Set Of Problems Research Articles

Collaborative Hyperspectral Image Processing Using Satellite Edge Computing

The advancement of nanosatellite techniques has boosted the growth of satellite-originated data and applications. Satellite edge computing (SEC) is envisioned to provide in-orbit processing of the sensed data to save the scarce terrestrial-satellite communication resources and support mission-critical services. While most of the existing SEC studies mainly focus on general computing tasks, we present a two-tier collaborative processing framework for the important and unique hyperspectral image (HSI) processing task. Our framework carefully selects bands out of the collected HSIs and sends them back for further analysis. We first conduct a comprehensive data analysis to reveal the non-trivial relationship between the band selection and the eventual analytic performance. We then formulate the band selection problem in this collaborative setting as a utility maximization problem that jointly considers the analytic, energy, and communication factors. A novel multi-agent reinforcement learning approach, named MaHSI, is proposed to solve it in the dynamic SEC environment. Our multi-agent design judiciously embeds the complex correlations among bands as collaborations among agents and significantly reduces the exploration space. Extensive experiments on real-world HSI datasets prove that our approach not only outperforms the existing classical band selection algorithms in accuracy and inference speed but also brings the highest utility to the satellites.

No-Show Rates in a Urogynecology Clinic.

No-show appointments, or scheduled appointments that patients do not attend without giving notice of cancellation, are a prevalent problem in the outpatient setting. The objective of this study was to compare the proportion of patients by decades of life who "no-show" to their urogynecology appointments. This retrospective cohort included women 20 years and older who did not show to their urogynecologic clinical encounters at an academic practice between January 1, 2022, and December 31, 2022. Demographics and visit history were recorded. The primary outcome was the proportion of patients by decade of age who were a "no-show" to their appointments. All decades were compared with women in their 70s, the decade with the most patients seen. Secondary outcomes included descriptive data of patients. Descriptive statistics and χ2 analyses were used. The cohort of 450 no-show encounters (composed of 391 patients), out of 6729 encounters, demonstrated an overall no-show rate of 6.7%. Baseline demographics of "no-show" patients were 67.3% White and 27.4% Black. The odds of women in their 20s-50s who no-show was 2-3 times higher than women in their 70s (P < 0.01). The highest no-show rates occurred in 20s (12.6%) and 40s (11.8%). Forty-six patients missed multiple appointments. The odds of a Black patient having multiple no-shows was 3.15 times higher than the odds of a White patient. No-show rates are low in this urogynecology practice. Younger women are more likely to no-show. This knowledge can facilitate potential double bookings necessary for urgent appointments and to maximize resource utilization.

A review study of space perception and navigation of people with low vision: is simulated low vision a reliable methodology?

The inclusion of visually impaired participants in research protocols concerning their perception of space and navigation is essential for the reliability of the results, given the strategies developed by the people concerned in everyday life. However, the diversity of visual impairments, the scarcity of participants and possible safety issues due to obstacles in the physical space induce limitations and prompt researchers to look into alternative methodologies. Simulated low vision is seen as an option. This method involves sighted participants wearing goggles with customized filters or watching processed images in virtual environments. The objective of this study is to investigate the reliability of simulated low vision as a research method to describe the space perception and navigation of people with visual impairment. We conducted a literature review and identified 36 quantitative studies on low vision spatial performance involving multiple user groups. Simulated low vision proved effective in small-scale spatial ability evaluation, such as object detection and distance estimation, but remained challenging regarding large-scale capacity, such as navigation with mobility requirement. Advances in virtual environments suggest that they are a good alternative to goggles and screen displays because of their precision in mimicking ocular problems in simulation settings. Finally, the use of head-mounted-display (HMD) by people with real low vision could open up the possibility of greater testing in safer and controlled conditions, but requires confirmation of the validity of the protocols.

