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It has been shown that, after the shutdown of the nuclear reactor at the Rosatom Mining and Chemical Combine (MCC) in 2010, anthropogenic radionuclides (60Co, 137Cs, 152Eu, and 241Am) are still detected in the bottom sediments (BS) of the Yenisei River in the MCC-affected area. Data from a comparative analysis of the potential mobility of anthropogenic radionuclides in the Yenisei BS surface and lower layers long after the shutdown of the MCC nuclear reactor are presented for the first time. The mobility of radionuclides was estimated using the well-known procedure of sequential extraction of BS. For 60Co, an increase in potential mobility in the lower layers (which are earlier in time of formation) was shown compared to the upper ones for the first time. The radionuclide 137Cs retains high binding strength to mineral matter of BS (close to 100%) regardless of the time factor. The long-term preservation of the high potential mobility of the radionuclides 241Am and 152Eu in BS, as well as the increase in potential mobility of 60Co over time, for the first time makes it possible to consider these radionuclides as potentially bioavailable in the ecosystem of the Yenisei River. [ABSTRACT FROM AUTHOR]

Considering the high importance of the optimization problem, this study evaluated mathematical programming models by considering various methods of estimating model coefficients. Correct and accurate data must be entered into the model to get accurate and robust result from the model. Most input data to the presented model are technical and objective function coefficients. Therefore, it is necessary to determine the information related to these coefficients with the utmost precision and, as much as possible, to develop a suitable scientific method to estimate the value of these coefficients [5]. Finding the best method for estimating the coefficients of mathematical programming models can significantly optimize the final values of the variables extracted from the mathematical programming model. For this reason, it is essential to study the methods used so far in this field and examine their advantages and disadvantages. This review study investigated various methods of estimating technical coefficients of mathematical planning models in the conditions of possible decision-making and uncertainty after reviewing 117 articles published between 1955 and 2022. These methods include fuzzy methods, statistical methods, and data analysis methods. Statistical methods such as regression methods, time series methods, exponential smoothing, and linear non-linear and non-parametric, machine learning and data mining methods were investigated in this article. The methods of data-driven analysis explained in this article can be referred to as decision trees, random forests and the Lasso methods. After evaluating and comparing these methods, suggestions for choosing the best method were provided. [ABSTRACT FROM AUTHOR]

Issue Addressed: While definitions of impact may vary, they often refer to the wider benefits of research evidenced beyond academia. We evaluated case studies featuring randomised trials from the 2018 Engagement and Impact Assessment to better understand how the impacts of health research are evidenced and assessed within Australia. Methods: We collated and evaluated 'high' scoring case studies submitted by higher education institutions with a focus on randomised trials across all areas of health research. A qualitative coding system was used for manual content analysis to assess the key characteristics of trials reported, subsequent impacts and the methods used to evidence impacts. Results: A total of 14 case studies were identified citing 35 clinical trials. The majority of interventions were behavioural with a focus on mental, behavioural or neurodevelopmental disorders. Most trials were phase III, focused on the treatment of the indication and were funded by industry. Contribution to clinical guidelines was the highest cited research impact. While there was evidence of researchers seeking to maximise trial impact, case studies lacked details on the role of trial participants and other beneficiaries in generating impact. Conclusions: The impacts of health research can be improved through a better understanding of the priorities and agendas of funders, providing evidence of tangible impact rather than information that is contextual or predictive, and through the early development of impact strategies involving both researchers and beneficiaries. So What?: Large‐scale impact exercises intended for a broad range of disciplines may not be reflective of the depth and scope of health sciences research including trials. [ABSTRACT FROM AUTHOR]

This paper uses big data analysis to determine user similarity and calculate the bias in selecting nearest neighbors. The temporal factor is introduced to fully reflect the changing status of users’ interest degrees so that the recommendation accuracy can be significantly improved. According to the nearest neighbors’ rating of experimental teaching resources, the collected data on the effectiveness of vocal music teaching in colleges and universities are clustered, and the results are reflected using degree weights and biases for updating. It was discovered that seven samples had actual student vocal rating values above 0.5, and the overall vocal test scores could reach 86 or higher. To be more energetic in contemporary times, vocal music teaching in colleges and universities should be reformed and innovated to incorporate big data analysis technology.

In order to have a qualitative improvement in the education management of universities in the background of the era of big data technology, it is necessary to integrate information technology and technological means in the process of education management. Based on this background, this paper constructs an informatization education management platform. In the process of platform architecture, the cloud storage module obtains the exact nearest neighbors by the time factor, optimizes the construction process by using the weighting function and hybrid coordination filtering algorithm, and simplifies the teaching management process. In the teaching management system, the fixed rules of the feature selection algorithm are used to achieve teaching resource selection prediction in the optimal feature subset and establish the best classification surface. Finally, the performance of the platform proposed in this paper is verified from two levels: resource recommendation and education management. The experimental results show that the MAE (Mean Absolute Error) value of this platform is always under 0.7, with a mean value of 0.523, and the experimental teaching resource recommendation effect is better than that of the IE virtual experiment teaching platform and data platform. And the maximum number of visits that the platform can bear is 884, and the maximum amount of government processing can reach 839, which is much larger than the number of 800 users of experimental teaching in the university and fully meets the needs of university education management.

