Time Demands Research Articles

Impact of warranty and green level of the product with nonlinear demand via optimal control theory and Artificial Hummingbird Algorithm

Due to the current environmental situation and human health, a green manufacturing system is very essential in the manufacturing world. Several researchers have developed various types of green manufacturing models by considering green products, green investments, carbon emission taxes, etc. Motivated by this topic, a green production model is formulated by considering selling price, time, warranty period and green level dependent demand with a carbon emission tax policy. Also, the production rate of the system is an unknown function of time. Per unit production cost of the products is taken as increasing function of production rate and green level of the products. In our proposed model, carbon emission rate is taken as linear function of time. Then, an optimization problem of the production model is constructed. To validate of our proposed model, a numerical example is considered and solved it by AHA. Further, other five metaheuristics algorithms (AEFA, FA, GWOA, WOA and EOA) are taken to compare the results obtained from AHA. Also, concavity of the average profit function and convergence graph of different metaheuristics algorithms are presented. Finally, a sensitivity analysis is carried out to investigate the impact of different system parameters on our optimal policy and reach a fruitful conclusion from this study.

Logistics integration between the Manaus Free Trade Zone (ZFM) and Venezuela: evaluation of the modal through the Fuzzy logic for the flow of production in the electronics sector

The Logistics Integration between the Manaus Free Trade Zone (ZFM) and Venezuela regarding the flow of production in the electrical and electronic sector, has specificities in its commercial activity. Competition and the rapid increase in consumption form a more competitive environment, where the ability to meet fluctuating demand in time stands as a central component of competitive advantage. The main objective of this research was: To develop a Fuzzy inference model for the evaluation of the most viable modal for the integration between the Manaus Free Trade Zone (ZFM) and Venezuela in the flow of production in the electronics sector. The Methodological Process was developed in three phases: 1. Identification of the indicators for the Evaluation of Available Modals; 2. Fuzzy "Inference" System Modeling; 3. Experiment of the Proposed Model, with its respective stages: Phase 1 (definition of the inference levels of the indicators), Phase 2 (Development of Fuzzy Sets, Development of the "Inference" and Simulation Rules in the MatlabR2013 software), Phase 3 (Compilation of the Indicator Aggregation Algorithm, Simulation of the Results in 3D and Analysis of the Results Obtained). Based on the observed data from the 3D results, in figure 16, a detailed analysis of the behavior of the variables was allowed. The results obtained with the proposed model were satisfactory in the analysis of the performance classifications of the modes, it was possible to identify the most relevant variables for the flow of the production of the electrical and electronic sector between the Manaus Free Trade Zone and the Venezuela.

Developing a context-relevant psychosocial stimulation intervention to promote cognitive development of children with severe acute malnutrition in Mwanza, Tanzania.

More than 250 million children will not meet their developmental potential due to poverty and malnutrition. Psychosocial stimulation has shown promising effects for improving development in children exposed to severe acute malnutrition (SAM) but programs are rarely implemented. In this study, we used qualitative methods to inform the development of a psychosocial stimulation programme to be integrated with SAM treatment in Mwanza, Tanzania. We conducted in-depth interviews with seven caregivers of children recently treated for SAM and nine professionals in early child development. We used thematic content analysis and group feedback sessions and organised our results within the Nurturing Care Framework. Common barriers to stimulate child development included financial and food insecurity, competing time demands, low awareness about importance of responsive caregiving and stimulating environment, poor father involvement, and gender inequality. Caregivers and professionals suggested that community-based support after SAM treatment and counselling on psychosocial stimulation would be helpful, e.g., how to create homemade toys and stimulate through involvement in everyday chores. Based on the findings of this study we developed a context-relevant psychosocial stimulation programme. Some issues identified were structural highlighting the need for programmes to be linked with broader supportive initiatives.

