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This paper proposes a new family of estimators for population mean with a non-response using a simple random sampling. It specifically applies this method to estimate the mean in Turkey's Education sector data sets, accounting for non-response. The study integrates additional information in the form of mean and cdf of the auxiliary variable which is highly positively correlated with the study variable to develop a general class of estimators tailored for the non-response using a simple random sampling scheme. Through numerical research, the characteristics of these estimators – namely, their biases and mean square errors – have been carefully investigated and deeply evaluated. The basic estimator of a population mean is under-performed by the suggested estimators. The empirical investigation validates the results, demonstrating the improved relative efficiency of the proposed estimator over the existing estimators, as demonstrated by theoretical and numerical comparisons. [ABSTRACT FROM AUTHOR]

Recurrent aphthous stomatitis (RAS) is a common oral mucosal disease. Despite a variety of scientific articles have been available till date, merely a few scientometric analyses have been systematically carried out in this field. The objective of this study was to recognize the hotspots and research trends related to RAS via bibliometric approach. The Elsevier's Scopus database was searched to retrieve qualified literature through an advanced search strategy on 9 Feb 2023. The basic information was collected as following: article type, publication year, journals, impact factor, the count of citations, citation density, keywords, authors, contributing institutions and country. A list of 986 publications were identified from 1933 to 2022, and the number of citations for each paper varied from 0 to 283. A steady increasing trend in the number of documents could be observed each decade with the summit in 2010s. Controlled study (n = 334) and major clinical study (n = 192) were the most common types of study design. Scully C (n = 26) was identified as the most productive author. United States (n = 166) and Turkey (n = 101) top the list of dedicating countries. This report would offer profound insight into the current status of RAS research and serve as a reference source for anyone planning to enhance the quality of future work. [ABSTRACT FROM AUTHOR]

Southeastern Turkey perished by the M w 7.8, Kahramanmaras earthquake on February 6, 2023, at 01:17 UT (04:17 LT). The Kahramanmaras-centered event, hit by the break of the East Anatolian Fault Line, is felt in Syria, Egypt, Lebanon, Iraq, and Cyprus on the border of Turkey. Together with Syria, it causes loss of lives and severe damage in 10 cities. This work is dedicated to the people of the region. The study discusses the causality of anomalies by conducting ionospheric anomaly research via the interpolated TEC map obtained from the CODE according to the coordinates of the epicenter of the earthquake (latitude, longitude). The TEC amplitudes are observed by evolving the time-domain TEC map into the frequency-domain with the Fourier transform. The peaks of the amplitudes allow preliminary information for the anomaly days. For the anomaly, the TEC (TECU) boundaries are drawn by the statistical specification. The TEC map outside these boundaries is marked as an anomaly. The causality of abnormalities is tried to be read through the triad of the geomagnetic storm(s) , solar activity , and the earthquake effect. One can find seven anomaly days in the paper. January 30, February 2, 3, 11, 15, 16, and 21 are the anomaly days. The essay probably detects the anomaly before 3 days related to the Southeastern earthquake. • Reads the causality of the earthquake in southeast Turkey (Mw 7.8), through the ionosphere-earth crust relationship. • Notices potential anomalies by presenting the time-domain TEC atlas in the frequency domain with Fourier spectral analysis. • Considering all possible TEC anomaly essentials detect the precursors of the earthquake 3 days before the event day. [ABSTRACT FROM AUTHOR]

In Turkey, the transportation industry's greenhouse gas (GHG) emissions increased by 147.1% between 1990 and 2019. Today, this transportation industry (i.e., freight and passenger) is among the significant contributors to greenhouse gas emissions in Turkey's megacities. Moreover, 65.43% of short-distance trips between home to work and home to school have been made by private automobiles in Istanbul and increasing concerns about environmental pollution have led practitioners to seek practical, robust, and effective solutions to reduce GHG emissions. Shared electric scooters have rapidly become popular for end-users and practitioners in megacities, depending on their valuable advantages. However, the rapid spread of micro-mobility, characterized by e-scooters, has also raised questions about this system's sustainability, suitability, and applicability. Thus, there are some critical and noteworthy gaps in this issue. This study investigates the factors affecting the suitable e-scooter selection for a sustainable urban transport system. Besides, it aims to develop a methodological framework for assessing the available e-scooter alternatives. For this purpose, a novel negotiation approach, a new form of the Delphi technique, was developed with the help of Fermatean fuzzy sets to identify the influential criteria. Also, the current paper presents a consensus-based MULTIMOORA (Multiple Objective Optimization on the basis of Ratio Analysis plus Full Multiplicative Form) decision-making model based on Fermatean fuzzy sets to address the appraisal problem concerning e-scooter selection. The current paper indicated that economic measures such as acquisition price and upkeep costs affect the e-scooter selection processes. In addition, an optimization model based on cross-entropy and dispersion measures is utilized to compute criteria weights. It highlighted that the costs of e-scooters are still high, and operators consider these criteria instead of the technical and operational features of the e-scooters. Finally, the validity check executed to test the robustness and trustworthiness of the model affirms the model's firmness and trustworthiness. [ABSTRACT FROM AUTHOR]

Developing knowledge-based and usable climate change policies requires establishing science-policy interfaces through which knowledge producers and policymakers cooperate. Current research reveals that co-production-based interfaces dominated neither by knowledge producers nor policymakers succeed in facilitating the transformation of knowledge into politics. From this point of view, this paper explores how Izmir Development Agency (IZKA), a regional development agency in Turkey, utilizes scientific knowledge and science-policy interfaces in its activities on climate change. Based on the analysis of agency documents and semi-structured interviews with experts from and outside IZKA, the findings indicate that IZKA thrives in integrating scientific knowledge on climate change in its activities drawing from multi-stakeholder and multi-level science-policy interfaces that have significant similarities with the co-production model. In these interfaces, international, national, regional, and local stakeholders share their resources, knowledge, and experience on climate change. Although legal, geographical, and demographic conditions also have an impact, the main reason behind IZKA's success is the agency's climate-focused vision that shapes decisions, strategies, organizational structure, and activities. In parallel to the indicated finding, this paper also discloses that the perspective of not prioritizing climate change and considering the potential benefits as only "co-benefit" does not yield results with the desired efficiency. Another finding is that even though the national government is the main actor on climate in Turkey, development agencies are capable of carrying out transformative activities in their regions, albeit limited. • Effective climate policies necessitate multi-stakeholder and multi-level science-policy interfaces. • The concept of "co-production" provides a useful theoretical framework for science-policy interaction. • To improve science-policy interfaces, barriers and facilitators must be well identified. • Climate change should be integrated into the local policy agenda as "the main focus", not as "a co-benefit". • Although Turkey has a centralized political system, local and regional actors can also be influential in climate policies. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to explore the success factors and barriers to digital co-creation between Higher Education Institutions (HEI) and Non-Governmental Organizations (NGO). The analysis is based on a qualitative approach, and particularly 80 semi-structured interviews with experts in digital co-creation from HEIs and NGOs from Poland, Germany, Turkey and Italy. The article identifies key success factors, including country's level of digitalization, individual's motivation to engage with digital platforms and methods, common goals, attitudes, and expectations. The study also highlights several barriers to successful digital co-creation, including variety of digital platforms, interpersonal connection, disengagement, and decreased attention spans. Among the success factors and barriers both external and internal factors were identified, all comprising a network of driving fosters and limitations to digital co-creation. Main of them include country's level of digitalization, motivation to engage with digital technology, digital literacy, incentives for engagement, shared goals and mutual understanding, variety of digital platforms, fear from losing interpersonal interactions, disengagement, and decreased attention span. The findings of this study will be of interest to HEIs, NGOs, policymakers, and other stakeholders involved in digital co-creation initiatives, providing insights on how to overcome barriers and optimize success factors to achieve effective and sustainable partnerships. [ABSTRACT FROM AUTHOR]