Abstract Aim To describe the associations of psychological burnout and time factors on hospital nurses' medication errors. Design A cross‐sectional survey design was used. Methods A structured questionnaire pertaining to psychological burnout, time factors and medication error was administered to 200 bedside nurses working at two tertiary university hospitals in Korea. The associations between the psychological burnout, time factor and medication error were analysed with the zero‐inflated negative binomial regression for over‐dispersed and over‐abundant zeros count data. Results Higher psychological burnout, shorter meal time during duty and longer weekly overtime were associated with an increased likelihood of medication error of nurses working in tertiary university hospitals. For medication safety, nurse managers should provide appropriate administrative support to nurses to cope with psychological burnout of nurses. Work time management should also be considered as human factors to satisfy the needs of nurses, such as securing meal times and maintaining a low level of weekly overtime.

In fault diagnosis of gas turbines, it is necessary to determine the exact time when the fault occurs, so as to obtain the operating and environmental conditions of the turbine. However, the health and fault data of the turbine are mixed and hard to be distinguished because of the noise disturbance. In order to eliminate the influence of noise on clustering results, the time factor was introduced based on the traditional t-SNE algorithm. Then, a two-dimensional grid clustering method was used to process the dimension-reduced data of the t-SNE algorithm, so as to rapidly distinguish the health status of different data to determine the exact time of failure. Besides, a simulation dataset for the performance attenuation of compressors was created with the performance attenuation mechanism model of a compressor established in Matlab Simulink, and based on that, the t-SNE algorithm with a time factor was verified. The results indicate that the new algorithm is superior to other processing methods. The fault diagnosis method of gas turbines based on the t-SNE algorithm with time factor could rapidly and accurately determine the exact time when the fault of the turbine occurs, thus providing technical support to the health condition monitoring and fault diagnosis of gas turbines.

Abstract Background The potential effects of time factor and albuminuria on the morbid alterations in patients with type 2 diabetes (T2D) and COVID-19 are still unclear. We aimed to address the morbid alterations and the potential effects of time factor and albuminuria on the patients’ characteristics before, during, and 1 year after COVID-19 recovery. Methods 83 patients with T2D were included, at Mansoura University Hospital, Egypt (July 2021-December 2021). Data of detailed history, physical examination, laboratory tests were recruited from files of the patients. Diagnosis and resolution of COVID-19 were established by Real time polymerase chain reaction (RT-PCR) test of SARS-CoV2. Complete blood count (CBC), renal and hepatic function tests, multiple measures of morning spot urine albumin to creatinine ratio (urine ACR), glycosylated hemoglobin (HBA1c), lipid profile, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), Ferritin, neutrophil to lymphocyte ratio (NLR), vitamin D3, intact parathyroid hormone (intact PTH), serum calcium were applied to all participants. Results Our participants had a mean age of 45 years, 60.2% male, 56.6% were hospitalized, and 25.3% were admitted to ICU for severe COVID-19. Albuminuria was prevalent in 71.1% before, 98.8% during, and 92.8% after COVID-19 recovery. Patients with albuminuria showed older age, longer duration of T2D, more frequent severe COVID-19 and hospitalization (p = 0.03, p

Background: This study aimed to investigate the natural growth history of peripheral small-cell lung cancer (SCLC) using CT imaging. Methods: A retrospective study was conducted on 27 patients with peripheral SCLC who underwent at least two CT scans. Two methods were used: Method 1 involved direct measurement of nodule dimensions using a calliper, while Method 2 involved tumour lesion segmentation and voxel volume calculation using the "py-radiomics" package in Python. Agreement between the two methods was assessed using the intraclass correlation coefficient (ICC). Volume doubling time (VDT) and growth rate (GR) were used as evaluation indices for SCLC growth, and growth distribution based on GR and volume measurements were depicted. We collected potential factors related to imaging VDT and performed a differential analysis. Patients were classified into slow-growing and fast-growing groups based on a VDT cut-off point of 60 days, and univariate analysis was used to identify factors influencing VDT. Results: Median VDT calculated by the two methods were 61 days and 71 days, respectively, with strong agreement. All patients had continuously growing tumours, and none had tumours that decreased in size or remained unchanged. Eight patients showed possible growth patterns, with six possibly exhibiting exponential growth and two possibly showing Gompertzian growth. Tumours deeper in the lung grew faster than those adjacent to the pleura. Conclusions: Peripheral SCLC tumours grow rapidly and continuously without periods of nongrowth or regression. Tumours located deeper in the lung tend to grow faster, but further research is needed to confirm this finding. [ABSTRACT FROM AUTHOR]

