Preferred Framework Research Articles

Interrogating the growth of Africa through the opportunity act and the Nigerian textile industry

The study examined the impact of AGOA on the textile industry in Nigeria between 2000 and 2015. The African Growth and Opportunity Act (AGOA) was signed into law on May 18, 2000 as Title 1 of The Trade and Development Act of 2000. The Act offers tangible incentives for African countries to continue their efforts to open their economies and build free markets. This paper attempts to answer the question: Did the preferential access to the U.S. market under AGOA increase the volume of textile export from Nigeria to U.S. between 2000 and 2015? Secondary data were used and data collected were analysed using tables and content analysis. The study adopted the Complex Interdependence theory as its preferred framework of analysis and argued that the letter and spirit of AGOA were both in consonance with the neoclassical model of growth for SSA designed by the U.S. We remarked that this liberalised trade introduced by United States in Sub-Saharan Africa through AGOA was equally deepened to strengthen U.S. involvement capacity to this under developed markets in Sub-Saharan Africa. The study found out that the preferential access to U.S. market under African Growth and Opportunity Act did not increase the volume of textile export from Nigeria to the U.S. between 2000 and 2015. Finally, the study made profound recommendations aimed at maximizing the benefits inherent in the Nigeria-United States trade relationship.

Ambidextrous Innovation in Project Management: A Systematic Literature Review

Organizations increasingly rely on innovation to achieve sustainable competitive advantage and ensure longer-run market performance. Ambidextrous innovation is a critical strategic framework that guides managers to channel their innovation into exploring new ideas for products, services, and processes and exploiting ideas for improving existing ones. Ambidextrous innovation is one of the key frameworks that the projects can use to build innovation that best satisfies the stakeholder demands of higher performance at the lower resources. Ambidextrous innovation is becoming the most preferred framework for organizations to undertake innovations due to its ability to balance both the development of new and the improvement of existing products. However, research on ambidextrous innovation in the context of project management is widely lacking.

AN ANALYSIS OF BEHAVIORAL BIASES AGAINST VARIOUS PARAMETERS

Behavioral finance attempts to question the efficiency of the financial market. Several anomalies of human behavior are considered as interfering factors while investors make a financial decision, which leads to the criticism of the rationality of investors. Considerable evidence has been provided by psychologists and behavioral economists which demonstrates that investors are exposed to psychological and behavioral biases. Additionally, investors resort to shortcuts during the process of decision-making, and are affected by their past investment profits or losses. They make decisions within a framework of preference, and are influenced by the investment patterns of other investors. This study examines the relationship between behavioral heuristics and biases such as overconfidence, self-attribution, and availability biases and parameters such as age, gender, profession, and income of investors.

P6 EMA/EUnetHTA Qualification of the IMI PREFER Patient Preference Framework and Points to Consider for Method Selection: Key Experiences, Outcomes, Value and Implications

P6 EMA/EUnetHTA Qualification of the IMI PREFER Patient Preference Framework and Points to Consider for Method Selection: Key Experiences, Outcomes, Value and Implications

The music of the hemispheres: Cortical eigenmodes as a physical basis for large-scale brain activity and connectivity patterns.

Neuroscience has had access to high-resolution recordings of large-scale cortical activity and structure for decades, but still lacks a generally adopted basis to analyze and interrelate results from different individuals and experiments. Here it is argued that the natural oscillatory modes of the cortex-cortical eigenmodes-provide a physically preferred framework for systematic comparisons across experimental conditions and imaging modalities. In this framework, eigenmodes are analogous to notes of a musical instrument, while commonly used statistical patterns parallel frequently played chords. This intuitive perspective avoids problems that often arise in neuroimaging analyses, and connects to underlying mechanisms of brain activity. We envisage this approach will lead to novel insights into whole-brain function, both in existing and prospective datasets, and facilitate a unification of empirical findings across presently disparate analysis paradigms and measurement modalities.

Describing the state of a research network: A mixed methods approach to network evaluation.

