Central Unit Research Articles

New Turkey, social policy, and a daytime talk show as a remedy: Müge Anlı as a modern Çalıkuşu

ABSTRACT ‘Müge Anlı ile Tatlı Sert’, which investigates unresolved criminal cases with interactive audience support, is one the most popular daytime television shows in contemporary Turkey. The neoliberal restructuring of the welfare system in New Turkey posits that providing protection and security against social risks is the sole responsibility of individuals and families. In line with this approach, the show individualizes social problems ready to be resolved by ‘Müge Abla’ (Sister Müge) and her audience. Müge Anlı's image, which conforms to the ideal modern and modest Republican woman, allows her to gain authority, trust, respect, and popularity. The show’s discourses, structure, and communicative strategies are the product of the New Turkey where ‘the sacred family’ is the central political unit and the nation is an extended, happy family reproduced through this show.

Multifunctional Intelligent Reconfigurable Metasurface.

The emergent reconfigurable metasurfaces (RMs) have attracted a lot of attention due to their potential in broad applications. As a general platform, RMs are able to control the reflection (or refraction) of incident waves with predefined functionalities. Nevertheless, the operation of RMs is highly dependent on the arrival direction of incidence. The self-adaptive design of an RM, so that it can respond to varied incident waves automatically, is highly requested in practical implementation, which is actually challenging. This study reports the realization of an intelligent RM (IRM) system, which can detect the arrival direction of impinging waves and respond to the incidence with a predefined functionality accordingly. This IRM system is constructed by integrating a direction of the arrival estimation module, a frontend by the varactor-based metasurface, and a central control unit. In experiments, an IRM system designed for TM polarization is demonstrated to perform various functions, i.e., retroreflection, directional reflection, and fixed-point energy focusing, which are highly requested by edge communication and sensing. The measured results imply that this IRM system responds quite well within a wide incident range from -60° to 60° in a frequency range from 9 to 9.5 GHz. The proposed IRM can be a good candidate for boosting 5G communication and Internet of Things applications, including beam shaping/steering, RCS manipulation, object imaging, and sensor recharging.

Ionic Bent-Core Pillar[n]arenes: From Liquid Crystals to Nanoaggregates and Functional Applications.

Herein, we report the first examples of supramolecular systems from bent-core-based pillar[n]arenes through ionic bonds. These ionic materials have been prepared by the interaction of an amino-ended pillar[5]arene (P5N10) and three different carboxylic acids, including bent-core moieties. The bent-core units are based on ester, biphenyl, and azobenzene structures bearing two different flexible spacers between the carboxyl group and the central bent-core aromatic units. The ionic pairs segregate the molecular blocks, leading to columnar liquid crystal organizations. These ionic supramolecular compounds exhibit interesting results as proton-conductive materials. Furthermore, the introduction of azobenzene units in the bent-core structure has provided a photoresponse to the proton conduction materials. Interestingly, the amphiphilic character generated by the ionic pairs and the hydrophobic bent-core structures allows their molecular self-assembly in water solution, resulting in aggregates of appealing morphologies. The structural modifications of the bent-core units (i.e., connecting bonds at the lateral structure and spacer lengths) provide an attractive analysis on the relationship between the chemical structure and the morphology of the aggregates (fibers, chiral ribbons, nanotubes...). Additionally, the self-assembly process and evolution of the aggregates from fibers to nanotubes have been studied with several techniques.

Struggling with strategizing in public client organisations: Managing strategic projects in inter-organisational contexts

The efficiency of using strategic projects to accelerate long-term change is questioned, especially in inter-organisational contexts. Strategic projects operationalised in inter-organisational contexts are decoupled from the parent – permanent – organisation, creating a need for project actors to manage in-between. The aim of this paper is to increase the understanding of practices for managing strategic projects in inter-organisational contexts, initiated by public client organisations. Applying a practice perspective, this paper presents a case study exploring a strategic project to test collaborative contracting in road maintenance contracts for a public infrastructure client in Sweden. The findings revealed a lack of structure regarding both participation and responsibilities, as well as uncertainties and ambiguities with respect to the goal of the strategic change. The study shows how strategic practices move from central units at the permanent organisational level to the contract level where the strategic project is operationalised and tested, isolating the project and making project actors act as middle managers in-between the project and parent organisation. Thus, the study contributes with a discussion on the efficiency of strategizing through projects in public organisations, especially those operating in a network of suppliers through outsourcing.

