Complex Problems Research Articles

Etiology of Acute Febrile Illnesses in Adults in the Defense Community in French Guiana.

In tropical countries, acute febrile illnesses represent a complex clinical problem for general practitioners. We describe the prevalence of different etiologies of acute febrile illnesses occurring among French service members and their families, excluding children, in general practice in French Guiana. From June 2017 to March 2020, patients with a fever ≥ 37.8°C with a duration of less than 15 days who sought medical care at the army medical centers in Cayenne and Kourou were prospectively enrolled. Based on clinical presentation, blood, urine, nasopharyngeal, and stool samples were collected for diagnostic testing for viruses, bacteria, and parasites (by direct examination, microscopic examination of blood smears, culture, serology, or polymerase chain reaction), and standardized biological tests were systematically performed. Among 175 patients retained for analysis, fever with nonspecific symptoms was predominant (46.9%), with 10 Plasmodium vivax malaria cases, 8 dengue infections, and 6 cases of Q fever. The second most frequent cause of acute febrile illness was upper respiratory tract infections (32.0%) due to influenza virus (n = 18) or human rhinovirus (n = 10). Among the causes of acute febrile illness in French Guiana, clinicians should first consider arboviruses and malaria, as well as Q fever in cases of elevated C-reactive protein with nonspecific symptoms and influenza in cases of signs and symptoms associated with upper respiratory tract infections. Despite an expanded microbiological search, the etiology of 51.4% of acute febrile illnesses remain unknown. Further investigations will be necessary to identify the etiology of acute febrile illnesses, including new pathogens, in French Guiana.

Golden jackal optimization for economic load dispatch problems with complex constraints

This research paper uses the golden jackal optimization (GJO), a novel meta-heuristic algorithm, to address power system economic load dispatch (ELD) problems. The GJO emulates the hunting behavior of golden jackals. GJO algorithm uses the cooperative attacking behavior of golden jackals to tackle complicated optimization problems efficaciously. The objective of ELD problem is to distribute power system load requirement to the different generators with a minimum total fuel cost of generation. ELD problems are highly complex, non-linear, and non-convex optimization problems while considering constraints namely valve point loading effect (VPL) and prohibited operating zones (POZs). The proposed GJO algorithm is applied to solve complex, non-linear, and non-convex ELD problems. Six different test systems having 6, 10, 13, 40, and 140 generators with various constraints are used to validate the usefulness of the suggested GJO method. Simulation outcomes of the test system are compared with various algorithms reported in the algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), and backtracking search algorithm (BSA). Results show that the proposed GJO algorithm produces minimal fuel cost and has good convergence in solving ELD problems of power system engineering.

To Reach Flourishing, Turn Right Sharply at the Next Corner

Modern humans are failing to attain their existential potential, expressed as flourishing. Simultaneously, megaproblems like climate change have arisen as unintended consequences of normal societal behaviors. Both failings lie at the deepest root of modern culture, the way we think we think, and consequently choose our norms and design our cultural institutions. The paper explicates a transformative model of cognition, recently developed by Iain McGilchrist, that raises the possibility of flourishing. According to the model, each brain hemisphere attends to the world differently and, thus, produces two distinctive actors. When the right is master, actions reflect the immediate external world, that is, they care for it. When the left controls, actions reflect a virtual world of facts and theories that have been abstracted from experience and stored in the left hemisphere. Its actions are instrumental and treat the real world as a resource. The ills and failings of modernity spring from an excess of left-brain domination. Flourishing, as the desired state of human and natural affairs, requires rejuvenating the right side as master. Some practical approaches, for example, mindfulness exercises, are offered. Pragmatic inquiry, importantly, should become the primary method for dealing with such large, complex problems.

Complexity Column

Descriptive complexity theory initiated by celebrated Fagin's theorem aims to characterize complexity classes by the kind of logic necessary to express problems in the class. For many complexity classes such a characterization has been found; this includes the class NP that, according to Fagin's theorem, can be characterized by the monadic second order logic. Some classes, however, eludes such a characterization, and this famously includes the class P. Due to the importance of the class P, finding a logic for P is one of the central open problems in descriptive complexity.

