Regular Tasks Research Articles

QC Speller: User Interface Design of a Hands-Free Touch-Free Speller with Brain Electroencephalogram Sensory Rhythm

Nowadays, brain-computer interfaces (BCIs) are being extensively explored by researchers to recover the abilities of motor-impaired individuals and improve their communication. Text entry is one of the most important regular tasks in communication, and BCI has high application potential to develop a speller. Although BCI has been a growing research topic for the last decade, more progress is yet to be made for BCI-based spellers. Two challenges are there in BCI speller development: designing an effective graphical user interface with an optimal arrangement of symbols enabling a minimum number of control commands and reducing users’ efforts in error correction following an efficient error-correction policy. With this scope in mind, this work proposes a novel BCI speller paradigm with an efficient symbol arrangement to improve the text entry rate. Additionally, it provides a user-friendly error correction approach through six text-cursor navigation keys to enhance the accuracy of text entry. The proposed speller includes 36 target symbols and is operated with two control commands obtained from electroencephalogram motor-imagery signals. The experimental results revealed that the proposed speller outperforms the existing motor imagery-based BCI spellers when tested on ten motor-impaired users. This speller achieved a mean performance of 5.20 characters per minute without any typing error and 6.04 characters per minute with a 2.9 percent mean error. The mean correction efficiency was 0.69; that is, users corrected 69 percent of incorrect inputs with a single correction key pressed.

Intelligent standalone eye blinking monitoring system for computer users

Purpose: Working on computers for long hours has become a regular task for millions of people around the world. This has led to the increase of eye and vision issues related to prolonged computer use, known as computer vision syndrome (CVS). A main contributor to CVS caused by dry eyes is the reduction of blinking rates. In this pilot study, an intelligent, standalone eye blinking monitoring system to promote healthier blinking behaviors for computer users was developed using components that are affordable and easily available in the market. Methods: The developed eye blinking monitoring system used a camera to track blinking rates and operated audible, visual and tactile alarm modes to induce blinks. The hypothesis in this study is that the developed eye blinking monitoring system would increase eye blinks for a computer user. To test this hypothesis, the developed system was evaluated on 20 subjects. Results: The eye blinking monitoring system detected blinks with high accuracy (95.9%). The observed spontaneous eye blinking rate was 43.1 ± 14.7 blinks/min (mean ± standard deviation). Eye blinking rates significantly decreased when the subjects were watching movie trailers (25.2 ± 11.9 blinks/min; Wilcoxon signed rank test; p<0.001) and reading articles (24.2 ± 12.1 blinks/min; p<0.001) on a computer. The blinking monitoring system with the alarm function turned on showed an increase in blinking rates (28.2 ± 12.1 blinks/min) compared to blinking rates without the alarm function (25.2 ± 11.9 blinks/min; p=0.09; Cohen’s effect size d=0.25) when the subjects were watching movie trailers. Conclusions: The developed blinking monitoring system was able to detect blinking with high accuracy and induce blinking with a personalized alarm function. Further work is needed to refine the study design and evaluate the clinical impact of the system. This work is an advancement towards the development of a profound technological solution for preventing CVS.

Effective Noise Reduction in NDR Systems: A Simple Yet Powerful Apriori-Based Approach.

Noise (un-important) alerts are generally considered a major challenge in intrusion detection systems/sensors because they require more analysts to review and may cause disruption to systems that are shut down to avoid the consequences of a compromise. However, in real-world situations, many alerts could be raised for automatic tasks being completed by some software or regular tasks by users doing their daily job. This paper proposes an approach to reduce the number of noise alerts, assuming that frequent long-term security alerts can be considered noise if their frequency is meeting some criteria, such as the minimum occurrence ratio. We prove that to effectively reduce the level of noise alerts in Network Detection and Response (NDR) systems, we are able to use simpler algorithms; sometimes, the answer is in simpler solutions, and not always in complex solutions. We study data from a real customer of a Danish NDR solution and propose an Apriori-based approach to find frequent noisy alerts. Our comparison of the detected noise before and after applying our solution shows high performance in reducing noise alerts for most of the alert types for a real customer. Our experiments show that our method can filter more than 40% of the alerts by setting the minimum occurrences to 70%. Moreover, our results show that we were able to filter out more than 90% for some alert categories.

