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The role of the Jordanian legislator in regulating both the rights of preemption and preferences in the legislation in force in Jordan in the Real Estate Law of 2019 is of great significance. Appropriating the descriptive-analytical research method, this article examines the procedures for reforming the preference right under the pre-emption right regulatory provisions. The results indicate a great deal of similarity between the legal provisions regulating and preference in various substantive and procedural provisions. The findings also show that both legal provisions regulating and preference share the fact that they apply to real estate, not movable property. Given the said results, several key recommendations are suitably incorporated into the paper. [ABSTRACT FROM AUTHOR]

ABSTRACT: Background: Uncontrolled diabetes persists despite guideline-based treatment, partly attributed to inadequate patient involvement. This research addresses shared decision-making by eliciting patient preferences in Type 2 Diabetes Mellitus (T2DM) treatment based on certain key attributes and explores their correlation with socio-demographic-clinical profiles. Methods: A discrete choice experiment (DCE) was conducted among T2DM outpatients in an Indian tertiary care center. A choice card was developed using the contextual choice framework, having six second-line antidiabetic drugs (ADs) from different classes incorporating seven attributes. Face-to-face interviews were conducted with patients, and elicited preferences were analyzed using descriptive statistics, Chi-square analysis, and multinomial logistic regression. Results: Out of the 87 evaluated participant choices, the most preferred drug was Glimepiride (51.7%), followed by Dapagliflozin (22.9%) and Teneligliptin (17.2%). Overall, the most important attributes were the effect on weight (29%), followed by route of administration (24%), and additional benefits offered by the drug (18%). Significant associations were found between participants' drug preferences and their age (P = 0.002), socioeconomic status (P = 0.04), occupation (P = 0.004), and monthly income (P = 0.03). Age was not a significant predictor of drug choice for any of the drugs. Multinomial logistic regression showed that the overall model was statistically significant (P = 0.025), and it correctly predicted drug choice for 58.6% of the participants. Conclusion: Glimepiride was the most preferred option overall while the effect on weight was the most important attribute for patients in determining their preference. The study highlighted the importance of shared decisions and can guide practitioners in considering patient preferences when prescribing antidiabetic drugs. [ABSTRACT FROM AUTHOR]

Introduction: Older adults with cancer facing competing treatments must prioritize between various outcomes. This study assessed health outcome prioritization among older adults with cancer starting chemotherapy. Methods: Secondary analysis of a randomized trial addressing vulnerabilities in older adults with cancer. Patients completed three validated outcome prioritization tools: 1) Health Outcomes Tool: prioritizes outcomes (survival, independence, symptoms) using a visual analog scale; 2) Now vs. Later Tool: rates the importance of quality of life at three times—today versus 1 or 5 years in the future; and 3) Attitude Scale: rates agreement with outcome‐related statements. The authors measured the proportion of patients prioritizing various outcomes and evaluated their characteristics. Results: A total of 219 patients (median [range] age 71 [65–88], 68% with metastatic disease) were included. On the Health Outcomes Tool, 60.7% prioritized survival over other outcomes. Having localized disease was associated with choosing survival as top priority. On the Now vs. Later Tool, 50% gave equal importance to current versus future quality of life. On the Attitude Scale, 53.4% disagreed with the statement "the most important thing to me is living as long as I can, no matter what my quality of life is"; and 82.2% agreed with the statement "it is more important to me to maintain my thinking ability than to live as long as possible". Conclusion: Although survival was the top priority for most participants, some older individuals with cancer prioritize other outcomes, such as cognition and function. Clinicians should elicit patient‐defined priorities and include them in decision‐making. In this secondary analysis of 219 older participants (median age 71 years) from a randomized trial addressing vulnerabilities in older adults with cancer, 61% prioritized survival over other outcomes such as maintaining their independence or being free from symptoms. Clinicians should elicit patient‐defined priorities and include them in decision‐making when discussing treatment. [ABSTRACT FROM AUTHOR]

Objective: Patients with advanced ovarian cancer face a range of treatment options, and there is unwarranted variation in treatment decision-making between UK providers. Decision support tools that produce data on treatment outcomes as a function of individual patient characteristics, would help both patients and clinicians to make informed, preference- and values-based choices. However, data on treatment outcomes to include in such tools are lacking. Methods: Following a literature review, a questionnaire was designed for use in a Delphi process to establish which treatment outcomes are important to both patients and clinicians in decision-making for treatment for advanced ovarian cancer. Patient and clinician panels were established. Results: Following 2 Delphi rounds, consensus was achieved for 7/11 items in the patient panel and 8/11 items in the clinician panel. Consensus across both panels was achieved for inclusion of both overall survival and progression free survival as important items in the decision-making process, although there remained differences of opinion as to whether these should be presented as relative or absolute values. Conclusion: Information needs for treatment decision-making in ovarian cancer differ between and within patient and clinician groups. Whilst overall survival and progression free survival are universally accepted as important data items, decision support tools will need to be nuanced to allow presentation of a range of outcomes and associated probabilities, and in a range of formats, that can be tailored to the preferences of clinician and patients. [ABSTRACT FROM AUTHOR]

