Siirt Üniversitesi Kurumsal Akademik Arşivi
DSpace@Siirt, Siirt 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 telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Molecular detection, epidemiology and phylogenetic evaluation of Babesia ovis in apparently healthy goats
(Elsevier BV, 2025-08) Asia Taqddus; Muhammad Naeem; Hira Muqaddas; Ceylan Ceylan; Onur Ceylan; Ferda Sevinc; Maryam Rahravani; Meysam Moravedji; Alireza Sazmand; Furhan Iqbal
Babesia (B.) ovis is an intra-erythrocytic protozoan parasite that infects small ruminants globally, causing economic losses. This study aimed to investigate the molecular prevalence of B. ovis in 1200 asymptomatic goats of various breeds across four districts in Punjab, Pakistan: Layyah, Lohdran, Dera Ghazi Khan, and Rajanpur. The enrolled goats represented ten breeds, including Daira Din Pannah (n = 890), Pahari goat (n = 68), Nukri (n = 44), Teddy (n = 37), Lail Puri (n = 36), Beetal (n = 36), Dessi (n = 32), Makhi Cheena (n = 27), Muhammad Puri (n = 19) and Fazil Puri (n = 11). The hematological and biochemical profiles of the goats, risk factors associated with the infection, and the phylogenetic relationship of the detected isolates were also evaluated. In total, 105 blood samples (9.6 %) tested positive by PCR. Sanger sequencing of a partial fragment of the 18S rRNA gene confirmed B. ovis. Phylogenetic analysis of the 18S rRNA gene sequences revealed 99-100 % similarity with isolates previously reported from Iran, Iraq, Turkey, and Spain. The infection rate varied across districts, with the highest prevalence observed in goats from Rajanpur (13 %), followed by Dera Ghazi Khan (11 %), Layyah (7 %), and Lohdran (5 %) (P = 0.003). The susceptibility to infection varied among goat breeds, with Lail Puri breed showing the highest susceptibility (P = 0.03). Risk factor analysis revealed that goats under one year of age and those kept on farms where other animals and dogs were also present had higher B. ovis infection rates. Babesia ovis-infected goats showed reductions in white and red blood cells, hemoglobin concentration, and alterations in serum aspartate aminotransferase and creatinine levels. This study provides updated data on the prevalence of B. ovis in local Pakistani goat populations, emphasizing the need for integrated control strategies against this tick-borne pathogen.
A comprehensive method for exploratory data analysis and preprocessing the ASHRAE database for machine learning
(Elsevier BV, 2025-08) Amir Rahmanparast; Muhammed Milani; Muhammet Camci; Yakup Karakoyun; Ozgen Acikgoz; Ahmet Selim Dalkilic
Thermal comfort prediction is crucial for building energy efficiency and occupant comfort. ML methods are commonly used to predict thermal comfort. This research presents a comprehensive process for exploring and preprocessing the ASHRAE Database, providing a substantial dataset comprising 107,583 records of thermal comfort observations to create ML algorithms that can estimate Fanger's PMV. With the most detailed cleaning and preprocessing stages in the literature, which included the imputation of missing values and the management of outliers, the final dataset is reduced to 55,443 records for the analyses. For practical applications and indoor comfort assessments, its estimation offers significant advantages due to its speed, ease of use, and cost-effectiveness. This study aimed to investigate which parameters are important in Fanger's PMV model and which subset of variables is best for variable selection using different feature selection and analysis methods. The Ta and Tr had a high correlation value of 0.92, indicating a robust link between these two variables. The study employed Feature importance, the SelectKBest, SHAP, P-box, and PDP analyses, which showed consistency and suggested condensing the first six elements into three, and also was validated with the Chinese Database with 41,977 entries. The study targeted three parameters: Ta, clo, and M, using less expensive and simple measurement devices. To evaluate the accuracy of the research performance, RF and SVM models were created based on these three parameters. The results indicated that they have the accuracies of 85% and 70%, respectively, which are far better than the conventional models.
Exploring the fixed point theory and numerical modeling of fish harvesting system with Allee effect
(Springer Science and Business Media LLC, 2025-04-24) Muhammad Waqas Yasin; Mobeen Akhtar; Nauman Ahmed; Ali Akgül; Qasem Al-Mdallal
Fish harvesting has a major role in nutritive food that is easily accessible for human nourishment. In this article, a reaction-diffusion fish harvesting model with the Allee effect is analyzed. The study of population models is a need of this hour because by using precautionary measures, mankind can handle the issue of food better. The basic mathematical properties are studied such as equilibrium analysis, stability, and consistency of this model. The Implicit finite difference and backward Euler methods are used for the computational results of the underlying model. The linear analysis of both schemes is derived and schemes are unconditionally stable. By using the Taylor series consistency of both schemes is proved. The positivity of the Implicit finite difference scheme is proved by using the induction technique. A test problem has been used for the numerical results. For the various values of the parameters, the simulations are drawn. The dynamical properties of continuous models, like positivity, are absent from the simulations produced by the backward Euler scheme. Implicit finite difference scheme preserves the dynamical properties of the model such as positivity, consistency, and stability. Simulations of the test problem prove the effectiveness of the Implicit finite difference scheme.
Effects of irrigation level, plant density, and nitrogen doses on sweet corn yield and water productivity
(Pakistan Journal of Botany, 2024-11-01) Hayrettin Kuşçu; Halis Seçme; İpek Karakuş
Plant density, nitrogen and irrigation management are three important agricultural inputs that affect plant yield and quality. This study was undertaken to ascertain the impact of varying plant densities, irrigation water levels and nitrogen rates on the yield, some yield components and irrigation water productivity (IWP) of sweet corn (Zea mays L.) cultivated in an open-field environment. To this end, two-year field experiments were carried out using the Challenger F1 corn variety on clay-textured soil in the Bursa province located in the Southern Marmara Region of Turkey. In the first year of the experiment, plant density in the main plots and irrigation levels in split plots were randomized. Accordingly, two plant density levels (57000 and 95000 plants ha-1) and three irrigation levels (100%, 67%, and 33% of crop evapotranspiration (ETc)) were applied. In the second year of the experiment, three irrigation levels in main plots (100%, 80%, and 60% of ETc) and three N fertility ratios (150, 300, and 450 kg ha-1) in split plots were assigned. A combination of 95000 plants ha-1 population and 100%ETc irrigation ratio provided maximum fresh ear yield. The irrigation treatment 80%ETc, accompanied by 300 kg N ha-1, and 76000 plants ha-1 population was determined as the optimal management system for maximum yield, yield components, and IWP. To preserve soil and water resources, the optimal management system at maximum yield and IWP should be implemented for sweet corn production in the Marmara region.
Evaluation of machine learning applications in building life cycle processes for energy efficiency: A systematic review
(Elsevier BV, 2025-06) Gevher Nesibe Kaya; Figen Beyhan; Zeynep Yeşim İlerisoy; Jan Cudzik
In recent years, machine learning has been increasingly applied to achieve energy efficiency in buildings. This study analyzes the utilization of machine learning across the building life cycle by reviewing literature on building energy efficiency. In this context, a systematic literature search was conducted using the Web of Science (WOS) search engine, and 868 publications were found. The publications were analyzed according to their year, subject scope, and qualification results, and 84 publications were selected. These publications were discussed under five categories: objective function and control variables, programs, simulations, machine learning, and optimization algorithms. The relationships between these categories and each phase of the building life cycle were examined. The findings suggest that machine learning can effectively optimize factors related to energy efficiency and building sustainability throughout the life cycle, and it is anticipated that interdisciplinary studies incorporating machine learning will experience exponential growth in the future.