Diabetes Action Canada Strategy for Patient-Oriented Research (SPOR) Network in Chronic Disease was formed in 2016 and is funded primarily through the Canadian Institutes of Health Research (CIHR). We propose a novel mixed-methods approach to a network evaluation integrating the State of Network Evaluation framework and the Canadian Academy of Health Sciences (CAHS) preferred framework and indicators. We measure key network themes of connectivity, health and results, and impact and return on investment associated with health research networks. Our methods consist of a longitudinal cross-sectional network survey of members and social network analysis to examine Network Connectivity and assess the frequency of interactions, the topics discussed during them, and how networking effectively facilitates interactions and collaboration among members. Network Health will be evaluated through semistructured interviews, a membership survey inquiring about satisfaction and experience with the Network, and a review of documentary sources related to funding and infrastructure to evaluate Network Sustainability. Finally, we will examine Network Results and Impact using the CAHS preferred framework and indicators to measure returns on investment in health research across the five domains of the CAHS framework, which include: advancing knowledge, capacity building, informing decision making, health impact, and economic and social impact. Indicators will be assessed with various methods, including bibliometric analyses, review of relevant documentary sources (annual reports), member activities informing health and research policy, and Patient Partner involvement. The Network Evaluation will provide members and stakeholders with information for planning, improvements, and funding future Network endeavors.

Which online learning resources do undergraduate economics students’ value and does their use improve academic attainment? A comparison and revealed preferences from before and during the Covid pandemic

The rapid shift to online learning during the Covid-19 pandemic led to widespread migration to online / blended delivery across UK Higher Education. This has prompted renewed interest in identifying the features of virtual learning environments (VLEs) which students value and are most helpful in academic development and attainment. Using the experience of delivery on an undergraduate module both before and after the introduction of an online delivery model, we use a revealed preference framework to analyse the influence of cognitive load on the value students attach to VLE features in combination with other learning resources. We also use regression analysis to examine which learning resources are crucial to attainment. Our findings suggests that students avoid cognitive overload by being selective in their use of learning resources. They showed a greater preference for both familiar and passive learning resources like lectures, lecture recordings and seminars in both learning environments. Students exhibited a lower preference for active VLE features - multiple-choice quizzes, open-ended questions and discussion forums. Nonetheless, use of open-ended questions along with lecture recordings and lecture slides had a significantly positive impact on academic attainment in the online learning environment. This supports instrumentalism in the use of these resources. Students were more selective in developing deeper understanding using online open-ended questions. Our results imply that module designers need to accept that such resources will be used more selectively. However, they should still be provided to encourage active, deeper learning.

Single-phase front-end bridgeless modified Landsman-Canonical Switching Cell PFC converter for arc welding applications

ABSTRACT This research paper proposes an enhanced power quality in bridgeless modified Landsman-Canonical switching cell (BMLCSC) converter used for arc welding power supplies. This proposed converter topology is presented for circuit component reduction to eliminate the need for a diode bridge rectifier and input filter to make the circuit more compact. The proposed converter is operated on discontinuous conduction mode (DCM). A closed loop PI control circuit with a single voltage sensor is utilized to achieve load regulation, line regulation with high efficiency and quick response. The exploratory performance of preferred framework is simulated using Matlab/Simulink and validated with a hardware prototype of 100 W, 48 V.

Risk assessment of hazardous materials transportation: A review of research progress in the last thirty years

In recent years, hazardous materials transportation accidents occur frequently, causing huge casualties and property losses. Risk assessment of hazardous materials transportation has been constantly investigated by researchers and decision-makers. In order to study the research progress of risk assessment of hazardous materials transportation, so as to better reveal relevant problems. This review systematically analyzes the existing literature, from 1991 to 2020, on risk assessment models and methods of hazardous materials transportation, and segregates the relevant studies based on unimodal and intermodal transportation systems. Additionally, an example is considered to analyze the characteristics of each risk assessment model of unimodal transportation, and a novel detailed classification is proposed for the risk assessment problems. The results indicate that the research on the risk assessment model of road transportation of hazardous materials is comprehensive, whereas that based on the railway and intermodal transportations is deficient. In comparison with the traditional risk assessment model, the conditional value-at-risk model presents a flexible decision-making framework for decision-makers and controls risk preference between risk neutrality and risk aversion. Owing to the difficulty in achieving door-to-door railway transportation and the increased risks associated with the intermodal transportation, the conclusion that the risk of railway and multimodal transportation of hazardous materials is lower than that of road transportation is not universal. Transport operators and governments can combine the advantages of different transportation modes, such as low risk, low cost, high flexibility, and high reliability, to achieve public safety and improve the competence of the system. Based on the proposed classification, this review identifies the existing trends and research gaps and summarizes future research directions. • Development process of risk assessment models is summarized. • Advantages and disadvantages of various transportation modes are outlined. • Characteristics of the unimodal transportation risk assessment model are analyzed using an example. • New classification for unimodal transportation is proposed.

