Balıkesir Üniversitesi Kurumsal Akademik Arşivi
DSpace@Balıkesir, Balıkesir Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Mathematical analysis and optimal control of a Caputo fractional diabetes system with parameter identification
(Elsevier B.V., 2026) Evirgen, Fırat; Uçar, Sümeyra; Özdemir, Necati
In this study, mathematical modeling of diabetes disease is handled with the Caputo fractional
derivative, and the resulting fractional differential equation system is examined in detail.
The existence of the solution of this system was mathematically proven under appropriate
conditions. Then, for the management and treatment of diabetes, three control parameters are
added, and the number of people with diabetes is tried to be kept in the desired range with
the optimal control theory. In addition to this, our model gains from parameter estimation and
fitting with more accurately generated parameters. The numerical results of the fractional model
and the effect of the control parameters are determined with the optimal control approach on
glucose levels, examined with various simulations, and presented graphically.
Meta-heuristic algorithms for optimization of hybrid flowshop scheduling problems: A comprehensive review of the state of the art
(Elsevier B.V., 2026) Çolak, Murat; Keskin, Gülşen Aydın
The hybrid flowshop scheduling problem (HFSP), which combines classical flowshop and parallel machine
scheduling environments, has gained significant attention in recent years and has various application areas such
as manufacturing, healthcare management, seaport operations, agricultural activities, and cloud computing. The
HFSP considers multiple stages, at least one of them includes identical, uniform, or unrelated parallel machines,
and aims to determine machine assignments and job sequences simultaneously at each stage. Since the HFSP has
an NP-hard structure as a combinatorial optimization problem, exact methods like the branch & bound algorithm
are not capable of obtaining promising solutions for large-sized problems within a reasonable time. At this point,
meta-heuristic algorithms inspired by nature and based on mathematical concepts are effectively utilized to solve
this type of complicated optimization problem. Therefore, in this paper, a state-of-the-art review on metaheuristics applied to solve HFSPs has been carried out using the Preferred Reporting Items for Systematic Re
views and Meta-Analyses (PRISMA) methodology, which enables the realization of systematic reviews and metaanalyses in a specified research domain. As a result of the execution of this systematic review methodology, 328
articles published in a decade from 2015 to 2024 have been determined and these articles have been statistically
and mathematically analyzed in terms of various characteristics such as year, country, journal, publisher,
objective functions, meta-heuristic optimization methods, performance metrics, test instances, and parameter
optimization techniques. The analysis results have been presented through charts and tables for visual demon
stration with the aim of revealing the current state of the existing literature, recent developments, and future
research suggestions related to meta-heuristic algorithms used to solve the HFSPs. Thus, it has been desired to
provide a beneficial road map for researchers conducting research in this area.
Phytochemical composition and biological activities of bark extracts from seven industrially relevant tree species
(Springer Science and Business Media Deutschland GmbH, 2025) Akça, Mehmet; Gür, Mahmut; Güder, Aytaç
This study presents a comprehensive analysis of the phytochemical profiles and biological activities of bark extracts from seven industrially significant tree species: Pinus sylvestris, Pinus nigra, Pinus brutia, Picea orientalis, Abies nordmanniana subsp. equi-trojani, Fagus orientalis, and Quercus robur. Bioactive compounds, including phenolics, flavonoids, and alkyl esters, were identified and quantified using GC-MS and HPLC techniques. A methanol: water mixture (65:35, v/v) was found to be the most effective solvent, yielding the highest total phenolic content in Abies nordmanniana and the highest flavonoid concentration in Pinus brutia. GC-MS analysis revealed species-specific distributions of key compounds such as 2-ethylhexanol, methyl stearate, and mono(2-ethylhexyl) adipate, reflecting the chemical diversity among the species. The extracts were further evaluated for their antioxidant, antimicrobial, and enzyme-inhibitory activities. Notably, inhibition of PPO and PON1 enzymes was observed, and DNA protection assays confirmed the ability of extracts to mitigate oxidative damage. These findings highlight the potential of industrial tree bark, typically a waste product of the wood industry, as a valuable source of bioactive compounds. The study advocates for its integration into the circular economy by developing high-value products for pharmaceutical, industrial, and environmental use.
Exploring the intentions of generation Z to pursue environmental and green careers
(Springer Science and Business Media B.V., 2025) Ibrahim, Blend; Tayşir, Nurgül Keleş; Görener, Ali
As global environmental concerns intensify, understanding the career motivations of sustainability-minded youth has become increasingly important. This study extends the theory of planned behavior (TPB) to investigate the factors influencing green job pursuit intention (GJPI) among Generation Z. Specifically, it incorporates perceived environmental responsibility (PER) and expected treatment as theoretical extensions to enhance explanatory power. Using a sample of 292 participants, the study examines how PER influences attitudes (ATT), subjective norms (SN), perceived behavioral control (PBC), and ultimately GJPI. Structural equation modeling (AMOS) and SPSS are employed for data analysis. Results reveal that PER significantly predicts ATT and PBC, which, along with SN, are positively associated with GJPI. Additionally, ATT and PBC mediate the relationship between PER and GJPI, while expected treatment moderates the effect of PER on ATT.
Novel Q-Rung orthopair fuzzy correlation measure based on Spearman’s correlation scheme with application in vehicle selection problem
(Springer, 2026) Ejegwa, Paul Augustine; Daniel, Wanzenke Tidoo; Kausar, Nasreen; Aydın, Nezir
Background Q-rung orthopair fuzzy sets (Q-ROFS) have been widely employed in decision-making problems due
to their strong ability to handle uncertainty, indecision, and imprecision. Consequently, several q-rung orthopair fuzzy
correlation measures (Q-ROFCM) have been developed and applied in various decision-making contexts. However,
many existing correlation measures exhibit inherent limitations, which reduce their effectiveness in addressing practical, real-world problems.
Methods In this study, a novel q-rung orthopair fuzzy correlation coefficient (Q-ROFCC) based on Spearman’s correlation scheme is proposed to overcome the shortcomings of existing approaches. The fundamental mathematical
properties of the proposed correlation measure are rigorously analyzed to ensure compliance with the standard
axioms of correlation coefficients. Furthermore, the proposed method is incorporated into a multi-attribute decisionmaking (MADM) framework.
Results The results demonstrate that the proposed Spearman-based Q-ROFCM technique is reliable, effective,
and accurate when compared with existing methods. Its applicability is illustrated through a vehicle selection problem, where the most suitable alternative is identified based on optimal performance and user satisfaction. Comparative analysis confirms the superiority of the proposed approach over Pearson-based Q-ROFCM approaches.
Conclusions The proposed Q-ROFCM technique provides a robust and efficient alternative for solving MADM
problems under uncertainty. Owing to its improved performance and practical applicability, the method is well suited
for real-life decision-making scenarios.
