Systematic Literature Review of Health Tourism Innovation

Innovation is an important issue for any business and especially vital concept for the establishment and growth of a successful health tourism businesses for gaining business improvement and differential advantage in competition. Purpose of this study is to figure out gaps in the innovation in health tourism entrepreneurship literature, show the comprehensive whole situation, areas where the related areas focused on and indicate the related promising areas for research in health tourism innovation. Thus, the significance of this study lies in 2 sub topics as || 1) providing a brief literature review on health tourism innovation 2) indicate the research gaps in health tourism innovation. Results indicate that there is a serious research gap in the innovation studies in spa and wellness tourism, since studies are concentrated on health and medical tourism. Moreover, it is understood that qualitative research is preferred by the majority of the authors followed by the mixed methods which leads to a research gap for quantitative studies in the area. Hence, most researched areas consist of determinants of innovativeness, impact on costs, innovation's relationship with sustainability, innovation drivers, collaboration, aspects of innovation on success, innovation types' effectiveness on health tourism establishments and successful innovation applications.Hospitality, Leisure, Sport & Touris

A two-stage real world serial batching scheduling problem: a case study

This paper concerns a real-life production management problem composed of two stages, which includes interdependent batch scheduling problems. Moreover, both stages require setup operations at the beginning and between the subsequent batches. From the optimisation point of view, this production management problem is complying with customers' deadlines and hence minimising total lateness. To achieve this objective, the production planner must identify optimum or near-optimum batch schedules for both stages. Correspondingly, this paper aims to develop a methodology to manage the related production problem as accurately as possible. We formulate an optimisation model that employs the mathematical programming method in line with this. Afterwards, an algorithmic proposal based on the simulated annealing algorithm is also developed to solve the problem in realistic sizes. The computational capabilities of the developed model and the algorithm are evaluated on the randomly generated problem sets. A direct comparison between the mathematical model and the algorithm shows how efficiently the proposed algorithm solves real-world problems. Also, computational results indicate that the proposed algorithm satisfactorily solves the related real-world batch scheduling problem.Management || Operations Research & Management Scienc

How are energy transition and energy-related R&D investments effective in enabling decarbonization? Evidence from Nordic Countries by novel WLMC model

Public interest in climate change-related problems has been developing with the contribution of the recent energy crisis. Accordingly, countries have been increasing their efforts to decarbonize economies. In this context, energy transition and energy-related research and development (R&D) investments can be important strategic tools to be helpful to countries in the decarbonization of economies. Among all, Nordic countries have come to the force because of their well-known position as green economies. Hence, this study examines Nordic countries to investigate the impact of energy transition, renewable energy R&D investments (RRD), energy efficiency R&D investments (EEF) on carbon dioxide (CO2) emissions by performing wavelet local multiple correlation (WLMC) model and using data from 2000/1 to 2021/12. The outcomes reveal that (i) based on bi-variate cases, energy transition and RRD have a mixed impact on CO2 emissions in all countries across all frequencies || EEF has a declining impact on CO2 emissions in Norway (Sweden) at low and medium (very high) frequencies || (ii) according to four-variate cases, all variables have a combined increasing impact on CO2 emissions || (iii) RRD is the most influential dominant factor in all countries excluding Norway, where EEF is the pioneering one. Thus, the reach proves the varying impacts of energy transition, RRD, and EEF investments on CO2 emissions. In line with the outcomes of the novel WLMC model, various policy endeavors, such as focusing on displacement between sub-types of R&D investments, are argued to ensure the decarbonization of the economies.Environmental Science

Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence

This article presents a literature review of the past five years of studies using Deep Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic manipulation tasks. The reviewed articles are examined in various categories, including DRL and IRL for perception, assembly, manipulation with uncertain rewards, multitasking, transfer learning, multimodal, and Human-Robot Interaction (HRI). The articles are summarized in terms of the main contributions, methods, challenges, and highlights of the latest and relevant studies using DRL and IRL for robotic manipulation. Additionally, summary tables regarding the problem and solution are presented. The literature review then focuses on the concepts of trustworthy AI, interpretable AI, and explainable AI (XAI) in the context of robotic manipulation. Moreover, this review provides a resource for future research on DRL/IRL in trustworthy robotic manipulation.Computer Science, Information Systems || Engineering, Electrical & Electronic || Telecommunication

Modelling and analysis of heat pump integrated Photovoltaics-Wind systems for an agricultural greenhouse in Turkey

