Superior Degree Research Articles

Simultaneous reactions of sulfonation and condensation for high-yield conversion of polystyrene into carbonaceous material

Polystyrene (PS) was chemically treated with sulfuric acid to prepare stabilized precursors, which were converted into carbonaceous materials with high yield via subsequent carbonization. In addition to sulfuric acid, paraformaldehyde (PFA) was used to increase the crosslink density within the sulfonated PS chain networks by forming methylene bridges between phenyl rings via condensation. This transformed PS into a black glassy powder with increased crosslinking points. The carbon yield of the stabilized PS obtained after carbonization showed was considerably higher (45.3 wt%) than those of pristine PS (0 wt%) and sulfonated PS without PFA (22.4 wt%). This was attributed to its superior degree of crosslinks and the additional contribution of sulfur bridges to its thermal stability during carbonization. Further thermal treatment up to 2800 °C resulted in the formation of an intermediate structure between hard and soft carbons, which was directly revealed by microscopic images. These simultaneous reactions of sulfonation and condensation can be universally applied to various PS wastes, regardless of their shape and morphology. This suggests that the solid-to-solid transformation from PS to carbonaceous substances can be an effective and affordable method of upcycling general PS wastes.

Topological interference management via low-rank tensor completion for time-varying topology networks

In this paper, we introduce a tensor-based topological interference management framework, in which a novel low-rank tensor completion (LRTC) model is proposed to manage interferences in time-varying topology networks. We use particular channel decomposition to design the precoding tensor and interference suppression tensor for TIM with multi-antenna users. Furthermore, we formulate the low-rank tensor completion model as a tensor rank minimization problem, which is then solved by resorting to the tensor nuclear norm (TNN) and the tensor sum of nuclear norm (SNN). Extensive simulation results show that the proposed two low-rank tensor completion TIM algorithms have superior degree of freedom (DoF) and sum rate performance in general time-varying networks.

The origin of the guardianship action against financial and/or banking entities

The guardianship action is a mechanism for the protection and guarantee of fundamental constitutional rights. Its origin is delimited by what is established in article 86 of the national constitution, and developed by Decree 2591 of 1991 and by the repeated pronouncements of the Constitutional Court that have been issued on the matter. Specifically, the constitutional court has developed a jurisprudential line related to the origin of the protection action against individuals who provide a public service, framing within this issue what is related to the origin of the protection action against banking and/or financial entities, who, by showing a degree of superiority in the contractual relationship with users, may commit abuses or make decisions that affect fundamental constitutional rights. Although there have been some pronouncements from the highest constitutional body, there is no easily understandable academic work among ordinary society that makes it possible to accurately identify when the protection action is appropriate against this type of individuals who provide a banking and financial service, for which reason It is necessary to address the subject of study, for which we will initially delimit the guardianship action conceptually, later its origin against individuals will be explained and then, based on inductive reasoning, its origin will be identified against banking and financial entities, managing to demonstrate what are the problematic situations that can generate the violation of fundamental rights and what are the most violated or thre

Differential impacts of fiscal consolidation policies under alternative monetary regimes: evidence from Indian economy

The study tried to examine the effects of fiscal consolidation policies under alternative monetary stances in Indian economy. Using structurally adjusted vector autoregression (SVAR) we found that both Monetary and Fiscal-policies interact in cooperative as well as competing manner depending on the type and timing of shocks. Although fiscal policy enjoys a certain degree of superiority, the potency of monetary policy has not waned away. In terms of effectiveness, the study found that the fiscal policy is a better tool of economic stabilisation in the short run but in long and medium run it may harm growth. A consolidation program aimed at infusing fiscal prudence was found to have differential impacts on economic growth depending on whether such a policy is undertaken in an expansionary or contractionary monetary regime. Ironically the fiscal-policy was found to have more influence on inflation than monetary policy pointing towards the fiscal theory of price level; FTPL.