Endgroup engineering of the third component for high-efficiency ternary organic solar cells

In this study, we carefully designed and synthesized three A-D-A type molecules, namely YF-C8-CN, YF-C8-S, and YF-C8-O. These molecules feature a 2,2′-bithiophene core, which bears two 2,4,6-tripropylbenzene steric hindrance groups, as the central electron-donating unit, and utilize rhodanine (Rh), thiazolidinedione, and dicyanoyl-modified benzotriazole (BTA) respectively as the electron acceptor unit (A-unit). We investigated their impact as the third component on the performance of ternary organic solar cells (OSCs). Our studies indicate that YF-C8-S, with rhodanine terminal groups, exhibits complementary absorption characteristics and cascaded energy levels when combined with the host binary system (D18:eC9-4F). Compared to YF-C8-CN and YF-C8-O, ternary OSCs based on YF-C8-S demonstrate significantly higher exciton diffusion and charge transport efficiency, favorably impacting the short-circuit current density (JSC) and fill factor (FF) of the OSCs. Furthermore, devices based on YF-C8-S exhibit reduced non-radiative energy loss, contributing to an enhanced open-circuit voltage (VOC). Consequently, ternary OSCs based on YF-C8-S achieve a remarkable efficiency of 19.12%, positioning it among the highest reported values. This comprehensive research on the third component of the YF-C8 series underscores their significant potential for fabricating high-performance ternary OSCs.

Rapid patient-specific organ dose estimation in computed tomography scans via integration of radiomics features and neural networks.

Computed tomography (CT) offers detailed cross-sectional images of internal anatomy for disease detection but carries a risk of solid cancer or blood malignancies due to exposure to X-ray radiation. This study aimed to develop a new method to quickly predict patient-specific organ doses from CT examinations by training neural networks (NNs) based on radiomics features. CT Digital Imaging and Communications in Medicine (DICOM) image data were exported to DeepViewer, a clinical autosegmentation software, to segment the regions of interest (ROIs) for patient organs. Radiomics feature extraction was performed based on the selected CT data and ROIs. Reference organ doses were computed using Monte Carlo (MC) simulations. Patient-specific organ doses were predicted by training a NN model based on radiomics features and reference doses. For the dose prediction performance, the relative root mean squared error (RRMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were evaluated on the test sets. The robustness of the NN model was evaluated via the random rearrangement of patient samples in the training and test sets. The maximal difference between the reference and predicted doses was less than 1 mGy for all investigated organs. The range of MAPE was 1.68% to 5.2% for head organs, 11.42% to 15.2% for chest organs, and 5.0% to 8.0% for abdominal organs; the maximal R2 values were 0.93, 0.86, and 0.89 for the head, chest, and abdominal organs, respectively. The radiomics feature-based NN model can achieve accurate prediction of patient-specific organ doses at a high speed of less than 1 second using a single central processing unit, which supports its use as a user-friendly online clinical application.

GPU parallel implementation of a finite volume lattice Boltzmann method for incompressible flows

This work presents a graphics processing units (GPU) parallel algorithm of a cell-centered finite volume lattice Boltzmann method (FVLBM) on unstructured meshes. In the present GPU parallel algorithm, the parallelization is performed in the physical space. To reduce the frequency of GPU memory accesses, this algorithm develops coalesced access to GPU memory. In addition, to avoid the race for resources leading to data anomalies, such as dirty read or phantom read etc., and the double counting for flux calculation, the efficient face-based data structure often used for flux calculation in cells in the central processing unit (CPU) version of FVLBM is modified into a face-based data structure used for the fluxes on all faces, followed by a cell-based loop for the final residuals in all cells. Therefore, the proposed GPU parallel algorithm does not need to use the resource lock and retains the high efficiency of the face-based data structure in the fluxes computation to enhance its’ parallel efficiency. Additionally, to demonstrate the computational efficiency of the proposed GPU parallel algorithm, various benchmark studies are performed in this work by the proposed parallel scheme on a double precision NVIDIA GeForce RTX 3090Ti GPU card, including (a) the lid-driven flow in a two-dimensional (2D) square cavity, (b) a 2D flow past a cylinder, and (c) the lid-driven flow in a three-dimensional (3D) cubic cavity. The numerical results show that the proposed GPU parallel algorithm can be as accurate as the original CPU serial scheme with 1 to 2 orders of speedup.

