WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14517/18

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Pharmacists Role in Cost Saving and Waste Minimization Study on Antineoplastic Drugs: A Multi-Central Study in Turkey
(Marmara University, Fac Pharmacy, 2025) Kanmaz, O.; Macit, C.; Eroglu, I.; Berk, B.
Cancer diseases are the most common cause of death and chemotherapeutics used in cancer treatment have an important place in the budget allocated to health due to their high costs. This study aims to reduce disposal costs and reduce chemotherapy drug expenses through cost savings. Our study was conducted between November 2017 and July 2018 (1st semester) and between August 2018 and April 2019 (2nd semester) in three hospitals using a common chemotherapy drug preparation center. Chemotherapy sessions were rearranged in accordance with the drugs included in the treatment plans to reduce drug costs. The drug quantities billed through hospital automation programs and the disposal costs of unused drugs were reported by the pharmacists on a milligram (mg) basis. The obtained data was analyzed by the GraphPad Prism program. P<0.05 was considered significant. While the total amount of waste medicine in the 1st. period was reported as 371,866 mg, it was reported as 303,056 mg in the 2nd. period (p<0.05). While the total disposal cost was calculated at € 87,867.02 in the 1st. period, it was calculated € 26,392.48 in the 2nd. period under the control of pharmacists (p<0.01). In this study, it was observed that providing and monitoring drug preparation services by well-equipped pharmacists specialized in the field of oncology gave positive results in terms of reducing both drug expenditures and drug disposal costs. Further studies are needed to determine the safety of chemotherapy pharmacists and patients. © 2025 Marmara University Press.
Enhancing Heat Transfer Across Applications with Triply Periodic Minimal Surface (TMPS) Structures: A Comprehensive Review
(Elsevier Science Sa, 2025) Rashid, Farhan Lafta; Al Maimuri, Najah M. L.; Al-Obaidi, Mudhar A.; Eleiwi, Muhammad Asmail; Ameen, Arman; Ahmad, Shabbir; Agyekum, Ephraim Bonah
The current research evaluates how triply periodic minimal surface (TPMS) structures, specifically Gyroid configurations, enhance heat transfer in thermal management systems by addressing heating issues caused by miniaturized electronic devices. TPMS structures composed of Gyroid and Fischer-Koch varieties demonstrate up to a 50.6 % improvement in cooling efficiency compared to traditional fin structures. Additionally, the FischerKoch structure facilitates internal flow heat transfer, achieving efficiency levels 12 times greater than conventional designs. The Nusselt number exceedes 80 in TPMS configurations, although pressure drops increases when porosity fell below 0.7. However, the performance evaluation criterion remains above 70 at porosities of 0.8. The effective thermal management of advanced electronic systems benefits from the integration of phase change materials (PCMs) with TPMS structures, as they enhance heat dissipation and reduce melting durations. The review concludes that implementing TPMS components would significantly improve heat transfer, besides enabling designers to optimise thermal management systems within constrained spaces.
Molecular Imaging Using (Nano)Probes: Cutting-Edge Developments and Clinical Challenges in Diagnostics
(Royal Soc Chemistry, 2025) Samadzadeh, Meisam; Khosravi, Arezoo; Zarepour, Atefeh; Jamalipour Soufi, Ghazaleh; Hekmatnia, Ali; Zarrabi, Ali; Iravani, Siavash
Molecular imaging has emerged as a transformative approach in the field of medical diagnostics, enabling the visualization of biological processes at the molecular and cellular levels. Additionally, the integration of molecular imaging with other imaging modalities such as positron emission tomography (PET), magnetic resonance imaging (MRI), computed tomography (CT), photoacoustic imaging (PAI), and fluorescence imaging (FI) has further broadened the scope of diagnostics. Despite significant advances in probe design, including multifunctional and targeted nanomaterials, their clinical translation remains limited by critical challenges. Key obstacles include nanoprobe stability in physiological environments, nonspecific accumulation in the reticuloendothelial system, potential toxicity, and difficulties in achieving optimal biocompatibility and controlled biodistribution. Moreover, the complexity of nanoprobe synthesis and batch-to-batch variability hinder scalable manufacturing and regulatory approval. The primary goal of this review is to critically analyze the current challenges hindering the clinical translation of molecular imaging nanoprobes in biomedicine. While existing literature extensively covers imaging techniques, this review uniquely emphasizes the persistent obstacles-such as nanoprobe stability, biocompatibility, off-target effects, and limited sensitivity-that impede their effective application. Unlike previous reviews, which tend to focus broadly on advancements, we offer a nuanced perspective by identifying specific barriers and proposing promising strategies to overcome them. We explore how surface modification, novel targeting ligands, and smart responsive systems can enhance nanoprobe performance. Furthermore, the review discusses how addressing these challenges is crucial for accelerating the development of multifunctional nanoprobes capable of simultaneous diagnosis and therapy, ultimately advancing personalized medicine. By highlighting these hurdles and potential solutions, this review aims to provide a comprehensive roadmap for researchers striving to optimize molecular imaging nanoprobes, thereby bridging the gap between laboratory innovation and clinical reality.
