Ideal Scenario Research Articles

The Process of The Transfer of Hate Speech to Demonization and Social Polarization

We are living through a time of major political changes due to the rise of populist leaders and the resurgence of extreme ideological movements. The emergence of this phenomenon is due, to a large extent, to the ease with which these political actors can disseminate and spread their messages without any limits through social networks, leaving aside the former “fourth power” of the media as filterers and reinterpreters of information. Generally, the formula used by these leaders and movements is usually based on symbolic social division and polarization through hate speeches that allow demonizing their adversaries while antagonizing the issuers: a discursive “us” against “them” based on verbal violence to dehumanize an “exogroup.” We want to discuss the importance of understanding the process of communicational transfer—which begins with hate speech and evolves into demonization and social polarization—as a strategic basis for creating an ideal scenario for the growth and strengthening of populist discourse, which is reductionist and simplifying in nature.

A bioinspired stiffness tunable sucker for passive adaptation and firm attachment to angular substrates.

The ability to adapt and conform to angular and uneven surfaces improves the suction cup's performance in grasping and manipulation. However, in most cases, the adaptation costs lack of required stiffness for manipulation after surface attachment; thus, the ideal scenario is to have compliance during adaptation and stiffness after attachment to the surface. Inspired by the capability of stiffness regulation in octopus suction cup, this article presents a suction cup that adapts to steep angular surfaces due to compliance and has high stiffness after attachment. In this design, the stiffness after attachment is provided by using granular jamming as vacuum driven stiffness modulation. Thus, the design is composed of a conventional active suction pad connected to a granular stalk, emulating a hinge behavior during adaptation and creating high stiffness by jamming granular particles driven by the same vacuum as the suction pad. During the experiment, the suction cup can adapt to angles up to 85° with a force lower than 0.5N. We also investigated the effect of granular stalk's length on the adaptation and how this design performs compared to passive adaptation without stiffness modulation.

Autonomous Single-Molecule Manipulation Based on Reinforcement Learning.

Building nanostructures one-by-one requires precise control of single molecules over many manipulation steps. The ideal scenario for machine learning algorithms is complex, repetitive, and time-consuming. Here, we show a reinforcement learning algorithm that learns how to control a single dipolar molecule in the electric field of a scanning tunneling microscope. Using about 2250 iterations to train, the algorithm learned to manipulate the molecule toward specific positions on the surface. Simultaneously, it generates physical insights into the movement as well as orientation of the molecule, based on the position where the electric field is applied relative to the molecule. This reveals that molecular movement is strongly inhibited in some directions, and the torque is not symmetric around the dipole moment.

Electrodialysis Processes an Answer to Industrial Sustainability: Toward the Concept of Eco-Circular Economy?-A Review.

Wastewater and by-product treatments are substantial issues with consequences for our society, both in terms of environmental impacts and economic losses. With an overall global objective of sustainable development, it is essential to offer eco-efficient and circular solutions. Indeed, one of the major solutions to limit the use of new raw materials and the production of wastes is the transition toward a circular economy. Industries must find ways to close their production loops. Electrodialysis (ED) processes such as conventional ED, selective ED, ED with bipolar membranes, and ED with filtration membranes are processes that have demonstrated, in the past decades and recently, their potential and eco-efficiency. This review presents the most recent valorization opportunities among different industrial sectors (water, food, mining, chemistry, etc.) to manage waste or by-product resources through electrodialysis processes and to improve global industrial sustainability by moving toward circular processes. The limitations of existing studies are raised, especially concerning eco-efficiency. Indeed, electrodialysis processes can be optimized to decrease energy consumption and costs, and to increase efficiency; however, eco-efficiency scores should be determined to compare electrodialysis with conventional processes and support their advantages. The review shows the high potential of the different types of electrodialysis processes to treat wastewaters and liquid by-products in order to add value or to generate new raw materials. It also highlights the strong interest in using eco-efficient processes within a circular economy. The ideal scenario for sustainable development would be to make a transition toward an eco-circular economy.

