Current Choice Research Articles

A Comparison of Financial Risk-Tolerance Assessment Methods in Predicting Subsequent Risk Tolerance and Future Portfolio Choices

This study explores the effectiveness of various methods for measuring risk tolerance, with the aim to better understand the risk-taking attitudes and behaviors of financial decision-makers. Using data collected between October 2020 and March 2021, the research investigates three key areas: (a) the stability of risk tolerance over a six-month period, (b) the individual and household characteristics that predict future risk tolerance, and (c) the predictive accuracy of various risk-tolerance assessment methods in relation to portfolio choices made by financial decision-makers. The results show that risk-tolerance scores derived from a psychometrically developed scale provide the most accurate insights into future risk-taking attitudes and portfolio decisions. For those looking for a simple way to assess both current and future risk tolerance and portfolio choices, a stated-preference item can be effective. Although less consistent, a revealed-preference test can also be used to predict risk tolerance and risk-taking behavior. Findings provide guidance for financial decision-makers and financial advisors by comparing the key features of the three primary risk-tolerance assessment methods evaluated in this study. The study also establishes a foundational basis for selecting the most appropriate evaluation approach, based on the variables identified in the findings.

Beyond conventional therapy: Synthesis of multifunctional nanoparticles for rheumatoid arthritis therapy

Abstract Rheumatoid arthritis (RA) is a persistent inflammatory illness that causes joint destruction and dysfunction due to the activation of macrophages and the generation of reactive oxygen species. Current therapy choices frequently limit the effectiveness of targeting the inflammatory areas. To reduce inflammation and oxidative stress in RA, this research will create and assess multifunctional nanoparticles that selectively target inflammatory cells and deliver therapeutic medicines. Tannic acid, ferric chloride hexahydrate, methotrexate (MTX), and bovine serum albumin were conjugated using sonication and centrifugation to create the nanoparticles. Folic acid was added to improve the ability to target. Transmission electron microscopy, dynamic light scattering (DLS), UV-vis spectroscopy, and in vitro release experiments were used to characterize the nanoparticles. RAW 264.7 macrophage cells were used to test the cellular uptake of the nanoparticles using confocal microscopy and fluorescence-activated cell sorting (FACS). TFMBP-FA achieved 65.56%, and TFMBP reached 68.96%, indicating a high drug delivery rate for the synthesized nanoparticles. Confocal microscopy showed that the TFMBP-FA group had a greater density of fluorescent markers, indicating that the cells effectively targeted and absorbed the inflammatory environment. These results imply that the created nanoparticles may improve how medications are delivered during RA therapy.

Developing a hybrid-built pre-hardened alloy steel for injection moulding tools using the laser powder bed fusion process

Hybrid additive-subtractive manufacturing has been adopted as a cost-effective alternative for manufacturing plastic injection moulding tools with conformal-cooled inserts created by fusing powder and wrought material. This article reports the development of a hybrid power-wrought pre-hardened alloy steel to supplement the current material choice for fabricating injection mould inserts using this advanced manufacturing strategy. In this study, MS1 (maraging 300) steel powder was additively deposited onto pre-machined wrought Nimax steel to form a hybrid alloy material. The mechanical and microstructural properties of the fusion-bonded interface were examined. Microstructural observation revealed a 280 μm thick interfacial region consisting of a homogenous mixing of powder and substrate materials. As a result of solid solution strengthening within the region, tensile tests established robust powder-substrate bonding with tensile ruptures occurring well away from the interface. The as-built hybrid-alloy steel possessed excellent mechanical properties, with 1200 MPa in ultimate tensile strength, 12.4 % in elongation at fracture and 39 HRC (Nimax)/42 HRC (MS1) in hardness. The overall results suggested that hybrid MS1-wrought Nimax steel is a suitable pre-hardened material for manufacturing durable and high-performance injection mould inserts as part of a cost-effective hybrid additive-subtractive manufacturing strategy.