Способы выражения сравнения в ульчском языке

The paper summarizes the mechanisms used in the Ulch language to denote comparison as a broad cognitive category. Referring to numerous studies of various languages, the author examines a variety of possible comparative relations between objects and events and divides them into four types: comparative and superlative (inequality group), equative and simulative (equality group). Each type features a set of linguistic means used to verbalize a specific degree of similarity or superiority, including syntactic constructions, grammatical markers, and lexical units. The analysis has revealed that all four types can be found in the Ulch folklore texts. A conclusion is drawn that the Ulch language lacks a holistic grammatical category of comparison of adjectives or adverbs, resulting in a diverse but economic set of means of expressing comparison. Overall, the comparative mechanisms in Ulch cover the syntactic (two-noun constructions in nominative and instrumental cases in comparative structures or the conjunction umutu/omoto in simulative ones), grammatical (suffixes -achi/machi and mat/mot/meti), and lexical (adverbs of degree: chӯ, tes, badi, ӡin-ӡin, and others) levels. Considerable overlap between equative and similative subtypes has been identified. In addition, the adverbs of degree typically describing superlative relations can also be used in comparative constructions and, in some cases, in constructions describing equality relations. Consideration is also given to the basic components of various comparative structures (objects of comparison, standards, parameters, etc.) for each subtype, highlighting the elements essential for modeling a comparative situation and those that may be included or omitted depending on the context.

Developing an Intelligent Model Based on Fuzzy Multilayer Neural Network for Improving Credit Risk Management under Uncertain Conditions

This study utilizes the advantages of soft calculations, represented as intelligent combined methods and computational intelligence methods, to enhance credit risk management. For this purpose, the proposed method contains the fuzzy regression and artificial neural network (ANN). In this way, the parameters of neural network are fuzzy, encompassing weights and errors to model under uncertain conditions. Then, fuzzy neural networks form the system where the optimal decision is obtained using the highest degree of superiority by fuzzy inferences. Finally, using the credit information of some countries, the efficiency of the proposed combined model in credit scoring analysis has been shown.

Efficient complexity based adaptive system for cloud resources

Cloud computing is a new IT concept, that is no longer solely applicable to the economic system however as nicely very beneficial in science. The issue of asset distribution and income expansion is likewise similarly significant, particularly about cloud security. This achieves the need of various displaying strategies including however not restricted, to security danger, asset assignment, and income boost models. These offerings are billed on a utilization basis. The cloud services are provided by the CSPs to the end users in an optimized way by using our mathematical proposed algorithm. This proposed algorithm is simulated in the cloud simulator. It prompts financially savvy arrangements by lessening the execution season of enormous application testing. As a piece of framework assets, cloud testing can accomplish its productivity by dealing with the boundaries like organization traffic, Circle Stockpiling, and Smash speed. In this paper, we propose another fluffy numerical model to accomplish a superior degree for the above boundaries. The results of the outcomes informs that the proposed algorithm CROS (Cloud Resource Optimized System) performances are better.

Tirzepatide, A New Antidiabetic Drug with Change in the Treatment of Patients with Type 2 Diabetes

Mounjaro® (tirzepatide) was approved by the U.S. Food and Drug Administration for type 2 diabetes treatment in August 2022. Tirzepatide, a synthetic peptide consisting of 39 amino acids, is a dual GLP-1 (glucagon-like peptide-1)/GIP (glucose-dependent insulinotropic polypeptide) receptor agonist. Tirzepatide showed marked reductions in glycated hemoglobin and body weight in the SURPASS study among participants with type 2 diabetes that surpassed existing diabetes drugs. Other benefits included blood pressure reduction and lipid lowering effects. On meta-analysis and post-hoc analysis, tirzepatide did not increase the risk of major cardiovascular events and is expected to have cardiovascular benefits in future studies. In addition, it showed a protective effect on the kidney by slowing the rate of renal function decline and reducing new-onset macroalbuminuria. However, the fact that it is an injectable drug, the cost of the drug, and the high rate of gastrointestinal side effects are disadvantages to tirzepatide. Considering the superior degree of glucose reduction and weight reduction observed in tirzepatide clinical trials compared to clinical trials of other anti-diabetic drugs, we expect tirzepatide to bring about a paradigm shift in the treatment of obese type 2 diabetes patients.

