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Growth and optical characterization of PbMo<sub>0.5</sub>W<sub>0.5</sub>O<sub>4</sub> single crystals for UV optoelectronic applications
No full textPbMo0.5W0.5O4 single crystals were successfully grown via the conventional Czochralski method, and their structural and optical properties were comprehensively investigated. x-ray diffraction analysis confirmed the phase purity and tetragonal scheelite-type structure with lattice parameters determined to be a = 5.3209 & Aring; and c = 12.8099 & Aring;. UV-Visible absorbance and transmission spectroscopy revealed a sharp absorption edge, indicative of a direct allowed electronic transition. The band gap energy was determined to be around 3.15 eV using Tauc plot analysis, supported by relatively low Urbach energy, suggesting minimal structural disorder. Photoluminescence measurements showed prominent green emission peaks at 488 and 512 nm, attributed to intrinsic structural defects and radiative recombination processes. The high optical quality and tunable band gap of PbMo0.5W0.5O4 make it a promising candidate for ultraviolet optoelectronic devices and photocatalytic applications
Three-level sustainable supply chain model with cap-and-trade policy and green technology
No full textThis study proposes a sustainable model involving a single supplier, a logistics provider, and multiple retailers. The presented model incorporates deteriorating items by accounting for carbon emissions with green technology investment. Emissions arising from inventory storage, transportation, and storage of deteriorating items. To minimize both production and overall system costs, the supplier adopts discrete setup cost reduction. The paper evaluates the effectiveness of green technology investment within a supply chain model under the effect of a cap-and-trade policy. Preservation technology is also applied to mitigate item deterioration. Economic sustainability is maintained by minimizing total cost via optimized shipments, setup cost reduction, cycle time, and investments in preservation and green technologies. Environmental sustainability is preserved through carbon emission reduction and the implementation of green technology, while social sustainability is supported by ensuring a healthier, low-carbon environment for future generations. Sensitivity analysis is performed and graphical representations are provided to evaluate the effectiveness of key parameters in the study. An important perspective of the study is on reducing carbon emissions while minimizing total cost. Numerical experiments illustrate that a global optimum solution is obtained at the optimum values of the decision variables, and carbon emission reduction is sustained. This study contributes to sustainable supply chain research in developing countries by exploring investment strategies such as green technology implementation, cap-and-trade policies, and preservation technology to reduce product deterioration and emissions
Intention mining and intention reshaping: surfacing deep intentions by proactive visuals to morph them into new intentions
No full textIntention mining through human-computer interaction (HCI) has been studied in various domains such as psychology, health, communication, and transportation. Unlike previous studies focusing solely on recognizing intentions, our work introduces a novel HCI-based approach to reshape recognized human intentions into desired ones defined by the computer. We address the psychological challenge of modifying human intentions, which depend on multiple psychobiological variables. To fill this research gap, we propose the Intention Risk and Stimulus factor Impact (IRSI) framework, which enables intention reshaping by considering the emotional effects of shape and color in proactive visuals, the risk status of intentions, and human habituation to stimuli. Our system operates in two phases: (1) intention recognition through deep learning-based mining, and (2) reshaping of these intentions into new, non-premeditated ones via robotic stimuli. As an experimental setup, we employ a computer-based bluff card game that allows both mining and reshaping of player intentions. The system analyzes recorded bluff sessions to generate intention matrices and trains a CNN-based model for recognizing bluff-related moves. During gameplay, the robotic interface delivers adaptive proactive stimuli based on risk levels to psychologically influence and reshape the player's intentions. Experimental results demonstrate the effectiveness of our IRSI-based HCI system in achieving successful intention reshaping performance
