We developed a novel, rapid and efficient high performance liquid chromatography (HPLC) method and validated to analyse the quantification of Lamivudine (LAM) in different pharmaceutical formulations, including pure form, commercial tablet, and nanostructured lipid carrier (NLC), a novel drug carrier system. Accurate analysis of the amount of active ingredient in pharmaceutical formulations is very important for assessment of the quality and therapeutic efficacy of formulations. In method, we used distilled water: methanol (MeOH) (60:40 v/v) as mobile phase and analysed on C18 column. To analyse the eluent, the method was performed at 270 nm, with a flow rate of 1 mL/min, in 10 min. The calibration curve obtained showed linearity in the concentration range 2-60 ppm. The average recovery of pharmaceutical preparations (Zeffix, GlaxoSmithKline tablets and NLC formulation) was 99.552%. Our method’s limit of detection (LOD) was 1.494 μg/mL. Our method’s limit of quantification (LOQ) was 0.514 μg/mL. The method also allowed the determination of the amount of LAM contained in the existing commercial formulation and the newly developed NLC formulation and the verification of the homogeneity of the pharmaceutical formulations. The results obtained show that the developed HPLC method can be used reliably in both formulation development and stability studies in NLC drug carrier systems.
Adhesively bonded joints are widely employed as components in a wide range of industrial applications. These joints can be subjected to quasi-static forces as well as dynamic loads. Therefore, the determination of the dynamic properties of these joints is very critical in terms of the strength and service life of the structure. In this study, previously manufactured and mechanically characterised aramid and carbon fiber reinforced composites were modelled as adherend components in joints and the effects of interply/intraply hybridization on the vibration behaviour of joints were investigated by finite element analysis (FEA). The first natural frequencies and mode shapes of the joints were obtained for three different boundary conditions (free-free, clamped-free and clamped-clamped). It was observed that the clamped boundary condition had a significant effect on the first natural frequencies of joints with intraply adhrends.
The pursuit of cost-effective, high-performance, and eco-friendly energy storage solutions has driven increasing interest in aqueous ammonium-ion batteries. These systems provide enhanced safety, sustainability, and affordability, attributed to the low molar mass and small hydrated ionic radius of ammonium ions. However, identifying a cathode material capable of reversible ammonium-ion storage in aqueous electrolytes remains a key challenge. This study explores lithium manganese oxide (LiMn₂O₄) as a promising cathode material for ammonium-ion batteries. The spinel LiMn₂O₄ structure, known for its cubic symmetry and interconnected 3D ion-diffusion channels, ensures efficient charge transport and robust electrochemical performance. Additionally, its low-cost raw materials and environmental advantages make it an attractive alternative to conventional transition metal oxides. With a theoretical capacity of ~148 mAh g⁻¹, LiMn₂O₄ exhibits substantial specific capacity, contributing to improved battery energy density. The material was synthesized via a high-temperature solid-state reaction, and X-ray diffraction (XRD) confirmed the formation of a stable orthorhombic structure. Electrochemical analysis using cyclic voltammetry indicated a two-step lithium extraction process in ammonium-ion electrolytes. As cycling progressed, redox peaks associated with ammonium-ion insertion and extraction became more defined, highlighting the material's capability for efficient and reversible charge storage. Galvanostatic charge-discharge tests revealed that the MnO₂-based electrode delivered a stable specific capacity of approximately 47 mAh g⁻¹ during NH₄⁺ intercalation/de-intercalation. The study demonstrates that LiMn₂O₄ effectively supports ammonium-ion storage, offering a sustainable and high-performance cathode option for next-generation aqueous batteries. These findings provide crucial insights into the material’s electrochemical behavior and potential for advancing ammonium-ion battery technology.
