University of Kyrenia

This study aimed to develop an algorithm to automatically segment the oral potentially malignant diseases (OPMDs) and oral cancers (OCs) of all oral subsites with various deep convolutional neural network applications. A total of 510 intraoral images of OPMDs and OCs were collected over 3 years (2006-2009). All images were confirmed both with patient records and histopathological reports. Following the labeling of the lesions the dataset was arbitrarily split, using random sampling in Python as the study dataset, validation dataset, and test dataset. Pixels were classified as the OPMDs and OCs with the OPMD/OC label and the rest as the background. U-Net architecture was used and the model with the best validation loss was chosen for the testing among the trained 500 epochs. Dice similarity coefficient (DSC) score was noted. The intra-observer ICC was found to be 0.994 while the inter-observer reliability was 0.989. The calculated DSC and validation accuracy across all clinical images were 0.697 and 0.805, respectively. Our algorithm did not maintain an excellent DSC due to multiple reasons for the detection of both OC and OPMDs in oral cavity sites. A better standardization for both 2D and 3D imaging (such as patient positioning) and a bigger dataset are required to improve the quality of such studies. This is the first study which aimed to segment OPMDs and OCs in all subsites of oral cavity which is crucial not only for the early diagnosis but also for higher survival rates.

This study aims to determine which signs are cognitively sufficient to convince the viewer to enter a social place, through the integration of semiotics and design. Photographs of 6 social places were obtained through personal photographs. A semiotic model of entrances was created and qualitative data were analyzed. An online survey was used to find out the perceived effectiveness of signs influencing customer decisions. A total of 595 respondents from all over the world responded to the questionnaire. Chi-square analysis was used for statistical analysis for differences between countries and age groups. Results of this study showed that specific colors, lights, transparency, right symbols, large scaled forms and gold lettering on black background raise the visibility of social place entrances and convince customers to enter the place and explore further. According to color preferences of countries, differing from previous literature; black or gray tones followed by red was preferred in Turkey and Cyprus. Red was the most preferred color in the The U.S.A., Middle East and Far East. In order to convince the customers to enter sociable places; this study has pointed out the importance of right signs to be used at sociable place entrances considering the different cultural connotations.

This paper discusses the impact of film industry on CO2 emission of the USA by using four different single-equation cointegration methods, namely, fully modified least squares (FMOLS), dynamic least squares (DOLS), canonical cointegrating regression (CCR), and autoregressive distributed lag (ARDL) to check the robustness of the results. The data has chosen in line with the environment Kuznets curves (EKC) and pollution haven hypothesis, and the models use communication equipment in millions of dollars and capital in entertainment, literacy, and artistic originals as determinants of motion picture and sound recording industries with other control variables such as income per capita and energy use to examine their nexus. Moreover, we also employ the Granger causality test to determine whether one variable is a predictor of another. The results approve the validity of EKC hypotheses for the USA. As expected, increase in energy use and capital assets results in rise in CO2 emission, while communication equipment improves the environmental quality.

Type 1 diabetes (T1D) is characterized by destruction of pancreatic insulin-producing beta cells by immune cells. In general, environmental and genetic factors can lead to immunological self-tolerance in TID. It is clear that the innate immune system, especially natural killer (NK) cells, is involved in the pathogenesis of T1D. Aberrant NK cell frequencies associated with dysregulation of inhibitory and activating receptors contribute to the initiation and progression of T1D. As T1D is incurable and the metabolic disturbances caused by T1D severely impact patients, a better understanding of NK cell behavior in T1D may facilitate disease treatment strategies. The current review focuses on the role of NK cell receptors in T1D and also highlights ongoing efforts to manipulate key checkpoints in NK cell-targeted therapies.

