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Islanding detection has become a serious concern due to the extensive integration of renewable energy sources. The non-detection zone (NDZ) and system-specific applicability, which are the two major issues with the islanding detection methods, are addressed here. The purpose of this paper is to devise an islanding detection method with zero NDZ and, which will be applicable to all types of renewable energy sources using the sequence components of the point of common coupling voltage.

Here, a parameter using the sequence components is derived to devise an islanding detection method. The parameter derived from the sequence components of point of common coupling voltage is analysed using wavelet transform. Various operating conditions, such as islanding and non-islanding, are considered for several test systems to evaluate the performance of the proposed method. All the simulations are carried out in Simulink/MATLAB environment.

The results showed that the proposed method has zero NDZ for both inverter- and synchronous generator-based renewable energy sources. In addition, the proposed method works satisfactorily as per the IEEE 1547 standards requirement.

Performance of the proposed method has been tested in several test systems and is found to be better than some conventional methods.

This study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand is a residential area that includes 20 houses.

The daily operational strategy of the proposed MG allows to vend and procure utterly between the main grid and MG. The smart metre of every consumer provides the supplier with the daily consumption pattern which is amended by demand side management (DSM). The daily operational cost (DOC) CO2 emission and other measures are utilized to evaluate the system performance. A grey wolf optimizer was employed to minimize DOC including the cost of procuring energy from the main grid, the emission cost and the revenue of sold energy to the main grid.

The obtained results of winter and summer days revealed that DSM significantly improved the system performance from the economic and environmental perspectives. With DSM, DOC on winter day was −26.93 ($/kWh) and on summer day, DOC was 10.59 ($/kWh). While without considering DSM, DOC on winter day was −25.42 ($/kWh) and on summer day DOC was 14.95 ($/kWh).

As opposed to previous research that predominantly addressed the long-term operation, the value of the proposed research is to investigate the short-term operation (24-hour) of MG that copes with vital contingencies associated with selling and procuring energy with the main grid considering the environmental cost. Outstandingly, the proposed research engaged the consumers by smart meters to apply demand-sideDSM, while the previous studies largely focused on supply side management.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

Parallelogram linkages are used to increase the stiffness of manipulators and allow precise control of end-effectors. They help maintain the orientation of connected links when the manipulator changes its position. They are implemented in many palletizing robots connected with binary, ternary and quaternary links through both active and passive joints. This limits the motion of some joints and hence results in relative and negative joint angles when assigning coordinate axes. This study aims to provide a simplified accurate model for manipulators built with parllelogram linkages to ease the kinematics calculations.

This study introduces a simplified model, replacing each parallelogram linkage with a single (binary) link with an active and a passive joint at the ends. This replacement facilitates countering motion while preserving subsequent link orientations. Validation of kinematics is performed on palletizing manipulators from five different OEMs. The validation of Dobot Magician and ABB IRB1410 was carried out in real time and in their control software. Other robots from ABB, Yaskawa, Kuka and Fanuc were validated using control environments and simulators.

The proposed model enables the straightforward derivation of forward kinematics and transforms hybrid robots into equivalent serial-link robots. The model demonstrates high accuracy streamlining the derivation of kinematics.

The proposed model facilitates the use of classical methods like the Denavit–Hartenberg procedure with ease. It not only simplifies kinematics derivation but it also helps in robot control and motion planning within the workspace. The approach can also be implemented to simplify the parallelogram linkages of robots with higher degrees of freedom such as the IRB1410.

This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model.

In dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts.

The research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management.

This study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management.

This research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.

The need to enhance student support is evident in higher education (HE) curricula. In addition to the complications created by the COVID-19 pandemic, the current strategies used in academia are criticised for their lack of appropriate student support in HE. The study focused on the themes under Section 4 of the National Student Survey (NSS): availability to contact tutors, receiving good advice and guidance and availability of good advice. The study aimed to provide recommendations for enhancing academic support by developing drivers that need implementation during course delivery.

A documental analysis and a qualitative survey were adopted for this study. A documental analysis of 334 mid-module reviews (MMRs) from levels three to six students in the built environment (BE) discipline. Critical themes identified from the MMRs were fed forward in developing a questionnaire for academics. A sample of 23 academics, including a Head of school, a Principal lecturer, Subject leads and Lecturers, participated in the questionnaire survey. Content analysis is adopted through questionnaire data to develop drivers to enhance academic support in BE. These drivers are then modelled by interpretive structural modelling (ISM) to identify their correlation to NSS Section 4 themes. A level partition analysis establishes how influential they are in enhancing academic support.