Feasibility of randomized controlled trials and long-term implementation of interventions: Insights from a qualitative process evaluation of the PEDAL trial.

A multi-site randomized controlled trial was carried out between 2015 and 2019 to evaluate the impacts on quality of life of an intradialytic exercise programme for people living with chronic kidney disease. This included a qualitative process evaluation which gave valuable insights in relation to feasibility of the trial and of the intervention in the long-term. These can inform future clinical Trial design and evaluation studies. A constructivist phenomenological approach underpinned face-to-face, semi-structured interviews. Purposive recruitment ensured inclusion of participants in different arms of the PEDAL Trial, providers with different roles and trial team members from seven Renal Units in five study regions. Following ethical review, those willing took part in one interview in the Renal Unit. Audio-recorded interviews were transcribed (intelligent verbatim) and inductively thematically analyzed. Participants (n = 65) (Intervention arm: 26% completed; 13% who did not; Usual care arm: 13%; 46% women; 54% men; mean age 60 year) and providers (n = 39) were interviewed (23% PEDAL Trial team members). Three themes emerged: (1) Implementing the Intervention; (2) Implementing the trial; and (3) Engagement of the clinical team. Explanatory theory named "the Ideal Scenario" was developed, illustrating complex interactions between different aspects of intervention and trial implementation with the clinical context. This describes characteristics likely to optimize trial feasibility and intervention sustainability in the long-term. Key aspects of this relate to careful integration of the trial within the clinical context to optimize promotion of the trial in the short-term and engagement and ownership in the long-term. Strong leadership in both the clinical and trial teams is crucial to ensure a proactive and empowering culture. Novel explanatory theory is proposed with relevance for Implementation Science. The "Ideal Scenario" is provided to guide trialists in pre-emptive and ongoing risk analysis relating to trial feasibility and long-term intervention implementation. Alternative study designs should be explored to minimize the research-to-practice gap and optimize the likelihood of informative findings and long-term implementation. These might include Realist Randomized Controlled Trials and Hybrid Effectiveness-Implementation studies.

Detection of High Level of Co-Infection and the Emergence of Novel SARS CoV-2 Delta-Omicron and Omicron-Omicron Recombinants in the Epidemiological Surveillance of Andalusia.

Recombination is an evolutionary strategy to quickly acquire new viral properties inherited from the parental lineages. The systematic survey of the SARS-CoV-2 genome sequences of the Andalusian genomic surveillance strategy has allowed the detection of an unexpectedly high number of co-infections, which constitute the ideal scenario for the emergence of new recombinants. Whole genome sequence of SARS-CoV-2 has been carried out as part of the genomic surveillance programme. Sample sources included the main hospitals in the Andalusia region. In addition to the increase of co-infections and known recombinants, three novel SARS-CoV-2 delta-omicron and omicron-omicron recombinant variants with two break points have been detected. Our observations document an epidemiological scenario in which co-infection and recombination are detected more frequently. Finally, we describe a family case in which co-infection is followed by the detection of a recombinant made from the two co-infecting variants. This increased number of recombinants raises the risk of emergence of recombinant variants with increased transmissibility and pathogenicity.

EDITORIAL INTRODUCTION

Healthcare providers are formed to serve people and put people’s needs at the center. They are expected to inform their patients as experts in health problems and health risk factors. During the clinical encounter they must integrate their knowledge with the unique characteristics, feelings, needs, and life circumstances of the person seeking their help. While this is the ideal scenario, the pandemic erased this script completely. Everything had to be learned by everyone without an expert guide. What are the risk factors, are mouth masks necessary, should we close schools, borders, or do we apply watchful waiting? How can we define cases, diagnose people, and treat them if they become ill? What do we do with other chronic diseases, health problems, or preventive services? Which resources do we need, including human resources (student trainees, retired healthcare workers, volunteers)? What was the view of healthcare providers on their duty to work during this health emergency? The latter question is answered by Muñoz et al in their paper in this issue of the IJPCM for nursing students in Chile and Spain [1].

