Cyprus International University

The building sector includes energy used for constructing, heating, cooling and lighting houses and businesses, as well as the appliances and equipment installed in these buildings. This sector accounts for a significant part of the global energy demand. Hence, lowering down the energy consumption in this sector would significantly help the countries in terms of balancing the demand and supply and freeing up the financial resources. Reducing the energy consumption requires changing daily choices in energy consumption and adopting energy efficient technologies. Many different policies can be applied in building sector in order to increase the use of energy-efficient technologies. This chapter will focus on four major policy types mostly employed in building sector: Appliance standards, building codes, incentives and, labels and consumer information.

The economy in Lebanon has confronted noteworthy encounters in the past few years, depicted by economic uncertainty, political instability, and financial crises. This paper attempts to redeem a gap in the literature employing transaction money, balance of payments, total exports, Consumer Price Index, average interest rate on loans, total deposits in commercial banks, and foreign currencies at the central bank as macroeconomic indicators using the Vector Autoregression (VAR). Monthly data was collected from 2011 to 2022. The findings reveal significant short- and long-term interconnections between key macroeconomic variables, with shocks to money supply and balance of payments exerting notable impacts on inflation and interest rates. The model highlights the complex transmission mechanisms within the Lebanese economy, offering insights into how these variables influence macroeconomic stability. Previous studies were conducted in stable economic environments, making this study a valuable addition to the literature, particularly in understanding macroeconomic dynamics in crisis-stricken economies.

Modeling of different processes and phenomena in real-world is one of the most important fields of the mathematics in which qualitative dynamics of such systems are studied from mathematical point of view. In this paper, we discuss the qualitative properties of solutions of a temperature control system in the context of a mathematical model in fractional discrete calculus. We discretize our supposed control system with the help of two delta sum and difference operators in the sense of the Caputo and Riemann–Liouville. By the existing properties of the falling functions, we obtain the equivalent difference formula corresponding to the given discrete delta difference boundary value problems of temperature control system. To conduct an analysis on solutions of this fractional system, the existence results are investigated via fixed points and the stability bahaviors are proved from the Ulam–Hyers point of view. In two applied examples, we use numerical data to simulate solutions of such discrete fractional delta boundary value problems of temperature control system.

The biological activity of both cyclophosphazenes and peptides makes these compounds important for new studies in medicinal chemistry. For this purpose, five different phosphazene‐peptide conjugates synthesized from dichlorocyclotriphosphazene and tyrosine‐containing tripeptides. The synthesized compounds were evaluated for their in vitro cytotoxic activities against human breast (MCF‐7) and colon (Caco‐2) cancer cell lines using MTT assay. The derivatives induced cell death through DNA damage, with notable effects in Caco‐2 cell lines. Specifically, DTVV, DTVG, and DTVA were cytotoxic at 50 and 100 μM, while DTVP and DTVM were effective at 25, 50, and 100 μM. DTVM outperformed Tamoxifen at 50 μM in the MCF‐7 cell line. DNA damage studies of the compounds were performed using the comet assay method, evaluating tail length, tail density, olive tail moment, head length, and head density parameters. The findings indicated that cell death occurred via a DNA damage mechanism. The molecular intricacies of DTVA, DTVG, DTVM, DTVP and DTVV within the VEGFR2 kinase domain (3VHE) and Cyclophilin_CeCYP16‐Like Domain (2HQ6) binding pockets and various interactions, docking scores and potential activities of these derivatives were investigated. The differences in docking scores and interaction profiles highlight the potential efficacy and specificity of these compounds in targeting breast and colon cancer cells. These findings highlight the potential of phosphazene‐peptide derivatives as therapeutic agents in cancer treatment.