Review on Parking Demand Estimation Based on Crowdsourced Data

With the increase in urbanization and the growth of the economy, car ownership increases significantly, and at the same time demand for parking increases causing several issues like traffic congestion and resource wastage. There’s a critical significance of accurate parking demand estimation in the context of urban planning, addressing those issues. It emphasizes the emerging role of crowdsourced data as a novel solution for more efficient and cost-effective parking demand estimation. This review discusses the benefits of crowdsourcing, including real-time data and minimal infrastructure requirements, while acknowledging challenges like data accuracy, user privacy, and potential biases. Furthermore, it provides a comprehensive overview of the subject matter and it suggests future directions for improvement, proposing the integration of advanced technologies like Google APIs and IoT to augment parking demand estimation models and address the limitations associated with crowdsourced data.

Solving the guaranteed-service time inventory model for real-world supply chains by implementing it in commercial optimisers

The guaranteed service time inventory (GSTI) model aims to satisfy customers’ demand in an assured time. As multi-echelon supply chains (SC) grow in size and complexity, there is a need to develop exact algorithms to compute the amount of safety stock to deliver products to customers in the guaranteed service time. This work proposed an algorithm to minimise the safety stock in real–world SCs in minutes. The application reads the data set provided in [31]; then a continuous initial solution is computed in seconds using the software library (IPOpt); finally, that solution is used as the initial solution to run the branch–and–cut (B&C) algorithm implemented in a state-of–the–art commercial solver. In the proposed application, the GSTI model is modified to be implemented in commercial solvers when the demand is normally distributed. The reader can download the application’s source code from [24] to analyse the results of the 38 instances. The application computes an integer solution to each of the 38 SCs; in the worst case, it takes 150 min to find the solution. Seventeen instances are solved optimally, and the worst gap between the actual and best solution is about 24% in three instances. The biggest instance is solved in less than 10 min, but other smaller ones are solved in a longer time because of the echelons’ structure (degree and depth). Thus, the time to compute a solution does not depend on the size of the SC but on its structure. The findings indicate that by using a state-of–the–art commercial solver (based on the B&C method) and running it with powerful computer capabilities, real–world SCs can be solved with techniques that return optimal solutions.

Potassium permanganate oxidation of low-permeability phenanthrene contaminated soil under fracturing conditions

In situ chemical oxidation (ISCO) technology has limitations in the remediation of low-permeability polycyclic aromatic hydrocarbon (PAHs)-contaminated sites because of the difficulty in oxidant delivery. Horizontal fractures created by hydraulic fracturing provide convenient channels for rapid oxidant delivery. However, limited studies have focused on potassium permanganate (KMnO4) oxidation in low-permeability PAH-contaminated sites under fracturing conditions considering in situ hydrological environments. Therefore, this study aimed to explore the effect of KMnO4 concentration on the remediation efficiency (including the removal rate of phenanthrene (PHE), remediation time, and total oxygen demand (TOD)) of low-permeability PHE-contaminated soil through a one-dimensional penetration-oxidation test, and the effect of KMnO4 oxidation on the physicochemical properties of the soil through a series of laboratory experiments. The results showed that an increase in KMnO4 concentration shortened the remediation time and increased the removal rate of PHE and TOD from PHE-contaminated soil. The oxidation of KMnO4 resulted in an increase in the pH, oxidation-reduction potential, dissolved organic carbon, cation exchange capacity, specific surface area, and fine particle content of the soil, and a decrease in the total organic carbon of the soil, but did not alter the permeability of the soil.