The traditional method to authenticate users on mobile devices or applications requires usernames and passwords. This approach authenticates the user only at the entry point of an application without providing continuous authentication during the whole session. This paper explores the use of behavioral biometrics, which involves tracking the unique movements of a user while interacting with a device, for continuous authentication on a mobile banking application. As a methodology, we use binary classification and explore the performance of deep learning algorithms. A dataset is collected from 45 participants using a mobile banking application in Turkey. We train four different types of deep architectures, including Multilayer Perceptron (MLP), LSTM, bi-directional LSTM, and convolutional LSTM. The dataset includes data from both touch screens and motion sensors. The results of the experiments reveal that MLP and the convolutional-LSTM algorithms achieve the best performance on raw data from both motion sensors and touch screens. Accuracy rates are over 99.85%, and FAR, FRR, and EER are below 0.5%. [ABSTRACT FROM AUTHOR]

In recent years, the use of deep learning methods in railway defect detection has expanded rapidly due to the technology's ability to improve accuracy and efficiency in fault diagnosis. However, modeling deep learning applications requires a large amount of data, and the labeling process for such datasets demands significant manpower. To address this gap this paper introduces a semi-supervised learning approach using a student–teacher model with YOLOv4 for automatically labeling images, aiming to reduce the manual effort required for image classification. We also present a novel dataset generated from images provided by the TCDD (The State Railways of the Republic of Turkey), which includes five distinct railway defects. Results of the experiments on the same test dataset show that the proposed student–teacher model not only improves YOLOv4's detection performance according to several decision metrics but also extends the training set with high-confidence pseudo-labeled images. • A novel dataset is generated for five common railway defects. • Student–teacher model effectively generates accurate labels for railway defects. • Automated image labeling process via student–teacher model minimizes manual effort. • Significant increase is achieved in mAP and F1-score in defect detection. [ABSTRACT FROM AUTHOR]

Cross-laminated timber (CLT) has recently attracted significant interest in building timber or hybrid structures owing to the rapidly growing demand for low-carbon construction materials with excellent mechanical properties. To investigate the seismic performance of CLT structures adopting novel dissipative angle brackets and hold-downs with soft-steel and rubber (i.e., SRD-ABs and SRD-HDs), a series of shaking table tests were performed on two full-scale specimens, i.e., a benchmark specimen (Specimen Ⅰ) and a specimen with a multi-doorway at the ground (Specimen Ⅱ). Several representative ground motions, including the Turkey wave, El Centro wave, Wenchuan wave, and Shanghai artificial wave, were selected as excitations, whose peak acceleration increased gradually from 0.035 g to 0.80 g. The damage patterns of the structures were revealed, and the dynamic characteristics and responses were obtained and analyzed. The results show that the fundamental frequencies of Specimen Ⅰ and Specimen Ⅱ were 4.125 Hz and 3.625 Hz, respectively. The mode shapes of each specimen remained similar before tests with peak ground acceleration (PGA) of 0.51 g, with only a slight decrease of approximately 5 % in the fundamental frequency, indicating that the specimens only suffered minor damage because of the great energy-dissipating capacity of the SRD-ABs and SRD-HDs. After experiencing tests with PGA of 0.80 g, the specimen exhibited a maximum inter-story drift of 1/60 with a structural damping ratio of 13 %, and the damage was mainly exhibited at the SRD-ABs and SRD-HDs, which realized the performance objective of replaceability of dissipative connections and repairability of structures under the 9-degree rare earthquakes. Furthermore, non-linear numerical models were developed to duplicate the dynamic characteristics and responses of the test specimen, and the analytical results from the models show satisfactory agreement with the test results. Overall, the outcomes of this paper can provide valuable references for future research and applications of CLT structures with the SRD-ABs and SRD-HDs. • Full-scale shaking table tests on two CLT structures with novel dissipative connections. • Damage patterns, dynamic characteristics and responses were obtained and analyzed. • A non-linear numerical model was developed to duplicate the seismic performance. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the failure probability of buried pipelines under pulse-like ground motions based on the seismic records detected during the Kahramanmaraş earthquake in Turkey. The incremental dynamic analysis (IDA) method was used to evaluate the vulnerability of continuous and segmented steel pipes, taking into account the corrosion effect in alkaline and near-neutral soil environments. The results show that pulse-like ground motion can significantly impact the fragility of buried pipelines, particularly segment steel pipelines. Moreover, the failure probability of buried pipelines tends to increase with service age, although the rate of failure gradually decreases. Empirical models notably underestimate the failure probability of buried pipelines under pulse-like ground motion, so they should be used cautiously in engineering application. These insights contribute to advancing our understanding of the seismic fragility of buried pipelines. • Studied the influence of pulse-like ground motion form the Kahramanmaraş earthquake on buried pipelines failure probability. • Considered seismic fragility and failure probability of buried pipelines based on geological environments and pipeline type. • Revealed experiential model underestimates failure probability of buried pipelines under pulsating seismic motion. [ABSTRACT FROM AUTHOR]

Mobile broadband (MBB) services are rapidly growing, causing a massive increase in mobile data traffic growth. This surge in data traffic is due to several factors (such as the massive increase of subscribers, mobile applications, etc.) which have led to the need for more bandwidth. Mobile service providers are constantly improving their network efficiency by upgrading current networks and investing in newer mobile network generations. However, these improvements will not be enough to accommodate the future spectrum demands. This paper proposes a time series forecasting model to analyze future spectrum demands based on the spectrum efficiency growth of MBB networks. This model depends on two key input data: the average spectrum efficiency per site and the number of sites per technology. The model is used to predict the spectrum efficiency growth of three countries (Turkey, Malaysia, and Oman) from 2015 to 2025. The proposed model is compared with various traditional statistical models such as the Moving Average (MA), Auto-Regression (AR), Autoregressive–Moving-Average (ARMA), and Autoregressive Integrated Moving Average (ARIMA). The forecasted results indicate that the average spectrum efficiency and growth will continue to rise multiple times by 2025 compared to 2015. The data from this prediction model can be used as input data to forecast the required spectrum needed in future for any specific country. This study further contributes to the network planning of future mobile networks for Fifth Generation (5G) and Sixth Generation (6G) technology. The proposed model obtains higher accuracy (by 90%) compared to other models. The proposed model is also applicable to any country, especially when new wireless communication technologies emerge in future. It is customizable and scalable since spectrum regulators can add additional metrics that positively contribute towards accurately estimating future spectrum efficiency growth. [ABSTRACT FROM AUTHOR]

• 54 regolith simulants by 10 countries are presented in one list for the first time. • TBG-1 is a recent lunar soil simulant by Turkey with soils from 9 volcanic areas. • Using 3D printing in lunar construction materials can to transform ISRU capabilities. • Sintered simulants used as building blocks have promising compressive strengths. • Investigation results may vary greatly according to the simulants' characteristics. One of the hot subjects in space civil engineering is the research for establishing outposts on space bodies, starting with the Moon. This wide research topic mainly covers learning about ground conditions and soils of these extraterrestrial bodies, looking for means of using the soil in producing construction materials, designing structures appropriate for local conditions, and finally choosing construction techniques to be applied using mainly these materials. In this paper, as the title indicates, an overview is presented on three aspects of using local materials in lunar construction activities: nature of in-situ soils obtained with lunar missions, the variety of lunar soil simulants produced on Earth, and their use in producing lunar construction materials. [ABSTRACT FROM AUTHOR]