Advancing Ovarian Cancer Care: Recent Innovations and Challenges in the Use of MXenes and Their Composites for Diagnostic and Therapeutic Applications
(Royal Soc Chemistry, 2025) Farzizadeh, Neda; Zarepour, Atefeh; Khosravi, Arezoo; Iravani, Siavash; Zarrabi, Ali
Ovarian cancer remains the deadliest form of gynecologic malignancy, largely owing to the absence of reliable early diagnostic tools and the limited effectiveness of current therapeutic strategies. Recent advances in nanotechnology-particularly the emergence of two-dimensional materials known as MXenes-offer promising avenues to address these challenges. This review highlights the emerging role of MXenes and their composites in the management of ovarian cancer, focusing on their potential in biomarker detection and targeted treatment strategies. We provide a comprehensive analysis of the latest studies examining the physicochemical features of MXenes, their synthesis and surface functionalization approaches, and their application in ovarian cancer, including biosensing, drug delivery, and combinatorial therapeutic systems. MXene-based biosensors have shown remarkable detection limits in detecting ovarian cancer biomarkers, such as cancer antigen 125 (CA125), human epididymis protein 4 (HE4), lipolysis-stimulated lipoprotein receptor (LSR), and carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5). However, several challenges remain, including issues of biocompatibility, structural stability, and clinical scalability. Continued interdisciplinary research is essential to address these limitations, optimize MXene functionalization, and translate their laboratory success into clinical settings. With appropriate advancements, MXenes hold significant promise for enabling more precise, efficient, and patient-specific approaches to ovarian cancer diagnosis and therapy.
Effect of Preclinical Training in Periodontal Instrumentation on Undergraduate Students' Anxiety, Clinical Performance, Satisfaction
(BMC, 2025) Kayaalti-Yuksek, Sibel; Besiroglu-Turgut, Ekin; Agirman, Merve; Keles, Gonca Cayir
Objectives This study aims to assess the impact of preclinical training using instructional typodont-phantom head on undergraduate students' anxiety levels, clinical performance, and satisfaction. Materials & methods Sixty-fourth-year students from Istanbul Okan University with no clinical periodontal experience were randomly divided into two groups. Both groups received one hour of theoretical periodontal training on comprehensive examination and supragingival instrumentation. Group 1 received only theoretical training, while Group 2 additionally completed 60 min of hands-on preclinical training using a typodont-phantom head with artificial calculus. Before their first patient procedures, students completed a state anxiety test and afterward rated their training satisfaction on a VAS. Clinical performance was assessed using a scaling operation score sheet. Results Group 2 had significantly higher clinical performance (77.67 +/- 17.17) and satisfaction scores (8.23 +/- 1.79) compared to Group 1 (59.93 +/- 15.38 and 6.67 +/- 1.62, respectively; p < 0.05). No significant difference in state anxiety scores was observed between groups, nor any correlation between anxiety and clinical performance. Conclusion Preclinical training in periodontal instrumentation improved clinical performance and satisfaction but did not affect anxiety. Integrating theoretical and practical preclinical training with a typodont-phantom model can enhance learning outcomes. Clinical trial registration The study was retrospectively registered with ClinicalTrials.gov (ID: NCT06593873) on 10/09/2024.