Aim: To describe the associations of psychological burnout and time factors on hospital nurses' medication errors. Design: A cross‐sectional survey design was used. Methods: A structured questionnaire pertaining to psychological burnout, time factors and medication error was administered to 200 bedside nurses working at two tertiary university hospitals in Korea. The associations between the psychological burnout, time factor and medication error were analysed with the zero‐inflated negative binomial regression for over‐dispersed and over‐abundant zeros count data. Results: Higher psychological burnout, shorter meal time during duty and longer weekly overtime were associated with an increased likelihood of medication error of nurses working in tertiary university hospitals. For medication safety, nurse managers should provide appropriate administrative support to nurses to cope with psychological burnout of nurses. Work time management should also be considered as human factors to satisfy the needs of nurses, such as securing meal times and maintaining a low level of weekly overtime. [ABSTRACT FROM AUTHOR]

Electric load forecasting is crucial for the metallurgy industry because it enables effective resource allocation, production scheduling, and optimized energy management. To achieve an accurate load forecasting, it is essential to develop an efficient approach. In this study, we considered the time factor of univariate time-series data to implement various deep learning models for predicting the load one hour ahead under different conditions (seasonal and daily variations). The goal was to identify the most suitable model for each specific condition. In this study, two hybrid deep learning models were proposed. The first model combines variational mode decomposition (VMD) with a convolutional neural network (CNN) and gated recurrent unit (GRU). The second model incorporates VMD with a CNN and long short-term memory (LSTM). The proposed models outperformed the baseline models. The VMD–CNN–LSTM performed well for seasonal conditions, with an average RMSE of 12.215 kW, MAE of 9.543 kW, and MAPE of 0.095%. Meanwhile, the VMD–CNN–GRU performed well for daily variations, with an average RMSE value of 11.595 kW, MAE of 9.092 kW, and MAPE of 0.079%. The findings support the practical application of the proposed models for electrical load forecasting in diverse scenarios, especially concerning seasonal and daily variations. [ABSTRACT FROM AUTHOR]

Aim of the study: To analyze the hepatitis B virus (HBV) infection and mortality in Poland according to sociodemographic characteristics, trends over time, and the impact of the COVID-19 pandemic on hepatitis B epidemiology. Material and methods: We examined HBV infection cases and deaths reported by national registries and used Joinpoint analysis to estimate time trends in the years 2005-2021. To assess the impact of the COVID-19 pandemic on HBV infection, we used monthly information and compared 2020-2022 with 2019. Results: The Joinpoint analysis showed that in Poland between 2005 and 2021, there were pronounced decreasing trends of acute HBV infection, and during the pandemic period, acute HBV infection dramatically decreased (annual percent change, APC2019-2021 for men -57.65%, and women -42.10%, both ptrend < 0.05). There was a fluctuation in trends for chronic HBV infection, shifting from positive to negative in both genders in 2016, and over the pandemic, there were decreasing trends (APC2019-2021 for men -26.94% and women -28.96%, both ptrend < 0.05). From March to July 2022, the value of the diagnosis rate of HBV infection was lower compared to the respective months in 2019, but from September to December 2022, the rate changes were positive. Mortality due to HBV infection decreased in both genders, mainly within the 2005-2019 period. Conclusions: During the COVID-19 pandemic, a sharp decrease in HBV diagnosis rates in Poland, especially in acute cases, was observed. However, trends of hepatitis B infection require further monitoring. It is necessary to introduce a national screening program that also encompasses the population of migrants and improve the linkage to care. [ABSTRACT FROM AUTHOR]

Background: The potential effects of time factor and albuminuria on the morbid alterations in patients with type 2 diabetes (T2D) and COVID-19 are still unclear. We aimed to address the morbid alterations and the potential effects of time factor and albuminuria on the patients' characteristics before, during, and 1 year after COVID-19 recovery. Methods: 83 patients with T2D were included, at Mansoura University Hospital, Egypt (July 2021-December 2021). Data of detailed history, physical examination, laboratory tests were recruited from files of the patients. Diagnosis and resolution of COVID-19 were established by Real time polymerase chain reaction (RT-PCR) test of SARS-CoV2. Complete blood count (CBC), renal and hepatic function tests, multiple measures of morning spot urine albumin to creatinine ratio (urine ACR), glycosylated hemoglobin (HBA1c), lipid profile, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), Ferritin, neutrophil to lymphocyte ratio (NLR), vitamin D3, intact parathyroid hormone (intact PTH), serum calcium were applied to all participants. Results: Our participants had a mean age of 45 years, 60.2% male, 56.6% were hospitalized, and 25.3% were admitted to ICU for severe COVID-19. Albuminuria was prevalent in 71.1% before, 98.8% during, and 92.8% after COVID-19 recovery. Patients with albuminuria showed older age, longer duration of T2D, more frequent severe COVID-19 and hospitalization (p = 0.03, p < 0.001, p = 0.023& p = 0.025) respectively. Body mass index (BMI), mean arterial blood pressure, ESR, CRP, ferritin, NLR, HBA1c, triglycerides to high-density lipoprotein cholesterol (TG/HDL-C) ratio, vitamin D3, serum calcium, alkaline phosphatase (ALP), hepatic aminotransferases, and urine ACR showed significant alterations throughout the study (p < 0.001 for all). Although the interaction between time and albuminuria showed non-significant effect on all studied parameters, we noticed relevant main effects of time factor on Body mass index (BMI), HBA1c, glomerular filtration rate (eGFR), TG/HDL ratio, NLR, vitamin D3, (p < 0.001 for all). Moreover, albuminuria showed main effects on BMI, serum creatinine, and intact PTH (p = 0.019, 0.005 & <0.001), respectively. Conclusion: The characteristics of patients with T2D significantly altered throughout the study. Time factor and albuminuria exerted relevant main effects on the patients' characteristics without significant effect of their interaction. [ABSTRACT FROM AUTHOR]