In this article, ZrB2–SiC–Si layers were applied on graphite substrate employing SPS technology. Additives such as MoSi2 and WC were also used to improve sinterability, increase adhesion and proper bonding between the applied composite layer and the graphite substrate. To select the optimal sample in terms of densification, adhesion and bonding between the substrate and the applied composite layer, microstructural and phase studies were performed on the composite layers. The Si penetration depth in the graphite substrate in the ZrB2–14.3 vol% Si–14.3 vol% SiC–2.5 vol% MoSi2 –2.5 vol% WC sample, made at 1890 °C under 25 MPa for 5 min, was more than 165 µm, and the thickness of the penetration layer composed of SiC–C was more than other samples. The densification process in this sample was almost complete and a dense structure was obtained.

Objective: Brain tumors in childhood carry a high risk for endocrine disorders due to the direct effects of the tumor and/or surgery and radiotherapy. Somatotropes are vulnerable to pressure and radiotherapy; therefore, growth hormone deficiency is one of the most frequent abnormalities. This study aimed to evaluate endocrine disorders and recombinant growth hormone treatment outcomes in brain tumor survivors. Materials and methods: In this study, 65 (27 female) patients were classified into 3 groups as craniopharyngioma (n = 29), medulloblastoma (n = 17), and others (n = 19). "Others" group included astrocytoma, ependymoma, germinoma, pineoblastoma, and meningioma patients. Anthropometric data and endocrine parameters of patients and their growth outcome with/without recombinant growth hormone therapy were collected from medical records, retrospectively. Results: Mean age at the first endocrinological evaluation was 8.7 ± 3.6 years (range: 1.0- 17.1 years). Height, weight, and body mass index standard deviation score, mean ± standard deviation (median) values were -1.7 ± 1.7 (-1.5), -0.8 ± 1.9 (-0.8), and 0.2 ± 1.5 (0.4), respectively. Hypothyroidism (central 86.9%, primary 13.1%) was detected during follow-up in 81.5% of patients. Primary hypothyroidism in medulloblastoma (29.4%) was significantly higher compared to other groups (P = .002). The frequency of hypogonadotropic hypogonadism, central adrenal insufficiency, and diabetes insipidus was significantly high in the craniopharyngioma cases. Conclusion: In our study, endocrine disorders other than growth hormone deficiency were also frequently observed. In craniopharyngioma cases, the response to recombinant growth hormone therapy was satisfactory. However, there was no improvement in height prognosis during recombinant growth hormone therapy in medulloblastoma patients. A multidisciplinary approach to the care of these patients, referral for endocrine complications, and guidelines on when recombinant growth hormone therapy is required.

There has been considerable academic interest in the study of nonlinear dynamical models and their exact traveling waves over the past few years. The main aim of the present paper is to consider a nonlinear dynamical model known as the nonlinear Konno–Oono model and derive its exact traveling waves. Specifically, after applying a universal transformation, periodic and solitary waves of the governing model with applications in the electromagnetic field are derived using generalized methods. Through the consideration of two- and three-dimensional simulations, several case studies are considered to represent the dynamical behavior of soliton solutions.

The major issues and challenges of the Industrial Internet of Things (IIoT) include network resource management, self-organization; routing, mobility, scalability, security, and data aggregation. Resource management in IIoT is a challenging issue, starting from the deployment and design of sensor nodes, networking at cross-layer, networking software development, application types, environmental conditions, monitoring user decisions, querying process, etc. In this paper, computational intelligence (CI) and its computing, such as neural networks and fuzzy logic, are used to tackle the challenges of resource management in the IIoT. The incorporation of the neuro-fuzzy technique into the IIoT contributes to the self-managing intelligence systems’ self-organizing and self-sustaining capabilities, offering real-time computations and services in a pervasive networking environment. Most of the problems in IIoT are realtime based; they require fast computation, real-time optimal solutions, and the need to be adaptive to the situation of the events and data traffic to achieve desired goals. Hence, neural networks and fuzzy sets would form appropriate candidates for implementing most of the computations involved in the issues of resource management in IIoT networks. A real-time test-bed network is simulated and implemented on the Crossbow mote (sensor node) using TinyOS.