Recent Advances in Aptasensing Strategies for Monitoring Phycotoxins: Promising for Food Safety

Phycotoxins or marine toxins cause massive harm to humans, livestock, and pets. Current strategies based on ordinary methods are long time-wise and require expert operators, and are not reliable for on-site and real-time use. Therefore, it is urgent to exploit new detection methods for marine toxins with high sensitivity and specificity, low detection limits, convenience, and high efficiency. Conversely, biosensors can distinguish poisons with less response time and higher selectivity than the common strategies. Aptamer-based biosensors (aptasensors) are potent for environmental monitoring, especially for on-site and real-time determination of marine toxins and freshwater microorganisms, and with a degree of superiority over other biosensors, making them worth considering. This article reviews the designed aptasensors based on the different strategies for detecting the various phycotoxins.

The differential measure for Pythagorean fuzzy multiple criteria group decision-making

Pythagorean fuzzy sets (PFSs) proved to be powerful for handling uncertainty and vagueness in multi-criteria group decision-making (MCGDM). To make a compromise decision, comparing PFSs is essential. Several approaches were introduced for comparison, e.g., distance measures and similarity measures. Nevertheless, extant measures have several defects that can produce counter-intuitive results, since they treat any increase or decrease in the membership degree the same as the non-membership degree; although each parameter has a different implication. This study introduces the differential measure (DFM) as a new approach for comparing PFSs. The main purpose of the DFM is to eliminate the unfair arguments resulting from the equal treatment of the contradicting parameters of a PFS. It is a preference relation between two PFSs by virtue of position in the attribute space and according to the closeness of their membership and non-membership degrees. Two PFSs are classified as identical, equivalent, superior, or inferior to one another giving the degree of superiority or inferiority. The basic properties of the proposed DFM are given. A novel method for multiple criteria group decision-making is proposed based on the introduced DFM. A new technique for computing the weights of the experts is developed. The proposed method is applied to solve two applications, the evaluation of solid-state drives and the selection of the best photovoltaic cell. The results are compared with the results of some extant methods to illustrate the applicability and validity of the method. A sensitivity analysis is conducted to examine its stability and practicality.

DPT: An importance-based decision probability transformation method for uncertain belief in evidence theory

Dempster–Shafer evidence theory can combine multiple sources of uncertain information to form a consistent basic probability assignment (BPA). How to transform BPA into decision probability is still an open issue. To solve this problem, an importance-based Decision Probability Transformation (DPT) method is proposed in this paper. In this method, the importance weight is introduced into the belief interval for the first time, and then the importance is used to define the information volume and superiority degree, so that more reliable decisions can be achieved through the trade-off between conservative and aggressive. Comparative experiments on numerical examples and applications show that the proposed DPT method can weigh the aggressiveness of decisions, and its decision probability transformation behavior is the most rational and its application performance is the most reliable. Especially, the accuracy rate of classification application is 95.31%, which is at least 0.54% higher than other compared methods.

Deriving optimal operating rules for flood control considering pre-release based on forecast information

Pre-releasing before a flood can increase reservoir flood control storage capacity to effectively improve the flood disaster prevention and mitigation effects, but also might bring water shortage risk due to the uncertain forecasts. This paper proposes a framework to determine the optimal operating rules for flood control considering pre-release based on uncertain forecasts. The framework includes three models: a pre-release model at the early stage of the flood to enlarge the flood control storage capacity; a multi-objective optimization model with resilience and risk as objectives at the flood regulating stage to optimize the flood control operating rules; a fuzzy optimum selecting model to determine the satisfied operating rule considering forecast uncertainty and decision-maker's preferences. In the framework, forecast uncertainty has been considered from the perspective of the forecast error distribution. Nierji Reservoir in Northeast China is taken as a case study. Results show that compared with the existing operating rule, the recommended operating rule considering pre-release releases water more evenly and can not only decrease the peak flow at the downstream protection point but also improve the downstream flood resilience without increasing reservoir risk. The proposed framework that considers forecast error distribution provides higher superiority degrees of the rules than the traditional framework that considers the maximum and minimum forecast errors in the real-time operation of the 1969 and 2013 flood hydrographs.