• SEEA ecosystem accounts help to identify the role of forests in economy, but there have been limited tests so far. • We apply the SEEA EA to quantify hydrological ecosystem services provided by forests. • We develop SEEA ecosystem supply and use accounts for groundwater recharge, flood control and sediment control services. • The average total value of the three ecological services is about 201 million €/year (2001–2020) in the Southern Aegean Region of Turkey. • The methodology developed in this paper potentially has wide applications in sustainable regional development. This study aims to model hydrological ecosystem services provided by forests and explore how they can be captured in an ecosystem accounting framework. We test a dynamic GIS-based hydrological model to quantify and map key hydrological ecosystem services in the Southern Aegean Region of Turkey and integrate the services into an ecosystem account following the System of Environmental Economic Accounting – Ecosystem Accounting (SEEA EA). We map and account for services in physical and monetary terms, using an innovative model to quantify flood control services. Our paper connects forest management with water resource supply, eliciting the hydrological role of forests. The combined value of the three ecological services (groundwater recharge, flood control and sediment control) is about 201 million €/year. Reduction in forest coverage means that the economy will face the consequences of losses of these critical regulating services. Finally, we discuss opportunities, challenges, and limitations in the practical application of the ecosystem accounting concept. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of trends in drought variables at 29 stations across the Euphrates-Tigris Basin over the period 1965–2022. Drought variables were calculated using the Standardized Precipitation Index (SPI) for 1-, 3-, 6- and 12-month scales. The drought values have been decomposed for these time scales through application of Discrete Wavelet Transform (DWT). The components are defined as short, medium and long periodicities. DWT-based trend analysis was computed using Mann-Kendall (MK) and Innovative Trend Analysis (ITA) methods. As the time scale and the periodicity of drought increase, a strong increase in drought occurs. It was revealed that drought increased significantly at all time scales in almost all stations, especially in the long periodicity, according to both MK and ITA. The droughts and the serious increasing trend of droughts in this basin constitute a significant threat to water resources. • Drought in the largest basin of Turkey, the Euphrates-Tigris Basin, was scrutinized through an assessment of the Standard Precipitation Index (SPI). • Short, medium, and long periodicities acquired by decomposing Standardized Precipitation Index (SPI) time series have been introduced. • The trends of SPI time series and short, medium and long periodicity series were analysed by MK and ITA tests. • The most severe droughts began after 2020 throughout the basin and the drought intensified significantly during the long periodicity. [ABSTRACT FROM AUTHOR]

The growth of social networks is ever-increasing. Many available scientific publications evidence the interest of researchers in this area. Within a time span of eight years from 2011 to 2018, approximately 2600, 230, 150, and 110 scientific articles were published from the USA, Iran, Saudi Arabia, and Turkey, respectively around this area of research. To comprehensively survey all the sub-fields and interests within this research area, the present paper proposes a novel density-based method for finding topic descriptors from academic articles. By employing a robust to noise fuzzy clustering algorithm, the terms are clustered, and by utilizing a modified Parzen window, k topic descriptors from each cluster are extracted. Besides, an optimization problem has been designed to detect the similarity between word pairs. By conducting the experiments, the research priorities for four countries within this time span have been found. Moreover, the closeness of the research in developing countries to the developed country have been measured. The experimental results show that for four years, the research topics in Turkey were close to the research topics in the USA on average, and the research topics in Saudi Arabia were close to the USA topics during the past two years. Additionally, the experimental comparison of the proposed method with two clustering baselines indicates the superiority of the proposed method in terms of precision, recall, and accuracy. [ABSTRACT FROM AUTHOR]

Naturally occurring volcanic glassy rocks, obsidians, are widely used for medical tools and have great interests in alternative medicine, auxiliary therapy, spa, cosmetics and jewellery fields. In this paper, we studied the spectroscopic and radiation shielding properties of obsidian samples collected from four main volcanic regions, named as Nemrut Mountain, Erzurum-Pasinler, Kars-Sarıkamış and Rize-Ikizdere, eastern Turkey. In this regard, important and effective spectroscopic techniques; Electron paramagnetic resonance, X-ray diffraction, Fourier transform infrared spectrometry, Raman spectroscopy, Energy dispersive spectroscopy and Scanning electron microscopy were used to give information about structural, magnetic and morphological features of the obsidians. Additionaly, radiation shielding potentials of the obsidians were evaluated by determination of mass attenuation coefficients, effective atomic numbers, and buildup factors. Recently developed EpiXS code was used for obtaining radiation shielding parameters. It is concluded that the studied obsidians can be considered as radiation shielding materials. By this comprehensive study, both spectroscopic and shielding properties of obsidian samples from different volcanic regions in Turkey are presented and compared. [ABSTRACT FROM AUTHOR]

• This is the first report on phenolics and biological activity of C. oppositifolia and S. cilicius. • The plant extracts possessed significant antioxidant and enzyme inhibitory activity. • Kaempferol and quercetin were identified as major flavonoids in the extracts. • Protocatechuic acid and gallic acid were identified as major phenolic acids in the extracts. • The plant extracts could be potential sources of bioactive molecules. In the paper, the phenolic compounds and biological activity of Corydalis oppositifolia DC. and Senecio cilicius Boiss., the endemic plants growing in Turkey, were investigated for the first time. The ethanol extract of C. oppositifolia exhibited more potent antimicrobial activity while the ethanol extract of S. cilicius exhibited the highest radical scavenging activity on ABTS (81.4±1.1a mg TE g−1 extract) and DPPH (23.4±0.2a mg TE g−1 extract). In C. oppositifolia , the highest total flavonoid and phenolic acid contents were found to be in the ethanol (94.2±3.5a mg QE g−1 extract) and methanol (320.5±3.3c mg GAE g−1 extract) extracts, respectively. In S. cilicius , the highest total flavonoid and phenolic acid contents were found to be in the methanol (56.9±0.9c mg QE g−1 extract) and ethanol (568.0±3.3a mg GAE g−1 extract) extracts, respectively. The ethanol extract of C. oppositifolia has the most prominent activity against α-amylase, AChE and BChE whereas the methanol extract of this plant displayed the highest inhibitory activity against α-glucosidase. HPLC analysis of the ethanol extracts revealed that the major flavonoids were kaempferol and quercetin whereas the major phenolic acids were protocatechuic acid, gallic acid, gentistic acid and 2,4-dihydroxybenzoic acid. The findings suggested that the plants and their extracts have the potential to be used in the treatment of diseases such as diabetes and Alzheimer's diseases. [ABSTRACT FROM AUTHOR]

Monitoring large infrastructures is vital for determining structural safety over time. Dams are structures that influence agricultural activities and have high economic contributions. The dams' structural safety was mainly addressed using conventional measurement techniques. However, these might not be time-efficient and practical, considering the large surfaces of dams. This paper presents a long-term InSAR analysis of the Atatürk Dam, Turkey's largest and one of the five biggest dams in Europe using ERS, ENVISAT, and Sentinel-1 satellites images. Since only Sentinel-1 data have acquisitions in ascending and descending orbits, we could estimate surface movements also in the vertical and east-west directions using both lines of sight (LOS) displacements. The results showed that the east-west movement is higher than vertical movement. As a result, we observe a maximum of 10 mm/yr displacement in the LOS direction at the dam's crest for period of 2003 and 2010, decreasing down the slope. • The stability of the biggest dam of Turkey was monitored by InSAR technique. • Long term displacement at Ataturk Dam was determined. • The highest deformation was extracted at the crest of the dam. • The dam shows slow displacement after 28 years of its construction. • The total deformation in 28 years is up to 11 cm. [ABSTRACT FROM AUTHOR]