The mammalian target of rapamycin (mTOR) pathway plays a critical role in cancer progression, making it a key target for therapeutic intervention. Dysregulation of mTOR signaling is frequently observed in malignancies, highlighting the need for potent and selective inhibitors. In this study, a series of benzimidazole derivatives were designed and evaluated for their potential as mTOR inhibitors. Cytotoxicity assessments using MTT assays demonstrated that compounds 10 and 15 exhibited significant anti-proliferative effects against breast cancer cell lines, with IC₅₀ values of 6.63 µM and 5.28 µM, respectively. Further biochemical studies revealed that the most active compounds effectively suppressed mTOR phosphorylation at Ser2448 in MCF-7 cells, as confirmed by colorimetric enzymatic activity assays. These results suggest that compound 15, in particular, represents a promising lead for the development of novel mTOR-targeted therapies. This study provides valuable insights into the structure-based design of mTOR inhibitors, offering a foundation for future advancements in targeted cancer treatment.
The improvement of tribological properties of biomaterials in load-bearing implants is very important. In this study, the tribological properties of TiN and ZrN films deposited on surfaces by CA-PVD method on two different types of biomaterials (CP-Ti and CoCrMo alloy) were compared under dry wear conditions and 1N and 3 N loads. In addition, the microstructural and mechanical properties of TiN and ZrN films deposited on the surface on CP-Ti and CoCrMo materials were investigated. The crystal structure, elemental composition and surface morphology of TiN and ZrN coated CP-Ti and CoCrMo materials were determined using XRD, SEM and SEM-EDS analyses, respectively. According to the test results conducted in a dry environment after the wear test under 1N load, the lowest friction coefficient was found in the untreated CoCrMo sample at approximately 0.35, while the highest friction coefficient was found in the ZrN-coated CoCrMo sample at 0.55. While the lowest wear rate under 3N load was 0.38x10-6 mm3/Nm in the ZrN/CCM sample, the highest wear rate was 2.10x10-6 mm3/Nm in the untreated Ti sample. As a result, it was determined that the microhardness values and wear resistance of the TiN and ZrN-coated CP-Ti and CoCrMo samples increased.
In patients with cardiovascular disease, ECG (Electrocardiography) recording and pulse data must be monitored. ECG devices frequently used in hospitals provide output on paper but cannot transfer data to a computer environment. In this case, it is not possible to store, compare and analyze patient data. Portable holter devices used today are generally heavy and are of a size that will cause discomfort to the patient. The long cables used both increase noise and disturb the patient. In some of the new generation devices designed, the control unit and sensors are on separate cards and are combined with cables. This causes noise to be added. In this study; a lightweight, ergonomic, integrated on a single card, ECG/Pulse test device using short electrode cables was designed. An ergonomic case was produced to place the device in the middle of the rib cage. In the device designed using an embedded system, the data received from the user was recorded on the computer and a database using software created in Python. Patient data can be transferred to the specialist doctor thanks to the mobile software created for instant patient monitoring. The design will be very useful for patient monitoring, especially in intensive care units.
This paper presents an overview based on comparison of different machine and deep learning methods applied to perform the sentiment analysis of tweets related to mobile games. The dataset, gathered from Twitter (X) between 2020-2021, was preprocessed and vectorized using Count Vectorizer and TF-IDF methodology. Traditional machine learning (ML) models such as Linear Support Vector Classifier (SVC), Logistic Regression (LR), Ridge Classifier (RC), and Voting Classifier (VC) were benchmarked against a few deep learning (DL) architectures such as the TEMSAP-CNNLSTM model stand-alone and BERT-enhanced versions. The study used precision, F1-score, recall, accuracy, and the AUC to check the performance of the model. The results revealed that DL models outperformed traditional ML classifiers, with these models achieving the highest classification performance of 97,10% and achieving impressive success in minimizing false negatives and false positives. The Ridge Classifier exhibited the lowest performance, correctly classified twitter reviews at an accuracy of 76,76%, indicating its limitations in sentiment classification. In addition, ensemble learning techniques like the Voting Classifier performed much better than individual machine learning models, thus re-establishing the benefits of model aggregation. This study demonstrated that transformer-based models such as BERT have shown remarkable success in sentiment classification of text data related to mobile games. What is even more promising for furthering academic and industrial agendas is that it will provide informed insights into how to make the best selection for enhancing the analysis of user sentiment and identifying the best models to make the play of mobile games more entertaining.