Modeling content-attribute preference for personalized image esthetics assessment

Personalized image esthetics assessment (PIAA) models user's personalized esthetic preference and predicts a unique esthetic score for the user, which has become a hot topic due to its usefulness in social media, album curation, etc. People's esthetic preference is typically determined by a mixture of diversified factors, which makes it extremely challenging to model. Intuitively, people with different personalities have distinct preferences for various photographic contents, while image content and esthetic attributes are always tightly coupled. Motivated by this, this paper presents a content-attribute preference (CAP) framework for PIAA, which models the intricate relationship between user personality and image “content-attribute” coupling features. Specifically, a multi-task learning network is utilized to extract “content-attribute” coupling features, and another parallel network is used to extract personality features. Then, a personalized esthetic prior model is trained to represent the content-attribute preferences of people with different personalities based on the cross attention mechanism. Finally, a PIAA model is obtained by fine-tuning the esthetic prior model using target user data. Extensive experiments on three public databases demonstrate that the proposed CAP model outperforms the state-of-the-arts in terms of both prediction performance and generalization ability. • A new personalized image esthetics assessment model is proposed. • Esthetic preference is modeled using personality and content-attribute features. • Content, attribute and personality tasks are pre-trained for feature extraction. • The proposed method achieves better performance than the state-of-the-arts.

Handling qualitative preferences in SPARQL over virtual ontology-based data access

With the increase of data volume in heterogeneous datasets that are being published following Open Data initiatives, new operators are necessary to help users to find the subset of data that best satisfies their preference criteria. Quantitative approaches such as top-k queries may not be the most appropriate approaches as they require the user to assign weights that may not be known beforehand to a scoring function. Unlike the quantitative approach, under the qualitative approach, which includes the well-known skyline, preference criteria are more intuitive in certain cases and can be expressed more naturally. In this paper, we address the problem of evaluating SPARQL qualitative preference queries over an Ontology-Based Data Access (OBDA) approach, which provides uniform access over multiple and heterogeneous data sources. Our main contribution is Morph-Skyline++, a framework for processing SPARQL qualitative preferences by directly querying relational databases. Our framework implements a technique that translates SPARQL qualitative preference queries directly into queries that can be evaluated by a relational database management system. We evaluate our approach over different scenarios, reporting the effects of data distribution, data size, and query complexity on the performance of our proposed technique in comparison with state-of-the-art techniques. Obtained results suggest that the execution time can be reduced by up to two orders of magnitude in comparison to current techniques scaling up to larger datasets while identifying precisely the result set.

Farmers’ Demonstrate Rationality and Transitivity in Variety Choice: Empirical Evidence From two Rice Growing Niches in Coastal Kenya.

Purpose: A cross-sectional survey was designed to study farmers’ preference and choice framework for rice varieties in Kwale and Taita-Taveta counties of coastal lowland Kenya.
 Methodology: The survey used a multi-stage sampling technique; systematic selection for the study counties/niches, a purposive sampling approach for the study population (rice growers only) and a systematic random sampling for the respondents on an nth occurrence. Data was collected with the aid of a semi-structured questionnaire and key informant interviews from key rice value chain actors; including farmers as producers as well as consumers and agricultural extension service providers (AESPs) and other stakeholders. A total of 137 individual respondents were interviewed using the semi-structured questionnaire while the key informant interviews engaged a total of 29 AESPs. Data analysis focused on exploring on the varieties that farmers grew and whether they had a criteria or pattern for choosing the varieties to grow and seed source.
 Findings: Results confirmed that the Kwale rice growing niche had a higher rice variety diversity (more than 12 varieties) while Taita-Taveta had only around seven (7) common varieties. Rice was more traditionally grown in Kwale with low acreage and low yields compared to Taita-Taveta. Common varieties grown were Pachanga, Supa, Kitumbo, Makonde and Kibawa in Kwale while in Taita-Taveta, Japan, Supa and Hybrid 6444 rice varieties were common (with prevalence rating of 44.8%, 25.4%, 22.4%, 20.9 and 17.9% in Kwale respectively and 55.7%, 54.3% and 18.6% in Taita-Taveta respectively). Varieties grown by farmers in Kwale had a long history of seed recycling and were characterized by low productivity (less than 6, 90-kg bags per acre) and subsistent in nature than in Taita-Taveta. The general picture for variety prevalence in both (Kwale and Taita) growing niches was that farmers mostly relied on traditional (own-preserved, farmer-to-farmer exchange or market) seed systems for the recycled rice varieties. Variety choice by farmers who are both producers and consumers demonstrated a rational and transitive behavior with land resource allocation.
 Contribution to theory, practice and policy: The study provided evidence of an urgent need for strategic interventions towards improving the production system through on-farm researcher-led demonstrations for superior rice varieties, agronomic practices as well as an input subsidy and sustainable market linkages. Formation of farmer producer groups and strategic facilitation of the groups with rice certified seed and mills are proposed as positive drivers to improving rice marketing and increased productivity.