A threshold activation-based simplified Lv's transform algorithm for transient multi-component linear frequency modulation signals analysis.

The high sampling rate in modern digital systems generates a large scale of data. To address the computational burden, this paper proposes a threshold activation-based simplified Lv's transform (SLVT) algorithm to analyze the transient multi-component linear frequency modulation signals. Only the signal arrival can trigger the signal analysis. This mechanism alleviates the computation pressure because of the sparsity of signals. The threshold activation mechanism enables transient signal detection and sampling rate adjustment, thereby enhancing the efficiency and effectiveness of the analytical process. The simplified Lv's transform (LVT) removes redundant computations on stretch weighting and the Discrete Fourier Transform (DFT) in Lv's transform. SLVT uses the efficient Bluestein chirp-z algorithm to implement the stretch keystone transform. The comparison results show that SLVT reduces the computational complexity of the original LVT by at least 30.8%. This algorithm exhibits superior performance compared to other advanced signal processing methods, such as discrete chirp Fourier transform, fractional Fourier transform, and Radon Wigner transform algorithms, in terms of parameter extraction accuracy, computational complexity, and execution time. Moreover, the implementation of a field programmable gate array accelerates SLVT computing by a factor of 116 in comparison to the CPU (Central Processing Unit) platform.

Reduced Injection Rates and Shallower Depths Mitigated Induced Seismicity in Oklahoma

Abstract The proximity of wastewater disposal to the Precambrian basement is a critical factor influencing induced earthquake rates in the Central United States, but the impact of reducing injection depths has not been widely demonstrated. Beginning in 2015, state regulatory efforts in Oklahoma and Kansas mandated that wells injecting into the lower Arbuckle Group, a basal sedimentary unit, be backfilled with cement (i.e., “plugged back”) so that they inject into shallower formations. This plug back activity gives us a unique opportunity to investigate the relationship between injection depth and induced seismicity rate. To evaluate the impact that decreased injection rates and plug backs had on the seismicity rates, we created a suite of rate–state earthquake models. Observed seismicity rates are best fit when only lower Arbuckle volumes are considered, suggesting the lower Arbuckle injectors were primarily responsible for the seismicity and that plug backs were effective at isolating the injected volumes to shallower formations. Our models demonstrate that if these wells had not been plugged back, seismicity rates would be multiple times larger than they are today. We find that the combination of well plug backs and injection volume decreases can be an effective strategy for reducing induced seismicity rates.

A modified matrix model captures the population dynamics for the primary vector of Lyme disease in North America