The pandemic cause as a result of the outbreak of COVID‐19 disease continues to burden the healthcare system despite several interventions using vaccines and other preventive measures. Healthcare settings adopted the use of reverse transcription‐polymerase chain reaction (RT‐PCR) which is hampered by so many challenges such as miss‐diagnosis, false positive results, high cost, especially for those in remote and rural areas, the need for trained medical pathologists, the use of chemicals, and a lack of point‐of‐care detection. The use of radiographic images as an alternative or confirmatory approach has offered medical experts another option, but has some limitations, such as misinterpretation, and can be tedious for analyzing thousands of cases. In order to bridge this gap, we applied two AlexNet models for the classification of different types of pneumonia, including COVID‐19 using X‐ray. Considering the fact that the majority of articles in the literature reported binary classifications of radiographic images. This article utilizes X‐ray images for classification of COVID‐19, non‐COVID‐19 viral pneumonia, bacterial pneumonia, and normal cases using the AlexNet‐SoftMax classifier and the AlexNet‐SVM classifier. The research also evaluates performance based on 5k‐fold and 10k fold cross validation (CV). The results achieved in terms of accuracy, sensitivity, and specificity based on 70:30 partition, 5k, and 10k CV have shown that the models outperformed the majority of the state‐of‐the‐art deep learning architectures.

Intrauterine adhesions (IUA) are defined as the adhesion of opposing endometrial tissue with dense fibrous adhesive bands within the uterine cavity. With the increase in cesarean sections and endometrial surgical procedures, intrauterine adhesions have become a problem with increasing incidence and decreasing implantation. The purpose of the study was to investigate the effect of ellagic acid (EA), a phenolic compound, on fibrosis in IUA model rats. Another goal of the study was to increase endometrial receptivity with EA. The groups in the study were planned as control, DMSO, EA, IUA, IUA+DMSO, and IUA+EA, with 8 Sprague Dawley rats in each group. EA was administered at a dose of 100 mg/kg/day for 35 days. At the end of the experiment, the uterine tissues of the rats were removed. Histochemical staining was used to validate the IUA model and determine the degree of fibrosis. The levels of some fibrosis-related genes and proteins in the obtained uterine tissues were evaluated. In addition, implantation rates were determined. In our findings, it was observed that the fibrotic structure was decreased in the treated IUA+EA group compared to the IUA group, while fibrotic improvement was supported by down-regulation of TGFβ1 activity and up-regulation of BMP7 activity. The increase in the expression of the endometrial marker LIF with EA treatment was consistent with the increase in implantation rates with treatment. As a result of the study, it can be said that EA applied as a treatment against IUA causes healing in uterine tissue by reducing fibrosis and increases implantation rates by increasing endometrial receptivity.

Objective: Federated Learning (FL) enables collaborative training of artificial intelligence (AI) models from multiple data sources without directly sharing data. Due to the large amount of sensitive data in dentistry, FL may be particularly relevant for oral and dental research and applications. This study, for the first time, employed FL for a dental task, automated tooth segmentation on panoramic radiographs. Methods: We employed a dataset of 4,177 panoramic radiographs collected from nine different centers (n=143 to n=1,881 per center) across the globe and used FL to train a machine learning model for tooth segmentation. FL performance was compared against Local Learning (LL), i.e., training models on isolated data from each center (assuming data sharing not to be an option). Further, the performance gap to Central Learning (CL), i.e., training on centrally pooled data (based on data sharing agreements) was quantified. Generalizability of models was evaluated on a pooled test dataset from all centers. Results: For 8 out of 9 centers, FL outperformed LL with statistical significance (p<0.05); only the center providing the largest amount of data FL did not have such an advantage. For generalizability, FL outperformed LL across all centers. CL surpassed both FL and LL for performance and generalizability. Conclusion: If data pooling (for CL) is not feasible, FL is shown to be a useful alternative to train performant and, more importantly, generalizable deep learning models in dentistry, where data protection barriers are high. Clinical significance: This study proves the validity and utility of FL in the field of dentistry, which encourages researchers to adopt this method to improve the generalizability of dental AI models and ease their transition to the clinical environment.