Introduction: Prior discrete choice experiments (DCE) in oncology found that, on average, clinicians rank survival as the most important treatment attribute. We investigate heterogeneity in clinician preferences within the context of first-line treatment for advanced urothelial carcinoma in Spain, France, Italy, Germany, and the UK. Methods: The online DCE included 12 treatment choice tasks, each comparing two hypothetical therapy profiles defined by treatment attributes: grade 3/4 treatment-related adverse events (TRAEs), induction and maintenance administration schedules, progression-free survival, and overall survival (OS). We used a random parameters logit model to estimate attribute relative importance (RI) (0–100%) and generate preference shares for four treatment profiles. Results were stratified by country. Preference heterogeneity was evaluated by latent class analysis. Results: In August and September 2022, 498 clinicians (343 oncologists and 155 urologists) completed the DCE. OS had the strongest influence on clinicians' preferences [RI = 62%; range, 51.6% (Germany) to 63.7% (Spain)] followed by frequency of grade 3/4 TRAEs (RI = 27%). Among treatment profiles, the chemotherapy plus immune checkpoint inhibitor maintenance therapy profile had the largest preference share [51%; range, 38% (Italy) to 56% (UK)]. Four latent classes of clinicians were identified (N = 469), with different treatment profile preferences: survival class (30.1%), trade-off class (22.4%), no strong preference class (40.9%), and aggressive treatment class (6.6%). OS was not the most important attribute for 30.0% of clinicians. Conclusion: While average sample results were consistent with those of prior DCEs, this study found heterogeneity in clinician preferences within and across countries, highlighting the diversity in clinician decision making in oncology. [ABSTRACT FROM AUTHOR]

Purpose: This paper focuses on the different Shari'ah resolutions on preference shares. This study aims to provide a systematic review to cover all authentic, peer-reviewed literature on this issue between the years 2001 and 2020. Design/methodology/approach: This library research combines, compares and contrasts the discussions and the results of all these papers besides the opinions and discussions of some renowned scholars in the field. Findings: The aim of this paper was met as every research during that period was included and scrutinized which resulted in a comprehensive knowledge about the presence shares. Research limitations/implications: One of the limitations was the limited research on the Shari'ah issues in preference shares as a regulatory capital that meets Basel III accords. Originality/value: This paper will be the reference for any researcher who wants to add value on this issue and to start from where researchers ended. [ABSTRACT FROM AUTHOR]

This article reviews the recently issued Proposed Regulations on the application of the stock repurchase tax in Internal Revenue Code Section 4501. The Proposed Regulations address repurchases of foreign corporate stock “funded” by a domestic entity, repurchases of preferred stock, exemptions for redemptions in a complete liquidation, and application to M&A transactions. [ABSTRACT FROM AUTHOR]

The fundamental stages in designing a stock portfolio are each stock's selection and capital weighting. Selection and weighting must be conducted through diversification and price movement analysis to maximize profits and minimize losses. The problem is how the technical implementations of both are carried out. Based on this problem, this study aims to design these selection and weighting mechanisms. Stock selection is based on clusters and price movement trends. The optimal stock clusters are formed using the K-Means algorithm, and price movement analyses are carried out using the moving average indicator. The selected stocks are those whose prices have increasing trends with the most significant Sharpe ratio in each cluster. Then, the capital weighting for each preferred stock is carried out using the mean-variance model with transaction cost and income tax. After designing the mechanism, it is applied to Indonesia's 80 index stock data. In addition, a comparison is conducted between the estimated portfolio return and the actual one day ahead. Finally, the sensitivity of investors' courage in taking risks to their profits and losses is also analyzed. This research is expected to assist investors in diversification and price movement analysis of the stocks in the portfolios they form. [ABSTRACT FROM AUTHOR]