Purpose To investigate any correlation between the outcomes of the first euploid frozen-thawed blastocyst embryo transfer (FBT) and the subsequent euploid FBT derived from sibling oocytes. Methods This retrospective study analyzed data from 1051 women who underwent preimplantation genetic testing for aneuploidy and had a euploid FBT. Of these patients, 159 underwent a second transfer. The primary outcome was the live birth rate. Results Overall, 159 women who underwent a second euploid FBT were categorized into two subgroups depending on the implantation success of the first FBT. Of these patients, 94 (59.1%) belonged to the nonclinical group, signifying a negative result or a biochemical pregnancy. The remaining 65 (40.9%) patients belonged to the clinical group, indicating either a miscarriage or a live birth. In the binary logistic regression analysis, the live birth outcome during the first euploid FBT was a statistically significant and independent predictor of live birth in the subsequent FBT [odds ratio 4.14, 95% confidence interval (1.184–14.531), p < 0.026). Miscarriages, including those that occurred before intracytoplasmic sperm injection and in the first euploid FBT, reduced the live birth rate by approximately 34% (p < 0.027). No significant difference in the miscarriage rate was found between the two subgroups (19.2% (10/52) vs. 25.4% (14/55), p = 0.38). Conclusion The live birth outcome of the second euploid FBT is mainly determined by the live birth outcome of the first. Miscarriages that occurred before in vitro fertilization negatively affect the live birth outcome.

Purpose This study aims to investigate the evolving role and impact of ChatGPT in higher education. It seeks to understand the applications, benefits and challenges associated with ChatGPT, focusing on its potential to enhance teaching, learning and assessment while addressing ethical considerations in the educational context. Design/methodology/approach Using a comprehensive literature review approach, this study systematically examined 29 existing studies and scholarly works related to ChatGPT in higher education. Synthesizing these findings offers a multifaceted view of the subject, encompassing applications, advantages, limitations and ethical implications. Findings The study indicates that ChatGPT can have a significant impact on personalized learning, lesson planning, providing feedback, creating study materials, generating quizzes and exam questions, supporting language learning, offering virtual teaching assistance, tutoring and fostering critical thinking. However, it also sheds light on the ethical issues and challenges associated with its use, including privacy and data protection, transparency and alignment with educational principles. Practical implications This study underscores the practical applications of ChatGPT in higher education, offering insights that can significantly enhance teaching, learning and assessment practices. By leveraging ChatGPT, institutions can personalize learning experiences and provide tailored feedback and streamlined assessment processes, thereby improving student engagement and understanding. In addition, the integration of ChatGPT as a virtual teaching assistant can enhance teaching effectiveness and efficiency by supporting classroom activities, providing additional resources and answering students’ questions in real-time. Originality/value The originality of this study lies in its comprehensive exploration of ChatGPT’s applications in higher education, offering a balanced perspective on opportunities and ethical considerations. By providing valuable insights, it equips educators and institutions with a deeper understanding of the ChatGPT’s potential and challenges in the educational landscape.

Recognition of facial expressions is a challenging task in computer vision because of the complexity associated with individual facial features and social differences. Early studies classified human facial expressions into six basic categories which are anger, disgust, fear, happiness, sadness and surprise. The neutral expression is also taken into account. Furthermore, compound emotions are explored on human faces which are the representations of the expressions that entail the combination of more than a single basic facial expression. Including at least two expression categories, one is considered as the dominating expression and the other as the complementary expression. In this way, the categorization of compound facial expressions is done. In this study, a novel approach is proposed to recognize compound facial expressions. The main contribution of this paper is the proposed fusion of deep texture and geometric features. The texture features are the deep textures obtained from a deep learning model. The iCV-MEFED dataset is employed. It includes compound facial expressions consisting of all the combinations of basic facial expressions in the sense of dominating and complementary expressions. Therefore, 50 distinct classes of facial expressions are presented. The previous studies carried out on this dataset report high rates of misclassification due to the challenge of the complexity of facial expressions and correlations among the compound expressions. The proposed approach obtained encouraging results and has shown significant improvements in the recognition accuracy of compound facial expressions on the iCV-MEFED dataset.