The dual-channel graph collaborative filtering recommendation algorithm (DCCF) suppresses the over-smoothing problem and overcomes the problem of expansion in local structures only in graph collaborative filtering. However, DCCF has the following problems: the fixed threshold of transfer probability leads to a decrease in filtering effect of neighborhood information; the K-means clustering algorithm is prone to trapping clustering results into local optima, resulting in incomplete global interaction graphs; and the impact of time factors on the predicted results was not considered. To solve these problems, a dual-channel feature enhanced collaborative filtering recommendation algorithm (DCFECF) is proposed. Firstly, the self-attention mechanism and weighted average method are used to calculate the threshold of neighborhood transition probability for each order in local convolutional channels; secondly, the K-means++ clustering algorithm is used to determine the clustering center in the global convolutional channel, and the fuzzy C-means clustering algorithm is used for clustering to solve the local optimal problem; then, time factor is introduced to further improve predicted results, making them more accurate. Comparative experiments using normalized discounted cumulative gain (NDCG) and recall as evaluation metrics on three publicly available datasets showed that DCFECF improved by up to 2.3% and 4.1% on two metrics compared to DCCF. [ABSTRACT FROM AUTHOR]

This study considers learning in international military operations. It explores these questions: What are the obstacles to learning in a military context? How did these obstacles manifest themselves in contribution made by the Royal Norwegian Air Force (RNoAF) in Mali? How effective in overcoming these obstacles were the formal and informal learning processes that took place? And what does this tell us about how learning might be improved in a military context? Norway has supported the United Nations Multidimensional Integrated Stabilization Mission in Mali (MINUSMA) since 2013. The RNoAF contribution began in January 2016 and continued periodically until May 2021. The contribution consisted of a Hercules C130 transport aircraft with crew and other support personnel. The detachment from Norway consisted of approximately 60–70 women and men with backgrounds in various fields of expertise (aviators, navigators, cargo masters, technicians, security personnel, and other support personnel) required to execute the mission. Based on in-depth interviews of key personnel, this study looks at learning that took place among the various constellations of personnel from the force protection responsible for security during the operation. Much of the learning took place through last-minute efforts because of tight schedules and other pressing tasks. The study has resulted in a conceptual model for facilitating and better supporting the acquisition of knowledge through a continuous process rather than at the last minute. This will require top-down and bottom-up processes, both formal and informal.

“Learning” is a complex event.Individual's learning effect is affected by many factors.Moreover, different individuals have different learning habits.Therefore, it is challenging for students to plan their learning schedule reasonably according to their own characteristics.Although some general theoretical strategies for task management have been proposed, the differences among individuals are usually neglected.Furthermore, existing research cannot provide a calculation method to form a specific task mana-gement schedule.To this end, this paper tries to explore students'learning characteristics by deeply studying the relation between learning efficiency and time factor through data analysis.Based on this, it quantifies personalized learning efficiency.Furthermore, it exploits the bipartite graph method to construct the learning task assignment scenario, and designs adaptive utility function according to different learning goals.Then, a dynamic allocation algorithm TLTA based on transfer learning is proposed to formulate a reasonable schedule for students.Finally, a large number of experiments are carried out on real learning datasets, and the results validate the effectiveness and applicability of the proposed work.

BACKGROUND:Cerebral microhemorrhages (CMH) are commonly found in the aging brain. CMH are also the neuropathological substrate of cerebral microbleeds (CMB), demonstrated on brain MRI. Recent studies demonstrate the importance of systemic inflammation in CMH development, but the relationships among inflammation, aging, and CMH development are not well-defined. In the current study, we hypothesized that the pathogenesis of inflammation-induced CMH in mice differs by age. METHODS:We studied young (3 months, n = 20) and old (18 months, n = 25) C57BL/6 mice injected with low-dose lipopolysaccharide (LPS; 1 mg/kg, i.p.) or saline at 0, 6, and 24 h. Seven days after the first LPS/saline injection, brains were harvested, sectioned, and stained with hematoxylin and eosin (H&E) and Prussian blue (PB) to estimate acute/fresh and sub-acute CMH development, respectively. The relationships between microglial/macrophage activation (ionized calcium-binding adapter molecule-1), astrocyte activation (glial fibrillary acidic protein), blood-brain barrier (BBB) disruption (brain immunoglobulin G), aging, and CMH development were examined using immunohistochemistry. RESULTS:Aging alone did not increase spontaneous H&E-positive CMH development but significantly increased the number, size, and total area of LPS-induced H&E-positive CMH in mice. LPS- and saline-treated aged mice had significantly larger PB-positive CMH compared with young mice, but the total area of PB-positive CMH was increased only in LPS-treated aged mice. Aged mice had significantly increased microglial/macrophage activation, which correlated with H&E- and PB-positive CMH development. Aged mice treated with LPS had significantly increased astrocyte activation and BBB disruption compared with young LPS-treated mice. CONCLUSIONS:Aging makes the brain more susceptible to inflammation-induced CMH in mice, and this increase in CMH with aging is associated with microglial/macrophage activation.