Experimentally disambiguating models of sensory cue integration.

Sensory cue integration is one of the primary areas in which a normative mathematical framework has been used to define the “optimal” way in which to make decisions based upon ambiguous sensory information and compare these predictions to behavior. The conclusion from such studies is that sensory cues are integrated in a statistically optimal fashion. However, numerous alternative computational frameworks exist by which sensory cues could be integrated, many of which could be described as “optimal” based on different criteria. Existing studies rarely assess the evidence relative to different candidate models, resulting in an inability to conclude that sensory cues are integrated according to the experimenter's preferred framework. The aims of the present paper are to summarize and highlight the implicit assumptions rarely acknowledged in testing models of sensory cue integration, as well as to introduce an unbiased and principled method by which to determine, for a given experimental design, the probability with which a population of observers behaving in accordance with one model of sensory integration can be distinguished from the predictions of a set of alternative models.

Optimal health insurance with constraints under utility of health, wealth and income

<p style='text-indent:20px;'>We consider an optimal health insurance design problem with constraints under utility of health, wealth and income. The preference framework we establish herein describes the trade-off among health, wealth and income explicitly, which is beneficial to distinguish health insurance design from other nonlife insurance designs. Moreover, the work takes into account the case that if the insured is severely or critically ill, the insured may not fully recover even after necessary medical treatment. By taking these special features into account, the health insurance design problem is formulated as a constrained optimization problem, and the optimal solutions are derived by using the Lagrange multiplier method and optimal control technique. Finally, two numerical examples are given to illustrate our results. Our research work gives new insights into health insurance design.</p>

Exploration of the utility of the Nursing Process and the Clinical Reasoning Cycle as a framework for forensic mental health nurses: A qualitative study.

Forensic mental health nursing (FMHN) is a specialized field, designed to meet the needs of people who have a serious mental illness across the criminal justice system, inpatient services and community. Frameworks can assist assessment, planning, intervention, documentation and evaluation of nursing care. However, there is no prior research investigating frameworks in FMHN. This study aimed to explore the Nursing Process (NP) and the Clinical Reasoning Cycle (CRC), with nurses, to determine a suitable framework for use service-wide. A Nominal Group Technique was used to facilitate exploration of the two frameworks, where open-ended verbal and written responses collected from a Nominal Group were thematically analysed, and the participants voted on their preferred framework. Seventeen nurses from a state-wide forensic mental health (FMH) service participated. The four main themes were as follows: challenges to current practice, limitations of the NP, perceived benefits of the CRC and addressing implementation. Consensus was reached with the nurses selecting the CRC as the framework of choice. This is the first study to explore frameworks to guide practice in FMHN. Nurses in this study considered the CRC to be a suitable framework for novice through to expert, offering a contemporary framework to guide nursing care in complex FMH settings. Some adjustments to the existing cycle were suggested to emphasize recovery-oriented practice, and inclusion of family and carers. Any changes to the cycle warrant exploration with the interdisciplinary team and consumer carer workforce.

Graph model under grey and unknown preferences for resolving conflicts on discharging Fukushima nuclear wastewater into the ocean

Ten years after the Fukushima nuclear accident, on April 13, 2021, the Japanese government announced the discharge of nuclear wastewater into the Pacific Ocean starting from the late 2022 or early 2023. The implementation of this decision would harm the local and global marine ecology because of the radioactive substances in the nuclear wastewater, which has also triggered strong opposition from the Japanese people and neighboring countries. The discharge of nuclear wastewater is a complicated process. In the context of the COVID-19, options of decision makers (DMs) must take into consideration existing global uncertainty, potentially leading to a slew of uncertain outcomes. A novel grey and unknown preference framework of the graph model for conflict resolution (GMCR) was proposed in this study to resolve the conflict of discharging nuclear wastewater in the ocean. Four forms of unknown preferences were defined for different grey stability definitions fully considering consider the possibility of choosing unknown results by DMs and increasing the diversity of equilibrium results. The equilibrium results the other alternative methods to discharge or continue to store nuclear waste are more conducive to the development of various DMs, and the consultation and cooperation of all DMs would ensure effective conflict resolution.

Self-Reported Preferences for Help-Seeking and Barriers to Using Mental Health Supports Among Internal Medicine Residents: Exploratory Use of an Econometric Best-Worst Scaling Framework for Gathering Physician Wellness Preferences.