Management of Gridhrasi Through Panchakarma w.s.r to Sciatica

Among the Vatavyadhi brought on by vitiated Vata and Kaphanubandha vata is Gridhrasi. The symptoms of Gridhrasi can be correlated with the symptoms of sciatica. Sciatica is one of the most common disorders, affecting 10% to 40% of the population. Sciatica affects people of all ages, but it particularly affects older folks in their fourth decade of life. Sciatica is a condition in which pain or discomfort radiates down one or both legs, starting in the lower back and ending at the sciatica nerve. The degree of sciatica pain can sometimes make it difficult for a person to go about their everyday activities. It has traits with the traditional Ayurvedic remedies. In Gridhrasi, pain corresponds to the following locations: Jaanu (knee), Jangha (calf), Paada (foot), Prusta (back), Sphik purva Kati (hip), and Uru (thigh), internal pharmaceuticals. Materials and Methods: A 25-year-old patient complained of lower back pain that was radiating to both of his lower limbs, Shanoushadhi and Panchakarma procedure were recommended. Result: The patient's symptoms considerably subsided, and he was able to carry out his regular tasks without difficulty. Discussion: Together with internal medications, Sarvanga Parisheka, Churna Basti, Kati Basti, Sarvanga Abhyanga, Sthanika Patra Pottali Swedana, Sarvanga Bhaspha Swedana, Stha Lepa, and Yogabasti (Anuvasana Basti with Sahacharadi Taila, Erandamoola Niruha Basti) effectively managed the disease.

Improving Database Performance with SQL Server Optimization Techniques

Database performance is critical for ensuring efficient data management and retrieval, particularly in environments that utilize SQL Server. As the complexity and size of databases increase, performance optimization becomes essential for maintaining responsiveness and reliability. This paper examines a range of SQL Server optimization techniques that can significantly enhance database performance. Key strategies include effective indexing, which improves query execution speed; query optimization, which refines SQL statements for better efficiency; and proper database configuration, which ensures that SQL Server is tuned for optimal resource utilization. Additionally, we explore the importance of regular maintenance tasks such as updating statistics and monitoring performance metrics to identify bottlenecks. By employing these techniques, database administrators can minimize latency, reduce resource consumption, and enhance overall system performance. Furthermore, this paper highlights the significance of ongoing performance assessments to adapt to evolving application demands and data growth. Ultimately, implementing these optimization strategies not only boosts SQL Server performance but also contributes to improved user experiences and organizational productivity.

Virtual Digital Metaverse Chartered Accountant

A globally renowned financial expert that oversees customers' budgets, audits, taxes, and company strategy is a chartered accountant. You can work for companies, the government, or private citizens as a CA Assisting clients with money management and offering professional financial advice are your duties. For the accounting sector, the emergence of virtual reality (VR) and the metaverse offers an exciting opportunity. Modern technologies like blockchain, artificial intelligence (AI), and data analytics, which come together to form a virtual environment that reimagines the function of the accountant, are the foundation of this paradigm shift. Virtual Digital Metaverse Chartered Accountant is what we're working on. By using artificial intelligence (AI), one may automate repetitive processes related to financial reporting, taxation, predictive analytics, business intelligence, and automation of regular tasks. Chartered accountants are becoming increasingly important to the growth and recovery of the Indian economy. They guarantee honest, ethical trade and commercial practices, support the smooth operation of businesses and organizations, and apply their financial know-how to different economic and financial initiatives aimed at improving the country. They are therefore crucial to boosting the economy. Forecasting the budget, tax audits, creating monthly financial reports, capital planning, tax planning, and other duties are all part of the CA's job description. Additionally, since the GST was implemented, their function has grown stronger and more significant

Intestinal Parasitic Infection in Children and Adolescents With Ocular Diseases: The Prevalence, Spectrum, and Effect of Lockdown During the COVID-19 Pandemic.

