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Effect of insemination timing within a tai program on fertility using sex-sorted semen in lactating dairy cows
No full textThe objective of this study was to evaluate the effect of insemination timing with sex-sorted semen on fertility in dairy cows subjected to a timed artificial insemination (TAI) protocol. A total of 611 Holstein cows (46 +/- 3 DIM) were enrolled and subjected to a presynchronized Ovsynch protocol (G7G; PGF(2)alpha-2d-GnRH-7d-GnRH-7d-PGF(2)alpha-56 h-GnRH), and randomly allocated to four treatment groups. The control group (CONV-14, n = 154) was inseminated with conventional semen 14 h after the final GnRH, while cows in the sex-sorted semen groups were inseminated at 14 (SS-14, n = 152), 18 (SS-18, n = 153), or 22 h (SS-22, n = 152) after the same treatment. The same bull was used for all inseminations. All cows were examined by ultrasonography to individually evaluate ovarian responses to the protocol and pregnancy status. No significant differences were observed among groups in body condition score, milk yield, cyclicity at the beginning of the protocol, response to the protocol, or follicle size at TAI. Pregnancies per artificial insemination were similar with 50.0% (77/154) in the CONV-14 group, 42.8% (65/152), 48.4% (74/153), and 43.4% (66/152) in the SS-14, SS-18, and SS-22 groups, respectively. No significant difference was observed in embryonic loss rates among groups: 5.2% in CONV-14, 9.2% in SS-14, 4.1% in SS-18, and 13.6% in SS-22, while SS-22 was numerically higher (similar to 7%) than the average of the other SS groups. Overall, conception rates were higher in cows responding to the first GnRH than in non-responders (49.7% vs. 32.3%, p < 0.0005), with a significant difference observed only in the CONV-14 and SS-18 groups (p < 0.005). Estrous expression during TAI was associated with higher conception rates in the CONV-14 group (75.0% vs. 45.4%, p = 0.008), while no such difference was detected in the combined SS groups (51.8% vs. 43.3%). However, the conception rate in the SS-22 group (36.7%) was distinctly lower (p < 0.02) than in other SS groups (53.6% in SS-14, 68.0% in SS-18). In conclusion, contrary to the expectation that advancing insemination closer to ovulation with sex-sorted semen would be advantageous, fixed time insemination at 22 h within the TAI program showed poorer outcomes compared to 18 h, which achieved a relative conception rate of 97% compared with conventional semen. It was also concluded that TAI at 22 h should not be recommended in cows exhibiting estrus on the day of insemination
Framing the digital self: Development and validation of the social media ai filter scale
No full textPurposeThis study addresses the urgent need for psychometric measurement tools to investigate the use of artificial intelligence (AI) filters on social media. The increasing prevalence of AI filters in digital self-presentation has highlighted the need for a tool to measure their use. To meet this need, we have developed and validated the Social Media AI Filter Scale (AIFS).Design/methodology/approachThis study employed a systematic scale development approach, conducting a two-stage validity process using independent samples of young adults (n1 = 304, n2 = 326). Scale development was conducted in accordance with classical test theory (CTT), including expert panel reviews, structured interviews and pilot tests. Psychometric analyses included exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and measurement invariance tests.FindingsPsychometric analyses revealed a 5-factor structure, accounting for 58.57% of the total variance. These factors, including social interaction and self-presentation, technological awareness and risk perception, sociocultural integration, technological self-efficacy and future orientation, provide a comprehensive understanding of AI filter usage on social media. The scale also demonstrates reliable internal consistency (alpha = 0.908) and satisfactory construct validity, ensuring the reliability of our findings.Originality/valueThe findings confirm that AIFS enhances the understanding of AI filter usage on social media by integrating the dimensions of technological acceptance, self-presentation and sociocultural impacts.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2025-011
Dynamic current control of high-gain buck-boost power transfer for electric vehicle-to-vehicle (v2v) charging with pv integration