Profitability of Bank of Baroda: An Analysis

One of the industries in India with the highest growth is the banking sector. The banking industry is getting more complex today. In India, there are various banking organizations in operation. Bank of Baroda, the second- largest public sector bank, is the current choice. The study's goal is to anayse the Bank of Baroda's financial situation and performance both before and after the bank's merger. Bank profitability can be measured in a variety of ways, including WACC, regression analysis, the CAMEL model, and DuPont analysis. The study has used the DuPont model, which analyses the profitability of banks from each of the key parameters such net profit margin, assets turnover, and equity multiplier, since it makes it simple to analyze the profitability of businesses. Key words: DuPont model, Bank of Baroda, Profitability.

COVID‐19 as a financial opportunity for the millennials

AbstractThis study aims to examine how COVID‐19 affects the views of the members of Generation Y (millennials) regarding their financial future. The literature often describes and perceives millennials as spoiled, unindustrious and lacking in focus. This research uses data collected via interviews with 20 participants in their 20s and 30s who live in the centre of Israel. The participants were recruited by a polling company during September 2021. The study finds that participants’ behaviour during this crisis was more level‐headed than in the past due to their understanding that the future is uncertain and they can rely on no one other than themselves. The study's participants reported making decisions to improve their financial future. Most view COVID‐19 as a rare opportunity to make changes in their lives, primarily in their finances and their employment. This is reflected by current choices intended to create a better financial future for themselves. The main finding of this research is that following the shock caused by the COVID‐19 pandemic, millennials have been forced to take care of themselves. They understand that if their parents get sick, they will not be able to continue making a living and will be unable to provide financial help to their children.

Adapting to climate change in arid agricultural systems: An optimization model for water-energy-food nexus sustainability

Sustainable management of water, energy, and food (WEF) under climate change will be a significant challenge for arid agricultural systems. This study developed a fractional non-linear multi-objective programming (FNLMOP) model to optimize resource allocation and improve agricultural sustainability in these systems under climate change. The model was designed in the framework of the WEF nexus to simultaneously improved energy productivity (profit/energy), and water productivity (profit/water), while mitigating environmental damage (damage to groundwater resources/output) and ensuring food security in an arid watershed in Iran. The long Ashton research station weather generator (LARS-WG) and the coupled model intercomparison project 6 (CMIP6) were employed to project climate parameters for both future dry and wet conditions. The sustainability of the optimal solutions was then assessed using a hybrid criteria importance through intercriteria correlation (CRITIC)-VIKOR approach. The optimal solutions revealed a reduction in the land under cultivation and produced less water-intensive crops. The optimization model can ensure WEF security, enhancing agricultural system sustainability by optimizing crop cultivation patterns and resource allocation. Current crop choices were highly inefficient with the bigger changes being from the current crops to optimal crops. Climate change showed a substantial but lesser influence on optimal crop choice.

Teachers' Motivation After the Implementation of the Three-term School Calendar in Saudi Arabia

This study explored the perceptions of teachers of English as a foreign language regarding the effect of a new three-term calendar on their motivation. Identifying the effects of this new plan would assist the Saudi Ministry of Education in exploring the teachers’ perspective. In turn, these viewpoints can be useful for making current choices as well as enhancing and modifying future ones. Through semi-structured interviews, the results of this study found that the majority of teachers perceived the new calendar to have a positive effect on their motivation. Specifically, they reported feeling more motivated to plan and teach lessons, as well as to engage in professional development activities. Furthermore, they felt that the new calendar provided them with ample time for reflection and collaboration with colleagues. The results of this study suggest that a three-term calendar may be beneficial for teachers’ motivation levels. This change may provide teachers with more frequent breaks and opportunities for rest and rejuvenation throughout the year, potentially leading to higher overall motivation and job satisfaction.