People with visual impairments and blindness face challenges in performing regular tasks such as maintaining proper sanitation, which makes them vulnerable to intestinal parasitic infections. This study aims to examine the prevalence and distribution of intestinal parasitic infections in children and adolescents with ocular diseases and to assess if the lockdown during the COVID-19 pandemic affected these rates. This retrospective, hospital record-based study was conducted among children and adolescents attending the Regional Institute of Ophthalmology in Kolkata, India. It involved routine stool examinations as part of their treatment during 2019-2020. Early morning stool specimens were collected and brought to the institute laboratory in containers. Stools were examined under a microscope for cysts, ova, parasites, and adult worms. Findings were recorded in the laboratory record book. These data were then extracted into a spreadsheet and analyzed using IBM SPSS Statistics for Windows, Version 26 (Released 2019; IBM Corp., Armonk, New York). The prevalence of intestinal parasitic infections was 8.59% (59 out of 687 patients). Among those 59 positive cases, Ascaris lumbricoides, Giardia lamblia, Entamoeba histolytica, Trichuris trichiura, Taenia spp., Enterobius vermicularis, and Isospora belli were detected in 27 (45.8%), 15 (25.4%), 8 (13.6%), 6 (10.2%), 3 (5.1%), 2 (3.4%), and 1 (1.7%) patients, respectively. The positivity rate of stool samples was higher from September and thereafter from January to March. The sample positivity rate was higher post-pandemic and lockdown, but not statistically significant (11.5% vs. 5.3%; χ²=4.044, df=1, p=0.44). Ascaris lumbricoides was the most commonly observed intestinal parasite in children and adolescents with ocular disease in our setting. Seasonal variation was noted with higher case positivity at the end of the rainy season and thereafter in winter. Therefore, we propose to strengthen the routine deworming program during this period in Eastern India. Higher sample positivity after the pandemic may be attributed to school closures during the lockdown period, which might have caused some children to miss their routine deworming medication.

Effects of the use of face masks on the vital signs and headache of healthy healthcare workers during the COVID-19 pandemic: A hospital-based cross-sectional study

The study aimed to assess the impact of wearing protective facial masks (PFMs) on various health parameters of hospital staff amidst the COVID-19 pandemic. The aim of this study was to investigate the effects of wearing a face mask on blood oxygen saturation, heart rate, headache, fatigue, and shortness of breath in hospital staff during routine work shifts and COVID-19. In this study, participants from various clinical, administrative, and hospital cleaning professions who had at least one month of work experience were enrolled and their oxygen saturation (SpO2), heart rate (HR), and headache were measured during routine shifts with normal daily activities. The fatigue severity scale (FSS) and the modified Borg dyspnea scale were used to assess the participants. The significance level was set at 0.05. SpO2 and HR were lower at the end of the work shift than at the beginning (93.96 ± 1.57 vs. 95.03 ± 1.7 and 82.1 ± 11.83 vs. 83.93 ± 12.31, respectively). According to the result of the Borg scale, 59.2 % of the participants did not feel short of breath and 34.3 % had “mild-moderate” shortness of breath. The total FSS (21.46 ± 2.31) showed that the participants did not fall into the “fatigued” category. The Kruskal-Wallis test showed no significant difference in Borg score, FSS, HR, and SpO2 participants with different mask types (P > 0.05). The result of the Kruskal-Wallis test also showed no significant difference in Borg score between the occupational groups (P = 0.073). The results showed that the frequency of pre-existing headaches had increased in 18.2 % of the respondents. The findings indicated that wearing PFMs did not have a notable impact on oxygen levels and heart rate during the regular tasks of hospital staff. However, the use of PFMs led to the development of new headaches associated with PFMs or worsened existing headaches.