Harnessing the Power of Nanotechnology and Intelligent Wound Dressings to Transform Sports Injury Recovery and Healing
(Elsevier, 2025) Wei, Feng; Rong, Siyu; Baghaei, Sh; Salahshour, Soheil
As the field of sports medicine continues to evolve, the integration of nanoparticle-based technologies and intelligent wound dressings is poised to revolutionize the way athletes recover from injuries. In the coming years, we can expect to see a surge in the development of highly sophisticated, multifunctional wound care solutions tailored specifically for the unique demands of athletic populations. Advancements in smart materials, such as stimuli-responsive hydrogels and self-healing dressings, will enable precise control over the wound microenvironment, promoting accelerated tissue regeneration and minimizing the risk of complications. The incorporation of wireless sensors and real-time monitoring capabilities into these intelligent dressings will empower clinicians to make data-driven decisions, optimizing treatment strategies and ensuring timely interventions. Furthermore, the integration of novel drug delivery systems (DDS), including biodegradable nanoparticles and transdermal patches, will facilitate the targeted administration of therapeutic agents, enhancing the efficacy of wound healing while reducing systemic side effects. Innovations in gas-releasing dressings and nanoenzyme-based therapies will expand the arsenal of tools available to sports medicine professionals, addressing a wider range of wound types and complexities. As these cutting-edge technologies mature and transition into clinical practice, athletes will benefit from expedited recovery times, improved functional outcomes, and a swifter return to their respective sports. The convergence of nanotechnology, smart materials, and data-driven healthcare is poised to usher in a new era of personalized, precision-based wound care in the world of sports medicine.
Innovations in Cancer Treatment: Evaluating Drug Resistance with Lab-On Technologies
(Elsevier, 2025) Heydari, Parisa; Javaherchi, Pouya; Samadzadeh, Meisam; Azadani, Reyhaneh Nasr; Rad, Alireza Bahrami; Zarepour, Atefeh; Zarrabi, Ali
Lab-on-a-chip (LoC) technologies have emerged as transformative tools in cancer research, particularly in evaluating drug resistance, which remains a significant barrier to effective treatment. These miniaturized platforms allow for the integration of multiple laboratory functions onto a single chip, facilitating high-throughput screening and real-time monitoring of cellular responses to therapeutic agents. Despite their potential, several challenges hinder the widespread adoption of LoC systems in clinical settings. Key issues include the complexity of accurately replicating the tumor microenvironment (TME), which is critical for understanding cancer biology and drug interactions. Additionally, variability in chip design and fabrication raises concerns about standardization and reproducibility of results, complicating comparisons across studies. The integration of LoC technologies into clinical practice is further complicated by the need for translation from laboratory findings to patient-specific applications. High costs asSociated with advanced microfabrication techniques and the requirement for specialized technical expertise also limit accessibility for many researchers and clinicians. However, the future perspectives for LoC technologies are promising. Advancements in three-dimensional (3D) bioprinting and tissue engineering are expected to enhance TME modeling, while patient-derived tumor spheroids (PDTS) integrated into LoC platforms could facilitate personalized medicine approaches. Coupling LoC systems with omics technologies will provide deeper insights into the molecular mechanisms of drug resistance and help identify novel biomarkers. Furthermore, the integration of artificial intelligence and nanotechnology with LoC platforms has significantly enhanced their diagnostic accuracy, automation, and potential for personalized cancer treatment. As regulatory bodies increasingly accept LoC technologies as viable preclinical models, their integration into pharmaceutical development pipelines is likely to accelerate. This review aims to explore these challenges and future perspectives, highlighting the potential of LoC technologies in advancing cancer treatment paradigms. By examining the innovative applications of LoC systems, we aim to highlight their potential for enhancing our understanding of the complex interactions within the TME and their implications for personalized medicine. Additionally, it seeks to identify and discuss the key challenges that currently limit the widespread adoption of LoC technologies in clinical settings, including issues related to model complexity, standardization, and integration into existing drug development pipelines.
Short-Term Outcomes of Transcatheter Aortic Valve Implantation in Patients with Bicuspid Aortic Valve: Insights from Nationwide Readmission Analysis
(Springer Japan KK, 2025) Alhuneafat, Laith; Ghanem, Fares; Obeidat, Omar; Alzyoud, Anas; Ma'aita, Abdel Latif; Ajam, Mustafa; Altibi, Ahmed M.