We introduce a new Gray map Q(a+ub)=(a,a+3b), along with an automorphism θ(a+ub)=a+u(3b). Using these, we construct quasi-cyclic codes as left submodules of the skew polynomial ring R[x;θ] over R=Z_4+uZ_4. Although the original codes are skew cyclic, we show that their Gray images under Q are invariant under a modified cyclic shift operator, and hence are generalized quasi-cyclic codes over Z_4. Our analysis of the Lee weight transformation, supported by examples, demonstrates that this approach yields codes with predictable structure and favorable properties.
In this study, the strength and permeability properties of recycled concrete aggregate and waste brick powder obtained from construction and demolition wastes were investigated under the effect of different mixing ratios and alkali solution molarities. Sisal fiber, a natural fiber type, was applied to the soil samples prepared with recycled concrete aggregates and alkali solution-brick powder mixtures at different lengths, and the effect of the fiber additive was evaluated. For this purpose, alkali solution mixture-recycled soil was mixed at 15%, 20%, and 30% ratios. In order to determine the effect of an alkaline environment, alkali solution molarities were applied as 0.5M, 1M, 1.5M, 2M, and 2.5M. Fiber additive was applied at a constant rate, in lengths of 10 mm, 15 mm, and 20 mm. The results were evaluated with unconfined compressive strength and permeability tests. In addition, the behavior of soil samples under load was also investigated by considering the values of maximum unconfined compressive strength and axial strain corresponding to maximum strength test results. The results of this study showed that the mixture ratios were effective on the unconfined compressive strength and that the strength values increased as the mixture ratio increased. It was determined that the increase in the molarity of the alkali solution was more effective in increasing the strength compared to the mixture ratios and that this effect increased even more as the mixture ratio increased. It was observed that the fiber additive provided an increase in both the maximum strength and the strain values corresponding to these values and that it had positive effects on the load-carrying capacity and behavior of the soil. According to the permeability test results, it was evaluated that this soil improvement technique used could also be used for permeable soil surface layers.
This study aimed to determine the pomological, morphological, biochemical, antioxidant, and nutritional properties of 33 naturally grown kumquat (Citrus japonica) accessions using multivariate statistical methods. The analysis revealed significant variation among the accessions. Tukey’s multiple comparison test (p
This study aims to investigate the trace element composition, amino acid profile, and antioxidant activity of Verbascum orientale, a medicinal plant in Erzincan, Türkiye. The root and stem extracts were analyzed using ICP-MS to determine trace element levels, while LC-MS/MS was employed for amino acid profiling. Additionally, the antioxidant activities of methanol: water (70:30, v:v) extracts were evaluated using DPPH, FRAP, total phenolic content (TPC), and total flavonoid content (TFC) assays. The findings indicate that Verbascum orientale contains significant levels of sodium (Na) and silicon (Si), suggesting an adaptive response to arid and stressful environmental conditions. In addition, the plant exhibited high levels of amino acids such as L-proline, L-glutamine, and L-asparagine, which are essential for osmotic regulation and stress adaptation. A high iron (Fe) content suggests potential nutritional benefits, while the presence of aluminum (Al) raises concerns about possible toxicity. Despite its diverse phytochemical profile, Verbascum orientale exhibited moderate antioxidant activity compared to other Verbascum species, with the root extract demonstrating a stronger radical scavenging effect than the stem. These results underscore the importance of further research to evaluate the medicinal and nutritional applications of Verbascum orientale, particularly in terms of bioavailability and safety.
The mechanical, tribological, and chemical characteristics of nitride-based coatings make them crucial for industrial applications. C-BN films have better qualities than other nitride-based films. However, when ultra-high hardness values have been achieved, the adhesion characteristics of c-BN films produced by magnetic field sputtering with various power sources are often inadequate. High Power Impulse Magnetic Field Sputtering (HiPIMS) power sources have been applied recently to improve coating adhesion. In this study, c-BN films were coated on 4140 steels at increasing duty time and target voltage at constant N2 flow using HiPIMS technique. SEM and FT-IR analyses were performed to investigate the structural properties of c-BN films; microhardness and scratch tests were performed to investigate their mechanical properties; pin-on-disk tribo test was performed to determine their tribological properties. As a result of the tests, the film thickness increased with increasing duty time and target voltage, while the hardness value and critical load value decreased. In addition, friction coefficient decreased with increasing duty time and target voltage.
