Dicle University

In this paper, we determine the radial and orbital states mass spectra of the triply heavy baryon [Formula: see text] with total spin [Formula: see text] and 3/2 using the hypercentral constituent quark model (hCOM). The mass spectra are obtained by incorporating the color Coulomb plus linear confining term and the harmonic oscillator potential with first-order correction. Additionally, we considered spin–spin, spin-orbit and tensor interactions for hyperfine splitting calculation. Our computed mass spectra of the [Formula: see text] baryon is compared with existing literature. Further, we establish Regge trajectories of this baryon in ([Formula: see text]) and ([Formula: see text]) planes which are helpful in determining the unknown quantum number and [Formula: see text] values.

One of the main limitations of biopolymers compared to petroleum-based polymers is their weak mechanical and physical properties. Recent improvements focused on surmounting these constraints by integrating nanoparticles into biopolymer films to improve their efficacy. This study aimed to improve the properties of gelatin–chitosan-based biopolymer layers using zinc oxide (ZnO) and graphene oxide (GO) nanoparticles combined with spermidine to enhance their mechanical, physical, and thermal properties. The results show that adding ZnO and GO nanoparticles increased the tensile strength of the layers from 9.203 MPa to 17.787 MPa in films containing graphene oxide and zinc oxide, although the elongation at break decreased. The incorporation of nanoparticles reduced the water vapor permeability from 0.164 to 0.149 (g.m−2.24 h−1). Moreover, the transparency of the layers ranged from 72.67% to 86.17%, decreasing with higher nanoparticle concentrations. The use of nanoparticles enhanced the light-blocking characteristics of the films, making them appropriate for the preservation of light-sensitive food items. The thermal properties improved with an increase in the melting temperature (Tm) up to 115.5 °C and enhanced the thermal stability in the nanoparticle-containing samples. FTIR analysis confirmed the successful integration of all components within the films. In general, the combination of gelatin and chitosan, along with ZnO, GO, and spermidine, significantly enhanced the properties of the layers, making them stronger and more suitable for biodegradable packaging applications.

In order to improve the overall hydrothermal performance of minichannel heat exchangers (MCHEs), variations in the width and height of minichannels are investigated. A range of 3D numerical simulations are systematically conducted by varying the mass flux of water as the working fluid. The obtained results are comprehensively compared with the traditional design of MCHE, and new conclusions are drawn. The results indicate that different configurations of minichannel cause completely different effects on the thermal and hydraulic attributes of MCHE. It is found that by increasing the mass flux from 125 to 1000 kg m−2 s−1, the thermal enhancement index reaches 1.79 for the model that is converged on both sides of the cross section. Although converging the minichannels can ameliorate the thermal performance of MCHE, the hydraulic performance is unsatisfactory. However, when the cross section of minichannels diverges, MCHE exhibits better overall hydrothermal performance. Furthermore, the simultaneous divergence of both the width and height of the cross section leads to even better performance compared to diverging only one side. By comparing all minichannels, it seems that the minichannel model with complete divergence has the best performance in the studied ranges and achieved the maximum heat transfer rate to pumping power ratio of 25.4 × 10–5 at a mass flux of 125 kg m−2 s−1.

The objective of the present study is to develop a novel nanocomposite platform for drug delivery, and photothermal therapy. Molybdenum disulfide (MoS2) nanosheet, as one of the most stable transitional metal dichalcogenides, indicates unique structure, thermal, and optical properties. In this work, MFC nanocomposite was synthesized from MoS2 nanosheets, iron oxide (Fe3O4) nanoparticles, and copper monosulfide (CuS) nanoparticles. Then structural, morphology, and optical properties of the nanocomposite were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDS), fourier transform infrared (FTIR), and ultraviolet-visible (UV-Vis) spectroscopies. After that, a photothermal experiment was done for the MFC nanocomposite with different concentrations (50, 100, 150, 200, and 400 ppm). Photothermal experiments indicated that nanocomposite with the concentration of 400 ppm have produced the highest photothermal heat (58.3 $$\:℃\:$$ ) after 10 min near infrared (NIR) laser irradiation. Then, a doxorubicin (DOX) drug was loaded into the nanocomposite. It was further studied for in-vitro DOX release with, and without laser irradiation. Results indicated that in the presence of NIR laser irradiation (1 W/cm2), the optimized DOX/MFC nanocomposite show a controlled drug release of 63.5% in pH = 5.8 after 4 h. Finally, the cytotoxicity of MFC nanocomposite on Hela cells was assessed using an MTT assay. The result of the MTT assay shows that 69.9% of Hela cells were killed by the nanocomposite at the concentration of 400 µg/mL, and under an 808 nm laser irradiation. Finally, a DOX drug was loaded in the nanocomposite with different concentrations. Results illustrated that in the presence of NIR- laser radiation (1 W/cm2) cells viability were decreased when DOX concentration was increased in the nanocomposite. Therefore, the MFC nanocomposite at the concentration of 400 ppm was suggested as a good candidate in photothermal therapy, and drug delivery.