Transcatheter aortic valve implantation (TAVI) TAVI outcomes for patients with bicuspid aortic valve (BAV) and severe aortic stenosis are uncertain due to their exclusion from major clinical trials. We analyzed TAVI patients in the United States using data from the Nationwide Readmissions Database (2016-2019) identified using ICD-10 codes. We established matched cohorts of BAV and trileaflet aortic valve (TAV) patients using propensity-score matching (PSM). Primary outcomes were in-hospital mortality, 30-day mortality, and 30-day readmission rates. Out of 233,683 TAVI patients identified, 3169 (1.4%) had BAV. BAV patients were younger with fewer comorbidities. After PSM, 2,840 pairs were analyzed. Compared to TAV patients, TAVI in BAV patients showed comparable in-hospital mortality (1.2% vs. 2.0%; OR 0.62; 95% CI 0.36-1.04; p = 0.07) and 30-day readmission rates (10.0% vs. 12.3%; OR 0.79; 95% CI 0.60-1.03; p = 0.08), with lower 30-day mortality rates (0.88% vs. 1.96%; OR 0.44; 95% CI 0.23-0.84; p = 0.01). Post-TAVI in-hospital complications rates, including stroke, acute kidney injury, pacemaker need, and others, were similar between BAV and TAV patients. TAVI in BAV shows acceptable safety compared to TAV, but further randomized trials are needed to establish long-term outcomes and durability.
Functionalization of SBA-15 with TCPP for Pollutant Removal: Structural Characterization and Adsorption Performance
(Elsevier, 2025) Nouraei, Ali; Keyvani, Bahram; Aghayari, Reza; Moradbakhsh, Maryam; Toghraie, Davood; Salahshour, Soheil
In this research, first, the compound tetrakis(4-carboxyphenyl)porphyrin was synthesized and characterized using UV-Vis and FT-IR spectroscopy methods. Also, silicate mesopores SBA-15 and NH2-SBA-15 were synthesized and characterized using FT-IR spectroscopy and (scanning electron microscope)SEM image pattern methods. Then, through the immobilization of porphyrin on silicate substrates functionalized with amine groups, the compound TCPP-NH2-SBA-15 was synthesized and characterized using FT-IR spectroscopy and SEM image pattern methods. Finally, the efficiency of compounds NH2-SBA-15 and TCPP-NH2-SBA-15 in removing cadmium (II) metal from an aqueous solution was investigated. Factors such as time and concentration were examined. The results showed that porphyrin was used as an adsorbent only when it was immobilized on mesoporous silica. In addition, it was found that the more the substituents of the porphyrin ring, the more the metal removal rate decreased.
Brain Oscillations in Bipolar Disorder: Insights from Quantitative EEG Studies
(Sage Publications Inc, 2025) Bahadori, Amir Reza; Naghavi, Erfan; Allami, Pantea; Dahaghin, Saba; Davari, Afshan; Ansari, Sahar; Tafakhori, Abbas
Introduction Quantitative electroencephalography (QEEG) is a neurophysiological tool that analyzes brain oscillations across frequency bands, providing insights into psychiatric conditions like bipolar disorder (BD). This disorder, marked by mood fluctuations, poses diagnostic and treatment challenges, highlighting the need for reliable biomarkers.Objective This systematic review aims to evaluate QEEG changes in BD patients, investigate its diagnostic and therapeutic potential, and differentiate BD from major depressive disorder (MDD) and schizophrenia.Methods Following PRISMA 2020 guidelines, a comprehensive search of PubMed, Scopus, Web of Science, and Embase was conducted till 30th of October 2024 without timeline restrictions. Studies involving BD patients assessed using QEEG were included. Key outcomes focused on frequency band alterations, treatment responses, and diagnostic differentiation.Results The review included 20 studies with 475 BD patients. Increased gamma and beta activity were consistently observed in BD. However, the directionality of alpha and theta band changes varied, with differences observed depending on brain region and mood state. Delta band alterations were more prominent in BD I. Treatment responses showed reduced power in gamma, theta, and alpha bands. QEEG also distinguished BD from MDD and schizophrenia based on frequency band characteristics.Conclusion QEEG demonstrates significant promise as a diagnostic and therapeutic tool for BD. Despite methodological variability, its integration with machine learning could enhance diagnostic precision and guide personalized treatments. Further research is needed to standardize methodologies and validate findings.