This study focused on modelling and analysing photovoltaics and wind systems to meet the heating demand of a commercial greenhouse. The aim is to evaluate technical, economic, and environmental performances of the related systems and to determine the optimum configuration. A novel approach was introduced by integrating hybrid energy systems with large-scale wind turbines and developing a dynamic heat transfer model. A large commercial greenhouse with an area of 26,640 m2 located in Izmir, Turkey was selected for considering Mediterranean climate, and a detailed heat transfer model of the greenhouse were developed considering heat transfers by convection, radiation, ventilation, and infiltration. A combination of air source heat pumps, photovoltaic panels and wind turbines were used for meeting the heating demand of the related greenhouse. Five different on-grid energy systems scenarios, namely (i) Photovoltaics-Heat Pump, (ii) Photovoltaics-Wind Turbine- Heat Pump, (iii) Wind Turbine- Photovoltaics- Heat Pump (iv) Wind Turbine- Heat Pump, and (v) only Heat Pump were considered. The system modelling with a detailed heat transfer analysis of the greenhouse was made by MATLAB. The energy analysis of the systems was performed on an hourly basis for one calendar year. The annual heating demand and the corresponding electricity consumption of the greenhouse were calculated as 497.37 and 114.07 kWh/m2, respectively. Net Present Value, Levelized Cost of Energy and CO2 savings were used to evaluate economic and environmental performances of the systems. Among five on-grid energy system scenarios, the first scenario, consisting of 5271 photovoltaic panels and 20 heat pumps, emerged as the most economically attractive choice with Net Present Value and Levelized Cost of Energy of $547,440.40 and 0.080146$/kWh, respectively. Critical parameters affecting the economy of this scenario were found to be electricity prices, tomato yield, and photovoltaic panel prices. For environmental evaluation the fourth scenario, integrating wind turbines and heat pumps, achieves the highest CO2 savings of 2,064.73 tons due to increased renewable electricity production and lower life-cycle CO2 emissions of wind turbines compared to photovoltaic systems. This analysis enhanced the understanding of energy dynamics in greenhouse environments, contributing to the advancement of sustainable practices in agriculture.Thermodynamics || Energy & Fuels || Engineering, Mechanical || Mechanic

Modeling the link between environmental, social, and governance disclosures and scores: the case of publicly traded companies in the Borsa Istanbul Sustainability Index

This study constructs a proposed model to investigate the link between environmental, social, and governance (ESG) disclosures and ESG scores for publicly traded companies in the Borsa Istanbul Sustainability (XUSRD) index. In this context, this study considers 66 companies, examining recently structured ESG disclosures for 2022 that were published for the first time as novel data and applying a multilayer perceptron (MLP) artificial neural network algorithm. The relevant results are fourfold. (1) The MLP algorithm has explanatory power (i.e., R2) of 79% in estimating companies' ESG scores. (2) Common, environment, social, and governance pillars have respective weights of 21.04%, 44.87%, 30.34%, and 3.74% in total ESG scores. (3) The absolute and relative significance of each ESG reporting principle for companies' ESG scores varies. (4) According to absolute and relative significance, the most effective ESG principle is the common principle, followed by social and environmental principles, whereas governance principles have less significance. Overall, the results demonstrate that applying a linear approach to complete deficient ESG disclosures is inefficient for increasing companies' ESG scores || instead, companies should focus on the ESG principles that have the highest relative significance. The findings of this study contribute to the literature by defining the most significant ESG principles for stimulating the ESG scores of companies in the XUSRD index.Business, Finance || Social Sciences, Mathematical Method

New Turkey, social policy, and a daytime talk show as a remedy: Muge Anli as a modern Calikusu

'Muge Anli ile Tatli Sert', which investigates unresolved criminal cases with interactive audience support, is one the most popular daytime television shows in contemporary Turkey. The neoliberal restructuring of the welfare system in New Turkey posits that providing protection and security against social risks is the sole responsibility of individuals and families. In line with this approach, the show individualizes social problems ready to be resolved by 'Muge Abla' (Sister Muge) and her audience. Muge Anli's image, which conforms to the ideal modern and modest Republican woman, allows her to gain authority, trust, respect, and popularity. The show's discourses, structure, and communicative strategies are the product of the New Turkey where 'the sacred family' is the central political unit and the nation is an extended, happy family reproduced through this show.Area Studie

The analysis of critical success factors for successful kaizen implementation during the COVID-19 pandemic: a textile industry case study

PurposeThe primary objective of this research is to determine critical success factors (CSFs) that enable textile enterprises to effectively implement Kaizen, a Japanese concept of continuous development, particularly during disruptive situations. The study aims to provide insights into how Kaizen is specifically employed within the textile sector and to offer guidance for addressing future crises.Design/methodology/approachThis study employs a structured approach to determine CSFs for successful Kaizen implementation in the textile industry. The Triple Helix Actors structure, comprising business, academia and government representatives, is utilized to uncover essential insights. Additionally, the Matriced Impacts Croises-Multiplication Applique and Classement (MICMAC) analysis and interpretative structural modeling (ISM) techniques are applied to evaluate the influence of CSFs.FindingsThe research identifies 17 CSFs for successful Kaizen implementation in the textile industry through a comprehensive literature review and expert input. These factors are organized into a hierarchical structure with 5 distinct levels. Additionally, the application of the MICMAC analysis reveals three clusters of CSFs: linkage, dependent and independent, highlighting their interdependencies and impact.Originality/valueMajor contribution of this study is understanding how Kaizen can be effectively utilized in the textile industry, especially during disruptive events. The combination of the Triple Helix Actors structure, MICMAC analysis and ISM provides a unique perspective on the essential factors driving successful Kaizen implementation. The identification of CSFs and their categorization into clusters offer valuable insights for practitioners, policymakers and academia seeking to enhance the resilience and sustainability of the textile industry.Managemen