Proposal of a similarity measure for unified modeling language class diagram images using convolutional neural network

The unified modeling language (UML) represents an essential tool for modeling and visualizing software systems. UML diagrams provide a graphical representation of a system's components. Comparing and processing these diagrams, for instance, can be complicated, especially as software projects grow in size and complexity. In such contexts, deep learning techniques have emerged as a promising solution for solving complex problems. One of these crucial problems is the measurement of similarity between images, making it possible to compare and calculate the differences between two given diagrams. The present work intends to build a method for calculating the degree of similarity between two UML class diagrams. With a goal to provide teachers a helpful tool for assessing students' UML class diagrams.

Obesity and Aging: Optimizing Health at the Intersection of Ageism and Weight Bias

Obesity is a chronic and complex disease that can negatively impact health, functioning and quality of life. In the past decade, obesity's largest prevalence growth occurred in older adults, particularly among racial and ethnic minorities. By the year 2030, an estimated 50% of the population over 65 is likely to be living with obesity. Until recently, little attention has been given to obesity as a chronic disease, and those living with obesity have encountered challenges accessing high quality medical care. Despite growing evidence for bariatric and obesity medicine, these treatments remain inaccessible for most patients, especially for older adults. Obesity research focused on older adults has been very limited and ideal weight standards for older adults are more controversial, especially in later life where frailty and sarcopenia may develop. As concerns about the public health effects of obesity have risen in the U.S., so too have negative and stigmatized views of individuals with obesity. Weight stigma has serious negative emotional and physical health consequences including low self-esteem, negative body image, engagement in disordered eating patterns, reduced physical activity and may promote weight gain. Older adults with obesity may encounter the double stigma of both ageism and weight bias, which are two of the most socially acceptable forms of bias and discrimination in the U.S. This interactive session will focus on three areas related to obesity and older adults. Through case examples, small and large group discussions, the presenters will: i) explore potential weight bias amongst participants and discuss strategies to mitigate this bias in working with older adults; ii) discuss complex connections between obesity and frailty syndrome in older adults, and the risks associated with weight loss in later life; iii) introduce evidence-based assessment and treatment recommendations for older adults interested in obesity treatment. This session will focus on the role of psychiatrists and other mental health clinicians in the care of older patients with co-morbid obesity and mental health disorders. Also, presenters will discuss the importance of increasing awareness, advocacy, and practical strategies to combat the impact of weight bias and ageism in healthcare settings. Ultimately, the goal is to raise awareness of this complex problem and provide clinically helpful information to improve care for older patients with obesity. This symposium is sponsored by the AAGP's IDEA committee.

Graduate competencies based on the aspect of organizational activity in industry 4.0

<span lang="EN-US">Industrial revolution 4.0 has an impact, namely the demand for the labor market has changed, where the automation revolution replaces work that is usually capable of being done by humans and poses the risk of termination of employment. The purpose of this study is to identify the competencies that must be possessed by graduates based on aspects of organizational activity according to the needs of the industry. The data in this study are in the form of literacy studies, data from direct interviews with the industry and questionnaires to a number of respondents. Testing the validity of the questionnaire using the product moment correlation formula, followed by reliability testing using the Cronbach alpha. Meanwhile, to determine the sample size in this study using the purposive sampling technique with a total of 75 respondents consisting of practitioners and graduates of the Faculty of Engineering, Universitas Negeri Jakarta (FT-UNJ), Indonesia. The research method is carried out with a qualitative descriptive method which is intended to obtain results that describe in detail the competencies that must be possessed by graduates based on aspects of organizational activity according to the needs of the industry. The results of the research related to the competence of graduates needed by industry 4.0 based on aspects of organizational activity, namely open minded, decision making abilities, emotional intelligence, cognitive flexibility, service orientation, coordinating with others, leadership skills, critical thinking and creativity skills, skills in technology, and complex problem solving.</span>

A Review on Possibilities of Artificial Intelligence in construction Industry

Abstract: At present if we see construction industries, they are facing lot of problems which finally create project delay. The progression of the construction industry is strictly limited by the countless complex challenges it faces such as cost and time overruns, health and safety, productivity and labour scarcities. Working on these complex problems of construction industries is not easy in many aspects but if these problems are countered with the help of AI results will be robust. Therefore, many research efforts in the Architecture, Engineering, and Construction community have recently tried introducing AI into building asset management processes. AI is also able to make the process of decision making faster, decrease error rates, and increase computational efficiency. Among the different AI techniques, machine learning, pattern recognition, and deep learning have recently acquired considerable attention and are establishing themselves as a new class of intelligent methods for use in structural engineering. A present review of existing literature on AI applications in the construction industry such as activity monitoring, risk management, resource and waste optimization was conducted. Additionally, the opportunities and challenges of AI applications in construction were identified and presented in this study. This study focuses on AI and its application in construction industry.