In this study, we discuss the development of the stress recovery method and error estimation in analyzing Functionally Graded Material (FGM) problems using the isogeometric method. The paper furnishes evidence elucidating why Gauss points demonstrate enhanced accuracy in tackling these issues. Additionally, the effectiveness of this error estimation approach is explored in guiding a network improvement algorithm, which relies on the adjustment of control points. In this approach, which uses Gaussian points, each component of the improved stress tensor is considered as an imaginary surface. This surface is created with the same B-spline basis functions used to approximate the primary variables in the isogeometric method. The results show that by comparing the exact and approximate energy error norm in six numerical examples and estimating the effectiveness index of more than 85%, the proposed method can be used as a suitable approach to improve the stress and error estimation of functionally graded material problems by isogeometric method.

Renewable energy sources are particularly significant in global energy production, with wind and solar being the most prevalent sources. Managing the simultaneous connection of wind and solar energy generators to the smart grid as distributed generators involves complex control and stabilization due to their inherent uncertainties, making their management more intricate than traditional power plants. This study focuses on enhancing the speed and efficiency of the maximum power point tracking (MPPT) system in a solar power plant. A hybrid network is modeled, comprising a wind turbine with a doubly-fed induction generator (DFIG), a solar power plant with photovoltaic (PV) cells, an MPPT system, a Z-source converter, and a storage system. The proposed approach employs a motion detection-based method, utilizing image-processing techniques to optimize the MPPT of PV cells based on shadow movement patterns within the solar power plant area. This method significantly reduces the time required to reach the maximum power point (MPP), lowers the computational load of the control system by predicting shadow movements, and enhances the MPPT speed while maintaining system stability. The approach, which is suitable for relatively large solar farms, is implemented without the need for any additional sensors and relies on the system’s history. The simulation results show that the proposed approach improves the MPPT system’s efficiency and reduces the pressure on the control circuits by more than 70% in a 150,000 m2 solar farm under shaded conditions.

Aza-Diels–Alder cycloaddition reaction is a critical synthetic method for the production of bioactive tetrahydroquinolines. To this aim, an imine obtained from the reaction of an aniline derivative and a carbonyl compound is cyclized with an alkene in the presence of a catalyst. In this research, some tetrahydroquinoline compounds are synthesized through aza-Diels–Alder reaction in the presence of a prepared Ce(III) immobilized on the functionalized halloysite (Ce/Hal-TCT-IDA) as a catalyst. Ce/Hal-TCT-IDA was prepared by incorporation of aminopropyl silane on the halloysite surface, followed by treatment with 2,4,6-trichloro-1,3,5-triazine (TCT) and iminodiacetic acid (IDA), and loading cerium nitrate. Then, it was characterized and analyzed by different analytical methods, indicating the amorphous agglomerated grains (20–60 nm), containing 0.00196 mmol g−1 Ce(III) ions. The catalytic activity and reusability of Ce/Hal-TCT-IDA were studied in this research.

ABSTRACTBreast cancer is a leading cause of mortality among women, emphasizing the critical need for precise early detection and prognosis. However, conventional methods often struggle to differentiate precancerous lesions or tailor treatments effectively. Thermal imaging, capturing subtle temperature variations, presents a promising avenue for non‐invasive cancer detection. While some studies explore thermography for breast cancer detection, integrating it with advanced machine learning for early diagnosis and personalized prediction remains relatively unexplored. This study proposes a novel hybrid machine learning system (HMLS) incorporating deep autoencoder techniques for automated early detection and prognostic stratification of breast cancer patients. By exploiting the temporal dynamics of thermographic data, this approach offers a more comprehensive analysis than static single‐frame approaches. Data processing involves splitting the dataset for training and testing. A predominant infrared image was selected, and matrix factorization was applied to capture temperature changes over time. Integration of convex factor analysis and bell‐curve membership function embedding for dimensionality reduction and feature extraction. The autoencoder deep neural network further reduces dimensionality. HMLS model development included feature selection and optimization of survival prediction algorithms through cross‐validation. Model performance was assessed using accuracy and F‐measure metrics. HMLS, integrating clinical data, achieved 81.6% accuracy, surpassing 77.6% using only convex‐NMF. The best classifier attained 83.2% accuracy on test data. This study demonstrates the effectiveness of thermographic imaging and HMLS for accurate early detection and personalized prediction of breast cancer. The proposed framework holds promise for enhancing patient care and potentially reducing mortality rates.