No full textThis study presents an eco-friendly charging solution with an improved charging methodology for a high-efficiency off-board Vehicle-to-Vehicle (V2V) charging interface supported by photovoltaic (PV) power to facilitate energy exchange between light electric vehicles (EVs). The proposed method uses dynamic current control to adapt charging current in real time according to solar irradiance and the state of charge (SoC) of the vehicles. This approach improves energy transfer compared to conventional constant current (CC) and multistage CC methods, which use fixed or stepwise charging profiles and cannot fully utilize variable PV power. A high-gain quadratic buck-boost (QBB) converter is employed to enable both step-up and step-down operation, making the system suitable for vehicles with different voltage levels. The control strategy combines dynamic current control with an enhanced incremental conductance (InC) maximum power point tracking (MPPT) algorithm to maximize solar energy use. Performance results from processor-in-the-loop simulations show that the proposed system achieves more stable voltage regulation, better SoC improvement (+0.056 %), and higher charging efficiency than conventional CC and multi-CC methods under varying conditions. The performance findings show that the proposed V2V-PV interface provides a robust and efficient charging approach, supporting sustainable and grid-independent electric mobility
Exploring the use of silicone oil ash in cement-based mortars: Mechanical properties, high-temperature performance, and resistivity
No full textThis study investigates the utilization of silicone oil ash (SOA), a silicon-rich byproduct of polyacrylonitrile (PAN)-based carbon fiber production, in cement-based mortars to improve mechanical strength, high-temperature performance, and electrical resistivity. Due to its fine particle size and amorphous silica content, SOA has the potential to act as both filler and pozzolanic material in cementitious composites. Experimental studies were carried out on mortars containing different levels of SOA to evaluate their mechanical properties and durability. The findings of the present study demonstrated that the incorporation of SOA into concrete enhances its flexural and compressive strengths, particularly at moderate replacement levels. At the 28-day mark, both flexural and compressive strengths improved with the incorporation of SOA, particularly at 0.3% content, which yielded the most notable enhancements compared with the control sample. The findings of this study suggest that the optimal SOA dosage is one that maximizes strength benefits, and that excessive amounts may lead to particle agglomeration, which in turn limits further improvements. In addition, mortars containing SOA exhibited improved resistance to elevated temperatures, with reduced loss of strength at elevated temperatures. Electrical resistivity measurements also suggest that its inclusion may contribute to reduced chloride ion permeability, potentially improving durability. Microstructural analyses, including X-ray diffraction (XRD) and Fourier transform infrared (FTIR), revealed that no significant chemical interaction occurs between SOA and the cement hydration products. This emphasizes that its contribution is primarily through matrix densification. The results indicated that SOA does not exhibit significant pozzolanic reactivity but enhances mechanical strength and durability through a filler effect, improving particle packing and reducing porosity in the cementitious matrix. The results suggest that SOA could be a promising alternative material for sustainable construction applications, offering both environmental and performance benefits
Effect of structural modification of polycarboxylate-based grinding aids on hydration kinetics and rheological properties of cementitious systems
No full textStudies have examined the effects of polycarboxylate ether-based admixtures (PCEs) on clinker grinding, cement properties, and overall system performance; however, the influence of variations in chain structure and charge distribution on cement interactions remains insufficiently understood. To address this gap, the present study evaluates the effects of PCEs with controlled modifications in main chain length, side chain length, and anionic/ non-ionic charge ratio on cement particle size distribution, fresh-state properties, hydration kinetics, and compressive strength. Seven distinct PCE-type grinding aids (GAs) were synthesized, and 22 cement samples were produced at three GA dosages, including a GA-free control. The mechanism of PCE-based GA action was investigated through analyses of particle size distribution, hydration kinetics, water-reducing admixture adsorption, rheological properties, setting time, and compressive strength. These findings provide new insight into the mechanisms governing the performance of PCE-based GAs. The results indicate that cements produced with PCEs featuring medium main and side chain lengths or a medium chain combined with a high anionic/ nonionic ratio exhibit improved hydration kinetics and rheological properties. Overall, the study demonstrates that PCEs with an optimal chain length and a high anionic/non-ionic ratio deliver superior performance across the evaluated properties
Drug sensitivity prediction using machine learning on integrated cosmic, dgidb, and gdsc data