Understanding Food Loss Patterns Across Developed and Developing Countries Using a GDP, Growth Rate, and Health Expenditure-Based Typology
(Nature Portfolio, 2025) Baykoca, Buse; Yilmaz, Salim
Food loss and waste (FLW) threaten progress toward Sustainable Development Goals (SDG) 12.3, yet their distribution by development stage remains under-quantified. We created a time-weighted K-means typology for 105 countries (2000-2022) using Gross Domestic Product (GDP) per capita, GDP growth, and per-capita health expenditure-indicators chosen to capture economic capacity, growth momentum, and institutional investment. The scheme classified nations as developed (n = 13), developing (n = 92), or hybrid, with > 98% membership stability across weighting parameters. Linking this typology with FAO's FLW data, we modelled food loss percentages (FLP) across ten commodity groups and eight supply-chain stages using multilevel mixed-effects regression. Developed countries lost the most food at consumption (22.5%), dwarfing developing (6.8%) and hybrid cases (9.0-14.2%), whereas developing nations suffered greater upstream losses at harvest/on-farm (3.7%). FLP in developing economies was significantly lower for grains (beta = - 8.02, p = 0.007), oilseeds (beta = - 19.29, p = 0.016) and pulses (beta = - 5.43, p = 0.021). From 2000 to 2022, oilseed and sugar losses rose (beta = 0.26, p < 0.001), while roots/tubers and dairy/eggs declined (beta = - 0.31, - 0.89; p < 0.01). Stage analyses revealed pronounced development gaps at consumption (beta = - 16.06, p < 0.001) and processing (beta = - 5.58, p = 0.014), alongside a rising trend in marketing/retail losses (beta = 0.25, p = 0.005). Country-level random effects explained up to 90% of variance, underscoring the dominance of local conditions. The evidence supports consumer-behaviour interventions in high-income settings, upstream infrastructure investment in developing regions, and dual-track strategies in hybrids. Our typology provides a scalable, policy-ready lens for designing targeted FLW actions aligned with SDG 12.3.
Mechanical Ventilation Strategies in Buildings: A Comprehensive Review of Climate Management, Indoor Air Quality, and Energy Efficiency
(MDPI, 2025) Rashid, Farhan Lafta; Al-Obaidi, Mudhar A.; Al Maimuri, Najah M. L.; Ameen, Arman; Agyekum, Ephraim Bonah; Chibani, Atef; Kezzar, Mohamed
As the demand for energy-efficient homes continues to rise, the importance of advanced mechanical ventilation systems in maintaining indoor air quality (IAQ) has become increasingly evident. However, challenges related to energy balance, IAQ, and occupant thermal comfort persist. This review examines the performance of mechanical ventilation systems in regulating indoor climate, improving air quality, and minimising energy consumption. The findings indicate that demand-controlled ventilation (DCV) can enhance energy efficiency by up to 88% while maintaining CO2 concentrations below 1000 ppm during 76% of the occupancy period. Heat recovery systems achieve efficiencies of nearly 90%, leading to a reduction in heating energy consumption by approximately 19%. Studies also show that employing mechanical rather than natural ventilation in schools lowers CO2 levels by 20-30%. Nevertheless, occupant misuse or poorly designed systems can result in CO2 concentrations exceeding 1600 ppm in residential environments. Hybrid ventilation systems have demonstrated improved thermal comfort, with predicted mean vote (PMV) values ranging from -0.41 to 0.37 when radiant heating is utilized. Despite ongoing technological advancements, issues such as system durability, user acceptance, and adaptability across climate zones remain. Smart, personalized ventilation strategies supported by modern control algorithms and continuous monitoring are essential for the development of resilient and health-promoting buildings. Future research should prioritize the integration of renewable energy sources and adaptive ventilation controls to further optimise system performance.
The Relationship Between Nomophobia, Screen Addiction, Musculoskeletal Problems and Cardiovascular Risk Levels in University Students: A Cross-Sectional Study
(Springer Heidelberg, 2025) Aydin, Gamze; Kendal, Kubra; Karacan, Yildiz Yucel; Surenkok, Ozgur
AimNomophobia, a new concept born out of digital addictions, refers to the anxiety and fear that individuals experience when they are away from their cell phones. The aim of this study is to examine the effects of nomophobia and screen addiction on musculoskeletal problems and cardiovascular risk in university students and to reveal the relationship between these parameters.Subjects and methodsThe study adopted a cross-sectional design and included 293 participants (mean age: 21.29 +/- 2.20 years, 76.1% female). The presence of nomophobia was assessed using the Nomophobia Questionnaire (NMP-Q); screen addiction was assessed using the Smartphone Addiction Scale-Short Form (SAS-SF) and the Multiple Screen Addiction Scale (MSAS); musculoskeletal risk level was assessed using the Cornell Musculoskeletal Disorders Questionnaire (CMDQ); cardiovascular risk level was assessed using the Framingham Risk Score (FRS).ResultsParticipants had been using smartphones for 8.19 +/- 2.74 years, with a daily usage time of 6.64 +/- 3.38 h. The mean scores for NMP-Q, SAS, MSAS, and FRS were 74.51 +/- 31.74, 29.40 +/- 12.18, 39.37 +/- 13.14, and -1.16 +/- 5.00 respectively. The highest prevalence of musculoskeletal problems by body region was reported in the neck (71.7%), back (72.4%), and low back (68.6%). Significant differences were observed in the neck (p = 0.049) and back (p = 0.004), with severity increasing as nomophobia levels progressed. A significant positive correlation was found between NMP-Q scores and both SAS-SF (r = 0.560, p < 0.001) and MSAS (r = 0.468, p < 0.001). Regarding musculoskeletal pain, NMP-Q scores showed a significant positive correlation only with back pain (r = 0.166, p < 0.001).ConclusionThis study highlights the significant asSociation between nomophobia and musculoskeletal problems, particularly neck and back pain, among university students.