Why and how to collect representative study samples in educational neuroscience research

BackgroundEducational neuroscience research, which investigates the neurobiological mechanisms of learning, has historically incorporated samples drawn mostly from white, middle-class, and/or suburban populations. However, sampling in research without attending to representation can lead to biased interpretations and results that are less generalizable to an intended target population. Prior research revealing differences in neurocognitive outcomes both within- and across-groups further suggests that such practices may obscure significant effects with practical implications. BarriersNegative attitudes among historically marginalized communities, stemming from historical mistreatment, biased research outcomes, and implicit or explicit attitudes among research teams, can hinder diverse participation. Qualities of the research process including language requirements, study locations, and time demands create additional barriers. SolutionsFlexible data collection approaches, community engaugement, and transparent reporting could build trust and enhance sampling diversity. Longer-term solutions include prioritizing research questions relevant to marginalized communities, increasing workforce diversity, and detailed reporting of sample demographics. Such concerted efforts are essential for robust educational neuroscience research to maximize positive impacts broadly across learners.

Empowering the Traditional Indigenous Politics through Education: An Agent of Political Socialization

Empowering the Traditional Indigenous Politics through Education: An Agent of Political Socialization

Path Optimization of Container Multimodal Transportation Considering Differences in Cargo Time Sensitivity

To optimize the container multimodal transportation path selection problem with differences in cargo time sensitivity, this paper introduces the concept of mixed time windows and establishes an integer programming model from the perspective of carriers. The objective is to minimize the total transportation cost while meeting the customer’s on-time delivery requirements. The model imposes hard time window constraints on cargoes with on-time delivery requirements and soft time window constraints on general cargoes. A bi-level genetic algorithm is employed to solve the model. A case study is conducted to demonstrate the effectiveness of the proposed approach. Experimental results show that the bi-level genetic algorithm’s convergence ability, efficiency, stability, and global search capabilities are far superior to those of traditional single-level genetic algorithms. This indicates that the use of multi-stage solution approaches can mitigate the shortcomings of genetic algorithms in dealing with high-dimensional problems. In addition, when analyzing the effects of time penalty intensity and time window on decision-making results, it is found that the more time-sensitive cargoes are more likely to be affected by the time window setting, and will tend to sacrifice costs to ensure that the time demand is met. At the same time an increase in demand for time-sensitive services is accompanied by an increase in operating costs. Accurate assessment and classification of time sensitivity of cargoes can effectively prevent wasting resources in unnecessary areas, and ultimately maximize decision outcomes and ensure the most efficient use of resources.

Japanese investment for skills development program in attaining sustainable employability: The case of Bangladesh

Bangladesh lacks many trained workers. The existing technical training curriculums are not advanced enough to achieve the Sustainable Development Goals (SDGs) target for human capital development. Therefore, it is a time demand to address the need for skilled human resources. Japan prioritizes Bangladesh's skills development sector to assist the nation in achieving SDGs and importing skilled labor forces into Japan's already declined labor market. This research focuses on the prospects and challenges of Japanese investments in skills development projects in Bangladesh. To this end, this research adopts a need-based approach to prepare a uniform framework for skills development in Bangladesh and finds several challenges in the process of skills development programs that influence the development partners. This study employs a mixed-method research approach. This study finds that the trainees consider the skills development programs significant for their future careers and sustainable employability, although they face various challenges, such as a lack of qualified instructors and training programs and lack of training equipment apart from their lower socio-economic backgrounds. This study especially highlights the significance of Japanese investment in skills development programs in Bangladesh to meet human capital development requirements and develop a framework for sustainable accomplishment of those programs to achieve the SDGs. Finally, this research makes some policy recommendations for relevant stakeholders, including development partners engaged in skills development programs in Bangladesh.