No full textAnalyzing the relationship between drug efficacy and sensitivity to mutational profiles is necessary for the effective treatment of complex diseases such as cancer. Particularly, cancerous tissues undergo constant change as a result of ongoing mutations, and the sensitivity of drugs to cancer may change as a result of new mutations. For this purpose, this study aims to present a statistical analysis of drug-disease-gene interactions. Furthermore, a general processing pipeline and machine learning models were developed to predict the drug sensitivity of cancer cells according to genetic mutations. To achieve this, four well-known open-source databases, including drug sensitivity data from cancer cell lines, two somatic mutation data resources, and a gene-drug interaction database, were integrated to assess an enriched database. Next, various preprocessing techniques, including text encoding, filtering, and optimization, were implemented to attain an efficient new dataset for statistical analysis and machine learning. Statistical analyses were conducted to investigate gene-drug interactions on the enriched database and to quantify their relative contributions to drug sensitivity. On the other hand, developed machine learning models predict drug sensitivity from somatic mutation or drug interaction datasets. The research also includes ablation studies and feature importance to introduce a thorough analysis of gene and drug sensitivity. The developed pipeline not only yielded an R(2 )of 0.91 in initial evaluations but also demonstrated robust generalizability by maintaining a 0.73 R-2 score in predicting AUC values across independent data sources. Overall, statistical analysis, machine learning performances, and ablation studies offer a new perspective on drug sensitivity prediction
Responses of soil enzyme activities to urea amendment in microplastic-impacted soils
No full textThis study investigates the impact of microplastics (HDPE, PP, PET) on soil enzyme activities (urease, alkaline phosphatase, beta-glucosidase), which are crucial for biogeochemical cycling, and their interaction with urea, a widely used nitrogen fertilizer. Soil samples from farmland in Bursa, Turkey, were treated with microplastics at 0%, 0.5%, and 5% concentrations and urea at 0 and 20 mg/100 g, then incubated aerobically (at 28 degrees C and 70% field capacity) for 60 days. Enzyme activities and pH were measured at 20, 40, and 60 days, and two-way ANOVA was used for statistical evaluation. Results showed that higher microplastic concentrations (5%) significantly reduced enzyme activities, with urease decreasing by approximately 17-33%, beta-glucosidase by 14-34%, and alkaline phosphatase by 10-25%. Among microplastic types, PET had the least inhibitory effect, while PP and HDPE caused stronger reductions. Urea application partially alleviated enzyme inhibition at low microplastic concentrations, enhancing activity by 15-20%, but failed to counteract suppression at higher doses. These findings highlight the disruptive impact of microplastics on soil biochemical processes, reducing nutrient cycling efficiency and compromising soil health. While urea application offers some mitigation, its effectiveness is limited in microplastic-contaminated soils. This study underscores the urgent need for integrated soil management strategies to minimize the dual threats of microplastic pollution and declining fertilizer efficiency, ensuring long-term soil sustainability
Il-9 orchestrates immune regulation through cd39/cd73 dependent metabolic reprogramming
No full textAdenosine triphosphate (ATP), a principal component of cellular energy metabolism, also functions as a significant extracellular signaling molecule under pathological conditions, including tissue damage and inflammation. The hydrolysis of extracellular ATP (eATP) to adenosine, catalyzed by ectonucleotidases including CD39 and CD73, is a key pathway involved in the control of immune responses. The objective of this study was to systematically examine the capacity of interleukin-9 (IL-9) to regulate ATP-adenosine metabolism and to assess the resultant impact of this regulation on T-cell responses. Peripheral blood mononuclear cells (PBMCs) isolated from healthy donors were analyzed by flow cytometry (FC) and ELISA to characterize the phenotypic, functional, and metabolic changes induced by IL-9 and to investigate the underlying molecular mechanisms. Our findings revealed that while IL-9 did not significantly change the frequency of major T-cell populations, it potentiated the conversion of ATP to adenosine by upregulating the expression of CD39 and CD73. This activity fostered an immunosuppressive microenvironment, especially within regulatory T (Treg) cells. Furthermore, IL-9 treatment suppressed the production of pro-inflammatory cytokines, increased anti-inflammatory cytokine levels, and inhibited T-cell proliferation. The pharmacological inhibition of CD39 and CD73 largely abrogated these IL-9-mediated effects. Together, these findings suggest that IL-9 may act as a regulator of the CD39/CD73 axis and that its influence on ATP-adenosine metabolism may have relevance in inflammatory and immune-mediated conditions characterized by dysregulated purinergic signaling