Surface subsidence caused by underground coal mining has received wide attention due to its impact on the ecological environment. To obtain first-hand data on mining subsidence, we arranged line measurement stations in the mining area of the Weiqiang coalfield and implemented surface movement observation at its first mining working face and obtained the dynamic subsidence curve. The subsidence curves reflected the initial, active and declining stages of subsidence at each measurement point at a basic level, and were consistent with the general rule of surface subsidence. To accurately analyze the surface movement caused by coal mining, based on the probability integral method and considering the actual advancement progress of the working face, a new three-dimensional dynamic prediction model, namely the strip unit mining model, was proposed. The core assumption of this model was that the longwall mining is regarded as the result of superposition of many units of mining. Considering the influence of different mining times and periods after mining, the subsidence time function of two-time factors was introduced into the strip unit mining model, and the final formulation of the dynamic subsidence prediction of the three-dimensional surface is derived by considering the time factor. Based on the analysis of the measured data of the Weiqiang coalfield, the prediction parameters of surface movement were obtained, and the surface movement and deformation are predicted via the probability integral method. Finally, the measured surface subsidence was compared with the theoretically-predicted one. The good match showed that the parameters of the probability integral method, determined according to the measured data, meet the requirements of geological conditions and mining settings in the mining area, and the predicted subsidence curves agreed well with the field measurements, demonstrating the effectiveness of the newly-developed strip unit mining model. [ABSTRACT FROM AUTHOR]

Trade disciplines under the General Agreement on Trade in Services (GATS) apply to the treatment of products (i.e., services) and to that of producers (i.e., service suppliers). Unlike rules on merchandise trade, the GATS thus covers the conditions governing a producer's commercial establishment and, hence, its competitive position. That raises the question whether Members are free to modify these conditions over time or, instead, whether they are required under the GATSMost-Favoured-Nation (MFN) obligation to provide newly arriving producers with the most attractive conditions they accorded in the past. In the latter case, Members might shy away from launching trade-promoting initiatives beyond their existing GATS commitments, which have remained quite modest overall. As a way forward, Members could consider an informal understanding, possibly inspired by recent regional trade agreements (RTAs), to clarify the application of the GATS MFN obligation over time. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate the natural growth history of peripheral small-cell lung cancer (SCLC) using CT imaging. Methods: A retrospective study was conducted on 27 patients with peripheral SCLC who underwent at least two CT scans. Two methods were used: Method 1 involved direct measurement of nodule dimensions using a calliper, while Method 2 involved tumour lesion segmentation and voxel volume calculation using the “py-radiomics” package in Python. Agreement between the two methods was assessed using the intraclass correlation coefficient (ICC). Volume doubling time (VDT) and growth rate (GR) were used as evaluation indices for SCLC growth, and growth distribution based on GR and volume measurements were depicted. We collected potential factors related to imaging VDT and performed a differential analysis. Patients were classified into slow-growing and fast-growing groups based on a VDT cut-off point of 60 days, and univariate analysis was used to identify factors influencing VDT. Results: Median VDT calculated by the two methods were 61 days and 71 days, respectively, with strong agreement. All patients had continuously growing tumours, and none had tumours that decreased in size or remained unchanged. Eight patients showed possible growth patterns, with six possibly exhibiting exponential growth and two possibly showing Gompertzian growth. Tumours deeper in the lung grew faster than those adjacent to the pleura. Conclusions: Peripheral SCLC tumours grow rapidly and continuously without periods of nongrowth or regression. Tumours located deeper in the lung tend to grow faster, but further research is needed to confirm this finding.

Electric load forecasting is crucial for the metallurgy industry because it enables effective resource allocation, production scheduling, and optimized energy management. To achieve an accurate load forecasting, it is essential to develop an efficient approach. In this study, we considered the time factor of univariate time-series data to implement various deep learning models for predicting the load one hour ahead under different conditions (seasonal and daily variations). The goal was to identify the most suitable model for each specific condition. In this study, two hybrid deep learning models were proposed. The first model combines variational mode decomposition (VMD) with a convolutional neural network (CNN) and gated recurrent unit (GRU). The second model incorporates VMD with a CNN and long short-term memory (LSTM). The proposed models outperformed the baseline models. The VMD–CNN–LSTM performed well for seasonal conditions, with an average RMSE of 12.215 kW, MAE of 9.543 kW, and MAPE of 0.095%. Meanwhile, the VMD–CNN–GRU performed well for daily variations, with an average RMSE value of 11.595 kW, MAE of 9.092 kW, and MAPE of 0.079%. The findings support the practical application of the proposed models for electrical load forecasting in diverse scenarios, especially concerning seasonal and daily variations.