The Environmental Perception and Vegetarian Motivation of People in Ho Chi Minh City, Vietnam

Abstract Studies have previously shown that there were many vegetarian movements in the world, using many different models to indicate vegetarian food choice behavior. Among the factors affecting the motivation to become a vegetarian, perception and attitude are considered important factors influencing the decision to switch from using foods of animal origin to plant-based ones. There are many measured aspects of perception ranging from health, religion, love of animals, or animal rights. Among these, environmental perception increasingly shows a clear influence on vegetarian motivation, especially for young people. This current study aims to find out whether factors relating to perception including environmental perception, influence people’s motivation to become vegetarian in Ho Chi Minh City. To achieve this goal, the research surveyed 360 people living in Ho Chi Minh City (divided into three groups (students, office workers, and manual workers) using a direct questionnaire. Research results show that among the four independent variables (perception, ability, opportunity, barriers), there are three variables (1) perception; (2) opportunity; and (3) barriers having a significant relationship with the vegetarian motivation of people in Ho Chi Minh City. From the findings, this study points out key highlights in people’s current vegetarian food choices in this area, including environmental perception.

Thermal Modeling of Li-Ion Cylindrical Cells at Scale

The electric vehicle industry has created a demand for batteries with higher power densities. The current design choice for low-weight lithium-ion battery packs for electric vehicles is to create stacks arranged in parallel or series configurations to match the power demands. The individual cells are separated and placed in a cooling duct to pull heat away from the longitudinal surface convective. The amount of cooling is dependent on the internal temperature of batteries core. The current design of the cylindrical cell is to roll the cathode, anode, and separators into a jelly roll together and attach a tab to the cathode and anode at opposite corners of the sheet. The tabs connect to the positive and negative terminals of the cell to draw current away from the cell. The tabs may act as an agent for drawing heat away from the center of the battery. Currently, commonly used cylindrical cells have a form factor of 18650, 21700, or 26650. The thermal, geometrical, and electrical properties of 18650 and 21700 cylindrical cells are studied and it is found that the 21700 cells showed additional degradation and stronger heating. Recently, companies like Tesla have produced cylindrical cells with a 4680-form factor, which is a decent way to increase the ratio of active material vs. inactive material in a cylindrical cell. However, large diameter thick cylindrical cells suffer from thermal issues. Even large format pouch cells suffer through heat trapped within the stack.Since the thermal conductivity of the anode and cathode electrodes is often near 1 W/m-K, it becomes difficult for the heat to be extracted from the center of a large-diameter cylindrical cell without forming a considerable thermal gradient. PyBaMM, an open-source mathematical modeling software written in Python programming language, has the capability to simulate different electrochemical models such as the Doyle-Fuller-Newman (DFN), Single particle model (SPM), and SPMe, and varying chemistry. The software provides easy access to the cell parameters modification and graphical user interface capabilities. The PyBaMM framework also allows to simulation lumped, isothermal, and base thermal models which are electrochemically coupled. PyBaMM uses CasADi (computer algebra systems algorithmic differentiation). The thermal gradient and heat dynamics within a porous electrode are modeled using PoreSpy and openpnm, two open-source Python libraries used to design porous networks and model physical equations such as governing heat flow, concentration gradient, and movement of water/oil through porous shale media.We have explored and combined heat dynamic models for Li-Ion cylindrical cells at both cell and electrode levels with reduced-order Doyle-Fuller-Newman electro-chemical model frameworks implemented in the PyBaMM python library. Radial temperature gradient at cell and electrode levels were both generated from simulations with a distributed parameter model as a function of electrolyte and separator thickness and thermodynamic properties. Figure 1

Acute intermittent porphyria: A case report with an unlisted HMBS gene variant (c.345–2A>C)

We report a case of acute intermittent porphyria in a 19-year-old patient, linked to an unlisted variant of the gene encoding hydroxymethylbilane synthase c.345–2A>C. Given the very low prevalence of porphyria in the general population, diagnosis is rarely made initially and may mainly mimic Guillain-Barré syndrome. Considering this, we provide an overview addressing various ways the disease manifests, paraclinical investigations, pathophysiology, and available therapeutic options. Specifically, human heme therapy in the case of acute crises is nearly unanimous in the literature. However, there is no consensus on the management between crises if the current first line choice treatment, namely givosiran, is not accessible. We report the clinical follow-up proposed for this patient.