Aspects of occupational safety: a survey among European cancer nurses

PurposeNurses are particularly at risk for occupational exposure to hazardous cancer drugs, risking both acute and chronic health effects. Knowledge on the implemented safety precautions into minimizing these risks is limited. MethodsThe European Cancer Nursing Index (ECNI) was developed by the European Oncology Nursing Society (EONS) to illustrate the development and status of this profession. In this study, anonymous online survey data on occupational safety reported by European cancer nurses as part of the ECNI 2022, was analysed. ResultsA total of 630 cancer nurses from 29 countries responded to the survey. A majority reported that written guidelines (n = 553, 88%) on safe handling and administration of hazardous drugs, personal protection equipment (PPE) and cytotoxic spillage kits (n = 514, 82%) were available at their workplaces. 130 (21%) nurses reported that wipe testing to assess any residual hazardous drugs on workplace surfaces were conducted systematically at their workplaces. 185 (29%) nurses reported that nurses sometimes or always continued with their regular tasks (including handling hazardous cancer drugs) during pregnancy and breast feeding. 185 (29%) also responded that nurses at their workplaces did not receive an introductory education program before handling hazardous drugs. In total, 346 (55%) of the nurses reported that their workplace had a freedom to speak-up guardian or whistle blower policy for members of staff. ConclusionsEven if most nurses report that there are safety routines in place at their workplaces, the results reveal several serious occupational risks for European nurses handling hazardous cancer drugs. Actions are needed to improve and optimize occupational safety for nursing staff.

An efficient spatial-temporal transformer with temporal aggregation and spatial memory for traffic forecasting

Traffic forecasting technology has widespread applications in various domains, such as urban traffic planning and intelligent transportation systems. Traffic forecasting encounters challenges in effectively capturing the intricate spatial–temporal correlations in traffic data. While the latest methods have achieved satisfactory performance, they still suffer from two limitations: (i) Most methods overlook the memory of valuable traffic patterns at each traffic node, thus making it struggle to reveal dynamic spatial–temporal correlations using their inherent periodicity and trend characteristics from a broader perspective. (ii) As the research progresses, recently proposed models become increasingly complex and massive. To address these issues, we propose a Spatial-Temporal Aggregation Memory Transformer (STAMT) for traffic forecasting. Specifically, we propose a memory bank to enhance vanilla spatial attention and cache the traffic patterns of historical input. By querying these traffic patterns, which contain rich spatial–temporal semantic information, the model can optimize prediction performance by extracting trends and regularities across various periods. To reduce the computational costs, we introduce a temporal module to capture temporal correlations while reducing temporal dimension information. In addition, we leverage the random feature map and matrix multiplication associativity property, which reduce the quadratic complexity of spatial attention to linearity with regard to the number of nodes. Ultimately, our theoretical analysis concludes that a single-layer spatial attention network is sufficient to capture spatial–temporal correlations deeply without stacking. Extensive experiments on nine real-world datasets demonstrate that STAMT outperforms state-of-the-art baselines in regular, long-range, and large-scale traffic forecasting tasks while significantly reducing the computational costs. Codes are available at https://github.com/LiuAoyu1998/STAMT.

Accompanying study of the development process towards a smart city strategy-with a particular focus on social change

The development of a smart city strategy will take place in 73 selected cities in Germany as the result of a participation competition. The systematics of the requirements of the state as a funding provider and the approach of local actors will be examined in more detail in this study. The Objectives of the work are a supervised process of a model facility, on the basis of which observations for a process model are to be derived. This model is intended to enable various aspects as a starting point for non-subsidized institutions as well. A particular focus lies on the area of social change, which is difficult to quantify, in terms of social aspects. The Prior work shows that there are three different dimensions of strategy elements to be decided on in order to start subsidy-compliant design projects. On one hand, a team must be required that takes care of the smart city projects next to their regular tasks. Their composition and purpose in doing represent the first dimension. Secondly, the question of setting targets and the associated monitoring of results must be determined. Finally, the path of involving citizens and companies lead to the social legitimacy of the project team. The approach is based on a semi-structured participant observation by the authors. Qualitative insights into practical doing and abstracted findings based on an example case serve as practical recommendations for further and thus complementary consolidation and expansion of the findings. The first Results show that defining key performance indicators and objective key results is quite a new system of performance measurement for public institutions. In particular, the social aspects of living together in cities are difficult to put into key figures due to the diverse composition of society. Local differences with incentives and channels in different forms need to be translated into general insights. The Implications include enabling government leaders to initiate and manage transformative approaches to the digitization of public space. Social change as a field of action for the design of digital solutions will be opened up and brought closer by means of best-practice implementations. The value of the work lies in getting an example of steps towards a planning basis for comprehensive smart city agendas. Furthermore, management tools for digital projects are addressed and methods for the participation of service recipients of public authorities are presented. Through the classified experiences of the prime example, challenges, opportunities, risks but also weaknesses of the path to a smart city emerge, which can be a point of orientation for non-subsidized cities in their own roadmap.