Background Pre or postnatal exposure to pesticides can result in immune system disorders and development of allergic diseases. The study evaluates the association between breast milk organochlorine pesticide (OCP) levels and development of allergic diseases in the first year of life. Methods The study involved 93 infants and their mothers. Breast milk samples were collected within the first six months of birth and were analysed for the presence of six OCP residues (Endrin, DDD, DDT, DDE, Heptachlor, Lindane) using high-performance liquid chromatography and an acetylcholinesterase-based chronoamperometric biosensor. Infants were monitored for 12 months for any signs of allergies. Results 28 samples contained at least one of the six OCP residues, with Endrin being the most frequently detected OCP at 25.8%. Heptachlor had the highest mean estimated daily intake (EDI) value of 2.42 x10 and the highest mean hazard quotient (HQ) of 4.84. EDI of Endrin, DDE, DDD, and Heptachlor exceeded provisional tolerable daily intake (PTDI) levels in 9, 7, 11, and 11 infants, respectively. The presence of OCP residues in breast milk was not significantly associated with any type of allergy, including food allergies, rhinitis, wheezing episodes, or eczema in the first year of life. Conclusion Even though calculated HQ and EDI values higher than PTDI values are a matter of concern for health, no significant association between breast milk OCP’s and allergic diseases in the first year of life are shown in the present study.

Background: Sexual dysfunction may indicate severe endocrine diseases. Recent research has suggested a link between hypothyroidism, low testosterone (T) levels, and erectile dysfunction (ED); however, the exact cause is unknown. Aim: We sought to investigate possible beneficial effects of levothyroxine and T alone or in combination on ED in propylthiouracil (PTU)-induced hypothyroid rats. Methods: Adult Wistar rats (n = 35) were divided into 5 groups: control, PTU-induced hypothyroidism, PTU + levothyroxine, PTU + Sustanon (a mixture of 4 types of T: propionate, phenylpropionate, isocaproate, and decanoate) and PTU + levothyroxine + Sustanon. PTU was given in drinking water for 6 weeks. Four weeks after PTU administration, levothyroxine (20 μg microgram kg/day, oral) and Sustanon (10 mg/kg/week, intramuscular) were given for 2 weeks. Serum levels of total T, triiodothyronine (T3), and thyroxine (T4) were determined. In vivo erectile response and in vitro relaxant responses were measured. Localization of neuronal nitric oxide synthase (nNOS), endothelial NOS (eNOS), and phosphodiesterase type 5 (PDE5) were determined using immunohistochemical analysis. The relative area of smooth muscle to collagen was measured using Masson trichrome staining. Outcomes: Outcome variables included in vivo erectile function, in vitro relaxant and contractile responses of corpus cavernosum (CC) strips; protein localization of eNOS, nNOS, and PDE5; and smooth muscle content in penile tissue. Results: The rat model of hypothyroidism showed a significant decline in serum levels of total T, T3, and T4. Levothyroxine increased T3 and T4 levels, whereas Sustanon normalized only total T levels. Combined treatment enhanced all hormone levels. Rats with hypothyroidism displayed the lowest erectile response (P < 0.001 vs controls). Combined treatment returned reduced responses, while partial amelioration was observed after levothyroxine and Sustanon treatment alone. Acetylcholine (P < 0.01 vs controls), electrical field stimulation (P < 0.001 vs controls), and sildenafil-induced relaxant responses (P < 0.05 vs controls) were decreased in the CC strips from hypothyroid rats. The combined treatment increased the reduction in relaxation responses. Levothyroxine and Sustanon restored decreases in eNOS and nNOS expression in the hypothyroid group. There was no significant difference in PDE5 expression among groups. Monotreatment partially enhanced reduced smooth muscle mass, while combined therapy completely recovered. Clinical implications: The combination of thyroid hormones and T is likely to be a therapeutic approach for treatment of hypothyroidism-induced ED in men. Strengths and limitations: Beneficial effects of levothyroxine and Sustanon treatment were shown in vitro and in vivo in PTU-induced hypothyroid rats. The main limitation of the study was the lack of measurement of androgen-sensitive organ weights and luteinizing hormone, follicle-stimulating hormone, and prolactin levels. Conclusion: These findings demonstrate that neurogenic and endothelium-dependent relaxation responses are reduced by hypothyroidism, which is detrimental to T levels and erectile responses. Levothyroxine and Sustanon combination medication was able to counteract this effect.

