In recent years, as a result of the increasing demand for health services, medical waste (MW) generated from health facilities has increased significantly. Problems that threaten the environment and public health may arise as a result of inadequate medical waste management (MWM), especially in densely populated metropolitan areas. Therefore, it is very important that the disposal process of waste is carried out in a way that minimizes harm to human health and the environment. MW disposal site selection is among the most important decisions that local governments make. These decisions have many conflicting and similar criteria and alternatives. However, decision-makers may experience significant uncertainty when evaluating the alternatives. This study adopts the interval-valued neutrosophic (IVN) fuzzy EDAS method for the evaluation of MW disposal facility siting alternatives in Istanbul. This approach is used to evaluate potential sites based on a comprehensive, hierarchical criteria framework designed to address data uncertainty and inconsistency common in multi-criteria decision-making (MCDM) scenarios. Within the scope of the study, six main criteria (distance settlement area, social acceptance, costs, environmental impacts, infrastructure facilities, and disaster and emergency) and nineteen sub-criteria are meticulously analyzed. Considering the geographical location and dense urban texture of Istanbul, the study emphasizes the criteria related to distance to residential areas, logistics costs, and potential disaster risks. Among the identified criteria, land costs, topographical features, proximity to landfills, and distance to high-voltage lines are emphasized as the least important criteria. This study, which evaluated various alternatives, identified Pendik, located on the Anatolian side of Istanbul, as the most suitable site for MW disposal due to its minimal risk. The study also compares the four main alternatives and highlights their relative strengths and weaknesses.

Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability of these methods are subjects of paramount importance. This study rigorously investigates the effectiveness of three distinct machine learning techniques and two statistical approaches when applied to streamflow data from the Göksu Stream in the Marmara Region of Turkey, spanning from 1984 to 2022. Through a comparative analysis of these methodologies, this examination aims to contribute innovative advancements to the existing methodologies used in the prediction of streamflow data. The methodologies employed include machine learning methods such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) and statistical methods such as Simple Exponential Smoothing (SES) and Autoregressive Integrated Moving Average (ARIMA) model. In the study, 444 data points between 1984 and 2020 were used as training data, and the remaining data points for the period 2021–2022 were used for streamflow forecasting in the test validation period. The results were evaluated using various metrics, such as the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R²), and Nash–Sutcliffe efficiency (NSE). Upon analyzing the results, it was found that the model generated using the XGBoost algorithm outperformed other machine learning and statistical techniques. Consequently, the models implemented in this study demonstrate a high level of accuracy in predicting potential streamflow in the river basin system.

In the rapidly evolving and competitive sector of agricultural production, optimizing operational efficiencies is crucial for the sustainability of enterprises. This study introduces a novel approach to enhance the profitability and sustainability of perishable food production enterprises by optimizing harvest planning and logistics activities, which are significantly influenced by weather conditions. Using the weighted fuzzy c‐means (WFCM) method, a two‐stage solution approach was developed to improve the decision‐making process in both short‐ and long‐term operational planning. In the first stage, clustering analysis was conducted to determine optimal facility locations and assign fields to these facilities, thereby facilitating the efficient processing of perishable food products. Following this, an integer linear programming model was developed to optimize the harvest plan, considering the variable weather‐related costs and maximizing the total operating profit. This innovative approach not only considers the economic value of the product, which fluctuates over time, but also integrates weather precipitation data to dynamically adjust the harvesting plan, thereby minimizing costs and maximizing revenues. The model was rigorously tested using real data from 16 sugar factories in Türkiye and their corresponding sugar beet fields. The results demonstrated a substantial potential increase in operating profit by 27.47% compared with the current scenario. Furthermore, the model promises to reduce economic losses associated with improper storage and stacking and to stabilize seasonal fluctuations in vehicle supply and freight prices by distributing vehicle demand over a longer period. This study adds a significant layer to the existing literature, offering a comprehensive solution that addresses the complex interplay of various factors in agricultural production and setting the stage for more resilient and sustainable operations in the perishable food sector.

This paper proposes a model that forecasts the weather and then, based on that forecast, uses an income-oriented linear programming method to optimize the harvesting process. Data representing a total yearly output capacity of 472,878 tons from 214 different field locations were used to test the model for sugar beet production. Prior to optimization, long-term one-year weather rainfall forecasting was done using 10 years of actual weather data for the field locations. Weather precipitation was forecasted using logistic regression with an accuracy of 84.16%. The outcome of the weather precipitation prediction model was a parameter in the optimization model. The weather forecast for precipitation led to the 120-day harvest planning being optimized. Comparative analysis was done on the outcomes of the developed model and the current scenario. Comparing the current situation to the proposed one, revenue would have increased by 16.7%. Given that it incorporates weather forecasts into the harvest optimization process, the methodology presented in this paper is more practical than other harvest optimization models.

Sürekli veri akışının, veriye dayalı planlamaların daha etkin şekilde yapılabildiği akıllı şehir uygulamaları her geçen gün farklı uygulama alanlarıyla gelişmeye devam etmektedir. Akıllı şehir uygulamaları her ne kadar teknolojik olanaklarla donatılmış uygulamaları kapsasa da, bu uygulamaların teknolojiden bağımsız olarak sosyal konularda etkin çözüm önerileri sunması ve hayata geçirilmesi gerekmektedir. Dezavantajlı bireylerin sağlık hizmetlerine erişimi, farklı akıllı şehir uygulamalarındaki teknolojik altyapı ve üstyapı olanakları ile sistemler arası etkin iletişimi sağlayan yapıların planlanması ve hayata geçirilmesi önemlidir. Dezavantajlı bireyler ikametlerinden çıktığı andan itibaren, risksiz şekilde sağlık birimlerine ulaşmaları ve sağlık hizmetlerini almaları sağlandığında, bireylerin yaşam kalitesinin artmasının yanı sıra, erken teşhise bağlı olarak sağlık giderlerinde de ciddi tasarruflar sağlanacaktır. Bu çalışma, “Akıllı Şehirlerde Dezavantajlı Bireylerin Sağlık Hizmetlerine Erişimi” raporundan derlenmiştir.