From Announcing Their Pregnancy to Returning to Work: The Experience of Pregnant Street-Level Bureaucrats in non-Western Public Contexts

Drawing on the theory of social stigma, this article explores how female street-level bureaucrats experience pregnancy in the Egyptian public service and the main challenges they face. The empirical sample comprises semi-structured interviews with 40 female street-level bureaucrats working in four ministries in Egypt. Thematic analysis was subsequently used to determine the main ideas in the interview transcripts. The findings show that during pregnancy, female bureaucrats find it easy to announce their pregnancy without any fear or concern about being challenged with intensive job duties. However, during that stage, they predominantly suffer from abusive clients and a lack of organizational support. During maternity leave, female bureaucrats mostly feel forgotten by their colleagues and managers, and once they return to work, they find it difficult to balance the new obligations of motherhood and their regular job tasks. Furthermore, pregnant bureaucrats consider the marginal discretionary power to craft job duties, stigmatising the performance of pregnant bureaucrats and the primitive laws regarding pregnancy as the main challenges they face in public contexts.

THE CORRELATION AMONG LEVEL OF KNOWLEDGE ABOUT ERGONOMIC POSITION AND LOW BACK PAIN COMPLAINTS IN INPATIENT NURSES

Low Back Pain (LBP) is a collection of several clinical symptoms that are marked by pain or discomfort in the lower back, which may or may not extend to the legs. Incorrect ergonomics is the primary factor contributing to lower back pain (LBP) among nurses. The objective of this study was to establish a correlation between the extent of ergonomics knowledge and the occurrence of lower back pain (LBP) complaints among nurses who work with hospitalised patients. This study employed a correlational quantitative design, utilising a cross-sectional technique. The sample size for this study consisted of 158 nurses working in the inpatient unit of a private hospital in Indonesia. The sampling technique employed was a total sampling, resulting in a sample size of 158 nurses. The research employed questionnaires that were both valid and trustworthy. The questionnaires assessing knowledge of ergonomics were devised by Bunga et al. (2019), whilst the questionnaire pertaining to low back pain was formulated by Deria (2021). The findings of the Pearson chi-square statistical test indicated a p-value of 0.105, suggesting that there was no statistically significant association between the level of knowledge of ergonomics postures and complaints of low back pain among inpatient nurses. Inpatient nurses have the ability to discover and implement ergonomic postures in their regular tasks. Further research could be undertaken on the elements that can induce low back discomfort.

Approach for quality deep-ultraviolet nonlinear optical crystals via a substitution strategy of channel species in zeolite.

Exploring the connections between the ion substitution of a material, its crystal structure, and its performance in applications is a regular task in synthetic chemistry. However, there still remain major challenges in the substitution of polyanionic groups for single ions. Herein, a strategy of channel species substitution of single anions by polyanionic groups in a zeotype rigid framework to design a new deep-ultraviolet nonlinear zincophosphate, {[Li3Na3(SiF6)]Cs2}[(ZnPO4)6], and tune physicochemical properties was proposed.

Frequency of migraine and its associated triggers and relievers among medical students of Lahore: a cross-sectional study.

Headache is a widely prevalent illness that negatively impacts people's lives, leaving them functionally incapable of performing regular everyday tasks. The global burden of headache is 40%, of which migraine accounts for 47%. This study aims to determine the frequency of migraine among medical students of Lahore and its characteristics, associated triggers, and relievers among medical students. A cross-sectional study was conducted in medical colleges after obtaining ethical approval. Migraine was diagnosed using the criteria provided in the International Classification of Headache Disorders, Third Edition (ICHD-3), and data on triggers, relief, and demographics were collected. The information is analyzed using SPSS 22. The data were collected from 522 medical students. The average age of the students was 21.3 ± 2.0 SD (in years). About 146 (28.0%) of the medical students have migraine according to ICHD-3 criteria and were diagnosed initially. Gender was insignificantly associated with migraine (P-value=0.32). Students with psoriasis, hypertension, and polycystic ovarian syndrome were statistically significantly related to migraines with a P-value=0.002. Dehydration is the most frequent aggravator, and adequate sleep is the most frequent reliever of migraine. The findings show a high frequency of migraine. They are similar in both genders, depicting that stressful lifestyles, inadequate sleep patterns, and bizarre dietary habits make them more prone to migraine episodes. So, further, detailed studies should be done on evaluating triggers and relievers of migraine and their interrelations with migraine so we can focus on preventive strategies, diagnosis, and treatment of migraine.

