IYTE GCRIS Database (Izmir Institute of Technology)
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Investigating the Impact of Sprouting on Starch Properties of Wheat Flour and Non-Linear Rheological Behavior of Bread Dough
No full textThis study investigated the physical and technological properties of the sprouted refined (SR), sprouted whole (SW) and unsprouted refined (UR) wheat flours to explore their potential in bread production. The effects of sprouting on the starch properties, including molecular weight, amylase activity, pasting, and crystallinity, were examined. Rheological properties were measured using farinograph, extensograph, and mixograph instruments. Then, a mixture design was used to optimize the flour blends for bread dough formulation. The non-linear rheological properties of the dough samples prepared by these flour blends were analyzed using large amplitude oscillatory shear (LAOS) test. Molecular weight (UR: 669.6 kDa and SR: 390.4 kDa) and the falling number (UR: 485.5 s, SR: 132 s and SW: 62 s) decreased with sprouting. The water absorption of UR and SR were similar (55 and 54 %), but SW had higher water absorption being 61%. The mixograph peak torque (UR: 78.3 %Tq, SR: 63.6 %Tq and SW: 57.4 %Tq) values decreased with sprouting. Comparison of the optimum blends with commercial counterparts in bread dough production was conducted by principal component analysis (PCA) using different rheological properties (GL, eta L,S, and T) at different strain values. The results showed that using a blend of 50.5% UR, 48.5% SR, and 1% SW, as well as 5.1% UR, 51.3% SR, and 43.6% SW, in bread dough formulation resulted in non-rheological properties similar to those of their commercial counterparts: refined bread flour and whole wheat bread flour
Investigation of the Effect of Solar Energy Use in Buildings on Reducing Carbon Dioxide Emissions
No full textThe aim of this study was to examine the reduction in CO2 emissions by using solar modules. In the article, the energy analysis of Narldere Nursing Home and Rehabilitation Centre (NNHRM) an exemplary public building in Izmir, was discussed. In this study, it was determined that if 1,500 kWp PV Panels were used, approximately 63.58% of the facility's electricity needs would be met by PV panels. It was revealed that by using the use of PV panels, an improvement of 6.98% in primary energy, 12.27% in CO2 emissions and 7.51% in PER would be achieved. The repayment period is calculated as 7.1 years
Tourist Guides Versus the Technology Threat
No full textThe study aims to understand the technology threat to the jobs of tourist guides and the way they perceive technology's impact. An email interview method is preferred while collecting the data from 25 countries. The findings reveal that although many guides mentioned the essentiality of human touch in guidance, they are aware of the influence of AI, the metaverse and smart technologies. Loss of jobs is very much possible, according to the vast majority of guides. They believe that without training and adapting themselves to novel technologies like the metaverse, they will not attract new generations and guides may lose jobs due to a lack of applying new technology to their own ways of doing business. The study enlightens the tour guidance field and the metaverse perceptions of guides influencing jobs
Exploring the Contemporary Dynamics of Extended Urbanisation: a Comprehensive Analysis on the Case of Denizli, Turkey
No full textContemporary discussions about extended urbanization and its inherent practices of suburbanization particularly focus on metropolitan cities in the Global South. There is inadequate empirical evidence on the rapidly developing Anatolian cities in Turkey. To address this gap, this article analyzes Denizli's extended urban development process, elaborates on the dominant practices, and examines the driving forces shaping its rapid, contested, and fragmented socio-spatial landscape. As one of the most ubiquitous cases among rapidly developing Anatolian cities, Denizli highlights the leading role of fragmented urban development planning interventions, the stimulating impact of transportation and infrastructure investments, and the pivotal role of private sector projects. The research consists of urban spatial analysis using statistical data and urban planning documents, detecting land use/cover changes over time, and identifying the driving factors that have influenced and shaped the patterns of urban development in Denizli. The findings indicate that fragmented urban development planning interventions have both triggered and sustained extended urban development in Merkezefendi, Denizli. Moreover, key public investments and real estate projects have fostered this extended urban development process, leading to disjointed fragments in a socioeconomically polarized geography. As a diversified and relational formation of extended urbanization, Denizli provides genuine research findings, and includes remarkable similarities as well as differences in the comparative analysis of global urbanism practices
Towards Facile Deep Learning Architectures for Chemical Processes: Simultaneous Pseudo-Global Training and Economic Synthesis