5G and edge: A reinforcement learning approach for Virtual Network Embedding with cost optimization and improved acceptance rate

5G technologies are fueling a revolution across numerous industries, including manufacturing, healthcare, and entertainment, by enabling the development and deployment of novel applications at the network’s edge. To meet the demanding service level agreements of these industries, a dynamic and adaptable infrastructure strategy that combines cloud and edge computing models is needed. This hybrid approach offers the benefits of both centralized cloud processing and decentralized edge computing, optimized for responsiveness and efficiency. A key element for success is an orchestration mechanism that dynamically allocates resources to ensure the infrastructure can adapt to fluctuating demands in real time, optimizing resource utilization and meeting SLA requirements. Among these mechanisms, virtual network embedding (VNE) and dynamic resource management (DRM) have emerged as tools for defining where and how edge technology should be used. However, current VNE approaches struggle to adapt to real-time fluctuations in demand across geographically distributed edge resources. This work introduces a novel resource allocation algorithm, the VNE-CRS, which uses an Artificial Intelligence technique called Reinforcement Learning to orchestrate resources across multiple domains. This approach benefits from the strength of Reinforcement Learning: its ability to consider the entire problem from beginning to end while incorporating various aspects of 5G Quality of Service Indicators for optimal decision-making. Experiments were conducted to evaluate the performance of VNE-CRS against state-of-the-art algorithms for multi-domain edge environments. Results have shown that employing Reinforcement Learning techniques for VNE resource allocation yields performance gains of 12.32 percentage points in comparison with the GRC algorithm and 28.80 percentage points in comparison with the base edge environment, presenting an acceptability rate closer to the Public Cloud environment with all benefits of edge environment. In conclusion, VNE-CRS offers an efficient solution for resource allocation in 5G environments, achieving superior performance and transforming the VNE architecture into a comprehensive orchestration system that optimizes infrastructure utilization for strategic long-term benefits.

An Examination and Evaluation of the Rising Incidence of Breast Cancer among Women and Impact on Society

The number of deaths from breast cancer is rising rapidly year by year. The most common kind of cancer named as breast cancer is an overall and the leading cause for women death rate all over India. A cancer patient can go for a long and healthy life by early detection and proper treatment of cancer. In India, where advanced stages of the disease at diagnosis, combined with rising incidence and death rates, make breast cancer the most frequent cancer among women now a days. It is demand of time to spread a better cancer literacy awareness among women of India. Therefore, we did secondary data collection using literature review in order to know patient survival parameters and treatment strategies, with high degree of accuracy in breast cancer detection . Online and offline structured questioners has been used to collect primary data from various participants. This research paper also used a large dataset of breast cancer patients from the UCI Machine Learning Repository. Our paper examined that there is a very low cancer literacy about breast cancer risk factors in India , irrespective of socio-economic and educational background of participant women . There is an urgent need for offline and online awareness programmes to improve cancer literacy in Indian Women .

SAFETY RISKOLOGY

Problem statement. The rapid expansion of the technosphere, unbalanced geopolitical, ecological, and biological processes of modern times demand a complete reassessment, a deep understanding, and the study of risk in all spheres of human life and activity. War and the non-trivial actions of aggressors force us into the necessity of reactive risk management, not excluding the need for proactive risk management. This complicates the determination of the priority of preventive measures at each stage of the activities of subjects and objects of risk, requiring the combination of these principles and the continuous reconsideration of risks to develop effective positive-compensatory measures. The actual state of human (humanity's) protection indicates that without the creation of a “science of risks”, without the establishment of a corresponding academic discipline, further effective and rational risk management is no longer possible. The purpose of the article. Justification for the Introduction of a New Academic Discipline “Safety Riskology” in Engineering and Technical Specialties at Higher Educational Institutions in Ukraine. Conclusions. The conceptual notion of “safety riskology” has been “synthesized” for the first time. The need for teaching a new academic discipline, “Safety Riskology”, in higher education institutions has been justified. During the instruction of this discipline, it is proposed to shape in learners advanced competencies in the identification, analysis, assessment, and compensation (balancing) of risk, considering the types and characteristics of the activities of risk subjects and objects. The demand for professionals with contemporary knowledge in safety riskology is identified across all sectors of the economy. Discussions on the initiation of the independent science of “Safety Riskology” have been initiated.