An updated job-exposure matrix for occupational noise: development and validation.

The aim of this study was to create a quantitative job-exposure matrix (JEM) for noise including a large set of measurements for the Swedish workforce, a detailed exposure-level assessment, spanning over an extensive time period from 1970 to 2014. The JEM was developed by 2 teams, each with an experienced occupational hygienist and an occupational safety engineer. Each pair assessed the exposure using measurements performed and reported by occupational hygienists, occupational safety engineers, or similar, from 1970 to 2014. The measurements included either the original LAeq(8h) measurements or an LAeq(8h) levels calculated from partial measurements of the working day, provided that the measurement targeted a regular task usually performed during a full workday. The collection of measurement reports was done in 2008 and 2012 by contacting clinics working in the area of occupational health or occupational safety engineers and their submitted reports were added to our own material. Noise exposure assessments were inserted at the appropriate time period for the relevant job family. The final matrix was developed in a consensus procedure and the validity was investigated by comparison of the 2 team's individual results. The noise JEM contains 321 job families with information regarding occupational noise from 1970 to 2014. The time-period label has a 5-yr scale starting in 1970. The estimated average 8 h (TWA) noise level in decibels [dB(A)] for every job family and 5-yr period was coded as 1: <70 dB(A), 2: 70 to 74 dB(A), 3: 75 to 79 dB(A), 4: 80 to 84 dB(A) or 5: 85(+) dB(A). The validation showed no systematic difference in relative position and very high agreement in the ordering of paired ordinal classifications. The JEM has also successfully been applied in several epidemiological studies. We present a JEM for occupational noise using Swedish data from 1970 to 2014 with a higher degree of sensitivity in assessed noise exposure compared with the previously existing version. Repeated application of the JEM, in epidemiological studies, has shown consistent results and contributed to yielding important findings.

Anxiety levels of young people in Gazipur, Bangladesh, with different physical activity levels

Background: An occasional feeling of anxiousness is common in life. Concerns about one's health, finances, or family issues are common. However, anxiety disorders encompass more than just fleeting fear or worry. People who suffer from anxiety disorders often experience persistent anxiety that may worsen with time. The symptoms may make it difficult to carry out regular tasks including relationships, employment, work and schooling. It is crucial to comprehend the psychological well-being of the Bangladeshi population during this unpredictable global period. Less information is currently available about the patterns of anxiety among adult Bangladeshis across time. The people's mental health has been severely impacted by their fear of infection and confusion about the situation. The purpose of this study is to make it easier to counsel the young people on how to promote their psychological health and better lifestyle by boosting their physical activity. Method: Data was gathered using a well-structured questionnaire that included sections including one for demographics. Because of its thoughtful validity and practicability, we employed the generalized anxiety disorder scale to evaluate anxiety levels and the International Physical Activity Questionnaire scale to test various levels of physical activity. Subsequently, the two variables were compared to determine the relationship between anxiety levels and various degrees of physical activity. Results: Anxiety was determined to be 8.20 ± 3.67 on average in the sample and the prevalence of anxiety (GAD-8 score 10 or greater) was determined to be 31.2%. Using Pearson correlation analysis, it was discovered that growing older was a risk factor for anxiety. Anxiety and Physical Activity differed significantly, according to a one-way analysis of variance. There was a significant difference in anxiety levels between those who engaged in vigorous physical activity and those who did not, according to the post-hoc Tukey test.