Online services of product delivery apps are more critical than ever. The COVID-19 pandemic has irrevocably altered internet purchasing habits. Customers are getting comfortable using the apps for services to order and do the shopping via their smartphone using the apps. The pandemic has quickened the transition to a more digital world. However, the debate remains as to what elements influence customers' purchase intentions toward using Delivery Services Apps (DSA). This research is therefore carried out to examine the key factors affecting the intention of customers to use delivery services apps among household customers in Malaysia during Covid-19 in 2020 to 2021. The research explored the most significant factors that influence the customer purchase intention on using DSA. There are 4 independent variables which we are focusing on: time factor (TF), price factor (PF), and convenience motivation factor (CMF) and product illustration factor (PIF). From the research, we have found out that convenience motivation factor is the most important exogenous variable that can influence purchase intention directly, sequentially through shopping motivations. The other factors that have a significant impact on a customer's purchase intentions are price factor and time factors. The least important factor that influences a customer’s purchase intention is the product illustrations. The study is carried out by gathering responses from over 167 participants in Malaysia. The objective of the study is to examine the use of online delivery services apps (DSA) to run the businesses efficiently and more effectively in order to attract household customer’s purchase intention. The novelty of this study is by giving the strategies and opportunities to the online business entrepreneurs to enhance and boost their sales by using social media as a platform of online delivery services apps to the community.

This study employs a mixed design to investigate the effects of time factor and clause type on Chinese EFL learners’ implicit and explicit knowledge of relative clauses. Eighty Chinese EFL learners were randomly assigned to one of two-time factor conditions and then tested on a grammaticality judgment task as well as on a sentence combination task to elicit their implicit and explicit knowledge of three types of relative clauses. It was found that, the learners had more accurate performance in untimed tasks than in timed ones; this finding is consistent with some previous studies’ findings. Clause type exerts a significant effect on Chinese EFL learners’ implicit and explicit knowledge of relative clauses, besides time factor and clause type had interaction effects. According to these findings, the article deepens the study of NPAH and it is suggested that multiple timed and untimed tasks could be designed to measure learners’ implicit and explicit knowledge.

In order to solve the problem of low accuracy of traditional network security situation awareness research methods in the case of complex network information,combined with deep learning,this paper proposes a network security situation assessment model based on time factor and composite CNN structure,which combines volume integral solution technology and deep separable technology to form a four layer series composite optimal unit structure.The one-dimensional network data are transformed into two-dimensional matrix and loaded into the neural network model in the form of gray value,so as to give full play to the advantages of convolution neural network.In order to make full use of the time-series relationship between data,time factor is introduced to form fusion data,which makes the network to learn the original data and fusion data with time-series relationship at the same time,the feature extraction ability of the model is increased,the spatial mapping of time-series data is established by using time factor and point convolution,and the integrity of the model structure is increased.Experimental results show that the accuracy of the proposed model on two datasets is 92.89% and 92.60% respectively,which is 2%～6% higher than randomfo-rest and LSTM algorithm.

The dual-channel graph collaborative filtering recommendation algorithm (DCCF) suppresses the over-smoothing problem and overcomes the problem of expansion in local structures only in graph collaborative filtering. However, DCCF has the following problems: the fixed threshold of transfer probability leads to a decrease in filtering effect of neighborhood information; the K-means clustering algorithm is prone to trapping clustering results into local optima, resulting in incomplete global interaction graphs; and the impact of time factors on the predicted results was not considered. To solve these problems, a dual-channel feature enhanced collaborative filtering recommendation algorithm (DCFECF) is proposed. Firstly, the self-attention mechanism and weighted average method are used to calculate the threshold of neighborhood transition probability for each order in local convolutional channels; secondly, the K-means++ clustering algorithm is used to determine the clustering center in the global convolutional channel, and the fuzzy C-means clustering algorithm is used for clustering to solve the local optimal problem; then, time factor is introduced to further improve predicted results, making them more accurate. Comparative experiments using normalized discounted cumulative gain (NDCG) and recall as evaluation metrics on three publicly available datasets showed that DCFECF improved by up to 2.3% and 4.1% on two metrics compared to DCCF.

