emissions

Green innovation and pollutant emissions are currently among the hottest topics in corporate environmental behavior (CEB) research. However, a few previous studies have examined these two concepts together at the choice level of CEB. This paper discusses the internal selection logic of CEB by using multinomial logit (MNL) models that consider the choices of enterprises in green innovation and illegal emissions, both across China and separately in different regions. The objective of this paper is to consider the four choices available to heavily polluting enterprises’ dual-target corporate environmental behavior (DTCEB), including alienated, conservative, contradictory, and intimate environmental behaviors. The results of this study show that, under various external pressures, 61.2% of heavily polluting enterprises choose conservative environmental behavior. There is also generally a lack of synchronization between the goals of short-term economic and long-term social benefits. At the current stage, environmental regulation measures should focus on guiding enterprises to change from alienated, conservative, and contradictory behaviors, to intimate. Instead of only focusing on monitoring pollution emissions, the government should focus on green innovation guidance to effectively promote the transformation and upgrading of heavily polluting enterprises. A moderate adjustment of the intensity of command regulation may lead to better regulatory effects. Specifically, the enforcement of laws across China should be slowed; efforts should be made to increase the focus of public participation in the eastern region; the importance of incentive-based tax rate preferences should be increased in the central region, and the intensity of legislation should be strengthened in the western region. Compared with the emphasis on environmental performance in other literature, this article uses comparative thinking to explore the choice logic of the DTCEBs, which attaches different degrees of importance to long-term social benefits and short-term economic benefits.

Auto gain Ultra low signal transimpedance amplifier for Blood Diagnostic machine.

The front-end circuit of blood diagnostic machine consists of a light source, photodiode and transimpedance amplifier (TIA). Various researchers have used separate photodiode and the TIA circuit to measure absorbance for different wavelengths that made the circuit bulky and limited its application for measurement of specific parameters. At high gain of around 4000 plus normally any amplifier, including TIA becomes unstable. We are presenting here auto gain TIA that is stable and precise even at the gain of 4000. MSP432 32 BIT Arm Cortex 4 microcontroller with inbuilt precision analog to digital convertor is used for sensing, implementing auto gain selection logic and calculating the absorbance. With this circuit, we measured the absorbance up to 4.2 and satisfactory results with maximum error of 0.04 were achieved up to the range of 3.5 on the absorbance scale. Further, the performance of TIA is tested and found satisfactory for 300nm to 800nm wavelength range.

An efficient ANFIS based pre-harvest ripeness estimation technique for fruits

The ripeness estimation of fruits plays a significant role in marketing and evaluation of quality. However, due to the subjectivity and slow speed in the case of manual assessment, the agriculture industry leads to the need for automation. In this research work, an efficient ANFIS based Pre-harvest Ripeness Estimation (APRE) technique is proposed for the ripeness estimation of fruits based on color. There are three main phases of the proposed work: Data Processing, Feature Selection and Fuzzy Logic Implementation. In the first phase, the data set of images of fruits is prepared in the image acquisition phase. Then images are pre-processed to make them equal in size. In Image Segmentation phase, a fruit is extracted from its background. In the next phase, two color features: red-green color difference and red-green color ratio are calculated based on extracted RGB color attributes and R-G is chosen based on performance comparison in terms of classification accuracy. In phase three, Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized for designing and implementing the classification system which classifies the fruits into six ripeness phases. The experimental results show that the APRE performs better than SVM, Decision Tree, and KNN in terms of accuracy, precision, recall, sensitivity and F-measure.

Sociological Stoicism

This article initiates a conversation between sociological theory and the contemporary revival of Stoicism. Identifying four problems common to historical and contemporary incarnations of Stoicism and tracing them to their shared individualism, I contend that only a sociological Stoicism is viable. I then sketch this sociological Stoicism by redefining key Stoic terms in the collective register; outlining a Stoic logic of case selection; assessing the fit of redefined Stoic concepts and logic of case selection with Marxian, Weberian, and Bourdieuvian frameworks; and developing a Stoic research agenda. These exercises culminate in the proposal to significantly alter sociology’s methods and epistemology.

Francia: ¿instrumentalización de la COVID-19 contra la igualdad escolar?

La pandemia de COVID-19 ha puesto a prueba los sistemas educativos. Este artículo trata del caso francés, en el que tuvo lugar un confinamiento de dos meses, seguido de una reanudación muy parcial y selectiva del curso antes del verano, que no llegó a funcionar de nuevo realmente hasta septiembre, en condiciones sin precedentes. Muestra que lejos de ser un paréntesis excepcional, la crisis sanitaria fue utilizada por el gobierno para tratar de imponer cambios estructurales. Estas conclusiones se derivan de un análisis de las lógicas que estaban previamente en marcha en los proyectos de reforma de la enseñanza francesa, lo que permite distinguir entre las respuestas puntuales a una situación excepcional de los intentos de instrumentalizar la pandemia. De este modo, las tentativas del Gobierno suponen una renuncia a la igualdad y a la democratización escolar, pues reducen los contenidos que se enseñan a todo el alumnado y derivan a las familias la responsabilidad y la financiación de disciplinas enteras y de contenidos de otras asignaturas, mediante el desarrollo de la educación a distancia y la transferencia al sector privado y a las autoridades locales de contenidos que finalmente no se incluirían en los planes de estudios nacionales. Estas políticas de repliegue público y de competencia entre el alumnado se derivan de lógicas que ya están en funcionamiento, dado que el sistema educativo francés ha sido objeto hasta ahora de un compromiso inestable entre lógicas de selección social y lógicas de democratización de los estudios.

Fuzzy Extended Krill Herd Optimization with Quantum Bat Algorithm for Cluster Based Routing in Mobile Adhoc Networks

ABSTRACT MANET integrates a set of autonomous mobile nodes which move independently and send data through wireless links. Clustering and routing are the commonly employed energy efficient techniques, which can be treated as an NP hard problem and is resolved by computational intelligence algorithms. The mobility of the nodes leads to repeated link failures and low energy efficiency. In order to achieve high energy efficiency and network connectivity, this paper presents a new Fuzzy Extended Krill Herd Optimization with Quantum Bat algorithm (FEKHO-QBA) for Cluster Based Routing in MANET.The presented model uses FEKHO algorithm by integrating the concepts of fuzzy logic and KHO algorithm for clustering process and effective selection of cluster heads (CHs). Besides, the QBA is applied as a routing technique to determine the optimal paths to the destination nodes. The QBA involves the features of faster convergence rate, easier to implement, and improved accurateness. The application of FEKHO-QBA algorithm offers maximum energy efficiency and network longevity. For determining the effectual performance of the EKHO-QBA algorithm, a set of different experiments were carried out and highlighted the supremacy over the compared methods interms of different performance measures.