The impact of dynamic learning and training on the personnel staffing decision

This paper studies the personnel staffing decision accounting for the planning of workforce training and the inclusion of learning and forgetting, which dynamically change the workforce skill level, influencing the required staffing budget. This contrasts staffing models presented in literature, which determine the required number of workers under the assumption of a fixed and/or predefined skill mix of the workforce. We consider different types of on-the-job and off-the-job training activities, traditional learning associated with execution of regular tasks and forgetting according to which a worker’s efficiency is adapted dynamically throughout the time horizon. The acquired worker’s efficiency level determines the speed at which assigned tasks can be processed, which is modelled via the definition of multiple discrete modes per task. To improve the accuracy of the staffing decision and account for the effect of gradually changing the workers’ skill levels, a baseline schedule is composed that comprises the solving of an integrated days-off scheduling and task assignment problem. We propose a dedicated solution procedure that relies on a constructive heuristic to find an initial solution and subsequently a branch-and-price algorithm to derive a (near-)optimal solution. The branch-and-price algorithm is featured by a multi-stage pricing procedure to find promising columns, alternating between mathematical mixed-integer programming, a single-pass heuristic and a genetic algorithm. The computational experiments validate the performance of the proposed procedure and demonstrate the benefits of different training and learning methods under different conditions.

Collaborative computation offloading for scheduling emergency tasks in SDN-based mobile edge computing networks

Due to the growing demand for time-intensive tasks, several tasks coexist, some of which last for a long time or only occur once in a while, such as emergencies. Emergencies such as natural disasters, accidents or medical emergencies require immediate attention and often demand real-time execution. Such tasks are the most difficult to plan for and foresee. Planning, resource allocation, and clear communication between all involved parties and network layers are crucial for scheduling emergency tasks. In the extensive literature, when an emergency task triggers, it will be handled like any other regular task. If the deadline is missed, a catastrophe will ensue. They ignored the impact of insufficient mobile edge computing (MEC) capacity and network congestion, which might result in execution delays. Collaborative offloading, which involves distributing computation tasks across multiple servers, shows promise in enhancing MEC network performance, particularly in emergency scenarios. In this context, efficient task scheduling plays a vital role in minimizing the total execution time of regular tasks while meeting the deadlines of emergency tasks. Our proposed scheme utilizes a collaborative offloading approach to schedule emergency tasks in MEC networks, leveraging the computing capacity of edge-deployed MEC servers. By utilizing the software-defined networking (SDN)’s global view of the network, task requests are collected and allocated to suitable MEC servers capable of meeting the demands. To address key challenges, our scheme propose four scheduling algorithms to address the following issues: (i) ensuring tasks are assigned to the nearest MEC server with sufficient computational resources, (ii) controlling a threshold to prevent network congestion, (iii) selecting an optimal collaborative MEC server for executing overloaded tasks based on collaborative offloading decisions, and (iv) allocating resources for emergency tasks when triggered to meet urgent deadlines by stealing resources from regular tasks without compromising their deadlines. Extensive simulations were conducted to assess the effectiveness of the proposed scheme. The results clearly illustrate its enhanced performance in terms of the total execution time of regular tasks and the ability to meet deadlines for emergency tasks.

Adaptive task decomposition physics-informed neural networks

As a new approach for solving partial differential equations, physics-informed neural networks (PINNs) have received extensive attention in recent years and have made breakthroughs in various fields. Serving as a neural network-based universal solver, they possess advantages such as high continuity and being meshless. However, an increasing number of studies have pointed out that PINNs may fail to converge to accurate results when dealing with complex tasks. In this paper, we highlight the differences between training PINNs and regular neural network tasks, summarizing three key characteristics of training PINNs. Based on these findings, we propose a strategy of task decomposition and progressive learning, which successfully explains the effectiveness of existing methods for time-dependent problems and reveals their limitations. For time-dependent and time-independent problems, we partition the tasks into physically complete and progressively challenging sub-tasks. The division is based on the resolution requirements for time-independent problems and the coverage of the time domain for time-dependent problems. Through task parameters and the decomposition of the loss terms, we adaptively control the progressive increase in task complexity during the training process. The proposed method overcomes the limitations of existing approaches, incurs no additional computational cost, and is applicable to both time-dependent and time-independent problems, demonstrating higher generality. Furthermore, building upon the concepts of task decomposition and progressive learning, we introduce a stable and efficient minibatch training method for the first time. In a series of numerical experiments on baseline PDE problems, our approaches outperformed other state-of-the-art algorithms.