Biophotonic (Nano)Structures: From Fundamentals to Emerging Applications
(Royal Soc Chemistry, 2025) Amoozadeh, Masoomeh; Hariri, Amirali; Zarepour, Atefeh; Khosravi, Arezoo; Iravani, Siavash; Zarrabi, Ali
Biophotonics is a dynamic interdisciplinary field that merges biology, photonics, and optics to explore and manipulate biological systems through light. Its applications are particularly prominent in medical diagnostics, imaging, and therapy. Key uses of biophotonic (nano)structures include enhancing medical imaging and enabling biosensing to detect disease markers. In therapeutic contexts, these nanostructures show significant promise in photothermal and photodynamic therapies, improving imaging contrast and allowing for real-time monitoring of cellular processes. However, the field faces challenges such as fabrication complexities, scalability, biocompatibility, and integration with existing technologies. For instance, limited biocompatibility can lead to adverse immune responses or toxicity, hindering their safe use in vivo, while scalability issues restrict the mass production of nanostructures with consistent quality, both of which are critical for clinical translation. Moreover, integrating these materials with existing medical devices or workflows often requires redesigning current platforms, slowing down adoption. Despite these obstacles, the future of biophotonics appears promising, especially with advancements in nanotechnology, including 3D printing and self-assembly, which could streamline production. The potential integration of biophotonic nanostructures with emerging technologies like wearable devices and point-of-care diagnostics could revolutionize healthcare by facilitating continuous health monitoring and rapid disease detection. This review aims to provide a thorough examination of biophotonic nanostructures and their emerging applications in disease diagnosis, imaging, and therapy. Additionally, it will address the challenges and future directions of biophotonic research, enhancing our understanding of how these innovative technologies can tackle critical issues in modern medicine and deepen our knowledge of complex biological systems.
Intelligent Multi-Objective Decision Support System for Efficient Resource Allocation in Cloud Computing
(Springer, 2025) Qi, Bo; Manoranjitham, M.; Zhang, Guohua; Alwabel, Asim Suleman A.; Zayani, Hafedh Mahmoud; Ferrara, Massimiliano
The dynamic allocation of materials within cloud systems is essential for optimizing system architecture, enhancing energy efficiency, and ensuring compliance with Service Level Agreements (SLA). to address workload imbalance and resource overload issues, this research introduces an Intelligent Multi-Objective Decision Support System (IMODSS) for resource allocation in cloud systems. The proposed framework leverages the novel integration of the Modified Feeding Birds Algorithm (ModAFBA) with the Deep Reinforcement Learning (DRL)-based Q-Learning algorithm to enhance the adaptability and effectiveness of resource management. By combining the dynamic clustering abilities of ModAFBA with the adaptive decision-making of Q-learning, IMODSS effectively prioritises tasks, balances workloads throughout the virtual machine (VM), and improves energy efficiency. Experimental validation using Python and CloudSim demonstrates that IMODSS notably outperforms traditional methods. Specifically, the proposed system reduces makespan by 15% to 20%, energy consumption by 18% to 22%, and VM migrations by 20% to 25% compared to existing cloud-based resource allocation models of HBCA, MOPSO, and TPOSIS. Also, the integration of Q-Learning strengthens the system to manage QoS parameters, such as CPU and memory utilization efficiency and SLA violation control. Therefore, the IMODSS framework effectively scales under varying workload conditions and is a promising solution for next-generation cloud computing environments.