Multi-Sensor E-Nose Based on Online Transfer Learning Trend Predictive Neural Network

Electronic Nose (E-Nose) systems, widely applied across diverse fields, have revolutionized quality control, disease diagnostics, and environmental management through their odor detection and analysis capabilities. The decision and analysis of E-Nose systems often enabled by Machine Learning (ML) models that are trained offline using existing datasets. However, despite their potential, offline training efforts often prove intensive and may still fall short in achieving high generalization ability and specialization for considered application. To address these challenges, this paper introduces the e-rTPNN decision system, which leverages the Recurrent Trend Predictive Neural Network (rTPNN) combined with online transfer learning. The recurrent architecture of the e-rTPNN system effectively captures temporal dependencies and hidden sequential patterns within E-Nose sensor data, enabling accurate estimation of trends and levels. Notably, the system demonstrates the ability to adapt quickly to new data during online operation, requiring only a small offline dataset for initial learning. We evaluate the performance of the e-rTPNN decision system in two domains: beverage quality assessment and medical diagnosis, using publicly available wine quality and Chronic Obstructive Pulmonary Disease (COPD) datasets, respectively. Our evaluation indicates that the proposed e-rTPNN achieves decision accuracy exceeding 97 % while maintaining low execution times. Furthermore, comparative analysis against established Machine Learning (ML) models reveals that the e-rTPNN decision system consistently outperforms these models by a significant margin in terms of accuracy.Computer Science, Information Systems || Engineering, Electrical & Electronic || Telecommunication

Q-learning guided algorithms for bi-criteria minimization of total flow time and makespan in no-wait permutation flowshops

Combining Deep Reinforcement Learning and meta-heuristic techniques represents a new research direction for enhancing the search capabilities of meta-heuristic methods in the context of production scheduling. Q-learning is a prominent reinforcement learning in which its utilization aims to direct the selection of actions, thus preventing the necessity for a random exploration in the iterative process of the metaheuristics. In this study, we provide Q-learning guided algorithms for the Bi-Criteria No-Wait Flowshop Scheduling Problem (NWFSP). The problem is treated as a bi-criteria combinatorial optimization problem where total flow time and makespan are optimized simultaneously. Firstly, a deterministic mixed-integer linear programming (MILP) model is provided. Then, Q-learning guided algorithms are developed: Bi-Criteria Iterated Greedy Algorithm with Q-Learning (BCIGQL). Bi-Criteria Block Insertion Heuristic Algorithm with Q-Learning (BC-BIHQL). Moreover, the performance of the proposed Q-learning guided algorithms is compared over a collection of Bi-Criteria Genetic Local Search Algorithms (BC-GLS), Bi-Criteria Iterated Greedy Algorithm (BC-IG), Bi-Criteria Iterated Greedy Algorithm with a Local Search (BC-IGALL) and Bi-Criteria Variable Block Insertion Heuristic Algorithm (BC-VBIH). The complete computational experiment, performed on 480 problem instances of Vallada et al. (2015), which is known as the VRF benchmark set, indicates that the BC-BIHQL and the BC-IGQL algorithms outperform the BC-GLS, BC-IG, BCIGALL, and BC-VBIH algorithms in comparative performance metrics. More specifically, the proposed BC-BIHQL and BC-IGQL algorithms can yield more non-dominated bi-criteria solutions with the most substantial competitiveness than the remaining algorithms. At the same time, both are competitive with each other on the benchmark problems. Moreover, the BC-IGQL algorithm dominates almost 97% and 99% of the solutions reached by the BC-IG, BC-IGALL, and BC-VBIH algorithms in small and large datasets. Similarly, The BC-BIHQL algorithm dominates almost 98% and 99% of the solutions reached by the BC-IG, BC-IGALL, and BC-VBIH algorithms in small and large datasets, respectively. This means that, among all the features that have been compared, the Qlearning-guided algorithms demonstrate the highest level of competitiveness. The outcomes of this study encourage us to discover many more bi-criteria NWFSPs to reveal the trade-off between other conflicting objectives, such as makespan & the number of early jobs, to overcome various industries' problems.Computer Science, Artificial Intelligence || Computer Science, Theory & Method