BackgroundBurnout interventions are limited by low use. Understanding resident physician preferences for burnout interventions may increase utilization and improve the assessment of these interventions.ObjectiveThis study aims to use an econometric best-worst scaling (BWS) framework to survey internal medicine resident physicians to establish help-seeking preferences for burnout and barriers to using wellness supports by quantifying selections for 7 wellness support options and 7 barriers.MethodsInternal medicine resident physicians at our institution completed an anonymous web-based BWS survey during the 2020-2021 academic year. This cross-sectional study was analyzed with multinomial logistic regression and latent class modeling to determine the relative rank ordering of factors for seeking support for burnout and barriers to using wellness supports. Analysis of variance with Tukey honest significant difference posthoc test was used to analyze differences in mean utility scores representing choice for barriers and support options.ResultsOf the 163 invited residents, 77 (47.2% response rate) completed the survey. Top-ranking factors for seeking wellness supports included seeking informal peer support (best: 71%; worst: 0.6%) and support from friends and family (best: 70%; worst: 1.6%). Top-ranking barriers to seeking counseling included time (best: 75%; worst: 5%) and money (best: 35%; worst: 21%).ConclusionsOverall, our findings suggest that low utilization of formal mental health support is reflective of resident preferences to seek help informally and that increasing utilization will require addressing pragmatic barriers of time and cost. Assessing physician preferences for wellness-related initiatives may contribute to understanding the low utilization of formal mental health services among physicians, which can be determined using a BWS framework.

A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes

Skew-Gaussian Processes (SkewGPs) extend the multivariate Unified Skew-Normal distributions over finite dimensional vectors to distribution over functions. SkewGPs are more general and flexible than Gaussian processes, as SkewGPs may also represent asymmetric distributions. In a recent contribution, we showed that SkewGP and probit likelihood are conjugate, which allows us to compute the exact posterior for non-parametric binary classification and preference learning. In this paper, we generalize previous results and we prove that SkewGP is conjugate with both the normal and affine probit likelihood, and more in general, with their product. This allows us to (i) handle classification, preference, numeric and ordinal regression, and mixed problems in a unified framework; (ii) derive closed-form expression for the corresponding posterior distributions. We show empirically that the proposed framework based on SkewGP provides better performance than Gaussian processes in active learning and Bayesian (constrained) optimization. These two tasks are fundamental for design of experiments and in Data Science.

Fast Bounded Suboptimal Probabilistic Planning with Clear Preferences on Missing Information

In the real-world, robots must often plan despite the environment being partially known. This frequently necessitates planning under uncertainty over missing information about the environment. Unfortunately, the computational expense of such planning often precludes its scalability to real-world problems. The Probabilistic Planning with Clear Preferences (PPCP) framework focuses on a specific subset of such planning problems wherein there exist clear preferences over the actual values of missing information (Likhachev and Stenz 2009). PPCP exploits the existence and knowledge of these preferences to perform provably optimal planning via a series of deterministic A*-like searches over particular instantiations of the environment. Such decomposition leads to much better scalability with respect to both the size of a problem and the amount of missing information in it. The run-time of PPCP however is a function of the number of searches it has to run until convergence. In this paper, we make a key observation that the number of searches PPCP has to run can be dramatically decreased if each search computes a plan that minimizes the amount of missing information it relies upon. To that end, we introduce Fast-PPCP, a novel planning algorithm that computes a provably bounded suboptimal policy using significantly lesser number of searches than that required to find an optimal policy. We present Fast-PPCP with its theoretical analysis, compare with common alternative approaches to planning under uncertainty over missing information, and experimentally show that Fast-PPCP provides substantial gain in runtime over other approaches while incurring little loss in solution quality.

The effects of acute and chronic stress on choice consistency

Important decisions are often made under some degree of stress. It is now well-established that acute stress affects preferences and priorities in our decisions. However, it is hard to make a general case on the net impact of stress on decision-making quality in a normative sense as evidence for or against a direct effect of stress on decision-making quality is sparse. Here, we used the revealed preference framework of choice consistency to investigate decision-making quality without the assumption of an objectively correct choice. Specifically, we tested whether acute stress influences choice consistency in a time dependent fashion. A sample of 144 participants solved a food choice task before, immediately after and in the aftermath of the Trier Social Stress Test (TSST) or a matched control procedure. We confirmed the effectiveness of our stress manipulation via an array of subjective and physiological stress measures. Using Bayesian statistics, we found strong evidence against an effect of acute stress on choice consistency. However, we found exploratory evidence for a negative association of self-reported chronic stress and choice consistency. We discuss our results in the context of previous findings of stress effects on choice consistency and preference changes.