No full textChemical process data is usually not directly valorized in pure machine learning predictive models due to limited data availability. This limitation often caused from high sensor costs, data variety, and veracity issues. In response, this study proposes a novel formulation based on mixed-integer linear programming (MILP), called Approximated Deep Learning (ADL), to overcome these limitations and enable accurate modeling under data scarcity. The ADL simultaneously performs input selection, outlier filtering, and training of deep learning architectures within a single-level optimization problem. The method approximates the nonlinear and nonconvex components of traditional deep learning models in the mixed-integer domain through sophisticated reformulations, achieving a pseudo-global solution. A key feature of ADL is the integration of sensor pricing as a regularization mechanism, which promotes cost-efficient soft sensor design without compromising predictive performance. The proposed framework is validated on a publicly available bubble column dataset and benchmarked against four conventional deep learning methods. Results show that ADL achieves superior test accuracy with more than 50% reduction in input space, drastically reducing sensor cost. Furthermore, the optimized architecture is a high-quality initial guess for transfer learning on larger datasets. Overall, the method offers a practical and economically viable solution for data-driven chemical process modeling
The Architecture of Relational Materialism: a Categorial Formation of Onto-Epistemological Premises
No full textThis study formulates the basic premises of materialism, which has largely lost its visibility despite being one of the fundamental philosophical approaches that have been effective in the development of modern scientific practice and the construction of philosophy of science, in an alternative way, and aims to develop a new materialist interpretation of it that is non-reductive, pluralistic and open to the use of more than one scientific discipline. This interpretation, expressed with the term relational materialism, first addresses matter with the concept of signifier and foregrounds the concept of beable as the general philosophical category of matter. Secondly, it formulates the category of beable within the irreducible integrity of the categories of relationality, nonstaticity, and finitude; and positions knownability in terms of its correspondence to these general onto-epistemological categories. Thirdly, it clarifies the conditions of existence and knownability of particular entities under general categories based on specially corresponding onto-epistemological categories (interactability, structurability, contextuality, transformability, scale-dependency, actuality, contingency). In this respect, this study offers a pluralistic philosophical framework within which different methodological positions and scientific disciplines can be formulated and criticized based on combinations of different particular categories under general categories. In the conclusion of this article, the meaning and potential of relational materialism for the development of scientific research programs are evaluated
Dynamic Fluidic Manipulation in Microfluidic Chips With Dead-End Channels Through Spinning: the Spinochip Technology for Hematocrit Measurement, White Blood Cell Counting and Plasma Separation
No full textCentrifugation is crucial for size and density-based sample separation, but low-volume or delicate samples suffer from loss and impurity issues during repeated spins. We introduce the "Spinochip", a novel microfluidic system utilizing centrifugal forces for efficient filling of dead-end microfluidic channels. The Spinochip enables versatile fluid manipulation with a single reservoir for both inlet and outlet functions. It expels compressed air, facilitating fluid flow, and offers programmable filling mechanisms based on the hydraulic resistance of microfluidic channels. Compatible with a basic centrifuge, it allows sequential filling, internal mixing, and collection in straight microfluidic channels by simply adjusting the spinning speed, eliminating the need for complex valving. We demonstrated the Spinochip's efficacy in blood testing, where it successfully separated blood components, such as plasma, buffy coat, and red blood cells, from a single drop using centrifugation alone. This system enabled simultaneous hematocrit (R2 >0.99) and total white blood cell (R2 >0.93) quantification within a single microfluidic channel without the need for staining or special reagents. Remarkably, the Spinochip can perform hematocrit measurements on as little as 100 nL of blood, potentially paving the way for less invasive blood analysis. This innovative approach unlocks new possibilities in microfluidics, providing precise fluidic control and centrifugation for sample volumes as small as a few nanoliters
Modulating Cancer Stem Cell Characteristics in CD133+ Melanoma Cells through Hif1α, KLF4, and SHH Silencing