Optimization of the algorithms use ensemble and synthetic minority oversampling technique for air quality classification

&lt;p&gt;Rapid economic development, industrialization, and urbanization in Indonesia have caused a large increase in air pollution with negative impacts on the environment and public health. The aim of this research is to use machine learning techniques to categorize air quality and generate an air quality index (AQI) using a dataset that includes six prevalent air pollutants. Next steps are preprocessing and data extraction, K-nearest neighbors (KNN) classification, support vector machine (SVM), and random forest (RF) models are implemented. Furthermore, synthetic minority oversampling technique (SMOTE) is incorporated into the ensemble learning process to improve the results. This research uses K-fold cross validation for improve classification accuracy and reduce overfitting. Research findings show that the application of SMOTE brings a significant increase in model accuracy, effectively solving the problem of imbalanced data sets. These insights provide direction for effective air quality monitoring systems and informed decision making in air pollution management.&lt;/p&gt;

Application of the Parabola Method in Nonconvex Optimization

We consider the Golden Section and Parabola Methods for solving univariate optimization problems. For multivariate problems, we use these methods as line search procedures in combination with well-known zero-order methods such as the coordinate descent method, the Hooke and Jeeves method, and the Rosenbrock method. A comprehensive numerical comparison of the obtained versions of zero-order methods is given in the present work. The set of test problems includes nonconvex functions with a large number of local and global optimum points. Zero-order methods combined with the Parabola method demonstrate high performance and quite frequently find the global optimum even for large problems (up to 100 variables).

Возможности опосредствования решения арифметических задач третьеклассниками: процедура «комплектования»

&lt;p&gt;The purpose of our study was to reveal students&amp;rsquo; typical difficulties when performing trivial transformations of several quantities, which are to be coordinated. We have designed two tasks that required to build up the "sets", and to keep their ratio. 97 third-graders from three secondary Moscow schools were recruited for this study. The participants solved the first task individually in the written form, and afterwards there were 25 couples randomly selected to solve the similar task jointly in an oral interview with the experimenter. The analysis of the results revealed the weakness and confusion of most of the surveyed students in solving such problems. The in-depth consideration of the written works and interviews allowed us to characterize the model means, used by students, as formal or meaningful. Among them, only the &amp;ldquo;portion-by-portion&amp;rdquo; measurement of two independent quantities, performed through drawing or using the counting material, provided by the experimenter, turned out to be effective. The study showed that the source of difficulties in solving problems related to &amp;ldquo;assembling sets&amp;rdquo; is the lack of adequate model mediation, and confirmed the relevance of considering the "assembling sets problem" in the general line of development of the number concept in primary mathematics education within the framework of V.V. Davydov's theory of learning activity.&lt;/p&gt;

Sparsity-Constrained Invariant Risk Minimization for Domain Generalization With Application to Machinery Fault Diagnosis Modeling.

Machine learning has been widely applied to study AI-informed machinery fault diagnosis. This work proposes a sparsity-constrained invariant risk minimization (SCIRM) framework, which develops machine-learning models with better generalization capacities for environmental disturbances in machinery fault diagnosis. The SCIRM is built by innovating the optimization formulation of the recently proposed invariant risk minimization (IRM) and its variants through the integration of sparsity constraints. We prove that if a sparsity measure is differentiable, scale invariant, and semistrictly quasi-convex, the SCIRM can be guaranteed to solve the domain generalization problem based on a few predefined problem settings. We mathematically derive a family of such sparsity measures. A practical process of implementing the SCIRM for machinery fault diagnosis tasks is offered. We first verify our theoretical exploration of the SCIRM by using simulation data. We further compare SCIRM with a set of state-of-the-art methods by using real machinery fault data collected under a variety of working conditions. The computational results confirm that the machinery fault diagnosis model developed by the SCIRM offers a higher generalization capacity and performs better than the other benchmarks across the different testing datasets.