AbstractLyme disease, the most prevalent tick‐borne disease in North America, is caused by the bacterium Borrelia burgdorferi, and in the eastern and central United States, it is spread to humans by the black‐legged tick (Ixodes scapularis). Due to the complex, multiyear and multihost life cycle of this species, a matrix modeling approach is needed to effectively estimate subseasonal, multistage survival and transition dynamics in order to better understand and predict when population growth is high. Of the three questing tick life stages (larvae, nymphs, and adults), nymphs are most often associated with transmitting the bacteria to humans, and previous work suggests a mix of abiotic and biotic drivers are associated with nymph abundance. However, understanding tick population growth requires understanding mortality and transition probabilities for each stage and each stage may be individually and uniquely impacted by climate and host availability. A larval tick, for example, may experience warming temperatures differently than nymph or adults, because they are present on the landscape at different times. Here, we describe and validate a model that accounts for field sampling design and evaluates abiotic (temperature, relative humidity, precipitation) and biotic (host abundance) drivers of variation in tick population growth. To account for the drivers of subseasonal and interannual variability in demography, phenology, and population density, we built stage‐structured population models that account for variability in meteorology and host population abundance throughout the full tick lifecycle. Our model is fit and validated with 11 years of tick and host data from the northeastern United States. In this context, we found that a four‐stage model that includes unique transitions to and from a dormant, overwintering nymph state outperforms a model that only includes the three questing stages, and that incorporating the abundance of the predominant host species, Peromyscus leucopus, and weather variables improved predictions and model fit. Additionally, the model accurately predicted all three questing stages at sites different than where they were calibrated, showing that this model structure is generally transferable. Overall, this model lays a foundation for the real‐time iterative forecasting of tick populations needed to effectively protect public health.

МЕТОД ОБУЧЕНИЯ ИНТЕЛЛЕКТУАЛЬНОГО АГЕНТА С ПОМОЩЬЮ СЕТЕЙ DOUBLE DQN, ПУТЕВЫХ ТОЧЕК И ФУНКЦИИ ВОЗНАГРАЖДЕНИЯ

The problems of increasing the control efficiency of autonomous vehicles are considered. The problem of reducing the required computational resources for the intelligent vehicle control module is highlighted. A neural network training algorithm for Double DQN architecture with modified reward functions is proposed. The basis of the proposed solution is the use of lane segmentation, reward function and the use of additional waypoints in training. A software model has been developed and simulation of the learning process has been performed. The results obtained from a comparative analysis with known solutions show a stable increase in episode duration, and effective training in a realistic urban simulation. The study points to the possibility of reducing the need for high computing power, which will enable the use of central processing units (CPUs) for basic functions of unmanned vehicles instead of graphics processing units (GPUs).

IoT Based Smart Traffic Management System using Image Processing and Automated Street Lighting System

Through the integration of image processing, IoT, and automatic street lighting, this study presents a novel solution to urban traffic management. Real-time traffic data, such as vehicle counts and congestion levels, are continuously collected by smart cameras positioned at strategic intersections. A central control unit receives this data and uses sophisticated algorithms to dynamically modify traffic signal timings. Additionally, by including sensors to identify surrounding cars and pedestrians, an automatic street lighting system improves sustainability. This system helps save energy and money by optimizing energy consumption through the ability to change lighting intensity levels as needed. In general, this Internet of Things (IoT)-based smart traffic management system improves efficiency and safety for both commuters and locals by addressing congestion and fostering sustainable urban development

Smart Sensors and NodeMCU ESP8266-Based Automated Irrigation System for Effective Water Management in Agriculture

In India, agriculture is the biggest and most essential sector. The Global Internet of Things (IoT) is a vital aspect of smart farming. Internet of Things (IoT) is the interconnection of many devices over the internet. All devices are interconnected via unique identifiers, enabling data transfer without requiring interaction between humans. A sufficient amount of water is given to the crop in agriculture at a specific time through irrigation. The primary objective of this paper is develop a controlled watering system that uses smart sensors and NodeMCU ESP8266 to improve water efficiency in plants, integrated with things of speak platform. By integrating soil moisture sensors, the system ensures that irrigation is given only when the soil penetrates a predefined dryness threshold by continuously monitoring the moisture levels in the soil. By using an optimized method, water waste is reduced and plants are given the proper amount of water for healthy growth. DHT11 sensor also monitor humidity and temperature, which enables the system to modify watering schedules in response to current meteorological conditions. These environmental elements can be implemented in the system to help it adjust to changing conditions and increase overall water efficiency. Rain sensors are used to detect rainwater. When rainwater occurs, irrigation is automatically stopped to avoid overwatering and significantly preserve water resources. The central processing unit (CPU) is the NodeMCU ESP8266 microcontroller, which extracts data collected by the sensors and regulates of irrigation system in reaction to the real-time data. This automated approach improves the accuracy of irrigation techniques while also decreasing the need for human intervention, which saves time and labour