Statistics on occupational accidents published by international organizations show that mobile cranes are the riskiest heavy equipment among all crane types. The most significant reason for these crane accidents is the decision-makers' wrong selection of mobile cranes. Making misevaluations and selecting improper mobile cranes can cause many problems besides mobile crane accidents. In addition, selecting a mobile crane is a highly complicated decision-making problem, as there are many conflicting criteria and uncertainties. Besides, no commonly accepted criteria set is used to solve this decision-making problem in the literature. The current paper proposes the fuzzy BWM (Best and Worst Method) and fuzzy MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) combination as a robust and practical decision-making tool that can overcome many complicated ambiguities. The suggested model has been applied to solve a mobile crane selection problem encountered by a large-scale project logistics and heavy transport company in Turkey. The acquired outcomes demonstrated that technical criteria are more influential than economic and quality criteria. The criterion "Maximum hoist height" in the technical criteria group is the most influential factor in selecting the best mobile crane option. In addition, the A1 Brand-L. 4.2 is the most appropriate alternative to meet the company's requirements. Finally, a comprehensive sensitivity analysis performed to test the validity and applicability of the model approves the robustness of the model. • The current paper presents the fuzzy BWM and MARCOS approach to mobile crane selection. • The proposed model is applied to solve mobile crane selection problems, which are highly complex and include uncertainties. • It has identified the set of selection criteria for mobile cranes for the first time. • It presents a flexible decision-making approach, which is proper for real decision-making problems. • It demonstrates interrelations among factors for making it more understandable in complex situations. [ABSTRACT FROM AUTHOR]

In this study, the electricity demands of some Fortune 500 companies in Türkiye have been forecasted by using deep learning techniques. This is a quite harder problem than the forecasting of the aggregated electricity demand in which the negative and positive fluctuations are absorbed on paper. Forecasting of firm-level electricity demand is an important problem since it can help automating firms' routine forecasting operations, reducing the electricity supply costs of the firms by improving the quality of the forecasts, and improving the quality of the electricity on the transmission network. Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) techniques have been preferred concerning the successful results in the literature. As the originality of this paper, the Multiple Seasonal-Trend Decomposition using Loess (MSTL) technique is used for the electricity demand forecasting problem for the first time. The obtained results showed that although it is simple to implement, MSTL outperforms MLP and LSTM for most of the firms operating in mass production form. It is seen that the complexity of the model does not always guarantee good results and simple methods sometimes can work well. Load balancing studies are also very important for the economic sustainability of the industry since the electricity price and imbalance penalty have extremely increased (i.e., 8 times in Türkiye) during the post-pandemic period. Therefore, the energy cost reduction potential of the companies has also been assessed. This study resulted in cost savings of approximately 378 minimum wages for the pilot company. [ABSTRACT FROM AUTHOR]

The waste management subject is a very important issue today and in the future. Therefore, lots of scholars are endeavoring to investigate the valorization of wastes from different sources, such as paper, metal, glass, and vegetables. In this study, the pistachio soft and hard shells, as an industrial waste material, were used as natural dye sources. For this purpose, waste were collected from Gaziantep province in the south of Türkiye. Then, the soft and hard pistachio shells that had been dried in the shadow were investigated in terms of FTIR analysis and then used as a dyestuff source in the dyeing of cotton fabrics with the help of bio-mordants and metal mordants. Additionally, the antioxidant capacity of the soft and hard shell of pistachio was assessed through the DPPH and FRAP assay methods, and the antibacterial activities were tested against Staphylococcus aureus and Escherichia coli for selected dyed samples. A spectrophotometer was used to determine the CIE L*a*b* color values and (K/S) of cotton fabrics that had been dyed. The results exhibited that the tested wastes could be useful for the coloration of the cotton fabrics, and depending on the used natural dye source, mordanting agent, and mordanting method, the colors were found to have changed, but in general, light brown-ecru-brown colors were observed in the dyed samples. Meanwhile, the fastness of the samples was examined, and the sources were found to exhibit limited light fastness values but good or perfect washing, rubbing, and perspiration fastness. In conclusion, the evaluated conditions were found to be useful for the coloration of cotton, and even more importantly, the pistachio soft and hard shells were observed to reveal great potential for use in textile dye houses as a natural dye source. [ABSTRACT FROM AUTHOR]

On February 6, 2023, two heavy earthquakes with moment magnitudes (M W) of 7.7 and 7.6 hit the Kahramanmaraş region in southeast Türkiye. Aftershocks of moment magnitude of 6 occurred consequently. These earthquakes affected more than 14 million people, and caused severe damage to civil infrastructure including buildings, government offices, religious buildings, dams, bridges, airports, and roads. After the earthquakes, a group of researchers visited the Hatay province to assess the response of buildings. Hatay was one of the most heavily affected cities in the region. The researchers gathered data on damaged reinforced concrete buildings, steel structures, as well as masonry buildings. This paper focuses on discussing the findings and analyzing the gathered data from reinforced concrete buildings and steel structures. A wide variety of issues that resulted in heavy damage or, in some cases, complete collapse of the buildings are discussed. These problems include construction mistakes such as insufficient shear reinforcements, neglected corrosion, utilizing low-quality concrete, insufficient development length in flexural reinforcements, and strong beam- weak column failure. In addition, various design errors including excessive torsional response of buildings, rigidity difference in two orthogonal directions of east–west and north–south of the building plan, pounding effect, short column failure, and development of weak and soft stories are described using gathered pictorial data. Finally, recommendations were made, and conclusions were drawn based on previous experimental and numerical studies of the seismic behavior of buildings as well as Turkish Building Earthquake Code (TBEC- 2018) requirements. [ABSTRACT FROM AUTHOR]

[Display omitted] • The most profitable system is on-grid PV-Wind Turbine system with an NPC of $52.8 M. • For 0% and 25% sale constrained systems low penetration systems are optimal. • For 50% sale constrained and unconstrained systems high penetration systems are preferred. • Fire hawk Optimizer, Grey Wolf Optimizer and Particle Swarm Optimizer are used to size the system by power flow analysis. Fire Hawk Optimizer is found to be usable in optimal power flow analysis and system sizing. • Increase in penetrations leads to enlargement of the system in the region with high load demand because of optimizations. Today, with a human population of 7.5 billion, increasing power consumption, and widespread use of high-technology equipment, the need for energy in the world is increasing day by day. The damage to nature caused by non-renewable energy resources and the economic and political disadvantages they cause in countries that must import these resources have led the world to search for alternative energy sources. One of the most important benefits of renewable energy sources is that they can create hybrid energy systems with other energy sources. Hybrid energy systems are structures in that more than one energy generation unit works together to feed the electrical load. In this paper, a hybrid system will be designed by using Homer PRO, to supply the energy consumed in the residential areas of Karaburun and Urla districts of Izmir province in Turkey with hybrid renewable energy systems. Systems of different sizes are designed for 50%, 75%, and 100% penetration rates, and sales to the grid are constrained to 0%, 25%, 50%, and no constraint on sales. Sensitivity analyses have been performed for 10% and 20% more power required by the load. Techno-economic analyses were done for the systems and the optimal system has been identified. The power flow of the optimal system is inspected by defining the network in Python and Fire Hawk Optimizer (FHO), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) are used for optimal sizing of the system. The optimal system has a Net Present Cost (NPC) of $52.3 M, 34 wind turbines, and 954 kW PV with conditions of 100% penetration and no restrictions on sales to the grid. FHO proposed 26 wind turbines and 780 kW PVs for Urla and 8 wind turbines, and 174 kW PVs for Karaburun. GWO proposed 25 wind turbines and 747 kW PVs for Urla, 9 wind turbines, and 165 PVs for Karaburun. PSO offered 24 wind turbines and 708 kW PVs for Urla, 10 wind turbines, and 246 kW PVs for Karaburun. When there is no sale to and 25% sales to the grid, it is seen that the optimal scenario is the system with a 50% penetration rate. In fully sale-constrained systems, NPCs are higher than the base system and they can never pay for themselves compared to the base system. It is concluded that smaller-sized systems are more suitable for systems with high constraints. The proposed FHO algorithm and GWO algorithm react to increasing penetration ratios by enlarging the system size in the regions with high load demand. PSO sizes the system like predictions by load demand. FHO reacts quicker to increasing penetrations than PSO and GWO. FHO algorithm is found to be usable to solve optimal power flow problems and optimal system sizing. [ABSTRACT FROM AUTHOR]