Submerged pile groups are important components of complex piers in hydraulic engineering and are analogs for a range of subsea structures. These may sustain severe damages from local scour. The velocity intensity (U / Uc, the ratio of critical velocity to mean velocity) and aspect ratio (H / Dp, the ratio of pile height to pile diameter) are critical variables in this scour process. However, previous studies on scour around submerged pile groups were conducted in clear-water conditions (U / Uc < 1). In addition, many research studies are being conducted in shallow water flow conditions, which cannot eliminate the effect of the water depth on the scour process. Thus, these research studies cannot directly be applied to live-bed scour around submerged structures. To expand the velocity intensity and aspect ratio of scour around submerged pile groups, flume experiments were conducted with uniform quartz sand in both clear-water and live-bed conditions. Pile groups with different heights are adopted as experimental models. An improved time factor for both clear-water and live-bed scour around submerged structures is determined with the present experiments to extrapolate the scour depth of the present work to the equilibrium scour depth. This new time factor is then tested with experimental data from the literature. The tests demonstrate that this new time factor can reliably predict the scour process and equilibrium scour depth for submerged structures. Empirical relationships to demonstrate the effects of the studied variables including the equilibrium scour depth, scour area, and scour volume are presented. Different methods to predict the effects of the velocity intensity and aspect ratio on the scour depth are compared based on the present work. Furthermore, a correction coefficient is proposed to illustrate the effect of aspect ratio on scour depth. Then, equations of scour area and volume are derived according to the present experiments.

Solid backfill mining (SBM) is a form of green mining, the core of which is to control and minimize the deformation and movement of strata above longwall coal mines. Establishing a mechanical model that can reliably describe roof deformation by considering the viscoelastic properties of waste gangue is important as it assists in improving mine designs and reducing the environmental impact on the surface. In this paper, the time-dependent deformation characteristics of gangue under different stress levels were obtained by using lateral confinement compression, that reliably represents the compaction of goaf. The viscoelastic foundation model for gangue mechanical response is different from the traditionally used elastic foundation model, as it considers the time factor and viscoelasticity. A mechanical model using a thin plate on a fractional viscoelastic foundation was established, and the roof deflection, bending moment, time-dependent, viscous and other characteristics of SBM were included and analyzed. Compared with the existing elastic foundation model, the proposed fractional order viscoelastic foundation model has higher accuracy with laboratory data. The plate deflection increases by 50.9% and the bending moment increases by 37.9% after 100 days, which the elastic model would not have been able to predict.

The acquisition of professional knowledge is a core issue in the formation of auditor industry expertise; however, previous literature has neglected the time required for auditors to acquire professional knowledge. We examine the audit quality and fees of audit firms in different stages of an auditor acquiring professional knowledge and find that, in the initial stage of the process of knowledge acquisition, audit quality and audit fees decrease. However, in the long run, knowledge learning has a more obvious effect on the improvement of audit quality and audit fees. Specifically, knowledge learning has a positive effect on the development of audit firms. [ABSTRACT FROM AUTHOR]

A multi-objective full-parameter optimization particle swarm optimization (MOFOPSO) algorithm is proposed in this paper to overcome the drawbacks of poor accuracy, low efficiency, and instability of the existing algorithms in the inverse kinematics(IK) solution of the manipulator. In designing the multi-objective function, the proposed algorithm considers the factors such as position, posture, and joint. To improve PSO, the proposed algorithm comprehensively analyzes all factors affecting the global and local searching abilities. Based on this, the initial population is designed following the localized uniform distribution method. Meanwhile, the inertia weight, asynchronous learning factor, and time factor are respectively designed by introducing the iteration factor. Finally, this paper tests the performance of MOFOPSO with three typical functions to obtain a better inverse kinematics solution of the 6-DOF manipulator. Also, six other algorithms are taken for performance comparison. The experimental results indicate that the proposed method not only ensures the stability of the manipulator but also achieves high accuracy and efficiency in solving the inverse kinematics of the 6-DOF manipulator. [ABSTRACT FROM AUTHOR]

Many accidents such as drowning, electric shock, strangulation, traffic accidents, trauma, drug intoxication are closely related to abrupt cessation of vital functions. The cessation of vital functions (representative of respiration as well as the work of the heart), practically suggested death of the organism. Regardless of the cause, death (artificial, violent, or due to illness) can be clinical and biological.Clinical death is terminated by cessation of respiratory and circulatory function and is a prolonged cardiac arrest (P. Safar). Acute respiratory distress (ARS) is a broad term and includes a number of factors that occur suddenly and endanger the lives of patients due to disturbed balance of the internal environment of the body. The causes of such conditions are different with one common feature, difficulty in absorbing O2 and releasing CO2 by causing different forms of hypoxia. Most causes affect the respiratory system as well as the central nervous system. Cessation of heart function or cardiac arrest (CA), in the classical sense of the word, represents abrupt, out of complete health, stopping the work of the heart in people from whom it was not expected. Cardiac arrest is the most urgent and stressful situation in which the medical team can find itself. When the heart stops suddenly, the circulation stops and hypoxia occurs, from which the cells of the cerebral cortex suffer. Fainting occurs in less than 15 seconds, air starvation in 15 to 30 seconds lasting up to 1 minute, and absence of circulation for 3 to 5 minutes indicates irreversible brain damage. From this arises the most important factor for starting SBMO, it is time. [ABSTRACT FROM AUTHOR]