On the parameterized complexity of interval scheduling with eligible machine sets

We provide new parameterized complexity results for Interval Scheduling on Eligible Machines. In this problem, a set of n jobs is given to be processed non-preemptively on a set of m machines. Each job has a processing time, a deadline, a weight, and a set of eligible machines that can process it. The goal is to find a maximum weight subset of jobs that can each be processed on one of its eligible machines such that it completes exactly at its deadline. We focus on two parameters: The number m of machines, and the largest processing time pmax. Our main contribution is showing W[1]-hardness when parameterized by m. This answers Open Problem 8 of Mnich and van Bevern's list of 15 open problems in parameterized complexity of scheduling problems [Computers & Operations Research, 2018]. Furthermore, we show NP-hardness even when pmax=O(1) and present an FPT-algorithm with for the combined parameter m+pmax.

Can quantum computing be the solution to energy use in computation?

Today, humanity cannot advance without the help of computation. There are still many problems that need resolving that would never be solved without the help of a powerful computer. It is also impossible to build a large enough memory to allow us to solve these complex problems. However, even if we manage to achieve the impossible, with the unprecedented rise of data and the ever-evolving complexity of algorithms, computing the answers in the traditional way would require billions of MWs of power consumption. Not to mention the energy required to cool down the powerful computers from the significant amount of heat they produce. Humanity is quickly reaching the limits of computations that can be currently achieved, raising concerns about the sustainability and environmental impact these power-hungry machines require. Enter quantum computing – the revolutionary technology holding the promise of not only enhancing computational speeds and capabilities but also potentially alleviating the energy burden imposed by traditional computing methods.

A comparative study of machine learning-based regression models for supply chain management

The rise of machine learning technology has opened up unprecedented opportunities for the retail industry. Machine learning, as an essential branch of artificial intelligence, enables computers to improve their performance through continuous learning and experience. It has demonstrated its ability to handle large-scale data and complex problems effectively. In retail, machine learning predictions and methods can also lead to significant breakthroughs in supply chain management, helping businesses identify better ways to maintain economic stability and growth, which are crucial for improving people's living standards, eliminating poverty, promoting social stability, driving technological progress, and reducing inequality. This is achieved through different algorithmic regression methods, which can predict future trends and consumer behavior with high accuracy. Machine learning algorithms can analyze vast amounts of data to identify patterns and trends and make accurate predictions about future demand, product inventory levels, and other important factors that drive business success in the retail industry.

Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge

The problem of sample complexity of online reinforcement learning is often studied in the literature without taking into account any partial knowledge about the system dynamics that could potentially accelerate the learning process. In this paper, we study the sample complexity of online Q-learning methods when some prior knowledge about the dynamics is available or can be learned efficiently. We focus on systems that evolve according to an additive disturbance model of the form S_{h+1} = ƒ(S_h, A_h) + W_h, where ƒ represents the underlying system dynamics, and W_h are unknown disturbances independent of states and actions. In the setting of finite episodic Markov decision processes with S states, A actions, and episode length H, we present an optimistic Q-learning algorithm that achieves Õ(Poly(H)√T) regret under perfect knowledge of ƒ, where T is the total number of interactions with the system. This is in contrast to the typical Õ(Poly(H)√SAT) regret for existing Q-learning methods. Further, if only a noisy estimate ƒ_hat of ƒ is available, our method can learn an approximately optimal policy in a number of samples that is independent of the cardinalities of state and action spaces. The sub-optimality gap depends on the approximation error ƒ_hat − ƒ, as well as the Lipschitz constant of the corresponding optimal value function. Our approach does not require modeling of the transition probabilities and enjoys the same memory complexity as model-free methods.

Study of convergence speed of chaotic particle swarm optimization algorithm

AbstractThis study introduces the Chaotic Particle Swarm Optimization as an innovative variant of the traditional particle swarm optimization algorithm, addressing the issue of particle swarm optimization getting trapped in local minima with a low convergence characteristic during later iterations. Chaotic particle swarm optimization incorporates principles from chaos theory to enhance the swarm's exploration and exploitation capabilities. By introducing controlled chaotic behavior, particles exhibit more diverse and unpredictable movements in the search space, leading to improved global convergence and escape from local minima. The proposed method has been implemented and evaluated on benchmark problems to assess its effectiveness. The integration of chaos theory with particle swarm optimization offers promising opportunities for developing robust and efficient optimization techniques suitable for complex and dynamic problem domains in various real-world applications.