When bacterial species with the same resource preferences share the same growth environment, it is commonly believed that direct competition will arise. A large variety of competition and more general 'interaction' models have been formulated, but what is currently lacking are models that link monoculture growth kinetics and community growth under inclusion of emerging biological interactions, such as metabolite cross-feeding. In order to understand and mathematically describe the nature of potential cross-feeding interactions, we design experiments where two bacterial species Pseudomonas putida and Pseudomonas veronii grow in liquid medium either in mono- or as co-culture in a resource-limited environment. We measure population growth under single substrate competition or with double species-specific substrates (substrate 'indifference'), and starting from varying cell ratios of either species. Using experimental data as input, we first consider a mean-field model of resource-based competition, which captures well the empirically observed growth rates for monocultures, but fails to correctly predict growth rates in co-culture mixtures, in particular for skewed starting species ratios. Based on this, we extend the model by cross-feeding interactions where the consumption of substrate by one consumer produces metabolites that in turn are resources for the other consumer, thus leading to positive feedback in the species system. Two different cross-feeding options were considered, which either lead to constant metabolite cross-feeding, or to a regulated form, where metabolite utilization is activated with rates according to either a threshold or a Hill function, dependent on metabolite concentration. Both mathematical proof and experimental data indicate regulated cross-feeding to be the preferred model to constant metabolite utilization, with best co-culture growth predictions in case of high Hill coefficients, close to binary (on/off) activation states. This suggests that species use the appearing metabolite concentrations only when they are becoming high enough; possibly as a consequence of their lower energetic content than the primary substrate. Metabolite sharing was particularly relevant at unbalanced starting cell ratios, causing the minority partner to proliferate more than expected from the competitive substrate because of metabolite release from the majority partner. This effect thus likely quells immediate substrate competition and may be important in natural communities with typical very skewed relative taxa abundances and slower-growing taxa. In conclusion, the regulated bacterial interaction network correctly describes species substrate growth reactions in mixtures with few kinetic parameters that can be obtained from monoculture growth experiments. Author summary: Correctly predicting growth of communities of diverse bacterial taxa remains a challenge, because of the very different growth properties of individual members and their myriads of interactions that can influence growth. Here, we tried to improve and empirically validate mathematical models that combine theory of bacterial growth kinetics (i.e., Monod models) with mathematical definition of interaction parameters. We focused in particular on common cases of shared primary substrates (i.e., competition) and independent substrates (i.e., indifference) in an experimental system consisting of one fast-growing and one slower growing Pseudomonas species. Growth kinetic parameters derived from monoculture experiments included in a Monod-type consumer-resource model explained some 75% of biomass formation of either species in co-culture, but underestimated the observed growth improvement when either of the species started as a minority compared to the other. This suggested an important role of cross-feeding, whereby released metabolites from one of the partners are utilized by the other. Inclusion of cross-feeding feedback in the two-species Monod growth model largely explained empirical data at all species-starting ratios, in particular when cross-feeding is activated in almost binary manner as a function of metabolite concentration. Our results also indicate the importance of cross-feeding for minority taxa, which can explain their survival despite being poorly competitive. [ABSTRACT FROM AUTHOR]

Automatic facial beauty assessment has recently attracted a growing interest and achieved impressive results. However, despite the obvious subjectivity of beauty perception, most studies are addressed to predict generic or universal beauty and only few works investigate an individual's preferences in facial attractiveness. Unlike universal beauty assessment, an effective personalized method is required to produce a reasonable accuracy on a small amount of training images as the number of annotated samples from an individual is limited in real-world applications. In this work, a novel personalized facial beauty assessment approach based on meta-learning is introduced. First of all, beauty preferences shared by an extensive number of individuals are learnt during meta-training. Then, the model is adapted to a new individual with a few rated image samples in the meta-testing phase. The experiments are conducted on a facial beauty dataset that includes faces of various ethnic, gender, age groups and rated by hundreds of volunteers with different social and cultural backgrounds. The results demonstrate that the proposed method is capable of effectively learning personal beauty preferences from a limited number of annotated images and outperforms the facial beauty prediction state-of-the-art on quantitative comparisons. [ABSTRACT FROM AUTHOR]

Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms – including both shallow and deep ones – often model such dynamics independently, i.e., user static and dynamic preferences are not modeled under the same latent space, which makes it difficult to fuse them for recommendation. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences, namely translating sequence to preference. To this end, we formulate the sequential recommendation task as a dictionary learning problem, which learns: 1) a shared dictionary matrix, each row of which represents a partial signal of user dynamic preferences shared across users; and 2) a posterior distribution estimator using a deep autoregressive model integrated with Gated Recurrent Unit (GRU), which can select related rows of the dictionary to represent a user's dynamic preferences conditioned on his/her past behaviors. Qualitative studies on the Netflix dataset demonstrate that the proposed method can capture the user preference drifts over time and quantitative studies on multiple real-world datasets demonstrate that the proposed method can achieve higher accuracy compared with state-of-the-art factorization and neural sequential recommendation methods. [ABSTRACT FROM AUTHOR]

The number of inputs and outputs to a SIF (safety instrumented function)—including voting—affects the probability of failure on demand (PFD) and the SIL (safety integrity level), assuming the test interval remains the same. The larger the number of inputs and outputs that perform separate functions, the higher the PFD and potentially the lower the SIL. What if the SIF includes all the sensors and final elements that are involved in any trip of a large processing unit like a heater, a reformer, or a distillation column? What if the SIF includes actions to shut down upstream and downstream units? When SIL verification is done for such a SIF, the target PFD and SIL may not be possible to achieve. The temptation may be to add redundancy in sensors and final elements or to reduce the proof test interval in an attempt to reduce the calculated PFD. Frustration may abound as capital and operating costs rise steeply. This paper shows how to use the principles of layer of protection analysis and the information in the process hazard analysis to split up the massive SIF into smaller SIFs that are more manageable. An example is included. [ABSTRACT FROM AUTHOR]