Insight into greenhouse gas emission in freshwater aquaculture ponds in Jiangsu Province: Variation due to species used and ponds management practice
No full textAquaculture ponds have emerged as a significant contributor to greenhouse gas (GHG) emissions. We measured methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O) emissions in ponds, all located in Jiangsu Province, with different fish and management practices over an entire cycle. All ponds emitted these gases, with higher CH4 and N2O levels during fish growth than stocking period. The highest CH4 and N2O fluxes were found in the Crucian carp (Carassius auratus) pond with up to 16,512 +/- 3015 mu mol/(m(2)h) and 5.54 +/- 0.31 mu mol/(m(2)h), respectively. CH4 was the primary contributor to the global warming potential in traditional earthen ponds, accounting for an average contribution rate of 87.7 %. The dissolved oxygen (DO) concentration was the water quality parameter that most significantly influenced the CO2 flux, while pH acted as its primary regulator. The GHG emission intensity per unit of fish production in traditional earthen ponds was 197 times higher than that in-pond raceway systems. Largemouth bass (Micropterus salmoides) and Crucian carp ponds exhibited CH4 diffusion fluxes at the sediment-water interface, which were > 20 times higher than those at the water-air interface. Our results further suggest that stocking density and feed amount significantly influence the variations in GHG emissions among the ponds with the in-pond raceway system having low carbon emissions and being high yield aquaculture system compared to traditional earthen ponds. The water depth and DO concentration can be manipulated to reduce GHG emissions across the various interfaces
Overcoming Zn anode limitations in aqueous zinc ion batteries: The promise of ZnO nanowire coatings
No full textThe practical application of aqueous zinc (Zn) ion batteries (ZIBs) is limited due to challenges such as severe dendrite formation and undesired side reactions during cycling. In this study, we present zinc oxide nanowire (ZnO NW) decorated Zn anodes that enhance the electrochemical stability and performance of the aqueous ZIBs. ZnO NWs were directly grown on Zn foil in a controlled manner using the hydrothermal method. Various structural and electrochemical characterization techniques demonstrated that the vertically aligned ZnO NWs on Zn (ZnO/Zn) anodes provide remarkable electrochemical stability and significantly boost the battery performance during cycling. The ZnO NW decorated Zn anodes in symmetrical cells demonstrate remarkable stability for 1200 hat 2.0 mA cm-2 (2.0 mAh cm-2) and 800 hat 5.0 mA cm-2 (5.0 mAh cm-2). The Tafel and LSV curves show that the surface-modified anodes successfully impede side reactions such as hydrogen evolution reaction (HER) and anode corrosion. Moreover, the fabricated ZnO/Zn//V2O5 full cell delivers a high specific capacity of 325.1 mAh g-1 at 0.1 A g-1, which is significantly higher than that of the bare Zn//V2O5 cell (283.2 mAh g-1) and also has a capacity retention of 78 % at 1.0 A g-1 after 1000 cycles. This work provides a practical fabrication method and paves a new route for aqueous Zn-ion batteries
Fast investigation of control interaction risks in PV parks using eigenvalue analysis in Modelica
No full textThis paper contributes to the fast detection of control interaction risk in a PV park using the eigenvalue analysis in Modelica. The entire PV park and its interconnected network are represented by time-domain equations in Modelica, then linearized state space equations are extracted directly by leveraging the Modelica features. This constitutes an advantageous approach for fast finding eigenvalues and extracting potential instability conditions. The presented approach is verified with electromagnetic transient (EMT) simulation and impedance-based stability analysis (IBSA) that uses EMT-type impedance scanning methods. The results show an outstanding improvement in the simulation time and accuracy
Revisiting Erdoğan's century of Turkey: unmasking populism's political strategies through the Lens of the Canal Istanbul