A Comparative Analysis of Student Perceptions of Recommendations for Engagement in Design Processes

Abstract Engineering designers are tasked with complex problems necessitating the use and development of various supports for navigating complexity. Prescriptive design process models are one such tool. However, little research has explored how engineering designers perceive these models' recommendations for engagement in design work. In this exploratory study, we analyzed data from individual semi-structured interviews with 18 mechanical engineering students to identify participant perceptions of design process models. As many design process model visualizations lack explicit attention to some social and contextual dimensions, we sought to compare perceptions among two models drawn from engineering texts and one model that was developed with the intent to emphasize social and contextual dimensions. We identified perceptions of the recommendations from the design process models related to starting and moving through a design process, gathering information, prototyping, evaluating or testing, and what they should consider. Participant perceptions across the three process models suggest different design process models make perceptions of certain recommendations more salient than others. However, participant perceptions also varied for the same process model. We suggest several implications for design education and training based on participant perceptions of the process models, particularly the importance of leveraging multiple design process models. The comprehensive descriptions of participant perceptions provide a foundation for further investigations bridging designers' perceptions to intent, behavior, and, ultimately, design outcomes.

DESIGN THINKING FOR THE DEVELOPMENT OF CREATIVE ABILITIES OF ELECTRICAL TECHNICIANS IN VOCATIONAL COLLEGES

Relevance it arises from the necessity to look for new ways and means of professional training of future specialists in the context of the renewal of the educational paradigm and the expansion of society's technological capabilities. The article reveals the urgency of the research topic, analyses existing approaches to the interpretation of the term design thinking. In the course of the scientific research, it was found that in the context of applying effective ways to form the professional competence of electrical technicians, the design thinking technology deserves attention, the use of which is a powerful effective tool for developing innovative thinking of students, what is extremely important in an era of rapid change, innovation and increased mobility in all spheres of life in the modern world. Aim: to determine the prospects for introducing design thinking technology to develop the creative abilities of future electrical engineers in vocational colleges. Methods: analysis and synthesis – to determine the stage of problem progress under study and the peculiarities of applying design thinking technology for the improvement of future electrical engineers' creative abilities; generalisation – to formulate conclusions and recommendations for the implementation of design thinking technology for the students' development in vocational colleges; diagnostic (testing, conversation, interview) - to find out the development level of future electrical engineers' design thinking; application of Pearson's ꭓ2 (chi-square) consistency criterion for statistical analysis of the experimental study results. Results: A system for developing design thinking in future electrical technicians within the framework of the course "Power Supply of Enterprises and Civil Engineering Structures" has been designed to increase the effectiveness of using a modern approach to designing innovative products and developing creative abilities. To develop this system, we identified and analysed the methodological principles that characterise the design thinking process. The highlighted stages of applying the design thinking method in the professional training of future electrical technicians are focused on solving a problem situation and getting out of it, the ability to generate ideas, change and improve their project based on the identified shortcomings, and develop skills in working with digital technologies. Conclusion: design thinking is a tool that can be used to project the future and find innovative ways to solve complex problems. It is a technology that meets all the challenges of the times, improves the quality of professional training, and contributes to the formation of the professional competence of future electrical engineers in professional pre-higher education institutions.

Online 3D behavioral tracking of aquatic model organism with a dual-camera system

Behavioral tracking system of aquatic model organism is crucial for applications in aquaculture, environment and biomedicine, as it facilitates human to monitor subject states by automatically recognizing individual identities, and quantify their movement trajectories. Previous research has been devoted to this topic, but they are still not simple and effective enough. Therefore, this work introduces a novel online monitoring system implemented by dual-camera equipment and software modules consisting of an object detector and a multi-view multi-target tracker. The tracker provides the abilities of cross-view matching, underwater 3D reconstruction, and 3D target tracking. Specifically, our solution adopts a new paradigm, called tracking by early-reconstruction, which prioritizes the 3D reconstruction of targets’ coordinates on a frame-by-frame basis and then tracks them directly in 3D space rather than in a 2D image plane. This paradigm simplifies the complex multi-view tracking problem into a series of local association procedures, allowing us to achieve an online resolution through the iterative approach. To verify the effectiveness of the system, we employ zebrafish as the research subject, and evaluate the accuracy and robustness of the system on tracking benchmark, behavioral tasks and simulated data. Finally, we conducted extensive experiments and demonstrated the efficiency and effectiveness of the proposed system.