Registration of ‘MN‐Rothsay’ spring wheat with high grain yield and lodging resistance

Abstract‘MN‐Rothsay’ (Reg. No. CV‐1208, PI 702731) hard red spring wheat (Triticum aestivum) was released by the University of Minnesota Agricultural Experiment Station in 2022 because of its high grain yield and lodging resistance. MN‐Rothsay is a medium‐late maturity cultivar with below average plant height, containing the semi‐dwarfing alleles Rht‐B1b and Rht24b. The lodging resistance of MN‐Rothsay is similar to ‘Linkert’, a 2013 release that was the most widely grown cultivar in Minnesota from 2016 to 2020, largely due to its superior lodging resistance. MN‐Rothsay has moderate resistance to Fusarium head blight (caused primarily by Fusarium graminearum Schwabe), good resistance to prevalent races of pathogens causing leaf rust (Puccinia triticina Eriks.) and stem rust (P. graminis Pers.: Pers. f. sp. tritici Eriks. & E. Henn.), and is well‐adapted to the north central United States.

Joint Power Allocation and Hybrid Beamforming for Cell-Free mmWave Multiple-Input Multiple-Output with Statistical Channel State Information.

Cell-free millimeter wave (mmWave) multiple-input multiple-output (MIMO) can effectively overcome the shadow fading effect and provide macro gain to boost the throughput of communication networks. Nevertheless, the majority of the existing studies have overlooked the user-centric characteristics and practical fronthaul capacity limitations. To solve these practical problems, we introduce a resource allocation scheme using statistical channel state information (CSI) for uplink user-centric cell-free mmWave MIMO system. The hybrid beamforming (HBF) architecture is deployed at each access point (AP), while the central processing unit (CPU) only combines the received signals by the large-scale fading decoding (LSFD) method. We further frame the issue of maximizing sum-rate subject to the fronthaul capacity constraint and minimum rate constraint. Based on the alternating optimization (AO) and fractional programming method, we present an algorithm aimed at optimizing the users' transmit power for the power allocation (PA) subproblem. Then, an algorithm relying on the majorization-minimization (MM) method is given for the HBF subproblem, which jointly optimizes the HBF and the LSFD coefficients.

WITHDRAWN: Detection of Amblyomma maculatum and Rickettsia parkeri in prairies of Central Missouri

Prairie habitats are complex ecosystems experiencing destruction and decline due to landscape repurposing, thus resulting in significant changes to plant, animal, and insect wildlife diversity. In the Central United States, the reconstruction of prairie habitats from farmland is a widely applied strategy to raise diversity and recreate a healthy, complex ecosystem. In Central Missouri, we are examining the consequences of reconstruction efforts on the prevalence of zoonotic diseases and their associated pathogens and have observed large populations of Dog ticks, Dermacentor variabilis, and Lone Star ticks, Amblyomma americanum, as well as the first recorded instance of the Gulf Coast tick, Amblyomma maculatum, in Callaway County. The Gulf Coast tick is traditionally found mainly along the Atlantic and Gulf Coast regions, but has been undergoing geographic expansion in the last decade. Since detection in 2020, the number of Gulf Coast ticks has increased,. Upon screening 193 Gulf Coast ticks in 105 pools, we found that 6.67 % of ticks were positive for the pathogenic Rickettsia parkeri, which causes spotted fever rickettsiosis in animals and humans, with most ticks coming from the reconstructed prairie site. These observations suggest that reconstruction of prairie habitat has the potential to support the sylvatic cycle of Rickettsia parkeri. Collectively, our observations show that the reconstructed prairie are capable of harboring large tick populations as compared to remnant prairies and demonstrates a potential increase in disease risk as a result. This works highlights the importance of proactive surveillance of ticks, especially with land-use change.