Accurate Gross Domestic Product (GDP) prediction is essential for economic planning and policy formulation. This paper evaluates the performance of three machine learning models—Random Forest Regression (RFR), XGBoost, and Prophet—in predicting Somalia's GDP. Historical economic data, including GDP per capita, population, inflation rate, and current account balances, were used in training and testing. Among the models, RFR achieved the best accuracy with the lowest MAE (0.6621%), MSE (1.3220%), RMSE (1.1497%), and R-squared of 0.89. The Diebold-Mariano p-value for RFR (0.042) confirmed its higher predictive accuracy. XGBoost performed well but with slightly higher error, yielding an R-squared of 0.85 and p-value of 0.063. In contrast, Prophet had the highest forecast errors, with an R-squared of 0.78 and p-value of 0.015. For enhanced interpretability, SHapley Additive exPlanations (SHAP) were applied to RFR, identifying lagged current account balance, GDP per capita, and lagged population as key predictors, along with total population and government net lending/borrowing. SHAP plots provided insights into these features' contributions to GDP predictions. This study highlights RFR's effectiveness in economic forecasting and emphasizes the importance of current and lagged economic indicators.

Attention‐Deficit Hyperactive Disorder (ADHD) is a neurobehavioral syndrome affecting children aged 6–17 with symptoms manifesting before age 12. ADHD presents heterogeneously and is associated with various psychiatric disorders. The cause remains elusive, but genetic and environmental factors, brain region maturation delays, and neurotransmitter dysregulation are implicated. Effective treatment requires a multi‐disciplinary approach, primarily involving pharmacological and behavioral intervention. Stimulants like methylphenidate and amphetamines are first‐line medications, but non‐stimulants may be considered for some patients. However, stimulants face challenges related to misuse, dependence, and long‐term tolerability issues. The etiology of ADHD involved genetic predisposition, environmental influences, and prenatal, perinatal, and postnatal factors. Prenatal causes encompass maternal diet, alcohol consumption, viral infections, and stress. Postnatal factors include head trauma, meningitis, toxin, nutritional deficiencies, as well as iodine deficiency and hypothyroidism. The gut microbiome's role in ADHD is emerging, influencing neurodevelopment through microbiota–gut–brain axis. Understanding these diverse etiological factors is essential for comprehensive ADHD management.

Periapical periodontitis may manifest as a radiographic lesion radiographically. Periapical lesions are amongst the most common dental pathologies that present as periapical radiolucencies on panoramic radiographs. The objective of this research is to assess the diagnostic accuracy of an artificial intelligence (AI) model based on U²-Net architecture in the detection of periapical lesions on dental panoramic radiographs and to determine whether they can be useful in aiding clinicians with diagnosis of periapical lesions and improving their clinical workflow. 400 panoramic radiographs that included at least one periapical radiolucency were selected retrospectively. 780 periapical radiolucencies in these anonymized radiographs were manually labeled by two independent examiners. These radiographs were later used to train the AI model based on U²-Net architecture trained using a deep supervision algorithm. An AI model based on the U²-Net architecture was implemented. The model achieved a dice score of 0.8 on the validation set and precision, recall, and F1-score of 0.82, 0.77, and 0.8 respectively on the test set. This study has shown that an AI model based on U²-Net architecture can accurately diagnose periapical lesions on panoramic radiographs. The research provides evidence that AI-based models have promising applications as adjunct tools for dentists in diagnosing periapical radiolucencies and procedure planning. Further studies with larger data sets would be required to improve the diagnostic accuracy of AI-based detection models.

This study empirically investigates the role of blockchain technology awareness in the adoption of electronic government (e-government) services in the northern part of Cyprus. With data collected from a random sample of 374 individuals eligible to use e-government services, a conceptual model that combines the Unified Theory of Acceptance and Use of Technology (UTAUT2) and the e-Government Adoption Model (GAM) was assessed. In addition to finding support for some predictors already used in prior literature, the current study investigated whether awareness of blockchain technology through its role in increasing trust would also enhance users’ intention for e-government adoption. Findings have shown that increasing blockchain technology awareness can contribute to building trust and facilitate e-government adoption. Policymakers should consider developing awareness campaigns to enhance trust and get the public to adopt the offered online services.