In this work, anthropogenic and natural factors were used to evaluate and forecast climate change on a global scale by using a variety of machine-learning techniques. First, significance analysis using the Shapley method was conducted to compare the importance of each variable. Accordingly, it was determined that the equivalent CO2 concentration in the atmosphere was the most important variable, which was proposed as further evidence of climate change due to fossil fuel-based energy generation. Following that, a variety of machine learning approaches were utilized to simulate and forecast the temperature anomaly until 2100 based on six distinct scenarios. Compared to the preindustrial period, the temperature anomaly for the best-case scenario was found to increase a mean value of 1.23 °C and 1.11 °C for the mid and end of the century respectively. On the other hand, the anomaly was estimated for the worst-case scenario to reach to a mean value of 2.52 °C and 4.97 °C for the same periods. It was then concluded that machine learning approaches can assist researchers in predicting climate change and developing policies for national governments, such as committing firmly to renewable energy regulations.

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the field of advanced robotics in recent years. AI, ML, and DL are transforming the field of advanced robotics, making robots more intelligent, efficient, and adaptable to complex tasks and environments. Some of the applications of AI, ML, and DL in advanced robotics include autonomous navigation, object recognition and manipulation, natural language processing, and predictive maintenance. These technologies are also being used in the development of collaborative robots (cobots) that can work alongside humans and adapt to changing environments and tasks. The AI, ML, and DL can be used in advanced transportation systems in order to provide safety, efficiency, and convenience to the passengers and transportation companies. Also, the AI, ML, and DL are playing a critical role in the advancement of manufacturing assembly robots, enabling them to work more efficiently, safely, and intelligently. Furthermore, they have a wide range of applications in aviation management, helping airlines to improve efficiency, reduce costs, and improve customer satisfaction. Moreover, the AI, ML, and DL can help taxi companies in order to provide better, more efficient, and safer services to customers. The research presents an overview of current developments in AI, ML, and DL in advanced robotics systems and discusses various applications of the systems in robot modification. Further research works regarding the applications of AI, ML, and DL in advanced robotics systems are also suggested in order to fill the gaps between the existing studies and published papers. By reviewing the applications of AI, ML, and DL in advanced robotics systems, it is possible to investigate and modify the performances of advanced robots in various applications in order to enhance productivity in advanced robotic industries.

The titanium alloys are widely used in aeronautical engineering and medical device materials due to exceptional mechanical properties such as tensile resistance and toughness of fractures. High thermo-mechanical loads occur in metal cutting of Titanium alloy Ti6Al4V, which can decrease life of cutting tool and increase cost of part production. In this paper, the coolant effects on the cutting temperature, surface roughness and tool wear are investigated by using the developed virtual machining system. The cutting forces during turning operations of Ti6Al4V alloy are accurately calculated in order to be used in calculation of cutting temperature and tool wear. The modified Johnson-Cook methodology is utilized to obtain the cutting temperatures along machining paths. Then, the Coupled Eulerian-Lagrangian (CEL) approach is investigated to predict and evaluate the effects of coolants on the cutting temperature in turning operations of Ti6Al4V alloy. The finite element approach is employed to predict tool wear by using the Takeyama-Murata analytical model and modifying the cutting tool geometry during the chip production process. To verify the developed methodology in the study, the results of experiments for the measured cutting temperatures, surface quality and wear rate are compared to the results of virtual machining system obtained by the finite element simulation. Thus, utilizing the proposed virtual machining system in the study, cutting temperatures, surface quality and tool wear during the turning operations of Ti6Al4V alloys with and without coolant can be accurately predicted to enhance the accuracy as well as productivity in the CNC machining operations.