Deep Reinforcement Learning Evolution Algorithm for Dynamic Antenna Control in Multi-Cell Configuration HAPS System

In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the winds, the throughput of the users might decrease. Therefore, we propose a method that can dynamically adjust the antenna parameters based on the throughput of the users in the coverage area to reduce the number of low-throughput users by improving the users’ throughput. Different from other model-based reinforcement learning methods, such as the Deep Q Network (DQN), the proposed method combines the Evolution Algorithm (EA) with Reinforcement Learning (RL) to avoid the sub-optimal solutions in each state. Moreover, we consider non-uniform user distribution scenarios, which are common in the real world, rather than ideal uniform user distribution scenarios. To evaluate the proposed method, we do the simulations under four different real user distribution scenarios and compare the proposed method with the conventional EA and RL methods. The simulation results show that the proposed method effectively reduces the number of low throughput users after the HAPS moves.

The Future of Digital Publishing

Peer review has long been held as the gold standard for article evaluation. At its simplest, the goal of peer review is to ensure that a published article in a journal has been appropriately vetted by qualified scholars. Traditional models require that this process is either single- or double-blind; the editor assigns reviewers based on subject expertise and either/or reviewer and author names are hidden. In an ideal scenario, this fosters open and unbiased commentary, but attempts at evaluation and rigor can soon become gatekeeping and exclusion. The reality of peer review is often fraught with issues, including biases toward race, gender, and language proficiency.

Application of classical and advanced machine learning models to predict personality on social media

Knowing personality traits and how people tend to think, feel and behave has been always an appealing and studied topic. This interest together with the vast amount of data generated every day on social networks present an ideal scenario to address this problem. By properly processing this data, it could be useful for many aspects of people’s daily life. In this study, we applied different Machine Learning methods to solve this problem using a dataset labelled with the MBTI personalities, and we compared several algorithms such as Naïve Bayes, Logistic Regression and three different Artificial Neural Networks. Two main experiments were conducted. First, a clustering-oriented solution. Second, a classification approach. The latter turned out to outperform the clustering methods. On average, our models achieved around 90% accuracy. Finally, in order to show an example of our solution, we will validate our model with the latest news about COVID-19 and the La Palma Volcano.

Rhetoric in the discourse of human rights

The constitutional amendment of human rights, according to specialised jurists, is the most important innovation that the Political Constitution of the United Mexican States of 1917 has undergone in its more than 100 years of existence. While it is true that numerous studies have examined in depth the impact of this legislative transformation on national legal life, it is even more true that little or nothing has been discussed in relation to the rhetorical discourse emanating from it. Following Aristotle, rhetoric must be oriented towards convincing through reasons and not merely persuading by manipulating the emotions of the audience, as it was used in antiquity by the sophists, considering it as an argumentative skill and an art of suggestion, which has always had an undeniable usefulness. Through rhetoric, discourses are created, for example, that of human rights, which is widely used in the field of professional politics and as a practical tool should be used to express the truth, However, its use can also be seen on the contrary, that is, the creation of sophistries that carry with them power interests, as is the case here. The discourse of human rights in Mexico has become an accumulation of political dissertations that attempt to demonstrate a utopian reality, not innocently, nor much less involuntarily, since those who control these messages create hypothetical ideal scenarios and impose their will, protecting hegemonic interests and logics, They turn them into demagogy, in order to achieve public acceptance and affinity with the political project to which they are aligned, through false promises about the effective application of these rights, appealing to the emotions and false expectations of citizens.