Investigating the Effect of Variable Heat Flux on Buckling of Carbon Nanotube Using Non-Equilibrium Molecular Dynamic Simulation
(Pergamon-Elsevier Science Ltd, 2025) Hou, Guoliang; Al-Mussawi, Waqid; Khidhir, Dana Mohammad; Singh, Narinderjit Singh Sawaran; Saeidlou, Salman; Al-Bahrani, Mohammed; Hasanabad, Ali Mohammadi
It is critical to know the buckling behavior of carbon nanotubes under non-uniform heat flux for maintaining stability in thermal applications at the nanoscale. In this study, time-dependent external heat fluxes of 1, 3, 5, and 10 W/m(2) are applied to carbon nanotubes using non-equilibrium molecular dynamics simulations, and the resulting structural and energetic responses are analyzed systematically. The findings demonstrate that, in parallel with the evolution toward the post-buckling state, some kinetic energy and mean squared displacement increased during simulation before abruptly decreasing and stabilizing. Before buckling, potential energy peaked and then dropped to negative values, indicating structural relaxation. The center of mass displacement was constrained, and the interaction energy stabilized at 3.63 x 10(13) eV, reflecting the structure's stability following buckling. Additionally, kinetic energy increased from about 50 eV to 130-140 eV and then decreased to 80-90 eV after buckling when the heat flux increased from 3 to 10 W/m(2). With a slight increase in atom mobility, mean squared displacement went from 0.41 to 0.412. After initially reaching its maximum, potential energy began to gradually decline, with the decline being greater at higher heat flux values. The interaction energy increased at 2.25 x 10(-12) eV at 3 W/m(2) and then decreased at 3.75 x 10(-14) eV at 10 W/m(2), indicating that higher thermal energy generates higher molecular motion and structural relaxation, stabilizing the buckled shape. The center of mass displacement decreased with increasing heat flux, suggesting greater local deformation and less overall movement. The originality of this work lies in simulating an actual, spatially non-uniform heat flux and examining its direct effect on carbon nanotubes' thermomechanical behavior, a situation overwhelmingly unexplored by the literature. The results offer useful guidance for the design of carbon nanotube-based systems in nanoelectronics and thermal management systems operating under non-uniform thermal conditions.
Classification of Morphological Variations of Mandibular Condyle in Panoramic Radiographs with a Deep Learning Approach
(Springer Heidelberg, 2025) Yuce, Fatma; Ozic, Muhammet Usame; Buyuk, Cansu
Aim This study aims to employ the YOLO algorithm for the automatic classification of mandibular condylar morphology in panoramic radiographs. Materials and Methods A total of 1,056 panoramic radiographs, containing 2,112 healthy mandibular condyles, were used in the study. The dataset was split into training (similar to 80%), validation (similar to 10%), and test (similar to 10%) sets. Two experienced dentomaxillofacial radiologists annotated the training images and classified the condyles into four morphological categories: Round, Angled, Diamond, and Crooked Finger-shaped. The YOLOv8 deep learning model was trained using transfer learning, hyperparameter tuning, and fine-tuning techniques. Performance was assessed using metrics including precision, recall (sensitivity), F1-score, mean Average Precision (mAP), and training time. True positives, false positives, and false negatives were evaluated based on bounding box localization and class assignments. Results The model demonstrated balanced performance across classes in the training dataset. On the test dataset, the model achieved an overall F1-score of 0.769 and mAP@0.5 of 0.786. The highest performance was observed for the Crooked Finger class (0.795 precision, 0.870 recall, 0.831 F1-score, 0.837 mAP@0.5) and the Angled class (0.723 precision, 0.860 recall, 0.786 F1-score, 0.808 mAP@0.5). The Round class showed moderate results with 0.677 precision, 0.870 recall, 0.761 F1-score, and 0.798 mAP@0.5. The Diamond class had the lowest performance, with 0.528 precision, 0.696 recall, 0.600 F1-score, and 0.661 mAP@0.5. Conclusion The model effectively distinguishes the Angled and Crooked Finger classes but faces challenges with the Diamond and Round classes. Despite varied performance, the model demonstrates balanced performance overall, providing a foundation for further refinement and optimization.