Are functional foods used as self-medication? Subjective health, self-reported disease symptoms, current medical treatment and functional food choices

PurposeFunctional foods have been marketed as promoting health and reducing the risk of disease. While the market of functional foods is increasing across the globe, little is known about how actual and subjective health status are related to functional food choices and existing research evidence is inconsistent. Therefore, the purpose of this paper is to systematically explore the relationship between functional food choices and perception related dimensions vs medical dimensions.Design/methodology/approachThe study used data collected from a large-scale mail survey in Japan (N = 8,368) and a representative Internet survey in Lithuania (N = 900). It used structural equation modeling (SEM) to test the proposed conceptual model.FindingsThe general results indicated that functional foods could be used to maintain one’s subjective health status – the frequency of using functional food products was positively related to consumers’ subjective health status (p = 0.04). However, if consumers were experiencing health-related issues (self-reported disease symptoms or current medical treatment), there was no systematic relationship between such experience and the usage of functional food products.Originality/valueTo the best of the authors’ knowledge, this study is among the first to systematically analyze the relationship between subjective health status, self-reported disease symptoms, current medical treatment and the frequency of using different functional food product groups. The findings indicated that it is important to simultaneously consider different underlying factors, such as specific to functional food targeted disease symptoms and specific food product groups, which contributed to a more thorough understanding of functional food consumption.

Optical trapping of nanoparticles using all-dielectric quasi-bound states in the continuum

Plasmonic nanotweezers employing metallic nanostrctures provide a powerful tool for optical trapping of nanoscale objects; however, they are limited by strong heating effects resulting from the intrinsic loss in metallic materials. Alternatively, quasi-bound states in the continuum (q-BICs), supported in all-dielectric metasurfaces, offer a higher quality-factor (Q-factor) and electric field enhancement compared to plasmonic systems, making it a promising candidate for heatless nanotweezers with high performance. Here, we demonstrate an all-dielectric nanotweezer harnessing q-BIC for effective trapping of nanoparticles with low trapping power and negligible heating effects. The quasi-BIC system provides very high electromagnetic field intensity enhancement as well as high-quality-factor resonances. The q-BIC mode is induced by symmetry breaking of periodic double ring nanostructure, which leads to an increased optical force and potential energy in comparison to prior studies, enabling attractive promise in subwavelength particle trapping applications. Moreover, the q-BIC nanotweezer creates numerous optical hotspots characterized by significant field confinement and enhancement, generating multiple trapping sites and thus facilitating high-throughput trapping of nanoscale objects. Furthermore, the trapping wavelength (1047.59 nm) in our structure exactly corresponds with the current laser choice for working with biological samples. In addition, we show that trapped particles can improve the resonance mode of the cavity in a symmetry-broken system, which in turn enhances the trapping process. Our study paves the way for applying q-BIC systems to low-power particle trapping and sensing applications, especially for parallel analyses of biological cells and protein molecules.

Perennial Grains and Oilseeds: Current Status and Future Prospects

The release of cultivars of two perennial grains is a milestone in perennial grain and oilseed development. Agronomic studies can progress by having stable genetics upon which to conduct research. Agriculture has generally committed to enhancing soil health, with perennial grains and oilseeds offering potentially non-animal-related alternatives to our current choices. Utilizing perennial grains and oilseeds offers potential to small-grain producers to improve, or at least maintain, their soil quality as perennial grains generally have little soil disturbance post-seeding. Understanding perennial grain and oilseed development and how they interact with their growth environment will provide us with baselines upon which to gauge agronomic interventions as we attempt to increase productivity without negative environmental impacts.