International migrant flows: Coalition formation among countries and social welfare

In this paper, we consider policy interventions for international migrant flows and quantify their ramifications. In particular, we further develop a recent equilibrium model of international human migration in which some of the destination countries form coalitions to establish a common upper bound on the migratory flows that they agree to accept jointly. We also consider here a scenario where some countries can leave or join an initial coalition and investigate the problem of finding the coalitions that maximize the overall social welfare. Moreover, we compare the social welfare at equilibrium with the one that a supranational organization might suggest in an ideal scenario. This research adds to the literature on the development of mathematical models to address pressing issues associated with problems of human migration with insights for policy and decision-makers.

Scenario Prediction of Carbon Peak in Fujian Electric Power Industry Based on STIRPAT Model

The power industry plays a crucial role in achieving the carbon reduction objectives and facilitating the transition towards a low-carbon economy and society. This study employed the IPCC carbon emission coefficient method to calculate the carbon emissions of the power industry in Fujian Province from 2001 to 2021. To predict the carbon emissions of the power industry in Fujian Province from 2022 to 2030, this article established a STIRPAT model based on ridge regression. Empirical research was carried out in this study to investigate the timing of carbon peaking and peak carbon emissions in the power industry of Fujian Province, considering various scenarios. The calculation of carbon emissions indicates that the overall carbon emissions in the electricity industry in Fujian Province showed an upward trend from 2001 to 2021. By 2021, the emissions reached 9.646×107 tons, and the carbon emissions peak has not been reached. Scenario simulation analysis shows that under the energy-saving scenario, the electricity industry in Fujian Province is projected to reach its carbon emissions peak in 2025, with a peak value of 9.687×107 tons. However, in the baseline and ideal scenarios, the carbon emissions in the electricity industry in Fujian Province are projected to not peak before 2030. By 2030, the emissions are estimated to be 9.853×107 tons and 1.067×108 tons, respectively. The article concludes by presenting a comprehensive analysis of the most effective approach towards achieving carbon peaking in the power industry within Fujian Province. This is accomplished by examining the issue from various angles, including government planning, power generation structure, industrial structure, and public awareness.

A DRL Strategy for Optimal Resource Allocation Along With 3D Trajectory Dynamics in UAV-MEC Network

Advances in Unmanned Air Vehicle (UAV) technology have paved a way for numerous configurations and applications in communication systems. However, UAV dynamics play an important role in determining its effective use. In this article, while considering UAV dynamics, we evaluate the performance of a UAV equipped with a Mobile-Edge Computing (MEC) server that provides services to End-user Devices (EuDs). The EuDs due to their limited energy resources offload a portion of their computational task to nearby MEC-based UAV. To this end, we jointly optimize the computational cost and 3D UAV placement along with resource allocation subject to the network, communication, and environment constraints. A Deep Reinforcement Learning (DRL) technique based on a continuous action space approach, namely Deep Deterministic Policy Gradient (DDPG) is utilized. By exploiting DDPG, we propose an optimization strategy to obtain an optimal offloading policy in the presence of UAV dynamics, which is not considered in earlier studies. The proposed strategy can be classified into three cases namely; training through an ideal scenario, training through error dynamics, and training through extreme values. We compared the performance of these individual cases based on cost percentage and concluded that case II (training through error dynamics) achieves minimum cost i.e., 37.75 %, whereas case I and case III settles at 67.25% and 67.50% respectively. Numerical simulations are performed, and extensive results are obtained which shows that the advanced DDPG based algorithm along with error dynamic protocol is able to converge to near optimum. To validate the efficacy of the proposed algorithm, a comparison with state-of-the-art Deep Q-Network (DQN) is carried out, which shows that our algorithm has significant improvements.