Ergonomic Risk Factors and Musculoskeletal System Problems In Healthcare Professionals Working In Central Sterilization Unit

This study was conducted to determine ergonomic risk factors and musculoskeletal system problems in healthcare professionals working in the central sterilization unit. The study is of descriptive cross-sectional type. The sample of the study consisted of 87 health professionals working in the central sterilization unit of seven hospitals of a private health group in Istanbul. Data were collected by face-to-face interview method using the sociodemographic characteristics form, the validated and reliable Ergonomic Risk Factors and Musculoskeletal Disorders Scale (ERKISO) and the Cornell Musculoskeletal Disorder Scale (T-CMDQ) between February and September 2020. Factor analysis, descriptive statistics, Mann Whitney U test, Kruskal-Wallis H test, post-Hoc analysis test and correlation test were used in the evaluation of the data. The level of significance was accepted as p

Quasi-exchange rings

An element a in a ring R is called a quasi-idempotent if a 2 = ka for some central unit k of R. A ring R is called quasi-exchange if for any element x of R, there exists a ∈ R with a 2 = ka ∈ Rx and k − a ∈ R ( k − x ) , where k is a central unit of R. Quasi-exchange rings are a generalization of exchange rings and quasi-clean rings. This paper studies the properties of quasi-exchange rings and the relationship among quasi-exchange rings, exchange rings and quasi-clean rings. An equation xa − fx = 1 is used to characterize the quasi-exchange ring and we prove that quasi-exchange rings are left-right symmetric.

Investigation of structural, ultrasonic, and radiation shielding properties of PbO-B2O3 glasses reinforced MnO2

Glass composition systems of (65-x) B2O3+15 PbO +15 Na2O+ 5 Al2O3+ x MnO2, where x = 0, 0.1, 0.3 and 0.5 mol% were organized by the usual melting-annealing process. Electron spin resonance (ESR) displayed broad hyperfine before and after exposing glasses to 25 kGy of gamma radiation, while sharp ESR signals at magnetic field range 3200–3500 Gauss were detected after irradiating glasses with 50 kGy. FTIR and Raman spectroscopy displayed the high structural stability of the glasses towards 25 kGy of gamma radiation, signifying the central building units of various borate groups at the same intensities and positions. Different ultrasonic characteristics such as ultrasonic velocities, elastic properties, Poisson’s ratio, Lamé constants, acoustic impedance, and ultrasonic attenuation coefficient didn’t variate greatly with gamma radiation exposure to 25 kGy. Comparing results showed that 0.5 % MnO2 glass has the most stable structure against gamma irradiation, the lowest ultrasonic velocity and wave passage, and the highest attenuation of waves, ensuring the high ability of the prepared glasses to inhibit the echoes. Experimental shielding parameters such as mass (MAC) and linear attenuation coefficients (LAC), half value layer (HVL) and tenth value layer (TVL) were measured for gamma rays and removal cross section area was measured for neutrons. Theoretical studies by WinXcom program revealed satisfactory settlement, with the experimental examinations displaying a high efficiency of the glasses to shield gamma rays especially the glass with 0.5 % MnO2, which has the highest attenuation and the lowest HVL and TVL. Therefore, the high efficiency of the glasses to be used as radiation shielding applicants in radiation institutes before 50 kGy, i.e. as storage containers for radioactive wastes in nuclear medicine.

Cubic q-Bézier Triangular Patch for Scattered Data Interpolation and Its Algorithm

This paper presents an approach to scattered data interpolation using q-Bézier triangular patches via an efficient algorithm. While existing studies have formed q-Bézier triangular patches through convex combination, their application to scattered data interpolation has not been previously explored. Therefore, this study aims to extend the use of q-Bézier triangular patches to scattered data interpolation by achieving C1 continuity throughout the data points. We test the proposed scheme using both established data points and real-life engineering problems. We compared the performance of the proposed interpolation scheme with a well-known existing scheme by varying the q parameter. The comparison was based on visualization and error analysis. Numerical and graphical results were generated using MATLAB. The findings indicate that the proposed scheme outperforms the existing scheme, demonstrating a higher coefficient of determination (R2), smaller root mean square error (RMSE), and faster central processing unit (CPU) time. These results highlight the potential of the proposed q-Bézier triangular patches scheme for more accurate and reliable scattered data interpolation via the proposed algorithm.