The study identified nine drivers, where two drivers were categorised as fundamental, two as significant, four as important, and one insignificant in enhancing academic support in HE. Module leaders’/tutors’ improving awareness and detailing how academic support is provided were identified as fundamental. Differentiating roles in giving advice and the importance of one-to-one meetings were identified as significant. A level partitioning diagram was developed from the nine drivers to illustrate how these drivers need to be implemented to promote the best practices in academic support in HE.

The identified drivers and their categories can be used to set prioritised guidelines for academics and other educational institutions to improve students’ overall satisfaction.

Novelty from the study will be the developed drivers and the level partitioning diagram to assist academics and academic institutions in successfully integrating academic support into HE curricula.

This study aims to examine the impact of interlocking directorships on firm performance in Turkey, with a specific focus on the moderating role of board diversity.

Using a panel dataset comprising the top 100 firms listed on Borsa Istanbul from 2014 to 2018, this study employs regression analysis to investigate the relationship between interlocking directorships, board diversity, and firm performance. It firm-level financial data and directorship information to assess the effects of interlocking directorships on firm performance while also considering the moderating influence of board diversity.

The findings of this study reveal several important insights. First, the results confirm the “busyness hypothesis” as an increase in the number of interlocks per director negatively impacts firm performance, indicating reduced monitoring effectiveness. However, the study also demonstrates that board diversity plays a significant moderating role. Specifically, board diversity positively influences the relationship between interlocking directorships and firm performance, suggesting that a diverse board can mitigate the negative effects of interlocks and enhance overall firm performance.

This study contributes to the existing literature in several ways. First, this study extends our understanding of the relationship between interlocking directorships and firm performance, considering contingency factors in the Turkish market. Second, our findings imply that board diversity mitigates the negative impact of busy interlocking directorates and improves firm performance, which provides invaluable directions to firms in setting their boards. Moreover, this research enhances corporate governance practices in Turkey and beyond in other emerging markets with similar corporate governance mechanisms by identifying the importance of board diversity and its moderating influence.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

Flow induced by rotating disks is of great practical importance in several engineering applications such as rotating heat exchangers, turbine disks, pumps and many more. The present research has been freshly displayed regarding the implementation of an engine oil-based Casson tri-hybrid nanofluid across a rotating disk in mass and heat transferal developments. The purpose of this study is to contemplate the attributes of the flowing tri-hybrid nanofluid by incorporating porosity effects and magnetization and velocity slip effects, viscous dissipation, radiating flux, temperature slip, chemical reaction and activation energy.

The articulated fluid flow is described by a set of partial differential equations which are converted into one set of higher-order ordinary differential equations (ODEs) by using convenient conversions. The numerical solution of this transformed set of ODEs has been spearheaded by using the effectual bvp4c scheme.

The acquired results show that the heat transmission rate for the Casson tri-hybrid nanofluid is intensified by, respectively, 9.54% and 11.93% when compared to the Casson hybrid nanofluid and Casson nanofluid. Also, the mass transmission rate for the Casson tri-hybrid nanofluid is augmented by 1.09% and 2.14%, respectively, when compared to the Casson hybrid nanofluid and Casson nanofluid.

The current investigation presents an educative response on how the flow profiles vary with changes in the inevitable flow parameters. As per authors’ knowledge, no such scrutinization has been carried out previously; therefore, our results are novel and unique.

In order to meet the different quality of service (QoS) requirements of vehicle-to-infrastructure (V2I) and multiple vehicle-to-vehicle (V2V) links in vehicle networks, an efficient V2V spectrum access mechanism is proposed in this paper.

A long-short-term-memory-based multi-agent hybrid proximal policy optimization (LSTM-H-PPO) algorithm is proposed, through which the distributed spectrum access and continuous power control of V2V link are realized.

Simulation results show that compared with the baseline algorithm, the proposed algorithm has significant advantages in terms of total system capacity, payload delivery success rate of V2V link and convergence speed.

The LSTM layer uses the time sequence information to estimate the accurate system state, which ensures the choice of V2V spectrum access based on local observation effective. The hybrid PPO framework shares training parameters among agents which speeds up the entire training process. The proposed algorithm adopts the mode of centralized training and distributed execution, so that the agent can achieve the optimal spectrum access based on local observation information with less signaling overhead.