ANALYSIS OF FACTORS CONTRIBUTING TO WORK STRESS AMONG EXPATRIATE AIRLINES CREW IN SAUDI ARABIA

Work stress can significantly impact the performance of cabin crew in the airline industry, potentially leading to job errors with critical implications for safety, a paramount concern in the industry. The research focuses on factors influencing work stress among cabin crew, providing a valuable reference for improving both service quality and safety in the industry. The potential key factors that affect work stress, including workload, cross-cultural adjustment, rotating work schedules, interpersonal relationships, organizational relationships, and physical demands, are established through extensive literature reviews. The relevant data were collected from a sample of cabin crews in Saudi Arabia representing various nationalities. The most pertinent factors for the problem setting are determined by applying a multivariate regression analysis, where the theoretical understanding is statistically verified. The analysis suggests that the theory is supported by the sample data with a fitness level of 0.85 in terms of the determination coefficient, implying strong materialization of the theory. Furthermore, the agreement between theory and reality is supported by the ANOVA and t-tests. The study concludes that workload is the most dominant factor affecting work stress, followed by organizational relationships and rotating work schedules. Statistically less significant factors include cross-cultural adjustment, interpersonal relationships, and physical demands.

Risk factors for violence in an emergency department: Nurses' perspectives.

Work-related violence remains a significant problem in healthcare settings, including EDs. Violence risk assessment tools have been developed to improve risk mitigation in this setting; however, incorporation of these tools into standard hospital processes remains scarce. This research aimed to explore nurses' perspectives on the Bröset Violence Checklist used in routine violence risk assessment and their recommendations for additional items. Thirty nursing staff who used the Bröset Violence Checklist (BVC) as standard practice for 5 years participated in two focus groups where 23 violence risk factors were presented. Using multiple methods, participants were asked to select and elaborate from a pre-determined list what they considered most useful in violence risk assessment in respect to descriptors and terminology. Quantitative data showed most risk factors presented to the group were considered to be predictive of violence. Ten were regarded as associated with risk, and overt behaviours received the highest votes. The terms 'shouting and demanding' was preferred over 'boisterous', and 'cognitive impairment' over 'confusion'. Patient clinical characteristics and staff perceptions of harm, inability to observe subtle behaviour, imposed restrictions and interventions and environmental conditions and impact were also important considerations. We recommend that violence risk assessment include: history of violence, cognitive impairment, psychotic symptoms, drug and alcohol influence, shouting and demanding, verbal abuse/hostility, impulsivity, agitation, irritability and imposed restrictions and interventions. These violence risk factors fit within the four categories of historical, clinical, behavioural and situational.

Mental Disorders and Suicidality by Sexual Orientation Status Among First-Year College Students in France

ABSTRACT Studies indicate that non-heterosexual young adults experience higher rates of mental health problems in various settings. This study seeks to further explore these associations, distinguishing heterosexual, gay/lesbian, bisexual and individuals questioning their sexual identity. Data were drawn from the French portion of the World Mental Health International College Student survey initiative (n = 3,545). Logistic regressions were performed to estimate the associations of sexual orientation with 12-month mental disorders and suicidal ideation and behaviors. Overall, 81.6% (n = 2,894) of students identified as heterosexual, 2.9% (n = 103) as gay/lesbian, 8.5% (n = 302) as bisexual and 6.9% (n = 246) stated questioning their sexual orientation. In adjusted models, compared to heterosexuals, students identifying as bisexual reported greater odds of major depression, panic disorder, drug use disorder and suicidality, gay/lesbian students were more likely to present with panic disorder, suicidal ideation and plans, and students questioning their sexual orientation were at greater risk for generalized anxiety disorder, suicidal ideation, and plans. The study confirmed higher rates for mental health disorders for non-heterosexual young adults, with little evidence of significant differences between minority subgroups.