A service evaluation of an online intervention for adults with learning disabilities

A mixed methods service evaluation explored the feasibility and effectiveness of an online intervention for people with learning disabilities in a rural county. Psychological wellbeing and self-esteem were compared pre and post-intervention for twelve participants. Thematic analysis of semi-structured telephone interviews with five participants and seven caregivers was conducted. Outcome measures showed general improvement although not meeting significance. Qualitative feedback was positive, stating the intervention was beneficial in developing emotional and interpersonal skills and increasing confidence. Service benefits included interventions for referrals that might otherwise be declined or placed on a waiting list, with little time demand on facilitators.Easy to read summary■An online intervention was developed to help people access support quickly to learn skills to feel better.■At the end, people with a learning disability and their caregivers told us how they found the intervention. They filled in questionnaires about their wellbeing and self-esteem.■People with a learning disability enjoyed the online intervention. It helped them learn ways to manage their feelings, improve their relationships and build their self-esteem. They also liked talking to new people.■For the service, it was a cost and time effective way of providing intervention for low-risk referrals to help manage the demand.

Optimization Strategies for Real-time Energy Management of Electric Vehicles Based on LSTM Network Learning

The orderly control of electric vehicle load can improve the load characteristics of regional power grid and reduce the charging cost. Since it is impossible to predict the accurate access time and charging demand of electric vehicles in the future, it is impossible to make a global optimal arrangement for accessing the grid when electric vehicles are charging. Aiming at this problem, a real-time energy management system and optimization strategy of electric vehicle based on deep long-term and short-term memory neural network are proposed. Firstly, a three-tier electric vehicle management architecture including power grid layer, regional energy management system and charging station energy management system is constructed to manage large-scale electric vehicles in layers and regions; Then, a region station interaction strategy based on deep long-term and short-term memory neural network is proposed. The historical optimal solution solved by historical load information is used to train the learning network to guide the new real-time optimization; The proposed strategy can further reduce the charging cost and improve the peak valley characteristics of regional load on the premise of ensuring the charging demand of users. Finally, a simulation example is given to verify the effectiveness and superiority of the proposed layered architecture and management strategy.

In vitro human cell culture models in a bench-to-bedside approach to epilepsy.

Epilepsy is the most common chronic neurological disease, affecting nearly 1%-2% of the world's population. Current pharmacological treatment and regimen adjustments are aimed at controlling seizures; however, they are ineffective in one-third of the patients. Although neuronal hyperexcitability was previously thought to be mainly due to ion channel alterations, current research has revealed other contributing molecular pathways, including processes involved in cellular signaling, energy metabolism, protein synthesis, axon guidance, inflammation, and others. Some forms of drug-resistant epilepsy are caused by genetic defects that constitute potential targets for precision therapy. Although such approaches are increasingly important, they are still in the early stages of development. This review aims to provide a summary of practical aspects of the employment of invitro human cell culture models in epilepsy diagnosis, treatment, and research. First, we briefly summarize the genetic testing that may result in the detection of candidate pathogenic variants in genes involved in epilepsy pathogenesis. Consequently, we review existing invitro cell models, including induced pluripotent stem cells and differentiated neuronal cells, providing their specific properties, validity, and employment in research pipelines. We cover two methodological approaches. The first approach involves the utilization of somatic cells directly obtained from individual patients, while the second approach entails the utilization of characterized cell lines. The models are evaluated in terms of their research and clinical benefits, relevance to the invivo conditions, legal and ethical aspects, time and cost demands, and available published data. Despite the methodological, temporal, and financial demands of the reviewed models they possess high potential to be used as robust systems in routine testing of pathogenicity of detected variants in the near future and provide a solid experimental background for personalized therapy of genetic epilepsies. PLAIN LANGUAGE SUMMARY: Epilepsy affects millions worldwide, but current treatments fail for many patients. Beyond traditional ion channel alterations, various genetic factors contribute to the disorder's complexity. This review explores how invitro human cell models, either from patients or from cell lines, can aid in understanding epilepsy's genetic roots and developing personalized therapies. While these models require further investigation, they offer hope for improved diagnosis and treatment of genetic forms of epilepsy.