This paper discusses the concerns related to the issuance of voting preferred shares by selected listed Philippine companies. These concerns include the rationale behind the issuance of these voting preferred stocks, limited access to the issue to controlling stockholders, very low offer prices relative to the common shares, non-listing of these securities in the Philippine Stock Exchange (PSE), and the dilution of minority stockholders' voting rights as a consequence of issuing these stocks. [ABSTRACT FROM AUTHOR]

Objective: It is unknown how patients prioritize gadolinium-based contrast media (GBCM) benefits (detection sensitivity) and risks (reactions, gadolinium retention, cost). The purpose of this study is to measure preferences for properties of GBCM in women at intermediate or high risk of breast cancer undergoing annual screening MRI. Methods: An institutional reviewed board-approved prospective discrete choice conjoint survey was administered to patients at intermediate or high risk for breast cancer undergoing screening MRI at 4 institutions (July 2018–March 2020). Participants were given 15 tasks and asked to choose which of two hypothetical GBCM they would prefer. GBCMs varied by the following attributes: sensitivity for cancer detection (80–95%), intracranial gadolinium retention (1–100 molecules per 100 million administered), severe allergic-like reaction rate (1–19 per 100,000 administrations), mild allergic-like reaction rate (10–1000 per 100,000 administrations), out-of-pocket cost ($25–$100). Attribute levels were based on published values of existing GBCMs. Hierarchical Bayesian analysis was used to derive attribute "importance." Preference shares were determined by simulation. Results: Response (87% [247/284]) and completion (96% [236/247]) rates were excellent. Sensitivity (importance = 44.3%, 95% confidence interval = 42.0–46.7%) was valued more than GBCM-related risks (mild allergic-like reaction risk (19.5%, 17.9–21.1%), severe allergic-like reaction risk (17.0%, 15.8–18.1%), intracranial gadolinium retention (11.6%, 10.5–12.7%), out-of-pocket expense (7.5%, 6.8–8.3%)). Lower income participants placed more importance on cost and less on sensitivity (p < 0.01). A simulator is provided that models GBCM preference shares by GBCM attributes and competition. Conclusions: Patients at intermediate or high risk for breast cancer undergoing MRI screening prioritize cancer detection over GBCM-related risks, and prioritize reaction risks over gadolinium retention. Key Points: • Among women undergoing annual breast MRI screening, cancer detection sensitivity (attribute "importance," 44.3%) was valued more than GBCM-related risks (mild allergic reaction risk 19.5%, severe allergic reaction risk 17.0%, intracranial gadolinium retention 11.6%, out-of-pocket expense 7.5%). • Prospective four-center patient preference data have been incorporated into a GBCM choice simulator that allows users to input GBCM properties and calculate patient preference shares for competitor GBCMs. • Lower-income women placed more importance on out-of-pocket cost and less importance on cancer detection (p < 0.01) when prioritizing GBCM properties. [ABSTRACT FROM AUTHOR]

In the airline industry's very competitive landscape, being traveler-centric and offering an adapted set of products and services for different customer segments is crucial for airlines to resist and keep growing. By correctly designing and pricing product offers, airlines will reinforce their travelers' loyalty and open upsell opportunities and strengthen their competitive position and revenue. This paper presents a new approach to design and optimizing airline offers, aiming to better consider travelers' needs and preferences and improve airline revenue. The proposed method is based on market research and conjoint analysis techniques, combined with a revenue simulation framework. To better understand travelers, airline historical bookings data are used to cluster them in different segments based on their trip characteristics and booking behavior. In addition to the segmentation, a large sample of several thousands of travelers is surveyed using an adapted questionnaire per segment. The survey required participants to select one option among an assortment of a few products with different features. The data collected are used to build a choice model and evaluate the price elasticity. Travelers' segments, estimated utilities, and choice modeling combined with the price elasticity and product preference shares are used to design the new product offer. A revenue simulation framework is developed to evaluate the revenue impact of introducing these new products in a competitive landscape. It simulates the flight booking life cycle from the flight's opening for booking until the day of departure. Several scenarios of demand and willingness to pay are evaluated. Our approach is tested on a mid-size, full-service carrier. It allowed a better understanding of the travelers' segments and behavior and resulted in a revenue improvement ranging from 1.6 to 4%, depending on the cabin and simulation scenario. Following the obtained results, the study recommendations' are being implemented in production with an airline partner. [ABSTRACT FROM AUTHOR]