Monoculture farming pollutes the environment by increasing the use of inputs, accelerating soil erosion, polluting water resources, raising carbon level in the atmosphere, and decreasing biodiversity. Therefore, farmers are advised to implement diversified farming systems such as crop rotation. However, crop rotation, i.e., planting more than one crop in the same field over a given year, may also harm the environment. To combat the environmental problems of monoculture, Turkey announced to implement a new crop rotation policy in 2020. The government said it would not provide support payments if farmers planted the same crop in the same plot for three consecutive years. This paper estimates the impact of this crop rotation policy on monoculture and shows how agricultural fires changed with this policy. We find that Turkey's crop rotation policy significantly decreased monoculture practices. Farmers decreased their cotton acreages and replaced cotton with wheat as the primary and corn as the secondary crop. However, since farmers burn their fields after the primary crop is harvested, the number of fires tripled after the policy. This environmentally friendly policy unexpectedly polluted the environment. Our paper shows how policies may create unintended environmental problems if they do not consider farmers' behavioral constraints. • This paper estimates the impact of a crop rotation policy on monoculture and the number of fires in Turkey. • We find that the crop rotation policy significantly decreased monoculture farming. • Farmers decreased their cotton acreages and replaced cotton with wheat as the primary and corn as the secondary crop. • Since farmers burn their fields after the primary crop is harvested to plant the secondary crop, the number of fires tripled with the policy change. • Consequently, an environmentally friendly policy unexpectedly polluted the environment. [ABSTRACT FROM AUTHOR]

Likened to the economic calamity of World War Two, the COVID-19 pandemic has sparked fears of a deep economic crisis, killed more than six million people worldwide and had a ripple effect on all aspects of life. MCDM (multi-criteria decision making) methods have become increasingly popular in modeling COVID-19 problems owing to the multi-dimensionality of this crisis and the complexity of health and socio-economic systems. This paper is aimed to review 72 papers published in 37 leading peer-reviewed journals indexed in Web of Science that used MCDM methods in different areas of COVID-19 pandemic. In this paper, data retrieval follows the PRISMA protocol for systematic literature reviews. 35 countries have contributed to this multidisciplinary research and India is identified as the leading country in this field followed by Turkey and China. Also 36 articles, namely 50% of papers are presented in the form of international cooperation. " Applied Soft Computing " is the journal with the highest number of articles whereas "Journal of infection and public health" and " Operations Management Research " are ranked in the second place. The results indicate that AHP (including fuzzy AHP) is the most popular MCDM method applied in 37.5% of papers followed by TOPSIS and VIKOR. This review reveals that the use of MCDM methods is one of the most attractive research areas in the field of COVID-19. As a result, one of the main purposes of this work is to identify diverse applications of MCDM methods in the COVID-19 pandemic. Most studies i.e. 69% (49 papers) of the papers combined various fuzzy sets with MCDM methods to overcome the problem of uncertainty and ambiguity while analyzing information. Nevertheless, the main drawback of those papers has been the lack of theoretical justifications. In fact, fuzzy MCDM methods impose heavy computational load and there is no general consensus on the clear advantage of fuzzy methods over crisp methods in terms of the solution quality. We hope the researchers who applied fuzzy MCDM methods to COVID-19-related research understand the theoretical basis of MCDM methods and the serious challenges associated with basic operations of fuzzy numbers to avoid potential disadvantages. This paper contributes to the body of knowledge via suggesting a deep vision to critique the fuzzy MCDM methods from mathematical perspective. • We provide a literature review on peer-reviewed journal articles with high impact factor related to the application of MCDM methods in COVID-19 era. • A total of 72 articles are collected from 37 leading journals and categorized into 52 types of application. • We highlight some claims on fuzzy arithmetic which are at use in the fuzzy literature but having little theoretical support. [ABSTRACT FROM AUTHOR]

Ranking Intuitionistic Fuzzy Sets (IFS) using distance-based methods involves calculating the distance between an IFS and a reference point which represents either maximum (positive ideal solution) or minimum (negative ideal solution) value. These methods assume that similarity of an IFSs to the reference point increases as its distance from it decreases. While it is a common practice to use nonlinear distance functions for ranking IFSs, this paper proves that no nonlinear IF distance function can be robust. In this study, the shortcomings of the conventional procedure are demonstrated by providing a mathematical proof and an alternative ranking method based on the hypervolume metric is proposed. In addition, the suggested ranking approach is extended as a new multi-criteria decision making method called Hypervolume-based Evaluation and Ranking Technique (HEART). HEART is applied for multi-criteria assessment of Turkey's energy alternatives. Results are compared with three distance based multi-criteria decision making methods: TOPSIS, VIKOR, and CODAS. • The study uncovers the critical shortcomings of distance-based methods in ranking Intuitionistic Fuzzy Sets (IFS). • An alternative ranking method to distance based approaches, based on the hypervolume metric is proposed. • The hypervolume based ranking approach is extended for use in multi-criteria decision making problems. [ABSTRACT FROM AUTHOR]

This paper examines the role of trade credit in the transmission of global liquidity conditions. Specifically, we investigate whether trade credit complements or substitutes the decline in bank lending activity as global liquidity tightens. Using a detailed bank and firm-level dataset for Turkish firms, we find that firms which are more bank-finance constrained have less access to trade credit as global liquidity tightens. The rate of trade credit tightening is amplified during crisis periods. While bank-finance constrained firms receive significantly less trade credit from suppliers, they reduce the amount of trade credit that they extend to customers even more, implying that tightening trade credit conditions propagate further through supply chains. In addition, trade credit conditions deteriorate more for small firms and for cash-poor firms in times of crisis. • Bank-finance constrained firms have less access to trade credit in tight periods. • The rate of trade credit tightening is amplified during crises. • Tight trade credit conditions propagate further through supply chains during crises. • Trade credit conditions deteriorate more for small firms during crises. • Trade credit conditions deteriorate more for cash-poor firms during crises. [ABSTRACT FROM AUTHOR]