Privacy decision‐making and the effects of privacy choice architecture: Experiments toward the design of behaviorally‐aware privacy regulation

AbstractThe current notice and choice privacy framework fails to empower individuals in effectively making their own privacy choices. In this Article we offer evidence from three novel experiments showing that at the core of this failure is a cognitive error. Notice and choice caters to a heuristic that people employ to make privacy decisions. This heuristic is meant to distinguish between a party's good or bad intent in face‐to‐face‐situations. In the online context, it distorts privacy decision‐making and leaves potential disclosers vulnerable to exploitation.From our experimental evidence exploring the heuristic's effect, we conclude that privacy law must become more behaviorally aware. Specifically, privacy law must be redesigned to intervene in the cognitive mechanisms that keep individuals from making better privacy decisions. A behaviorally‐aware privacy regime must centralize, standardize and simplify the framework for making privacy choices.To achieve these goals, we propose a master privacy template which requires consumers to define their privacy preferences in advance—doing so avoids presenting the consumer with a concrete counterparty, and this, in turn, prevents them from applying the intent heuristic and reduces many other biases that affect privacy decision‐making. Our data show that blocking the heuristic enables consumers to consider relevant privacy cues and be considerate of externalities their privacy decisions cause.The master privacy template provides a much more effective platform for regulation. Through the master template the regulator can set the standard for automated communication between user clients and website interfaces, a facility which we expect to enhance enforcement and competition about privacy terms.

A 3D ray traced biological neural network learning model

Training large neural networks on big datasets requires significant computational resources and time. Transfer learning reduces training time by pre-training a base model on one dataset and transferring the knowledge to a new model for another dataset. However, current choices of transfer learning algorithms are limited because the transferred models always have to adhere to the dimensions of the base model and can not easily modify the neural architecture to solve other datasets. On the other hand, biological neural networks (BNNs) are adept at rearranging themselves to tackle completely different problems using transfer learning. Taking advantage of BNNs, we design a dynamic neural network that is transferable to any other network architecture and can accommodate many datasets. Our approach uses raytracing to connect neurons in a three-dimensional space, allowing the network to grow into any shape or size. In the Alcala dataset, our transfer learning algorithm trains the fastest across changing environments and input sizes. In addition, we show that our algorithm also outperformance the state of the art in EEG dataset. In the future, this network may be considered for implementation on real biological neural networks to decrease power consumption.

Rootstocks for the Mediterranean citrus industry: current choices and new releases

Modern citriculture relies on the use of rootstocks to guarantee adaptability to environmental and biotic constraints and to speed up the establishment of a potentially unlimited number of homogeneous plants of a specific clone, which in turn ensures uniformity in the production of high-quality fruit. In this contribution, the status of rootstock availability for the Mediterranean citrus industry is described along with its evolution during the last decades. The major turning points in rootstock choice were caused by the spread of new diseases, such as Citrus Tristeza Virus (CTV), that hastened the use of resistant or tolerant rootstocks. In Italy, the growers decisions on rootstock replacement were facilitated by information from rootstock trials performed for more than 70 years in several areas of southern Italy, as well as information from other countries. However, there is still the need to find alternatives to sour orange (which was the most commonly used rootstock before the CTV outbreak), especially for cultivation under suboptimal growing conditions. Nowadays, the process of replacing standard rootstocks with new ones with better performance should be assisted by a long-term evaluation strategy, since negative characteristics can become evident after many years of evaluation in combination with specific scion varieties. Ongoing rootstock breeding programs in different parts of the world and in Italy are critically described.

Investigating the Effects of Tube Current and Tube Voltage on Patient Dose in Computed Tomography Examinations with Principial Component Analysis and Cluster Analysis: Phantom Study.

The aim of this study was to investigate the effects of tube current and tube voltage choices on patient dose in adult and pediatric CT protocols by qualitative analysis using Principal Component Analysis (PCA), cluster analysis, and statistical analysis.Dose length product (DLP), Effective mAs (Eff. mAs), and volume-weighted CT dose index (CTDIvol) dose descriptors were obtained from 16 adult and pediatric head phantom CT examinations. Different tube voltage and tube current values were selected in both pediatric head and adult head CT imaging protocols, and PCA and cluster analysis were applied to the data obtained for qualitative analysis of the relationship between CTDIvol, Eff. mAs and Total DLP values. The two principial components (PC) with the highest values among those obtained as a result of the PCA method were used. PC1 was 70.97%, and PC2 was 28.03%. In the cluster analysis, it was observed that the values obtained from pediatric and adult phantom CT scans were classified into two different clusters. The correlation coefficient for adult patients was r = 0.998, and for pediatric patients, the correlation coefficient was r = 0.947. When the obtained clusters were examined, the degree of closeness or distance of the variables could be observed. In the study, as a result of the analysis of CTDIvol, Eff. mAs and Total DLP data based on manufacturer data at different kV and mA values with PCA and cluster analysis, it was shown that pediatric patients could be exposed to more radiation than the adult patients.