Integrated inventory replenishment and online demand allocation decisions for an omnichannel retailer with ship-from-store strategy

Retailing has changed dramatically from single-channel brick-and-mortar stores to multi-channel and omnichannel retailers over the last few decades. Omnichannel retailers employ different strategies to integrate online and offline sales channels as well as order fulfillment processes. Among these strategies, the ship-from-store is the most popular and widely accepted among retailers. It enables retailers to use inventory from store locations to fulfill online demand. An omnichannel retailer with a distribution center and a retail store has to make important, interlinked decisions — (1) how much inventory to keep at the retail store, and (2) where to fulfill the online demand from and how much. In this work, we model the integrated inventory replenishment and online demand allocation decisions for an omnichannel retailer employing the ship-from-store strategy. We analyze this problem for both single-period and multi-period settings. We extend the analytical framework of the single-period problem by providing a finite-horizon Markov decision process (MDP) formulation for the multi-period problem. Our findings suggest that for a single-period setting, decentralized inventory replenishment and demand allocation system maximizes the profit of the omnichannel retailer for low values of the incentive for fulfilling the online demand through store inventory, while for sufficiently high values of the incentive, a pooled system provides the optimal profit. An increment in the discount factor has the same effect on the optimal decisions in a multi-period setting as that of salvage value in a single-period setting for a given value of the incentive for the ship-from-store strategy. We also provide several extensions (such as cross selling, endogenous and correlated demand streams) of our analytical framework for the multi-period problem.

An RBF-LOD Method for Solving Stochastic Diffusion Equations

In this study, we introduce an innovative approach to solving stochastic equations in two and three dimensions, leveraging a time-splitting strategy. Our method combines radial basis function (RBF) spatial discretization with the Crank–Nicolson scheme and the local one-dimensional (LOD) method for temporal approximation. To navigate the probabilistic space inherent in these equations, we employ the Monte Carlo method, providing accurate estimates for expectations and variations. We apply our approach to tackle challenging problems, including two-dimensional convection-diffusion and Burgers’ equations, resulting in reduced computational and memory requirements. Through rigorous testing against diverse problem sets, our methodology demonstrates efficiency and reliability, underscoring its potential as a valuable tool in solving complex multidimensional stochastic equations. We have validated the method’s stability and showcased its convergence as the number of collocation points increases. These findings serve as compelling evidence of the suggested method’s convergence properties.

Global multi-sourcing network design with inventory planning under uncertainty

ABSTRACT In the current era of turbulence, demand uncertainty and significant supply delays have become recurrent challenges for every global supply chain, which are accentuated by geopolitical issues, regional conflicts, natural disasters, etc. Companies are keen to decide when and where nearshoring and offshoring their supply locations and to adapt their inventory control policies to cope with significant perturbations. To deal with these challenges, we investigate the integration of inventory planning at the strategic level through a novel setting of the location-inventory problem under uncertainty. This problem is characterised by inbound multi-sourcing, order splitting among multiple suppliers, and optimised inventory decisions from sourcing to customers under uncertain demand and lead time. A two-stage stochastic optimisation model is proposed and solved using a Benders decomposition algorithm, supported by a sample average approximation approach. The results show that both lead time and demand uncertainties impact strategic and tactical decisions of the global supply network. Specifically, multi-sourcing and inbound order splitting policies have proven beneficial when the network faces a high lead time uncertainty. Based on an illustrative case of a global automobile spare parts supply network under different supply-side scenarios, managerial insights are gained on the benefit of onshore, nearshore, and offshore decisions. When facing a high lead time uncertainty, as in the pandemic case or any geopolitical episode, our findings suggest the necessity to rely on nearshoring for the supply side.