This paper investigates the distribution problem of the COVID-19 vaccine at the provincial level in Turkey and the management of medical waste, considering the cold chain requirements and the perishable nature of vaccines. In this context, a novel multi-period multi-objective mixed-integer linear programming model is initially presented over a 12-month planning horizon for solving the deterministic distribution problem. The model includes newly structured constraints due to the feature of COVID-19 vaccines, which must be administered in two doses at specified intervals. Then, the presented model is tested for the province of Izmir with deterministic data, and the results show that the demand can be satisfied and community immunity can be achieved in the specified planning horizon. Moreover, for the first time, a robust model is created using polyhedral uncertainty sets to manage uncertainties related to supply and demand quantities, storage capacity, and deterioration rate, and it has been analyzed under different uncertainty levels. Accordingly, as the level of uncertainty increases, the percentage of meeting the demand gradually decreases. It is observed that the biggest effect here is the uncertainty in supply, and in the worst case, approximately 30% of the demand cannot be met. [ABSTRACT FROM AUTHOR]

This paper presents the modeling and identification of a walking-beam-type slab reheating furnace in one of the biggest Iron and Steel Industry in Turkey. Firstly, the dynamical model of the furnace is established as it is running with 12 control zones based on energy balance equations in the zones. Secondly, a disturbance observer is designed to estimate the disturbances that represents unmeasurable energy losses and the effects of unknown dynamics of the system based on Immersion and Invariance (I&I) approach. Then, model is identified with real data collected from the running furnace. The model and real furnace outputs are compared zone by zone. The results demonstrate the validity of the model. [ABSTRACT FROM AUTHOR]

• The 2023 Kahramanmaraş earthquakes had devastating effects. • Structural failure determination of masonry buildings was made. • This paper evaluates the failures of masonry structures in Adıyaman. • A large part of the existing building stock consists of such structures in rural areas in the world. The 06 February 2023 Kahramanmaraş Earthquakes, which were the disaster of the century for Türkiye and in which more than 50,000 people lost their lives, caused many buildings to collapse or receive significant structural damages. Adıyaman is one of the provinces that caused the most destruction by earthquakes affecting 11 different provinces, which occupy approximately 14% of the country's surface area. The masonry buildings have been severely damaged by earthquakes, which constitute a significant part of the building stock in the province's rural areas. In this study, the region's seismicity and the spectral acceleration values of the last earthquakes were analyzed, and the distribution of building damages was given statistically. The main goal of this study is to determine the collapse and failure mechanisms of existing unreinforced masonry buildings in detail. The study also includes reconstruction strategies or retrofitting procedures to prevent damage to masonry structures in the region. Non-compliance with seismic design codes is the leading cause of damage in masonry buildings constructed by local craftsmen using low-strength wall and joint building materials. The usage of heavy earthen roofs, insufficient wall-wall, and wall-roof connections, lack of vertical and horizontal beams, poor workmanship, and insufficient interlocking between layers are the causes of structural damages observed in the field. At the end of the study, critical clauses in the current earthquake code were emphasized, and the precautions to be taken for existing and new design masonry buildings were also mentioned. [ABSTRACT FROM AUTHOR]

• The 2023 Kahramanmaraş earthquake had devastating effects. • Structural Damage Determination of Mosques and Minarets was made. • Reinforced concrete minarets were damaged by the devastating earthquake. • The masonry minarets were badly damaged by the devastating earthquake. This paper evaluates the failures of masonry and RC minarets and mosques during the February 06, 2023, Kahramanmaraş earthquakes. Two big independent earthquakes (Mw = 7.7 and Mw = 7.6), which can be called the disaster of the century for Turkey and occurred at nine-hour intervals, caused a lot of loss of life and destruction in the region where approximately 16% of the country's population lived. It has been observed that the accelerations obtained from many stations exceed the response spectrum values predicted by the Turkish Building Earthquake Code (TBEC). Numerous aftershocks occurred after the earthquakes. The main purpose of the study is to investigate the field and evaluate the structural damages of 27 different mosques and minarets in Adıyaman province, which is one of the provinces with significant losses after these earthquakes and is located on the Eastern Anatolian Fault Zone (EAFZ). Significant structural damage and total collapses occurred in mosques and minarets, which were built in masonry and reinforced concrete style, as in other types of structures. However, due to the occurrence time of the earthquakes, no loss of life caused by these structures was experienced. Very few of the mosques and minarets examined in this study behaved like earthquake-resistant structures without any damage. As a result of the field investigations, the main reason for the damages in such structures is that they were built by local masters without any engineering service. [ABSTRACT FROM AUTHOR]

Health institutes are structurally growing depending on the population increase and escalating demands for health services. As a negative result, the health industry produces more healthcare waste daily. Its efforts and resources cannot be sufficient to dispose of such medical wastes as the quantity and variety continue to increase. Therefore, developing solution partnership relations with logistics service providers specialized in collecting, storing, and disposing healthcare waste may be a beneficial and fruitful step for solving this problem. However, the evaluation and selection of service suppliers for healthcare waste disposal may not always result in success. Hence, the present work develops a robust integrated methodology on the basis of Step-wise Weight Assessment Ratio Analysis (SWARA) and COmplex PRoportional ASsessment (COPRAS) techniques based on Interval-Valued Fermatean Fuzzy Sets (IVFFSs) for healthcare decision-makers to make more rational and optimal decisions concerning service supplier selection. The proposed model based on the IVFFSs can also overcome incredibly complicated uncertainties in the assessment processes. A large-scale international hospital chain in Turkey is investigated using the developed methodology to find reasonable and logical solutions to assess and select the best Medical Waste Disposal and Logistics (MWDL) firm. It is revealed that the most important criterion is "Storage Conditions" where "Republic Services, Inc" is the most successful supplier of MWDL. Finally, theoretical and practical implications are discussed in detail in order to provide useful managerial insights and decision aids to assist in improving the sustainability of healthcare industry. [Display omitted] • The paper suggests a frame to solve the waste disposal firm selection problems. • The suggested a novel tool the IVFFS-Delphi method to identify the criteria. • The model consisting of the IVFFS-SWARA method to calculate the criteria weights. • The IVFFS-COPRAS technique was applied to determine the preference of the options. • The validity of the presented decision tool is confirmed through a case study. [ABSTRACT FROM AUTHOR]

• Global climate policies are used by political actors seeking to advance their agendas. • National government in Turkey depoliticizes climate change via climate policy diffusion. • Local governments, however, push repoliticization via norm domestication. • Attention needed to the strategic use of climate policy diffusion among the largest global emitters. Although climate policy diffusion is widely studied, we know comparatively little about how these global policies and the norms that surround them are used by various political actors seeking to advance their own agendas. In this article, we focus on how global climate norms are diffused differently at national and local scales and used to repoliticize or depoliticize climate change. We focus on the case of Turkey, which carries the stark contrast of showing willingness to achieve global climate goals in the international arena but less so in domestic politics and actions. The article employs a novel methodological approach, using topic modeling and network analyses on a range of climate change–related policy documents, and interviews with high-level officers, conducted at the three jurisdictional levels in Turkey. The findings reveal that although global climate policy is diffused to both national and local governments, it is used in different ways at these levels. The national government uses climate policy diffusion to depoliticize climate change by creating ad hoc climate coalitions and limiting local climate actions to seeking external climate-related funds. Meanwhile, the metropolitan municipalities replicate nationally adopted climate goals, whereas the district municipalities domesticate ambitious climate norms and repoliticize climate change via local climate entrepreneurs and civic action. The paper contributes to understanding how climate policy diffusion and norm domestication can have different political outcomes in achieving global climate goals and argues for increased policy attention to the strategic use of climate policy diffusion for the depoliticization of climate change. [ABSTRACT FROM AUTHOR]