AbstractWe correct some unfortunate mistakes that appeared in the article D. Thaghizadeh, M. Zahraei, A. Peperko, and N. H. Aboutalebi, On the numerical ranges of matrices in max algebra, Banach J. Math. Anal., 14 (2020), 1773–1792, concerning certain notions of the numerical range in the max algebra setting. Along the way we also include a study of the characteristic max polynomial and correspondingly the max k-spectrum and the k-tropical spectrum. We also pose a related and intriguing open question.

AbstractIntroductionAs of 2020, about 21% of adults in the United States have a diagnosable mental health disorder, excluding substance use and developmental disorders. Depression, predicted by the WHO to be the leading cause of disease burden by 2030, is linked to various systemic conditions and has been associated with poor oral health. Both behavioral factors, like poor dental hygiene and irregular visits, and biological mechanisms, such as changes in salivary immunity, contribute to this connection, which impacts overall well‐being and quality of life. This systematic review aims include: (1) Does tooth loss affect depression? (2) Does oral pain, such as that experienced during chewing and speaking, impact depression? (3) Does oral functionality, including chewing and speaking, influence depression? (4) Does overall oral health affect depression?MethodsWe conducted a systematic search of PubMed, EBSCO host, Medline, and Google Scholar databases from January 2000 to June 2024 using relevant keywords. Studies examining the impact of oral health parameters (tooth loss, oral pain, oral functionality, overall oral health) on depression were included. Articles were included if (1) full text manuscripts in English were available, (2) the study described the association of oral health and depression, and (3) the independent value was an oral related factor and the dependent value was depression. The following were excluded from our analysis: (1) any articles where oral factors were not the independent value, (2) systematic reviews, (3) case reports, and (4) duplicate studies among our databases. Thirty‐one studies met the inclusion criteria.ResultsTooth loss, oral pain, and impaired oral functionality were consistently associated with increased depressive symptoms across the included studies. Greater tooth loss was linked to higher odds of both onset and progression of depression. Oral pain exacerbated depressive symptoms, while difficulties in chewing or speaking were associated with elevated risks of depression.ConclusionThere is a bidirectional relationship between oral health and depression, highlighting the urgent need for comprehensive public health initiatives. Integrating oral health assessments into routine medical care, and developing targeted interventions are crucial steps to mitigate the impact of poor oral health on mental health outcomes.

Intrusion detection in network systems is a critical challenge due to the ever-increasing volume and complexity of cyber-attacks. Traditional methods often struggle with high-dimensional data and the need for real-time detection. This paper proposes a comprehensive intrusion detection method utilizing a novel wrapped feature selection approach combined with a long short-term memory classifier optimized with the whale optimization algorithm to address these challenges effectively. The proposed method introduces a novel feature selection technique using a multi-layer perceptron and a hybrid genetic algorithm-particle swarm optimization algorithm to select salient features from the input dataset, significantly reducing dimensionality while retaining critical information. The selected features are then used to train a long short-term memory network, optimized by the whale optimization algorithm to enhance its classification performance. The effectiveness of the proposed method is demonstrated through extensive simulations of intrusion detection tasks. The feature selection approach effectively reduced the feature set from 78 to 68 features, maintaining diversity and relevance. The proposed method achieved a remarkable accuracy of 99.62% in DDoS attack detection and 99.40% in FTP-Patator/SSH-Patator attack detection using the CICIDS-2017 dataset and an anomaly attack detection accuracy of 99.6% using the NSL-KDD dataset. These results highlight the potential of the proposed method in achieving high detection accuracy with reduced computational complexity, making it a viable solution for real-time intrusion detection.

Glioma is a kind of brain tumor that develops in the central nervous system and is classified based on its histology and molecular genetic features. The lifespan of patients does not exceed 22 months. One of the motives for the low effectiveness of glioma treatment is its radioresistance and chemoresistance. Noncoding RNAs (ncRNAs) are a diverse set of transcripts that do not undergo translation to become proteins in glioma. The ncRNAs have been identified as significant regulators of several biological processes in different cell types and tissues, and their abnormal function has been linked to glioma. They are known to impact important occurrences, including carcinogenesis, progression, and enhanced treatment resistance in glioma cells. The ncRNAs control cell proliferation, migration, epithelial-to-mesenchymal transition (EMT), invasion, and drug resistance in glioma cells. The main focus of this study is to inspect the involvement of ncRNAs in the drug resistance of glioma.