How Deep Neural Networks Understand Motion? Toward Interpretable Motion Modeling by Leveraging the Relative Change in Position

Motion understanding plays an important role in video-based cross-media analysis and multiple knowledge representation learning. This paper discusses physical motion recognition and prediction by deep neural networks (DNNs), such as convolutional neural networks and recurrent neural networks. In physics, motion is the relative change in position with respect to time. To ablate the moving object and the background where the motion happens, we focus on an ideal scenario where a point moves in a plane. As the first contribution, we evaluate a few popular DNN architectures from video research on the relative position change modeling. Experiment results and conclusions can be insightful in action recognition and video prediction. As the second contribution, we propose a vector network (VecNet) to model the relative change in position. VecNet considers the motion in a short interval as a vector. Meanwhile, VecNet can move a point to the corresponding position given a vector representation. To obtain the representation of the motion for a long time, we use a long short-term memory (LSTM) to aggregate or predict vector representations over time. The resulting VecNet+LSTM approach is able to effectively support both recognition and prediction, proving that modeling relative position change is necessary for motion recognition and makes motion prediction easier.

Job Shop Scheduling with Limited-Capacity Buffers using Constraint Programming and Genetic Algorithms

This article aims to propose a new approach for solving production planning and scheduling in the process industries, in such a way to be adaptable to any manufacturing plant and exploring the use of innovative AI-style technologies. The main contributions of the work are: (i) the design of a specific data format to describe any manufacturing plant (including resources, layout and production recipes), being the input of the method; and (ii) the consideration of limited-capacity production lines with intermediate and final buffers in the optimization. The method involves two stages: the first one corresponds to a deterministic optimization algorithm based on Constraint Programming modelling to solve the JSSP in an ideal scenario with no storage limitation; while the second one is a Genetic Algorithm that only comes into play when the solutions obtained from the first one are infeasible for the available storage, so it is a complementary layer to try to solve the mismatches stochastically.

Transdermal opioids for acute postoperative pain: A road less travelled!

Dear Editor, Transdermal delivery system (TDS) is a simple, reliable, noninvasive analgesic delivery method that has been used extensively for chronic pain management. After application, drug in TDS forms a depot in the upper layers of skin that slowly increases serum concentration.[4] Both fentanyl and buprenorphine have high lipid solubility and low molecular weight which makes them ideal candidates for a TDS. The perceived advantages of TDS for acute postoperative pain include sustained blood level of the drug and reduced side effects. Transdermal buprenorphine (TDB) provides convenient once-weekly dosing, has ceiling effect for respiratory, lower abuse potential, and does not require dose alterations patients with renal dysfunction, unlike fentanyl (full μ-opioid receptor agonist).[1] Khandelwal et al. compared two doses of buprenorphine (10 μgh-1 and 20 μgh-1) with fentanyl (25 μgh-1) in adult patients undergoing lower limb arthroscopic surgery.[2] They found TDB patch (20 μgh-1) to be an effective postoperative analgesic without any significant undesirable effects. The concentration of TDS drugs tends to be higher in the first 24 h and several factors like variability in body temperature, factors that increase cutaneous blood flow (warming blanket, sepsis or regional anesthesia), previous opioid use, hemodynamics, and patient’s general condition may make TDS opioid absorption unpredictable.[3] There have been reports of serious, life-threatening respiratory depression and death following transdermal fentanyl.[4] Buprenorphine is 60 times while fentanyl is 100 times as potent as morphine and the duration of action for fentanyl (3d) and TBD (7d) is different. So, equianalgesic doses of both[ratio 3:5]should be compared but the authors in the study have used a non-equianalgesic dosage (10:25 and 20:25). The peak effect comes in 12 h (fentanyl) to 24 h (buprenorphine). So, the patches are generally applied a day before surgery, but this may not be the ideal scenario in daycare settings. Alternative analgesics should be started till the effect of TDS drugs comes in 12–24 h, whereas in the index study, the usual analgesics were withheld the day before surgery when the patches were applied.[2] Many dose formulations are available and according to recommendations from the manufacturer, opioid-naive patients should initially receive the lowest strength patch.[5] Authors of present study evaluating the use of TDS in postoperative pain management concluded that further prospective randomized studies are required to conclusively find the optimum dose.[2] TDS are amenable to abuse, serious side effects like respiratory depression, and cognitive dysfunction, may increase duration of hospital stay and overall cost of daycare procedures. Despite their success for chronic pain management, TDS opioids for acute postoperative pain is a new and off-label use and should be undertaken if the benefits outweigh the risks involved. The main rebuke for TDS is its lack of titrability, long latency for onset, and slow offset making it an inferior alternative compared to other commonly used modes of analgesic delivery such as patient-controlled analgesia. Moreover, they are an expensive alternative to parenteral and oral drugs and may not be ideal in our setting. Hence, we would urge caution in the indiscriminate usage of TDS as a sole modality of postoperative analgesia until further robust evidence proves the contrary. Whenever chosen, they should be used in monitored settings for opioid naïve patients. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