Development and Evaluation of a Novel Herbal Anti-Ulcer Gel Formulation Incorporating Bauhinia Racemosa Extract

This research paper focuses on the development and testing of an anti-ulcer gel containing Bauhinia Racemosa extract that is specifically designed for the treatment of mouth ulcers. The study's goal is to overcome the limits of current treatment choices by offering a natural and effective alternative for people suffering from mouth ulcers. The research aims include developing an anti-ulcer gel based on Bauhinia Racemosa extract, assessing its efficacy and safety, and adding to existing knowledge on ulcer treatment. The gel formulation combines Bauhinia Racemosa's distinct phytochemical composition, including flavonoids, tannins, and saponins, which are thought to have anti-inflammatory, antibacterial, and wound-healing activities. The anti-ulcer gel's efficacy will be evaluated using a variety of criteria, including pain reduction, ulcer healing promotion, and relief of oral discomfort associated with mouth ulcers. Safety and tolerability will be extensively investigated to guarantee that there are no negative side effects. The study will also investigate the probable processes by which the gel exerts its anti-ulcer properties. The anti-ulcer gel's effectiveness will be determined by comparing it to existing conventional therapies for mouth ulcers. The long-term impact and recurrence rates of mouth ulcers treated with the gel will also be studied. The purpose of this research is to contribute to the scientific literature on natural products for the treatment of mouth ulcers by making evidence-based recommendations for the use of anti-ulcer gel in clinical practice. The study will also highlight topics for future research and development of natural products for treating mouth ulcers. This research report aims to improve the quality of life for those suffering from mouth ulcers by offering a safe, effective, and natural treatment option.

A statistical learning approach to pasture, rangeland, forage (PRF) insurance coverage selection

AbstractThis study uses statistical learning methods to identify robust coverage alternatives for the Pasture, Rangeland, Forage (PRF) insurance program. Shrinkage and ensemble learning techniques are adapted to the context of the PRF coverage selection process. The out‐of‐sample performance of the proposed methods is evaluated on 116 representative grids throughout Texas during 2018–2022. Ensemble learning methods generated more stable coverage choices compared with the other selection strategies considered. Depending on the target return, a reduction in the prediction error between 5% and 14% was observed. Furthermore, the proposed coverages can provide a broader protection than current coverage choices made by farmers.

Transactive energy management system for smart grids using Multi-Agent Modeling and Blockchain

Technological approaches for effective energy regulation are required due to incorporating contemporary electrical systems with sustainable power resources. Our article suggests a Transactive Energy Managing System (T.E.M.S.) using Blockchain-based technologies and Multiple-Agent Modelling (M.A.M.) to improve the long-term viability and dependability of energy-efficient grids. The framework utilizes a decentralized methodology enabled by self-governing agents. These units include services, sellers, and customers in the electricity system. Such entities engage in vibrant interactions, trading energy according to current economic circumstances, choices, and facts. Blockchain-based technology promotes a more robust and decentralized power industry by eliminating the demand for centralized mediators and improving information security. The suggested T.E.M.S. aims to tackle issues such as demand-response administration, incorporation of sustainable energy resources, and grid reliability. The efficiency of the mechanism in maximizing power use, lowering load spikes, and fostering an improved power environment is proved via modelling and evaluation. The research advances intelligent grid technology by providing an extensive, decentralized approach that aligns with the changing power industry. In this era of connected layouts, integrating Blockchain-based technologies with Multiple-Agent Modelling offers a solid basis for creating flexible and adaptable power control technologies.