A substantial proportion of coronavirus disease 2019 (COVID-19) survivors continue to suffer from long-COVID-19 (LC) symptoms. Our study aimed to determine the risk factors for LC by using a patient population from Northern Cyprus. Subjects who were diagnosed with severe acute respiratory syndrome-2 (SARS-CoV-2) infection in our university hospital were invited and asked to fill in an online questionnaire. Data from 296 survivors who had recovered from COVID-19 infection at least 28 days prior the study was used in the statistical analysis. For determination of risk factors for “ongoing symptomatic COVID-19 (OSC)” and “Post-COVID-19 (PSC)” syndromes, the patient population was further divided into group 1 (Gr1) and group 2 (Gr2), that included survivors who were diagnosed with COVID-19 within 4-12 weeks and at least three months prior the study, respectively. The number of people with post-vaccination SARS-CoV-2 infection was 266 (89.9%). B.1.617.2 (Delta) (41.9%) was the most common SARS-CoV-2 variant responsible for the infections, followed by BA.1 (Omicron) (34.8%), B.1.1.7 (Alpha) (15.5%), and wild-type SARS-CoV-2 (7.8%). One-hundred-and-nineteen volunteers (40.2%) stated an increased frequency of COVID-19-related symptoms and experienced the symptoms in the week prior to the study. Of those, 81 (38.8%) and 38 (43.7%) were from Gr1 and Gr2 groups, respectively. Female gender, chronic illness, and symptomatic status at PCR testing were identified as risk factors for developing OSC syndrome, while only the latter showed a similar association with PSC symptoms. Our results also suggested that ongoing and persistent COVID-19-related symptoms are not influenced by the initial viral cycle threshold (Ct) values of the SARS-CoV-2, SARS-CoV-2 variant as well as vaccination status and type prior to COVID-19. Therefore, strategies other than vaccination are needed to combat the long-term effect of COVID-19, especially after symptomatic SARS-CoV-2 infection, and their possible economic burden on healthcare settings.

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

The dimensional, geometrical, thermal and tool deflection errors which have a big portion of the overall error of machined parts need more attention in precision of components produced by using CNC machine tools. As a result, it is essential to simulate and compensate the errors in the machined components in order to increase accuracy of machined parts. In order to simulate and analyse the real manufactured components in virtual environments, virtual machining systems are proposed. In this paper, application of virtual machining system is investigated in order to simulate and compensate dimensional, geometrical, thermal and tool deflection errors in 5-axis milling operations of free form surfaces. The volumetric error vectors regarding the dimensional, geometrical, thermal and tool deflection errors at each cutting tool location throughout the machining pathways are calculated and compensated utilising the study’s created virtual machining technology. In order to validate the study, a sample workpiece free from surfaces is milled by using the 5-axis CNC machine tool. The machine part is then measured by suing the CMM machine in order to obtain the dimensional, geometrical, thermal and tool deflection errors during milling operations of free form surfaces. Thermal sensors are also installed to the different locations of CNC machine tool in order to measure the thermal error of CNC machine tool during machining operations. Finally, in order to improve accuracy in 5-axis milling operations of free form surfaces, new cutting tool paths regarding the compensated volumetric errors of dimensional, geometrical, thermal, and tool deflection errors are generated. As a result, by utilising the proposed virtual machining system in the research work, precision as well as reliability during 5-axis milling operations of free form surfaces can be enhanced.