Design with Simon's Inner and Outer Environments: Theoretical Foundations for Design Science Research Methods for Digital Science

Design science research has traditionally been applied to complex real-world problems to produce an artifact to address such problems. Although design science research efforts have been applied traditionally to business or related problems, there is a large set of problems in the area of digital science that also require important, digital artifacts. The digitalization of science has resulted in the need to develop essential, specialized, devices and software before it is feasible for scientists to carry out their work. This research examines digital science to identify its challenges and demonstrate how it can be possible to progress digital science with design science research, thereby establishing digital science as an important area of transdisciplinary inquiry. These areas of research are examined for their synergies and explained by positioning artifact development challenges with respect to Simon's inner and outer environments and the interface between them.

Tricuspid Regurgitation: A Review of Current Interventional Management.

Tricuspid regurgitation is a common yet clinically complex problem, traditionally managed with diuretic therapy with no observable mortality benefit. Older studies on surgical intervention observed poor outcomes; however, this clinical reasoning predates current surgical approaches and novel transcatheter technology. The tricuspid apparatus is a complex structure that poses a technical challenge for surgeons and interventional cardiologists. Recent advances in surgical techniques and transcatheter therapy, particularly edge-to-edge repair devices, have demonstrated promising results for safety, reduction in tricuspid regurgitation, and improving quality of life. We review the clinical, imaging, and hemodynamic findings that characterize patients who should be considered for intervention, alongside the rapidly evolving approaches to interventional management.

Can the role of a personal tutor contribute to reducing the undergraduate degree awarding gap for racially minoritised students?

AbstractThe imperative to address the complex problem of degree awarding gaps within UK higher education institutions is multifaceted and messy. Various studies have continually highlighted this persistent undergraduate awarding gap (racial equity gap or/ethnicity gap) between White and racially minoritised students. This disparity in educational outcomes, where racially minoritised students compared to their White counterparts are less likely to graduate with good honours degrees, has gained considerable attention from policymakers and scholars. This underscores the need for comprehensive strategies that transcend conventional approaches and boundaries to achieve systemic change. Efforts to close the awarding gap have predominantly focused on decolonising the curriculum, which is vital. Still, it is also crucial to recognise personal tutoring (PT) as a pivotal and often underestimated role in addressing the awarding gap. The PT role, with its potential to provide a joined‐up experience for students, promoting student engagement and enabling them to navigate the ever‐changing academic landscape, can be a catalyst for racial equity and an antidote to the degree awarding gap. By reviewing the literature, empirical studies, policy frameworks and practical implementations, this paper sheds light on the diverse ways PT can serve as a potent tool for promoting equity and inclusion and enhancing equitable outcomes for all. Through a synthesis of existing literature and critical analysis, this scoping review highlights ways PT in higher education is conceptualised nationally and internationally and how it can drive transformational change in higher education when aligned with principles of equity.

Analysis and Improvement of Geographic Information Systems for Problem Solving and Decision Making

The complexity of our way of life has increased due to the numerous parts of life developing quickly, which results in ongoing issues. To address these issues and respond to these rapid changes in the environment, solutions that were practical, quick, and easy to implement were needed. The use of geographic information tools, a contemporary innovation, allows for the implementation of difficult issues by enhancing users' abilities to comprehend problems thoroughly through the analysis of spatial data and the creation of digital maps. This allows decision-makers to save time, effort, and money by making informed choices that will result in the best possible solution to the issue at hand. In addition to a significant urban expansion, an increase in the number of people using vehicles, and a significant emigration of people from rural and small towns, the traffic jams that have recently engulfed much of the world, particularly in the major capitals and cities, are a result of these factors for transportation, have contributed to a complex problem in modern times. The goal of this study is to examine proposed elements and gauge their impact on the issue of traffic bottlenecks. It then suggests both long- and short-term remedies for this issue based on the study's findings, which are generally not found in the pertinent departments. In order to create a unique surface for these components and provide a comprehensive picture of the research region, it also makes satellite photos of the area available. geographical database for each of them, evaluate each layer's influence on the traffic jam independently by analyzing it, and then merge these levels to create full map that shows their location and the extent to which they affect the study problems. This process was done in order to arrive at the aforementioned solution.