Frictionless Motion of Diffuse Interfaces by Sharp Phase-Field Modeling

Diffuse interface descriptions offer many advantages for the modeling of microstructure evolution. However, the numerical representation of moving diffuse interfaces on discrete numerical grids involves spurious grid friction, which limits the overall performance of the model in many respects. Interestingly, this intricate and detrimental effect can be overcome in finite difference (FD) and fast Fourier transformation (FFT)-based implementations by employing the so-called sharp phase-field method (SPFM). The key idea is to restore the discretization-induced broken translational invariance (TI) in the discrete phase-field equation by using analytic properties of the equilibrium interface profile. We prove that this method can indeed eliminate spurious grid friction in the three-dimensional space. Focusing on homogeneous driving forces, we quantitatively evaluate the impact of spurious grid friction on the overall operational performance of different phase-field models. We show that the SPFM provides superior degrees of interface isotropy with respect to energy and kinetics. The latter property enables the frictionless motion of arbitrarily oriented diffuse interfaces on a fixed 3D grid.

Effect of Roll Material on Strip Solidification between the Rolls of a Vertical-Type High-Speed Twin-Roll Caster

Tool steel for hot working is usually used for the rolls of conventional twin-roll casters for aluminum alloys. In a vertical high-speed twin-roll caster, copper rolls are used to rapidly solidify the aluminum alloys and to increase the casting speed, since the thermal conductivity of copper is higher than that of tool steel. However, the degree of superiority of copper rolls over steel rolls is not clear. In the present study, the effect of the roll material on strip solidification between the rolls of a vertical high-speed twin-roll caster was investigated by inserting a thermocouple in the strip during casting. The Al-4.8%Mg alloy was used, since it does not stick to the steel rolls in the absence of a parting material. Both copper rolls and mild steel rolls were used. The cooling rate, the supercooling of the liquidus temperature, and the temperature of the strip cast using the copper rolls were superior to those of that cast using the mild steel rolls. The copper rolls were better than the steel rolls in terms of achieving high-speed roll casting, rapid solidification of the strip, and high productivity.

Fabricating flame retardant polyacrylonitrile fibers modified by sodium lignosulfonate and copper ions

A facile and eco-friendly approach was developed to endow polyacrylonitrile fibers (PAN) with excellent flame retardancy and char-forming capability. First, aminated PAN (A-PAN) was prepared via ethylenediamine treatment. Next, sodium lignosulfonate (SLS) was adsorbed on the surface and interior of A-PAN through strong ionic bonds to obtain the SLS modified A-PAN, i.e., SLS/A-PAN. Afterwards, copper ions (Cu2+) were chelated with SLS/A-PAN to construct Cu/SLS/A-PAN. The modified PAN samples were characterized by solid-state 13C NMR, FTIR, XPS and SEM. TGA demonstrated that the preferential degradation of Cu/SLS/A-PAN was prone to forming much char residue whether in N2 or air atmosphere, which acted as an effective barrier to hinder the decomposition of PAN matrix so as to exhibit excellent flame retardancy. Additionally, the char residue of Cu/SLS/A-PAN showed numerous intumescent vesicles with continuous wavy-like char layer and its ID/IG value was 1.28, exhibiting superior degree of graphitization. Particularly, compared to the pristine sample, the peak of heat release rate of Cu/SLS/A-PAN decreased significantly. In addition, TG-IR indicated the volatile products of Cu/SLS/A-PAN were significantly reduced. SLS and Cu2+ were cooperative in endowing Cu/SLS/A-PAN with excellent flame retardancy and superior char-forming capability.