This article contributes to the conservation of modern architectural heritage buildings by emphasizing the retrofitting of their construction systems. Modern architectural heritage buildings in Turkey are crucial in terms of transferring not only the cultural identity of the nations to further generations but also architectural sustainability of its distinguished period. By time, where these buildings can have constructional problems with its original structural systems, proposing a new system related with new earthquake regulations is inevitable in order to conserve them. Because most of the buildings are under threat of being demolished. The purpose of the research is to evaluate existing modern architectural heritage buildings in terms of its existing structural system and to develop a constructional model for renewal of them. As a case study, two hotel examples in İstanbul as a modern architectural heritage is selected, one is Hilton Hotel and the other is Çınar Hotel respectively. Then, the architectural plans and sections of the buildings as a data has been collected through literature survey by content analysis. At the end, the model proposal has been developed for each hotel buildings according to existing structural systems. The proposed models can be applied to sustain the life of the hotel buildings with a retrofitted structural system. So, the paper tries to conserve/protect the modern architectural heritage buildings by retrofitting its construction via presenting a remarkable study of two hotel buildings. [ABSTRACT FROM AUTHOR]

Using a natural resource-based view and technology-organization-environment framework as the theoretical focus, this paper develops and tests a research model in which sustainable operations practices mediate the impact of industry 4.0 technologies on sustainable performance. The model also tests sustainable operations practices as a moderator of the effect of industry 4.0 technologies on sustainable performance. Data obtained from 302 participants in Turkey's technology development regions were utilized to gauge the aforesaid linkages via partial least squares structural equation modeling. As predicted, sustainable operations practices mediate the influence of industry 4.0 technologies on sustainable performance. Contrary to the study prediction, sustainable operations practices do not significantly moderate the impact of industry 4.0 technologies on sustainable performance. Theoretical and practical implications are discussed and future research directions are offered. [Display omitted] • Industry 4.0 technologies (I4Ts) increased sustainable performance (SP). • Sustainable operations practices (SOPs) mediated the effects of I4Ts on SP. • SOPs did not significantly moderate the effects of I4Ts on SP. [ABSTRACT FROM AUTHOR]

This paper aims to develop a roadmap for ISO 9001-certified construction companiesn to move towards effective Total Quality Management (TQM) implementation. Although ISO 9001-certified companies are more ready for TQM implementation, there is no roadmap developed for these companies to move towards TQM. Therefore, in this study, 8 main critical success factors (CSFs) and 24 sub-CSFs of effective TQM implementation were determined through a literature review and expert review meetings. Then, to reveal the application-level differences of these CSFs, a questionnaire survey was prepared and distributed to participants working at 75 Turkish construction companies. The 16 of 75 construction companies were selected among members of the Turkish Quality Association (Kalder). Finally, a total of 40 completed questionnaires were received and t -test analysis was performed on the survey data. The results revealed that the most significant differences observed between these companies are giving quality awareness education to employees, training of employees on how to use quality management methods and tools, quality data related to products provided by suppliers, and measurement of internal customer satisfaction. Consequently, a roadmap was proposed based on the revealed differences. With this roadmap, first, ISO-certified companies should provide quality awareness education to employees, train employees on how to use quality management methods and tools, collect quality data related to products provided by suppliers, and measure internal customer satisfaction to move towards TQM. [ABSTRACT FROM AUTHOR]

Renewable energy, especially wind power, is a practicable and promising solution to mitigate the existing dilemma associated with climate change. Efficient and accurate prediction of wind power could guide a variety of decisions for resource management. To improve the accuracy of wind power prediction, most existing studies are multistage, where signal processing methods are first employed to decompose a single time series, and then deep learning methods are utilized for prediction. The aforementioned approaches have shown satisfactory results but tend to involve a burdensome time series decomposition process. To address this problem, this paper proposes a hybrid attention-based deep learning approach to achieve more efficient and accurate wind power prediction. The essential idea behind the proposed approach is to incorporate the cumbersome decomposition process into a hybrid deep learning model consisting of different deep neural networks, where each deep neural network is designed to perform a specific part of the prediction task to maximize its corresponding advantages. Compared with the typical deep learning models for time series prediction, e.g., long short-term memory (LSTM) and gated recurrent units (GRU), the proposed deep learning model has the following two major advantages: (1) the model eliminates the time series decomposition process by time embedding layers to achieve efficient prediction, and (2) the model achieves more powerful high-level temporal feature extraction by leveraging the combination of a convolutional neural network (CNN), multiple stacked bidirectional long short-term memory (Bi-LSTM) networks, and the attention mechanism, thus providing high accuracy prediction. The proposed method is evaluated with a real-world wind power dataset in Turkey, and comparative experiments demonstrate the effectiveness and applicability of the proposed method. [Display omitted] • A hybrid attention-based deep learning model was developed for wind power prediction. • The time embedding layer improves the accuracy and efficiency of the prediction. • The combination of two deep neural networks improves the prediction accuracy. • An attention mechanism reduces the loss of information. • The predicted results are well consistent with the observed dataset. [ABSTRACT FROM AUTHOR]

[Display omitted] • A model is proposed to maximize market performance including tradable certificates. • The long-term impacts of regulations on market are discussed by fine and subsidy. • Three important objectives for government are investigated under four scenarios. • The obtained results are validated by Turkey's energy data as a real case study. • Fine policy is more effective than a subsidy to empower renewable electricity. In the last decades, due to the increasing environmental concerns such as energy-resource depletion and global warming, energy efficiency instruments and renewable energy production has been promoted more than ever. This paper investigates a comprehensive comparison between two energy markets, i.e., the tradable white certificate (TWC) market and the tradable green certificate (TGC) market. How to coordinate the two energy markets and what the coordination impacts become the main issues under a reward-penalty mechanism. This multi-agent problem is discussed in an electricity supply chain consisting of two populations of traditional energy suppliers who want to decide about using one of them under government intervention. In this regard, the behavior of the suppliers is analyzed under three different Scenarios, including traditional power generation, energy efficiency program (EEP), and renewable portfolio standards (RPSs). An evolutionary game is applied to formulate the problem. The results show that a mandatory fine policy is more useful than an energy-saving subsidy to motivate the traditional suppliers to move toward sustainable energy generation. This policy also helps governments to meet their sustainable-oriented goals, as we discussed in this work. The findings can be applied to extend sustainable-based energy projects in developing countries. [ABSTRACT FROM AUTHOR]

While studies indicate that collaboration between stakeholders plays a prominent role in reducing food loss and waste (FLW), they have not specified which stakeholder group's collaboration will be more effective in reducing FLW. To fill this gap in the literature, this paper aims to identify and classify fruit and vegetable supply chain (FVSC) stakeholders according to their salience level and offer mitigation strategies for different salient stakeholder groups to tackle FLW. The study was conducted in Turkish FVSC because fruit and vegetable loss accounted for 53% of the total food loss. A multi-method approach was utilised to achieve the aim. First, 26 semi-structured interviews were conducted with Turkish FVSC experts to determine FVSC stakeholders and FLW drivers. Second, to identify and classify salient stakeholders, stakeholder mapping was undertaken. Collaboration-related mitigation strategies are offered high salient stakeholders and other stakeholder groups to reduce the amount of fruit and vegetable loss and waste. According to content analysis results, 25 supply chain actors are identified as stakeholders, and 15 are classified as salient stakeholders who can be more effective in tackling FLW. In addition, based on the results, 26 FLW drivers are identified according to different supply chain stages. Collaboration-based mitigation strategies were developed to diminish the impact of FLW causes at different stages. This study is one of the early attempts to classify food supply chain stakeholders according to saliency level. This study offers collaboration-related mitigation strategies to eliminate FLW drivers that cause loss and waste between specific stages of the FVSC. • Salient stakeholders are identified to tackle food loss and waste. • Interviews and stakeholder mapping are used to identify and classify high salient stakeholders. • Interviews are applied to identify food loss and waste drivers. • Collaboration-related mitigation strategies are provided for high salient stakeholders. [ABSTRACT FROM AUTHOR]