Background One of the important factors affecting the biomechanics of the knee joint is the posterior tibial slope which is the tibial plateau’s anatomical inclination toward the posterior of the sagittal plane. This inclination, which affects anterior-posterior stability, is important for the kinematics of the knee joint. Changes in the tibial slope may cause a deficit in the stability and function of the knee joint. We aimed to examine the inclination of the posterior horn of the meniscus and posterior tibial slope in healthy individuals and investigate the effect of body mass index on these measurements. Methodology A total of 34 magnetic resonance images and lateral knee radiographs were evaluated in this study. The study included individuals aged 15 to 78 without a history of previous injury or surgery of their knee. Results In the measurements made on magnetic resonance images, a statistically significant difference was found between 25% lateral meniscus slope (mean ± SD = 28.08 ± 1.88) and 25% medial meniscus slope (mean ± SD = 27.31 ± 1.41) (p = 0.05). At the same time, a statistically significant difference (p = 0.011) was found between 25% medial combined slope (mean ± SD = 29.05 ± 3.80) and 25% lateral combined slope (mean ± SD = 30.62 ± 2.99). There was no statistically significant difference between tibial and meniscus slopes, body mass index, gender, and age. Conclusions Our study results have shown that the 25% lateral meniscus and combined slopes are greater than the 25% medial meniscus slope.

Introduction The effects of Kinesio‐taping (KT) and rigid‐taping (RT) on vertical jump performance have been investigated; however, remain unclear. The study was designed to compare the effects of KT and RT on vertical jump in individuals with pes planus. Methods A total of 74 participants were diagnosed with pes planus. The foot posture index (FPI) was used to determine pes planus. Participants were randomly divided into two groups. Before taping, the vertical jump height and power were measured using a VertiMetric device as baseline data. Jump measurement was repeated after Kinesio‐taping (KT) and rigid taping (RT) application to group 1 and group 2 respectively in the first period and after crossing in the second period following a 1‐week washout. Crossover and equivalence analyses were used for data analysis. Results KT and RT showed a statistically significant increase in jump height and power. However, the effect of the RT was higher compared to KT ( p < 0.05). Conclusions While both taping techniques increased jump height and power, RT was more effective than KT in improving jumping performance in individuals with pes planus, possibly because of its direct supporting function on the MLA. RT may also improve performance in various sports or clinical settings to accelerate recovery after injury or lower the risk of injury caused by poor foot posture.

This chapter considers ethical aspects of marketing. The authors examine the paradigm of humanistic marketing, which places an emphasis on ethical considerations and the welfare of individuals in the conduct of business. By referencing an extensive compilation of academic literature, this discourse illuminates the profound ramifications that can arise from implementing a humanistic methodology in the field of marketing. The objective of this study is to explicate the fundamental principles and values that form the basis of humanistic marketing, thereby furnishing students and practitioners with a solid comprehension.

To enhance the benefits derived from palm oil processing by the farmers, there is a need for the producers (farmers) and buyers of palm oil to understand the seasonal price for making informed decisions on when to buy and sell the commodity. In order to determine price at the appropriate time of the season, the study looked at the seasonal price variation analysis of palm oil and the relationship between producers and buyers. This was done by understanding buyers’ requirements, producers’ effort, measurements taken, and contracting with buyers’ credit from customers. A multiphase sampling strategy was used. Organized interviews and questionnaires were utilized to collect primary data, while the National Bureau of Statistics provided secondary time series data on the average monthly retail price of palm oil from 2016 to 2022. The need for bio-fuel, production growth, governmental regulations, market dynamics, and the seasonality of palm oil products are then identified by the study as important factors influencing palm oil price fluctuation throughout the year. The study then suggests that there’s a need for economic diversification to reduce reliance on a single sector. This study will serve as a basis for future research on palm oil price variation analysis.