A path model to infer k-8 students computer programming performance: The relation between classroom goal structure, self-efficacy, and classroom engagement
No full textIt is crucial to grasp how classroom environments shape students' psychological and behavioural outcomes, particularly in programming education, a field that is more vital than ever in today's digital age. This paper addresses a critical gap in the literature by examining the intersection of classroom participation and self-efficacy variables and their association with engagement in programming education among K-8 students. We surveyed 868 middle school students (405 females, 463 males) in the northwestern province of Turkey. Data were collected using the Survey of Classroom Goals Structures and the Student Engagement Questionnaire, and analyzed through preliminary, descriptive, and inferential statistical methods. The results showed that self-efficacy was a predictor of student engagement. Among classroom goal structures, the motivational task was found to be a robust predictor of both student engagement and self-efficacy beliefs, while autonomy support did not predict either construct. However, mastery evaluation dimension predicted cognitive and emotional engagement. These findings show the nuanced roles of classroom goal structures play in shaping student engagement and self-efficacy in programming education. The study offers implications for instructors and policymakers in fostering student participation and motivation in early programming pedagogy. Conducting studies across different grade levels and cultural contexts will enhance the generalizability of findings on student engagement in programming education
The multiple steady states in ventilation: Turbulence and buoyancy approach effects on flow pattern
No full textPurposeThe effects of inlet turbulence parameters on the flow pattern in the ventilation of the IEA Annex 20 room were investigated in detail by also considering the effects of the buoyancy approach and iteration number. The flow pattern under the effects of the inlet turbulence intensity (Tu) and the length scale (LS) in a wide range of intervals has been predicted from the solution multiplicity point of view to check the hypothesis of Hancock and Bradshaw. Boussinesq and ideal gas approaches for buoyancy have been used for both converged iteration numbers of 3,000 and 9,000.Design/methodology/approachBuoyant ventilation flow has been considered and the finite volume ANSYS-Fluent code was used to solve the turbulent conservation equations from the solution multiplicity point of view. RNG k-e turbulence model that is validated and the most used by ventilation community has been used in the computations with the enhanced wall treatment. Solution multiplicity is related to the extreme sensitivity on initial conditions and/or system parameters. Both the effects of initial conditions and system parameters on solution multiplicity have been considered.FindingsThe main flow pattern in the room may be clockwise, counter-clockwise or intermediate cases dependent on the used buoyancy approach and iteration number. The number of intermediate cases in the ideal gas approach is less than the Boussinesq approach for both iteration numbers. In any room ventilation study, turbulence inlet parameters have the same importance as Archimedes number, used software, computational scheme and iteration number. It was found that Tu has an effect on flow patterns not only for low and medium LS values but also for high LS values.Research limitations/implicationsOnly a mixed ventilation scenario has been considered. The current study is related to a turbulent flow regime, and flow is assumed two-dimensional, steady, incompressible and non-isothermal. Inlet turbulence parameters' effects on flow patterns have been investigated for an empty room with a single aspect ratio.Practical implicationsUnderstanding of solution multiplicity phenomenon in ventilation applications can be used to prevent multiple steady states that cause undesired thermal comfort conditions.Originality/valueThere are opposite arguments in the literature about the effect of inlet turbulence conditions on the flow field and flow reversal. The first aim of this study is to investigate the effects of the inlet turbulence parameters on solution multiplicity in detail by considering these controversial points. Furthermore, many of the available studies are restricted to transitional flows. Secondly, as inlet turbulence parameters, Tu and LS values in a wide range have been investigated for the effects on flow pattern to check the hypothesis of Hancock and Bradshaw. Additionally, solution multiplicity has been shown under the fixed Tu and LS values by changing the buoyancy approach and converged iteration numbers as initial conditions