No full textThis study delves into the nuanced dynamics of populism in Turkish politics, focusing on the Justice and Development Party (Adalet ve Kalk & imath;nma Partisi, AKP) under the leadership of Recep Tayyip Erdo & gbreve;an. Over the past two decades, we contend that the AKP's policy proposals and projects have transcended mere political promises, instead evolving into meticulously planned physical embodiments of the party's political dominance. Through an in-depth analysis of one of the AKP's recent mega undertakings, the Canal Istanbul project, we unveil a strategic shift in the party's populist tactics aimed at fortifying its political hegemony. Moreover, this paper sheds light on a covert process of polarization within the AKP's apparent transition from a divisive approach to one centred on persuasion, particularly targeting the opponent political party, the CHP Republican People's Party, Cumhuriyet Halk Partisi. We posit that the AKP strategically positions itself as a victim of its adversaries, strategically laying the foundation for its persuasive agenda. This research offers critical insights into the multifaceted strategies employed by the AKP and CHP in navigating the complex terrain of populist politics in Turkey and furthers the empirical studies in the populism debate in general
Gradient-Boosted Decision Tree Optimizer for Antenna Optimization
No full textThe use machine learning-assisted optimization methods in the design of antennas have been increasing. Although neural networks (NNs) and Gaussian process regression (GPR) are widely used, their scalability to higher dimensions poses several challenges, such as the requirement for excessive data, extensive hyper-parameter tuning, and longer training times. In contrast, gradient-boosted decision trees (GBDTs) exhibit superior performance with limited training data, along with faster training and more efficient hyper-parameter tuning. Motivated by these advantages, we introduce a GBDT-assisted optimization (GBDTO) algorithm tailored for high dimensional problems. Beginning with an initial sample set, GBDTO builds a GBDT model and sequentially samples the input parameter space while searching for an optimal objective value. Compared to Bayesian optimization (BO) and NN-assisted optimization (ONN), GBDTO achieves faster convergence and superior objective values, as demonstrated through benchmarks using the Black-Box Optimization Benchmarking test suite, and several antenna designs. Numerical experiments across 480 instances of 12-dimensional 24 functions demonstrate 13% and 31% improvement in mean rank count over BO and ONN, respectively. Moreover, high dimensional antenna design examples indicate more than 50% faster convergence for a given optimization target and 7 − 54% improvement in the objective value for a fixed number of iterations, compared to BO and ONN. In addition to its optimization efficiency, GBDTO offers inherent and efficient feature importance analysis, which is extremely useful for user guidance
Comparing the Interactions of Trichomonas vaginalis/gallinae Legumain-Like Cysteine Protease 1 (LEGU-1) and Human Legumain (LGMN) Protein Sequences with Proton Pump Inhibitor Drugs (Lansoprazole, Omeprazole, and Esomeprazole) by Bioinformatics Analyses
No full textPurpose: The flagellar parasite Trichomonas vaginalis is the main cause of trichomoniasis cases globally and is associated with a broad range of complications. Due to the diverse range of virulence factors participating in the attachment, proliferation and resistance of this pathogen, preventive and well-tolerated compounds are necessary. One of the virulence factors in T. vaginalis, the legumain-like cysteine protease LEGU-1 is of particular interest as a target due to its potential influence on trichomoniasis and tumor development in urogenital systems, as well as its closely related to the avian strain T. gallinae. Previous studies on antineoplastic proton pump inhibitors revealed they also have legumain (LGMN) inhibitory activities. Methods: Therefore, this study aimed to compare the molecular interactions of T. vaginalis/gallinae LEGU-1 and H. sapiens LGMN with proton pump inhibitor drugs (lansoprazole, omeprazole, and esomeprazole) through sequence analysis, 3D modeling, and molecular docking. Results: Although sequence analyses revealed low homology between T. vaginalis/gallinae LEGU-1 and H. sapiens LGMN, secondary and 3D structural comparisons uncovered their structural conservation. Possible binding sites in all three proteins identified via CB-DOCK2 were compared to the previously described sites for LGMN, followed by targeted docking using Autodock Vina. Identification of amino acids mutually interacting with all three ligands by both programs revealed the overall conservation of the binding pockets. The variations in the number of amino acids within the binding sites for all three proteins displayed the variations in the binding energies for each ligand. Lansoprazole, omeprazole and esomeprazole were shown to bind T. vaginalis/gallinae LEGU-1 and H. sapiens LGMN, with lansoprazole having the highest binding energy. Conclusion: Conclusion Beyond our promising bioinformatics results, this study can guide further research on the development of alternative therapeutic methods against trichomoniasis and concomitant conditions