Impact of Chemotherapy on Sexual Dysfunction in Turkish Women with Breast Cancer: A Single-Center Prospective Cohort Study
(Lippincott Williams & Wilkins, 2025) Cekin, Ruhper; Senocak, Didar; Arici, Serdar
Although numerous studies have explored the connection between breast cancer surgery and sexual function, research on chemotherapy's temporal effects is limited. Addressing the impact of chemotherapy on sexual dysfunction is critical for improving quality of life. The aim of this study is to investigate changes in sexual function before, during, and after chemotherapy treatment, with a focus on asSociated factors. A total of 101 sexually active, reproductive-aged women diagnosed with locally advanced breast cancer were included in the study. The sexual dysfunction was evaluated by using the female sexual function index (FSFI) across 3 treatment phases: before, during, and after chemotherapy. Covariates such as age, baseline sexual dysfunction, tumor localization, comorbidity, family history of cancer, and receptor-related factors were analyzed for their influence on score changes during specific periods. A mixed-effects model was employed to evaluate asSociations and interactions between these variables and sexual function outcomes. Sexual function scores significantly declined across treatment phases. Notable reductions were observed in desire (P < .001), arousal (P < .001), lubrication (P < .001), orgasm (P < .01), and satisfaction (P < .01), while pain scores increased (P < .01). Total FSFI scores significantly dropped during and after chemotherapy (P < .001 and P < .01, respectively). Patients with preexisting sexual dysfunction experienced significantly greater declines in desire, lubrication, and satisfaction, along with more pronounced increases in pain-related discomfort scores, particularly in the FSFI pain subscale (P < .01). Older age, comorbidity, and tumor localization were significantly asSociated with worsening sexual function, whereas receptor status and histopathology showed no meaningful effect. Our findings confirm a high prevalence of sexual dysfunction among women with breast cancer. These results highlight the multifaceted impact of chemotherapy on sexual function and reveal a clear temporal pattern of changes across treatment phases.
Influence of Graphene Nanoplate Size and Heat Flux on Nanofluid Heat Exchanger Performance: A Molecular Dynamics Approach
(Pergamon-Elsevier Science Ltd, 2025) Yang, Zhongxiu; Basem, Ali; Jasim, Dheyaa J.; Singh, Narinderjit Singh Sawaran; Saeidlou, Salman; Al-Bahrani, Mohammed; Hasanabad, Ali Mohammadi
This study aimed to enhance the thermal efficiency of nanofluid-based heat exchangers by exploring the simultaneous effects of external heat flux and graphene nanoplate sizes on thermal and structural characteristics. Effective heat transfer is a critical requirement for managing heat in microscale systems, where optimizing the thermal performance of nanofluids can improve device performance. Molecular dynamics simulations were carried out of a sinusoidal inner surface copper heat exchanger coated with silicon nanoparticles to demonstrate atomic-level interaction within the nanofluid. The significant findings showed that while an external rising heat flux decreased heat flux from 41.7 to 37.26 W/m2 and thermal conductivity of nanofluid from 14.53 to 13.80 W/ m & sdot;K, only an increase in viscosity from 0.32 to 0.49 mPa & sdot;s, the agglomeration time of nanoparticles decreased from 3.71 to 3.33 ns and friction coefficient from 0.022 to 0.015, could indicate a difference in particle behavior responding to the thermal stress. However, the size of the graphene nanoplate from 5 to 15 & Aring; increases the heat flux from 40.05 to 46.77 W/m2 and thermal conductivity of the nanofluid from 14.15 to 14.99 W/m & sdot;K, since the larger graphene nanoplate films can produce a more substantial covalent bonding and link interlayer coupling. In contrast, the larger nanoplate also enhanced viscosity from 0.30 to 0.39 mPa & sdot;s, aggregation time from 3.64 to 4.01 ns, and friction coefficient from 0.020 to 0.026, which indicated lower particle mobility. This study was the first of its kind to contribute to the existing knowledge gap by investigating the simultaneous effect of both the nanoplate size and external heat flux in an oscillating microchannel heat exchanger. The knowledge provided offers an experimental pathway in optimizing the nanofluid properties and the heat exchanger geometry for improved thermal management for compact and microscale applications.
AI-Assisted Floor Plan Design Incorporating Structural Constraints
(Kim Williams Books, 2025) Okuyucu, Elif Bahar; Kosencig, Kamile Ozturk
Automatic layout generation studies remain limited in their ability to ensure structural continuity in floor plans. In response, this study considers structural schema as a design constraint. Generative Adversarial Network algorithms for image-to-image translation were utilised to generate floor plans. The dataset, consisting of 552 housing floor plans with structural layouts, was labelled and trained to generate house plan alternatives to assist the interior layout design process in early design phases. The dataset was trained with the machine-learning algorithms Pix2Pix and BicycleGAN. The comparative evaluation of the results using Learned Perceptual Image Patch Similarity indicates that BicycleGAN performs better than Pix2Pix, and the suggested workflow is quite promising. However, the lack of circulation areas was identified as a common limitation in both models. This workflow also has the potential to be used for renovation purposes.

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