Mathematical modelling of HMT through porous stretching sheet using artificial neural network

The influence of heat radiation on HMT, MHD on a porous stretched sheet has been explained in this study. The governing PDEs are converted to ODEs via similarity transformations. The coefficient of skin friction, rate of HMT are calculated using the MATLAB software for various parameter values. The controlling boundary layer PDEs are turned into a system of coupled nonlinear ODEs via similarity techniques, which are numerically solved using the shooting technique in conjunction with the fourth order Runge Kutta method, and then ANN is applied to them. Validation of numerical results using ANN results. We discovered that using engineering standpoints, an ANN model may produce high-efficiency estimates for heat transfer rates. The achieved R squared values of 99% for predicting Skin friction, Nusselt number and Sherwood number coefficients highlight the remarkable effectiveness of neural network models in these predictions. This efficacy not only demonstrates the accuracy of the models but also results in a significant reduction in the computational time required compared to traditional numerical methods. Furthermore, when compared to alternative numerical approaches, the current ANN model stands out for its applicability to more complex mathematical models, because its efficiency in minimizing both time and processing capacity demands in solving such problems.

Exploring spatio-temporal impact of COVID-19 on citywide taxi demand: A case study of New York City.

Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents' urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model's dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.

The impact of cohort relationships on BIPOC genetic counseling students: Results from a longitudinal qualitative study.

The transition to graduate school is marked by stress, with academic demands and interpersonal interactions being primary concerns for genetic counseling students. For Black, Indigenous, and People of Color (BIPOC) graduate students, additional stressors caused by the "minority tax" and microaggressions impact their sense of belonging and inclusion. This prospective longitudinal study employed a constructivist grounded theory approach to investigate the experiences of first-year BIPOC genetic counseling students as they transitioned into the first year of their graduate training. We conducted semi-structured interviews with 26 first-year genetic counseling students at three key time points during their first year and analyzed them using reflexive thematic analysis. Here, we report themes related to stressors when transitioning into the genetic counseling training environment, the role of relationships as a source of support in navigating these challenges, and the impact of cohort dynamics on the training experience. Stressors included managing academic rigor and time demands, navigating microaggressions, reactions to discussions about diversity, equity, inclusion, and justice (DEIJ), and managing mental health. Peer relationships emerge as pivotal source of support, but challenging dynamics within the cohort negatively impacted participants, highlighting the importance of fostering an inclusive training environment. Since programs have less control over the composition of each cohort with the advent of the Match system in 2018, we recommend the use of community-building and debriefing activities to strengthen healthy relationships and address problematic dynamics. We recommend that training programs be proactive in creating mentoring relationships between faculty and students rather than waiting until students ask for help. Ultimately, we advocate for a holistic approach to genetic counseling training that maintains academic rigor but also prioritizes the creation of supportive, inclusive, and culturally sensitive learning environments for all students.

Fourth Party Logistics Routing Problem Under Uncertain Time and Random Demand

With the rapid development of the economy, the fourth party logistics (4PL) has become an indispensable part of economic development, and the path optimization is an important topic in the research of 4PL routing problem. This paper studies the fourth party logistics routing problem with random demand and uncertain transportation time and transfer time by considering the randomness and uncertainty of the environment. In order to solve the path optimization strategy that minimizes the total transportation cost, this paper establishes an uncertain random programming model under the constraints of the total transportation time, the carrying capacity of the logistics provider, the transfer capacity of the transfer station, and the demand of the distributor. Subsequently, this paper transforms the uncertain random programming model into an equivalent deterministic programming model based on the distribution functions of random demand and uncertain time, and verifies the effectiveness of the model through two examples. The results show that the uncertain random programming model can give a reasonable path optimization strategy.