Residential and commercial buildings consume around 40% of the total energy of the world. The building sector is also responsible for 30% of the global greenhouse gas emissions. There is an urgent need to assess the environmental carbon footprint (CFP) of global building industry. This study reviews the related studies and calculates the CFP of a residential building constructed in Turkey by considering the type of construction materials, energy consumption habits, maintenance and demolition stages in a 50 year of life cycle. The CFP of a common type of residential building constructed in Turkey found to be 48.87 kgCO 2 eq/m2/year. The CFP of structural elements used during the construction phase has been calculated to be 6.94 kgCO 2 eq/m2/year, which is about 14.21% of the total footprint. It is investigated that; the operational stage is the foremost contributor, representing 64.11% of CFP. The CO 2 emissions have been decreased by 38.05% by the implementation of basic methods. Unlike other studies, this paper revealed that the CFP from transportation of habitants is 6.5% more than the construction stage contributing 15.13% of overall emissions. The comparative studies of constructions in different countries show that CFP significantly depend on the behavioral factors of citizens of a country and the system boundaries selected. The results exposed that the effects of transportation should not be disregarded in the future studies. [Display omitted] • The total life cycle CFP of the building found to be 48.87 kgCO 2 eq/m2/year. • The CFP of construction materials is calculated to be 6.94 kgCO 2 eq/m2/year. • CFP from transportation of habitants is 6.5% more than the construction stage contributing 15.13% of overall emissions. • The daily energy use of the habitants is the main contributor to the CFP, representing the 64.11% of CFP. • The CFP of the building has been decreased by 38.05% by the implementation of low-carbon strategies. [ABSTRACT FROM AUTHOR]

Despite recent increase in microplastics-related studies, little is known about their abundance in seabed sediment areas. However, the quantity of microplastics (MPs) in sediments is imperative for an overall understanding of worldwide MPs pollution. To address the above-mentioned gap, this study was performed on 43 seabed sediment stations from the Marmara Sea of Turkey. Studied stations vary between marine (MRN), pier (PIER), stream (STR), sea discharge (SD), and deep-sea discharge (DSD) stations. Collected seabed sediment samples were analyzed for MPs abundance, shape, size and color distribution. Besides, an investigation on the effect of sea depth on MPs' total abundance, size, and shape was investigated using Pearson's and Spearman's product momentum correlation coefficient. Empirical data analysis showed that the highest MPs abundance at STR stations changed between 1956.5 ± 3031.8 and 3256.2 ± 5168.2 particle/kg (dry weight: d.w.) for small MPs particles (SMP) and large MPs particles (LMP), respectively. Contrarily, the lowest abundance was found as 224.8 ± 423.05 and 287.68 ± 218.6 particle/kg (d.w.) for SMP and LMP fractions, respectively at DSD stations. The MPs abundance were found at 5 categorized stations in the following order; STR>PIER>MRN>SD>DSD, noted that the highest MPs quantity was found at STR-6 station located in Golden Horn. The isolated MPs were predominantly filament and fragment in shape (42.34 ± 6.10% and 33.91 ± 6.92%), blue and white in color (40.46 ± 4.66% and 24.75 ± 3.83%). Mean MPs abundance was determined to be 1957.37 ± 4079.96 particle/kg (d.w) at all 43 stations and sizes between 1 and 5 mm was found to be predominant at depths between 5 and 71m. Furthermore, a negative correlation was found between sea depth and parameters such as total MPs abundance, shape, and size. The overall results revealed the widespread presence of MPs in the seas surrounding Istanbul. [Display omitted] • This paper focuses on microplastics in sea-sediments in the coastal areas of Istanbul. • Marine, pier, stream and sea discharge stations were comprehensively studied. • The highest microplastics were found for stream station pouring into Golden Horn. • The lowest microplastic particles were obtained for deep sea discharge stations. • Alarming microplastic pollution was noticed in the Golden Horn located stations. [ABSTRACT FROM AUTHOR]

Forests cover nearly a third of the Earth's land area, and they are a key factor for all life on Earth, but unfortunately, forest fires are the greatest danger to their presence. The wildfires jeopardize general wellbeing, security, and require high levels of government resources. They also lead to noteworthy debasement of nature, property loss, and high rates of human death and injury. This paper proposes an algorithm to use and route unmanned aerial vehicles (UAVs) to mitigate forest fire risks. The developed matheuristic algorithm hybridizes simulated annealing and local search metaheuristics with an integer linear programming model. The mathematical model was developed to solve the distance-constrained multi-based multi-UAV routing problem, and because of the complexity of the problem, the generated metaheuristics helps the model to find better solutions. The effectiveness of the proposed matheuristic is tested with a real-life case study for Turkey and is also compared with a genetic algorithm. The Turkish State Meteorological Service generates forest fire-risk maps countrywide every day to predict fire risks 3 days later by using meteorological data. These maps are used to generate the risky regions to be visited by the UAVs, and the existing airports are considered for the UAVs to take off and land. The algorithm is coded using MATLAB and ILOG. The metaheuristics are designed with problem-based operators, and their parameters are tuned by experiments. Computational results demonstrate the effectiveness of the algorithm and the hybridization procedures. Results demonstrate that the CPU times for the methods are acceptable. • Defines a distance-constrained multi-based multi-UAV routing problem. • Routes UAVs for managing forest fire risks in a countrywide manner. • Includes a matheuristic with metaheuristics and integer programming. • Tests the effectiveness of the method with real-life case scenarios from Turkey. • Illustrates the improvements achieved by the hybridization procedures. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel genetic algorithm-based seasonal fractional grey model is proposed. • Monthly electricity generation is forecasted since the Covid-19 lockdown started. • Seasonal grey and machine learning models are used for forecasting. • The selected countries are France, Germany, Spain, Turkey, and the UK. • The share of renewables in total electricity generation is forecasted. Balances in the energy sector have changed since the implementation of the Covid-19 pandemic lockdown in Europe. This paper analyses how the lockdown affected electricity generation in European countries and how it will reshape future energy generation. Monthly electricity generation from total renewables and non-renewables in France, Germany, Spain, Turkey, and the UK from January 2017 to September 2020 were evaluated and compared. Four seasonal grey prediction models and three machine learning methods were used for forecasting; the quarterly results are presented to the end of 2021. Additionally, the share of electricity generation from renewables in total electricity generation from 2017 to 2021 for the selected countries was compared. Electricity generation from total non-renewables in the second quarter of 2020 for France, Germany, Spain, and the UK decreased by 21%–25% compared to the same period of 2019; the decline in Turkey was approximately 11%. Additionally, electricity generation from non-renewables in the third quarter of 2020 for all countries, except Turkey, decreased compared to the same period of the previous year. All grey prediction models and support vector machine method forecast that the share of renewables in total electricity generation will increase continuously in France, Germany, Spain, and the UK to the end of 2021. The forecasting methods provided by this study open new avenues for research on the impact of the Covid-19 pandemic on the future of the energy sector. [ABSTRACT FROM AUTHOR]