A pilot protection scheme based on high‐frequency transient current waveform similarity for AC lines connected to HVDC inverter station

AbstractAffected by inverter station commutation failure and fast control, line fault characteristics are obviously different from traditional AC transmission line fault characteristics. The existing protection methods for AC line connected to line commutated converter based high voltage DC (LCC‐HVDC) inverter station (ACLs) have the problems of complex setting, weak anti‐noise and high demand for data synchronization. This paper proposes a pilot protection scheme based on high‐frequency transient current waveform similarity. Firstly, with analyzing the fault traveling wave propagation process, the obvious differences are found in the transient current waveforms at both ends of the line when internal and external faults occur. Then the similarity of transient current waveforms is measured with the improved longest common similar subsequence (LCSS) method. And the weak high‐frequency current is extracted with the improved synchronous squeezed S‐transform (SST) method. Finally, a large number of power systems computer aided design (PSCAD) simulation results show that the proposed protection method does not require strict data synchronization, has strong anti‐noise performance, and is tolerant to the influence of abnormal point fluctuations.

DIFFUSUP: A graphical user interface (GUI) software for diffusion modeling

Advancements in high-resolution in-situ analyses have led to the extensive use of mineral diffusion zonings in determining petrologic and orogenic rates. The diffusion simulation, especially in multi-element systems, is numerically complex in practice. To streamline the application, we developed DIFFUSUP, a software featuring a graphic user interface (GUI) that facilitates the numerical simulation of diffusion with intricate initial conditions and thermal histories. DIFFUSUP alleviates the need for the knowledge of diffusion formulae, numerical solutions, and programming while still necessitating a fundamental understanding of problem setting, including the initial profiles and P-T-t evolution. DIFFUSUP's intuitive interface significantly simplifies the simulation setup process, making it particularly beneficial for reconnaissance research. It provides users with a balance between simplicity and flexibility, catering to a wide range of applications. These include support for multi-component systems, linear or isotropic spherical settings, variations in P-T-fO2 conditions, initial profiles, and boundary conditions. The software is stand-alone, compatible with Windows and macOS, and can be adapted to diverse problem settings. The software, user's guide, and a few examples can be downloaded from www.diffusup.org.

Semi-supervised imbalanced multi-label classification with label propagation

Multi-label learning tasks usually encounter the problem of the class-imbalance, where samples and their corresponding labels are non-uniformly distributed over multi-label data space. It has attracted increasing attention during the past decade, however, there is a lack of methods capable of handling the imbalanced problem in a semi-supervised setting. This study proposes a label propagation technique to settle the semi-supervised imbalanced multi-label issue. Specially, we first utilize a collaborative manner to exploit the correlations from labels and instances, and learn a label regularization matrix to overcome the imbalanced problem in the labeled instance. After that, we extend to semi-supervised learning and explore to represent the similarity of instances with weighted graphs on labeled and unlabeled data. Then, the data distribution information and label correlations are fully utilized to design the loss function under the consistency assumption manner. At last, we present an iterative scheme to settle the optimization issue, thereby achieving label propagation to address the imbalanced challenge. Experiments on a variety of multi-label data sets show the favorable performance of the proposed method against related comparing approaches. Notably, the proposed method is also validated to be robust with a limited number of training instances.

A projected-search interior-point method for nonlinearly constrained optimization

AbstractThis paper concerns the formulation and analysis of a new interior-point method for constrained optimization that combines a shifted primal-dual interior-point method with a projected-search method for bound-constrained optimization. The method involves the computation of an approximate Newton direction for a primal-dual penalty-barrier function that incorporates shifts on both the primal and dual variables. Shifts on the dual variables allow the method to be safely “warm started” from a good approximate solution and avoids the possibility of very large solutions of the associated path-following equations. The approximate Newton direction is used in conjunction with a new projected-search line-search algorithm that employs a flexible non-monotone quasi-Armijo line search for the minimization of each penalty-barrier function. Numerical results are presented for a large set of constrained optimization problems. For comparison purposes, results are also given for two primal-dual interior-point methods that do not use projection. The first is a method that shifts both the primal and dual variables. The second is a method that involves shifts on the primal variables only. The results show that the use of both primal and dual shifts in conjunction with projection gives a method that is more robust and requires significantly fewer iterations. In particular, the number of times that the search direction must be computed is substantially reduced. Results from a set of quadratic programming test problems indicate that the method is particularly well-suited to solving the quadratic programming subproblem in a sequential quadratic programming method for nonlinear optimization.

