This study investigates the impact of a solar photovoltaic (SPV) system on electricity consumption and carbon emissions in a private hospital. In 2024, the system produced 7,122,802.80 kWh of renewable electricity, preventing approximately 3,148 metric tons of CO₂e emissions and reducing total electricity costs by 72.3%. Data were analyzed using national tariff coefficients and emission factors published by the Energy Market Regulatory Authority (EPDK) and the Turkish Electricity Transmission Corporation (TEİAŞ). The results show that SPV integration effectively lowers both energy expenditures and carbon intensity in healthcare operations. This case demonstrates the technical feasibility and economic advantage of renewable energy adoption in energy-intensive institutions.

In the era of digital transformation, the protection of sensitive multimedia data against growing cyber threats has become increasingly critical. Traditional cryptographic and steganographic techniques, while effective individually, often fall short when faced with advanced detection and attack methods, making hybrid security approaches a necessity. In this study, a hybrid security approach combining chaotic algorithms and the Least Significant Bit (LSB) embedding method is proposed. The method is enhanced through key-dependent parameter assignment, as well as additional steps such as transient periods and square matrix transformation. Furthermore, vectorization after square matrix transformation simplified indexing in embedding and extraction steps, thereby improving computational efficiency. As a result, high security and integrity were achieved for both visual and audio data. In the encryption process, seven different chaotic structures (Logistic Map, Lorenz System, Piecewise Linear Chaotic Map, Tent Map, Hénon Map, Chua Circuit, Chebyshev Map) were supported, and the method was tested on these algorithms. The original content was encrypted using XOR and then embedded into the audio signal via the LSB method. The proposed method was evaluated using the EBU SQAM audio dataset and standard test images with Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), and Bit Error Rate (BER) metrics. Experimental results demonstrated that for all tests, the extracted images achieved Visual_PSNR = ∞, Visual_SSIM = 1, and Visual_BER = 0. For audio data, PSNR values ranged between 102–107 dB and SNR values approximately 53–99 dB depending on the content type. These findings reveal that the proposed method ensures both the integrity of the content and the reliable preservation of the hidden data.

The analysis of musculoskeletal system movements using electromyography (EMG) signals is a fundamental requirement in fields such as prosthetic control, human-machine interaction, and neuromuscular rehabilitation. This study presents a comprehensive approach that not only evaluates movement recognition accuracy but also quantitatively assesses the level of muscle force required for each movement. In the study, the muscle loading profile of each hand movement was created using EMG signal energy normalized to the Rest state. Five different classifier models were compared under 5-fold cross-validation (CV) and Leave-One-Subject-Out (LOSO) protocols. The results showed that the Extension movement had the highest normalized power value and that classification accuracy reached its highest level with SVM-RBF (86.95%). Furthermore, Out-of-Bag (OOB) error analysis revealed that the model converged stably around 600–800 trees, while accuracy differences between individuals were attributed to physiological variations. The proposed framework offers a new evaluation perspective for both ergonomic task design and clinical performance monitoring by assessing gesture recognition performance alongside muscle strength requirements.

Rapid developments in mobile and cloud technologies in particular are increasing the need for data storage today. This growing demand for data storage is leading to a rapid increase in the number of data centers. Data centers not only store organizations' digital information, but also process this data, play a role in its distribution, and assist in its management. In these centers, where numerous servers are housed together, security and sustainability are of critical importance to organizations, and any disruptions that may occur here can be extremely difficult to mitigate. Therefore, ensuring that data centers are designed to operate 24/7 and implementing the necessary precautions are essential requirements. The growing number of data centers has led to a significant increase in energy demand, both globally and in Türkiye. This situation poses serious problems for countries in terms of ensuring an energy supply and creating the necessary infrastructure. In recent years, the excessive energy consumption of data centers has become an area of intense research, and great efforts have been made to solve this problem. In this context, a new field of study called “Data Center Efficiency” has emerged. This study focuses on 18 data centers operating in Türkiye. Within the scope of the study, total facility power, IT equipment power, cooling capacity and total CO₂ amount data were collected from these data centers. Using this data, energy efficiency values and power utilization efficiencies of the data centers were calculated. Based on the values obtained, the efficiency of data centers was compared and classified. In addition, several recommendations are presented to improve the energy efficiency of data centers.

The development of next-generation photovoltaic devices requires the integration of advanced nanomaterials with superior electrical, optical, and mechanical properties. Among such materials, fullerenes (C₆₀ and their derivatives such as PCBM) and graphene have attracted significant attention due to their complementary functionalities. Fullerenes act as efficient electron acceptors, facilitating charge separation in donor–acceptor systems. While graphene provides excellent electrical conductivity, high optical transparency, and mechanical flexibility, making it a promising alternative to conventional transparent electrodes such as indium tin oxide (ITO).

This paper reviews recent progress in the design and fabrication of fullerene–graphene hybrid photovoltaic devices. The typical device structure includes a flexible or rigid substrate, a graphene transparent electrode, a polymer–fullerene active layer, selective charge transport layers, and a metallic back contact. Experimental studies demonstrate that hybridization of fullerenes with graphene improves charge transport pathways, reduces series resistance, and enhances power conversion efficiency (PCE). Furthermore, fullerene–graphene composites contribute to device stability under prolonged illumination and thermal stress.

Overall, fullerene–graphene hybrid materials represent a promising strategy for the development of high-efficiency, cost-effective, and flexible solar cells. Their potential applications extend beyond traditional photovoltaics to include portable energy sources, building-integrated photovoltaics, and wearable electronic devices, contributing to the broader goal of sustainable energy technologies.

In this study, an artificial intelligence-based quality control system was developed for the automatic detection and classification of defects in a tile. The dataset created to reduce human-induced errors in the production process and increase inspection accuracy consists of a total of 405 images.
During the model development phase, CNN, MobileNetV2, ResNet50, and EfficientNetB0 architectures were used. The performance of the models was evaluated using the 10-fold cross-validation method for an objective comparison.
The experimental results show that the EfficientNetB0 architecture achieved the highest performance with an accuracy rate of 96.73%. ResNet50 achieved 95.45%, CNN achieved 94.91%, and MobileNetV2 achieved 92.36% accuracy. 

Traditional street lighting systems are inadequate in performing basic functions such as monitoring, control, and centralized management. The traditional lighting technologies used in these systems do not allow for the optimization of energy consumption and maintenance activities. A significant portion of the existing infrastructure is designed with older generation lamps and analog control units, which imposes major limitations in terms of energy efficiency and sustainability. However, rapid technological advances in recent years and increased industrial activity focused on smart lighting solutions have enabled the integration of sensor-based, communication-enabled, and remotely accessible Internet of Things (IoT) and LED-based systems into street lighting systems.
This study examines IoT and LED-based systems developed to increase energy efficiency and reduce operating costs in street lighting applications. The study describes current technological developments in IoT applications by bringing together smart poles equipped with LED lamp technology, smart sensors, communication networks, and monitoring unit components. Furthermore, LED-based systems used in lighting systems are evaluated in comparison with traditional high-pressure sodium (HPS) and mercury vapor (HPM) lamps. Technical parameters such as power consumption, light efficiency, color rendering index (CRI), and lifespan are analyzed. Furthermore, this study demonstrates the applicability of these systems in smart city infrastructures in line with Turkey's National Energy Efficiency Action Plan and contributes to future applications.