Global disease burden attributed to low physical activity in 204 countries and territories from 1990 to 2019: Insights from the Global Burden of Disease 2019 Study.

The purpose of this investigation is to estimate the global disease burden attributable to low physical activity (PA) in 204 countries and territories from 1990 to 2019 by age, sex, and Socio-Demographic Index (SDI). Detailed information on global deaths and disability-adjusted life years (DALYs) attributable to low PA were collected from the Global Burden of Disease Study 2019. The ideal exposure scenario of PA was defined as 3000-4500 metabolic equivalent minutes per week and low PA was considered to be less than this threshold. Age-standardization was used to improve the comparison of rates across locations or between time periods. In 2019, low PA seems to contribute to 0.83 million [95% uncertainty interval (UI) 0.43 to 1.47] deaths and 15.75 million (95% UI 8.52 to 28.62) DALYs globally, an increase of 83.9% (95% UI 69.3 to 105.7) and 82.9% (95% UI 65.5 to 112.1) since 1990, respectively. The age-standardized rates of low-PA-related deaths and DALYs per 100,000 people in 2019 were 11.1 (95% UI 5.7 to 19.5) and 198.4 (95% UI 108.2 to 360.3), respectively. Of all age-standardized DALYs globally in 2019, 0.6% (95% UI 0.3 to 1.1) may be attributable to low PA. The association between SDI and the proportion of age-standardized DALYs attributable to low PA suggests that regions with the highest SDI largely decreased their proportions of age-standardized DALYs attributable to low PA during 1990-2019, while other regions tended to have increased proportions in the same timeframe. In 2019, the rates of low-PA-related deaths and DALYs tended to rise with increasing age in both sexes, with no differences between males and females in the age-standardized rates. An insufficient accumulation of PA across the globe occurs together with a considerable public health burden. Health initiatives to promote PA within different age groups and countries are urgently needed.

S-53-5: BUILDING A MONOLITH OF CARDIOVASCULAR CARE - THE MEDICAL DX AND PATIENT WELL-BEING

Sustainable development is the indispensable challenge to make the world a better place. More specifically, sustainable development is essential for transforming our world better and better by ensuring that all human beings can enjoy their lives by harmonizing economic, social, and technological progress with nature. Simultaneously, it has been emphasized that no one should not be left behind over this action. To bold and transformative achievement, the 17 Sustainable Development Goals (SDGs) and 169 targets have been globally announced and stimulate any activity by 2030. Healthy lives are one of the essential SDGs, but how are we doctors to dedicate ourselves to achieving the SDGs via our practices? How do we commit to making the world a better place? In this opportunity, focusing on hypertension practice, I would like to discuss the possible and ideal scenario for the medical SDGs by mentioning not only e-health/ICT but also new enlightenment of the medical approach by reflecting on every concern and issue in the current practice that lacks the inclusive, sustainable, and well-being support for patient and doctor relationship.

The Medical Metaverse, Part 1: Introduction, Definitions, and New Horizons for Neuropsychiatry.

The Medical Metaverse, Part 1: Introduction, Definitions, and New Horizons for Neuropsychiatry.