Sustainability assessment of the Yangtze River Delta Urban agglomeration in China based on stochastic simulation techniques

ABSTRACT The economy, society, and environment of the world are undergoing profound changes. Achieving sustainable development has become the common aspiration of the international communities. In recent years, sustainability issues are gaining increasing prominence for cities to formulate long-term development strategies. The Yangtze River Delta (YRD) urban agglomeration, one of China’s most developed and densely populated industrial areas, has encountered environmental problems. Assessment of city sustainability of the YRD is a prominent issue in the process of urban construction. The paper used multi-criteria decision making (MCDM) framework to evaluate the sustainability level of 26 cities in the YRD by selecting 24 indicators from the economic, social, and environmental dimensions. In the weighting process, the paper proposed a new subjective and objective integrated weighting method based on stochastic simulation techniques. Relative results including pairwise priority matrix (PPM), comprehensive priority index (CPI), and the most likely ranking were obtained to reflect the superiority degree between two cities by adequate comparisons. The assessment results indicate that all the cities showed an upward trend from 2015 to 2019. Shanghai had overwhelming advantages over the other cities. The CPIs of Suzhou, Nanjing, Wuxi, Changzhou in Jiangsu province, and Hangzhou, Ningbo, Zhoushan in Zhejiang province were above 50 in 2015 and increased to over 85 in 2019. Additionally, most cities had unbalanced development on the three dimensions. Each city should firstly pay more attention to their own weak points, and then achieve balanced development and long-term sustainability.

War Game as a Method of Training, as a Method of Analysis

The purpose of the article is to present the development of a new simulation tool Superior Degree Demonstrator, which is intended to introduce the issue of fire support to students of the military college in a game-like manner. The simulator is intended primarily for the purpose of preparing future artillery officers at the Department of Fire Support of the University of Defence, which is directly involved in its development. At the same time, it aims to awaken in the younger generation of students a proactive approach to study. However, it can also be used as an objective tool of combat effectiveness analysis within the framework of the army acquisition programs.

A Study on the Usefulness of Stochastic Simulation Algorithms for Teaching and Learning in College Physical Education Classrooms

In order to address the problem of the absolute nature of the evaluation of superiority and inferiority in the evaluation of physical education classroom in universities and the problem of inconsistency in the conclusion of multiple evaluations, we develop an “autonomous advantage evaluation method to highlight one's own advantages,” which uses a probabilistic stochastic simulation algorithm to evaluate the advantages of the evaluated objects by calculating the degree of superiority among them. The method is based on an innovative “base to top” approach, with a high degree of independence. The method was validated by means of an algorithm, and the conclusions were obtained with probabilistic information.

Deep Learning Diagnostics of Gray Leaf Spot in Maize under Mixed Disease Field Conditions.

Maize yields worldwide are limited by foliar diseases that could be fungal, oomycete, bacterial, or viral in origin. Correct disease identification is critical for farmers to apply the correct control measures, such as fungicide sprays. Deep learning has the potential for automated disease classification from images of leaf symptoms. We aimed to develop a classifier to identify gray leaf spot (GLS) disease of maize in field images where mixed diseases were present (18,656 images after augmentation). In this study, we compare deep learning models trained on mixed disease field images with and without background subtraction. Performance was compared with models trained on PlantVillage images with single diseases and uniform backgrounds. First, we developed a modified VGG16 network referred to as “GLS_net” to perform binary classification of GLS, which achieved a 73.4% accuracy. Second, we used MaskRCNN to dynamically segment leaves from backgrounds in combination with GLS_net to identify GLS, resulting in a 72.6% accuracy. Models trained on PlantVillage images were 94.1% accurate at GLS classification with the PlantVillage testing set but performed poorly with the field image dataset (55.1% accuracy). In contrast, the GLS_net model was 78% accurate on the PlantVillage testing set. We conclude that deep learning models trained with realistic mixed disease field data obtain superior degrees of generalizability and external validity when compared to models trained using idealized datasets.

Interactive Effect and Spatial Characteristics of Agricultural Development Level and Transport Superiority Degree in Main Grain-producing Areas of the Central Jilin Province, China

Interactive Effect and Spatial Characteristics of Agricultural Development Level and Transport Superiority Degree in Main Grain-producing Areas of the Central Jilin Province, China