Drug delivery systems for the food-processing animals in the veterinary field is particularly limited to the disease prevention and growth promotion. Therefore, optimizing dissolution performance of a targeted formulation may be achieved using a performance enhancer together with a commercial binder. In this study, poly(vinyl pyrrolidone) (PVP) was modified with graphene oxide (GO) to form a more stable binder matrix. The dissolution profiles of the formulations with and without the addition of GO into the bolus matrix were examined using two different methods: the continuous dissolution rig based on an artificial saliva and an in vitro dissolution test by the means of Daisy-II incubator. The kinetic models of First Order, Higuchi, Hixson-Crowell and Korsemeyer-Peppas were compared for a best fit of the experimental results obtained using the continuous dissolution rig and Daisy II. The results showed that PVP-containing tablets dissolved more rapidly, whereas PVP-GO combination provided a more controlled and prolonged release profile. Daisy II results in higher rate of dissolution for both formulations compared to the ones in the continuous dissolution rig.
Service-oriented architecture, one of the popular software architectures that have become very popular in recent years, has scalability, isolation and flexibility as it consists of smaller and independent domain-specific services compared to monolithic systems. For this reason, the transition from monolithic monolithic systems to service-oriented architectures is becoming widespread for large-scale applications with millions of users to have an easily manageable, scalable and flexible structure. In this study, the effectiveness of various machine learning models and different types of tokenization methods were evaluated by analyzing static source code to decompose monolithic legacy systems into domain-specific services. Standard machine learning algorithms and transformer-based tokenizers were applied to the FXML-POS legacy system and model performance were evaluated using precision, recall, accuracy, and F1 scores. Experimental results indicate that all transformer models achieve strong performance with an F1 score of 91.9% using Random Forest and Logistic Regression classifiers. Furthermore, it has been observed in the experimental results that the Word2Vec vectorization method outperforms TF-IDF in most scenarios and a maximum F1 score of 97.2% is achieved using the Random Forest Classifier. These results underscore the utility of advanced embedding techniques and classifiers in the accurate identification of domain-specific service components.
Cervical cancer is one of the most common cancers in women. Due to the side effects and inadequate treatment methods of current cancer drugs used in the treatment of cervical cancer, it is important to develop new treatment strategies. Vulpinic acid (VA), a natural lichen secondary metabolite with many remarkable biological activities, has no detailed study describing its potential anti-cancer molecular mechanism in cervical cancer HeLa cell line. We reported in our previous study that VA exhibited anti-proliferative, apoptotic, and anti-migratory properties in HeLa cells and the IC50 dose of VA in HeLa cells was calculated as 66.53 µg/mL at 48 h. The effect of VA on the WNT/β-catenin signaling pathway, which plays a role in various biological processes including tumorigenesis, cell proliferation, cell cycle regulation, embryogenesis, metastasis, cellular differentiation, apoptosis and drug resistance, is unknown. In this study, we aimed to elucidate whether VA exerts its antimigratory effect on HeLa cells treated with IC50 dose through the WNT/β-catenin signalling pathway. In summary, this study demonstrated that the suppression of migration of HeLa cells by VA may be mediated by inhibition of the WNT/β-catenin signalling pathway. VA may be a natural active compound candidate for the therapy of human cervical cancer and may be among the inhibitory candidates of the WNT/β-catenin signalling pathway.