No full textMalignant melanoma is a highly aggressive form of skin cancer, partly driven by a subset of cancer stem cells (CSCs) with remarkable capacities for self-renewal, differentiation, and resistance to therapy. In this study, we examined how silencing three key genes-Hif1 alpha, KLF4, and SHH-affects CSC characteristics. Using small interfering RNA (siRNA)-based approaches, we observed significant changes at both the gene and protein levels, shedding light on how these pathways influence melanoma progression. Our results demonstrated that silencing these genes reduces the stem-like features of CSCs. Notably, Hif1 alpha silencing triggered a marked decrease in hypoxia-related gene expression, while targeting SHH led to a reduction in Gli1, a downstream effector of SHH signaling, highlighting its potential as a therapeutic target. We also observed changes in epigenetic markers such as HDAC9 and EP300, which play crucial roles in maintaining stemness and regulating gene expression. Interestingly, these interventions appeared to reprogram CSCs, pushing them toward a phenotype distinct from both traditional CSCs and non-stem cancer cells (NCSCs). Our findings emphasize the importance of targeting key signaling pathways in melanoma CSCs and underscore the value of mimicking the tumor microenvironment in experimental models. By revealing the dynamic plasticity of melanoma CSCs, this study offers fresh insights into potential therapeutic strategies, particularly using siRNA to modulate pathways associated with tumor progression and stem cell behavior
Combined Treatment of Ketogenic Diet and Propagermanium Reduces Neuroinflammation in Tay-Sachs Disease Mouse Model
No full textTay-Sachs disease is a rare lysosomal storage disorder caused by beta-Hexosaminidase A enzyme deficiency causing abnormal GM2 ganglioside accumulation in the central nervous system. GM2 accumulation triggers chronic neuroinflammation due to neurodegeneration-based astrogliosis and macrophage activity with the increased expression level of Ccl2 in the cortex of a recently generated Tay-Sachs disease mouse model Hexa-/-Neu3-/-. Propagermanium blocks the neuroinflammatory response induced by Ccl2, which is highly expressed in astrocytes and microglia. The ketogenic diet has broad potential usage in neurological disorders, but the knowledge of the impact on Tay-Sach disease is limited. This study aimed to display the effect of combining the ketogenic diet and propagermanium treatment on chronic neuroinflammation in the Tay-Sachs disease mouse model. Hexa-/-Neu3-/- mice were placed into the following groups: (i) standard diet, (ii) ketogenic diet, (iii) standard diet with propagermanium, and (iv) ketogenic diet with propagermanium. RT-PCR and immunohistochemistry analyzed neuroinflammation markers. Behavioral analyses were also applied to assess phenotypic improvement. Notably, the expression levels of neuroinflammation-related genes were reduced in the cortex of 140-day-old Hexa-/-Neu3-/- mice compared to beta-Hexosaminidase A deficient mice (Hexa-/-) after combined treatment. Immunohistochemical analysis displayed correlated results with the RT-PCR. Our data suggest the potential to implement combined treatment to reduce chronic inflammation in Tay-Sachs and other lysosomal storage diseases
Evaluation of Hydro-Geochemical Processes Controlling Groundwater Quality in Balkh Center (Mazar-E Northern Afghanistan)
No full textBackground: Groundwater in Afghanistan stands as the predominant water source employed for potable consumption, household utilization, irrigation, and industrial applications. Major cities of Afghanistan are largely dependent on groundwater resources. However, the groundwater quality of major cities in Afghanistan, including Mazar-e-Sharif city was not investigated in detail. Objective: This study aims to conduct a comprehensive analysis of the hydrochemical characteristics of the Mazar-e-Sharif groundwater, identify the factors influencing groundwater quality, and evaluate the groundwater contamination sources. Methods: A total of 18 groundwater samples were collected during the dry season (June 2020) and analyzed for various physico-chemical parameters. Methods such as multivariate statistical analyses, geochemical modeling, water quality index (WQI), and spatial distribution of groundwater quality were employed to evaluate the hydro-geochemistry of the study area. Results: The results reveal that 1) The prevailing groundwater within the study area is predominantly characterized by Na-(Ca)-HCO3 and Ca-(Mg)-SO4 water types. 2) Physicochemical variables such as NO3−, F−, TDS, and SO42− exceeded the World Health Organization (WHO) safe limits in many wells. 3) Hydro-geochemical processes such as silicate weathering, cation exchange, and gypsum dissolution controls the groundwater chemistry. 4) Cl/Br ratios reveal, that high salinity may originate from evaporitic lacustrine and evaporite deposits and found to be localized in nature. 5) The Water Quality Index (WQI) classification suggests that approximately 60 % of the groundwater samples fall into poor to very poor water quality categories, highlighting substantial public health concerns. Major contaminants like nitrate and fluoride were found to be higher than the safe limit in nearly half of the samples. Conclusion: The findings of this study hold value for decision-makers in formulating a proficient strategy for the management of groundwater resources in Mazar-e-Sharif City in achieving the UN sustainable goal (SDG) of providing sustainable water for all. Furthermore, new advanced techniques like environmental isotopes should be analyzed to evaluate groundwater hydro-chemical evolution in the future to enhance our understanding. © 202