Markov decision process design: A framework for integrating strategic and operational decisions

We consider the problem of optimally designing a system for repeated use under uncertainty. We develop a modeling framework that integrates the design and operational phases, which are represented by a mixed-integer program and discounted-cost infinite-horizon Markov decision processes, respectively. We seek to simultaneously minimize the design costs and the subsequent expected operational costs. This problem setting arises naturally in several application areas, as we illustrate through examples. We derive a bilevel mixed-integer linear programming formulation for the problem and perform a computational study to demonstrate that realistic instances can be solved numerically.

Enhancing the quality of teaching and learning through ChatGPT and similar large language models: Challenges, future prospects, and ethical considerations in education

The integration of Artificial Intelligence (AI) into the field of education has ushered in a transformative shift in the creation and dissemination of assignments. This research delves into the implications and complexities surrounding the incorporation of AI-enhanced ChatGPT submission in the realm of education. AI-enhanced ChatGPT submissions offer a multitude of benefits. First and foremost, they offer educators a convenient means to produce a wide array of content, encompassing quizzes, essay prompts, and problem sets, thus saving both time and effort. AI has the capacity to customize assignments according to the specific needs of individual students, ensuring personalized learning experiences. Additionally, AI's ability to analyze student performance data can aid educators in pinpointing areas where students might be facing challenges, allowing them to adapt their teaching methods accordingly. Nonetheless, this innovation also raises substantial concerns. The authenticity and quality of AI-generated submission may come under scrutiny, potentially giving rise to issues of plagiarism and academic integrity. It is crucial to ensure that AI-generated content aligns with educational objectives and standards. Before disseminating AI-generated submission to students, educators must meticulously curate and review them. Furthermore, the role of teachers in the educational process extends beyond merely assigning tasks. They provide mentorship, guidance, and feedback that surpass the capabilities of ChatGPT. This human touch is indispensable for nurturing critical thinking, fostering creativity, and enhancing emotional intelligence. Ethical considerations also play a pivotal role. Both students and educators must comprehend how AI-generated assignments are created, ensuring transparency in the process. Safeguards must be in place to safeguard data privacy, and measures need to be taken to address bias in AI algorithms. Hence, it is imperative for teachers and educational institutions to critically evaluate the place of AI in education and determine the circumstances under which AI-generated assignments can be integrated to enrich the educational landscape.

Developing the Minimum Data Set for a Disaster Mental Health Registry System

Introduction: There is a close relationship between mental health and psychosocial problems in disaster settings, as well as overlap in the support provided for these problems. Therefore, public health officials need to understand the burden of behavioral health conditions among survivors and the needs of the affected community. This study aimed to develop a mental health minimum data set for an electronic disaster registry system to provide timely, essential, and accurate information to personnel on the ground and policymakers to design a disaster response and develop an action plan rapidly.Material and Methods: The present study is a mixed‑method (sequential exploratory) study. In the qualitative phase, a literature review and semi‑structured interviews with experts were conducted to generate an item pool for the mental health response in disasters. In the quantitative phase the quantitative content validity, content validity ratio and content validity index were used.Results: proposed data elements, 85 data elements were confirmed according to the opinion of experts and categorized into two main parts, pre-disaster part with three sections; including region profile, mental health local background, regional mental health committee affairs, and post-disaster parts with five sections including disaster information, information of mental health teams, mental health status, mental health interventions, and mental health need assessment.Conclusion: Collecting this minimum data set is critical for helping policymakers and healthcare providers prevent, control, and manage the mental health impacts of disasters during the response phase. Besides facilitating and promoting disaster prevention and response programs and measures.