Traditional energy sources are incompatible with contemporary environmental sustainability goals, hence renewable and eco-friendly alternatives are more popular. Due to its polyunsaturated fatty acid make up, biodiesel is prone to oxidative breakdown despite its low toxicity, biodegradability, and emissions. This reduces gasoline storage life and engine performance. Tilia platyphyllos extract, a natural phenolic, improved biodiesel-diesel (50% biodiesel + 50% diesel) blends' oxidation resistance in this research. The soxhlet extract was added to the gasoline at 3000 ppm and compared to TBHQ, a synthetic antioxidant. Thermal and chemical characterization methods FT-IR, TGA, and DSC were used to assess antioxidant activity. We tested the extract's ability to eliminate free radicals using DPPH• and ABTS•⁺ assays. The results show that Tilia platyphyllos is a powerful antioxidant that prevents biodiesel-diesel blend oxidative degradation. Biofuel technology might benefit from Tilia platyphyllos.
Illicit drugs are a global problem, and a variety of selective techniques are urgently needed to detect drugs of interest. Biosensors are integrated devices that combine biological recognition elements with transducers. Aptasensors are aptamer-based affinity biosensors, which are known for their exceptional specificity and high binding affinity to target molecules, and are providing significant advancements in the field of health, food, environmental, and forensic applications. Many aptasensors are nearly identical to conventional immunochemical aptamer, and aptamers are analogous to antibodies. Electrochemical aptasensors are aptamer for biological recognition integrated with electrochemical transduction. Recent studies on the use of electrochemical aptasensors for the detection of illicit drugs, including cannabis, opioids and amphetamine derivatives, in the last decade are discussed in this section. This section aims to present current technologies and challenges, and highlights gaps in the literature that can be addressed. This review emphasises the need for further research and development to improve the sensitivity, selectivity and applicability of aptasensors for point-of-care applications.
This paper presents a new hybrid structure for transformer based multilevel inverter (MLI) topologies. The proposed topology integrates an asymmetric multiplexer circuit (push-pull), a high frequency link (HFL) circuit and PUC to produce 11 levels at the output voltage wave. The main structure includes 10 power switches and four rectifier diodes. Simulation results and experimental results confirm that the proposed inverter effectively synthesizes the 11-level AC voltage waveform with low total harmonic distortion (THD). The inverter is tested with 100Ω pure resistive, 30mH and 50mH inductive loads under different load conditions to verify its robustness and operational stability. Loss simulations are performed with PLECS software. The power loss analysis shows that the system operates with an efficiency of 98% in overall performance. The dynamic response of the inverter is analyzed under varying modulation indices and operating frequencies and shows reliable performance over a wide operating range. Due to its high efficiency and reduced component count, the proposed topology is particularly suitable for single-source applications such as renewable energy integration, stand-alone power systems and electric vehicle inverters.
This study examines the impact of Artificial Intelligence (AI) and Machine Learning (ML) technologies on the fields of Mechatronics Engineering, Robotics, and Automation. Through a comprehensive bibliometric analysis of publications indexed in the Web of Science (WoS) database, the historical development, key research trends, and prominent themes of this interdisciplinary domain are revealed. Additionally, six different time series forecasting methods—ARIMA, ETS, Theta, Holt-Winters, Polynomial Regression, and Naive Model—are employed to predict the number of scientific publications for the year 2025. The analysis results indicate a growing influence of AI/ML in the field of mechatronics and a clear upward trend in publication volume. This study offers a unique perspective on the research directions in the field by quantitatively illustrating how AI and ML interact with mechatronics engineering.
In this study, it is aimed to obtain the highest energy that can be obtained from hydroelectric power plants (HPP) by providing minimum water consumption. Accordingly, with the Particle Swarm Optimization (PSO) method applied according to the characteristics of the plants specified in the study (Atatürk, Karakaya, Keban, Altınkaya and Deriner), the highest electricity production capacities that can be produced from the plants were obtained by reaching the optimum flow rate, water consumption value from the Matlab/Simulink model diagrams. The best solution obtained according to the PSO algorithm method is presented in terms of global best (Gbest), particle best (Pbest) energy production and water consumption. Energy production and target function values (fitness) for each flow rate range were tried to be determined pointwise. As a result, existing or new HEPP’s to be established should be provided with a balance between energy production and water consumption, sustainable, well-manageable and more efficient.