Javier Hervada has been outlined for his research in natural law history; its theory and its application. The last two aspects become inseparable and lead to the following three functions of natural law: as a source of «positive» law or, more properly, «institutional » law; as a critical instance of positive law, and as an integral part of the juridical system. This paper deals with this last aspect for two reasons: it is the least well known in the field of comparative natural law theory, and it is where Hervada’s contribution is most innovate. For this purpose, a presentation will be made of the Hervada’s theory that there is a single juridical system, that is composed of natural «titles» and «measures» and positive «titles» and «measures»; then, within the first above-mentioned, it is considered one of its components or factors, «time», that recognized this integration of natural law in the juridical order and, also, the aforementioned duty to guarantee the material justice of the «Institutional» law. Finally, this notion is illustrated with legislative and jurisprudential cases of argentine law, although, the exercise is perfectly applied to any comparative juridical system. [ABSTRACT FROM AUTHOR]

The acquisition of professional knowledge is a core issue in the formation of auditor industry expertise; however, previous literature has neglected the time required for auditors to acquire professional knowledge. We examine the audit quality and fees of audit firms in different stages of an auditor acquiring professional knowledge and find that, in the initial stage of the process of knowledge acquisition, audit quality and audit fees decrease. However, in the long run, knowledge learning has a more obvious effect on the improvement of audit quality and audit fees. Specifically, knowledge learning has a positive effect on the development of audit firms.

A multi-objective full-parameter optimization particle swarm optimization (MOFOPSO) algorithm is proposed in this paper to overcome the drawbacks of poor accuracy, low efficiency, and instability of the existing algorithms in the inverse kinematics(IK) solution of the manipulator. In designing the multi-objective function, the proposed algorithm considers the factors such as position, posture, and joint. To improve PSO, the proposed algorithm comprehensively analyzes all factors affecting the global and local searching abilities. Based on this, the initial population is designed following the localized uniform distribution method. Meanwhile, the inertia weight, asynchronous learning factor, and time factor are respectively designed by introducing the iteration factor. Finally, this paper tests the performance of MOFOPSO with three typical functions to obtain a better inverse kinematics solution of the 6-DOF manipulator. Also, six other algorithms are taken for performance comparison. The experimental results indicate that the proposed method not only ensures the stability of the manipulator but also achieves high accuracy and efficiency in solving the inverse kinematics of the 6-DOF manipulator.

This study aimed to investigate whether early surgical decompression was associated with favorable neurological recovery in patients with traumatic spinal cord injury (tSCI). We searched PubMed and Embase from the database inception through December 2020 and selected studies comparing the impact of early versus late surgical decompression on neurological recovery as assessed by American Spinal Injury Association Impairment Scale (AIS) for adult patients sustaining tSCI. We pooled the effect estimates in random-effects models and quantified the heterogeneity by the I2 statistics. Subgroup analysis and meta-regression analysis was conducted to identify significant outcome moderator. We included 26 studies involving 3574 patients in the meta-analysis. The pooled results demonstrated significant association between early surgical decompression and an improvement of at least one AIS grade (odds ratio [OR], 1.85; 95% confidence interval [CI], 1.41-2.41; I2, 48.06%). The benefits of early surgical decompression were consistently observed across different subgroups, including patients with cervical or thoracolumbar injury and patients with complete or incomplete injury. The meta-regression analysis indicated that cut-off timing defining early versus late decompression was a significant effect moderator, with early decompression performed before post-tSCI 8 or 12 h associated with greatest benefits (OR, 3.37; 95% CI, 1.74-6.50; I2, 53.52%). No obvious publication bias was detected by the funnel plot. In conclusion, early surgical decompression was associated with favorable neurological recovery for tSCI patients. However, there was a lack of high-quality evidence and the results need further examination. [ABSTRACT FROM AUTHOR]

We consider a clustering-based construction of a soft sensor with adaptive model structure and parameters and with the time factor taken into account by the example of an industrial reactive process. We propose to improve an earlier-developed adaptive soft sensor operation algorithm using a moving window and clustering by updating the model on the basis of the alternating conditional expectation algorithm with the time factor taken into account. The proposed adaptive soft sensor is shown to be advantageous in accuracy over the earlier-developed adaptation algorithm neglecting the time factor and over an adaptive soft sensor based on a neural network. [ABSTRACT FROM AUTHOR]

The article is devoted to the problems of public placement of companies on the Russian stock market. The arguments proving necessity of raising capital through public offering in the Russian Federation have been presented. Foreign and domestic researches of the influence of the time factor during public offering have been considered. The necessity of taking into account the time factor when making a decision on the company’s IPO has been identified and justified. The Russian stock market and its main trends have been analysed, the possibility of applying foreign experience on the Russian market has been assessed. Based on the conducted research, it has been proposed to promote the creation of favorable «Windows», which will help eliminate weaknesses, as well as provide evidence-based regulation in order to create favorable conditions for IPO.

In this paper, we introduce the notion of dynamic interval-valued neutrosophic soft sets (DIVNSSs) by embedding the time factor to interval-valued neutrosophic soft sets (IVNSSs). We also present some related set theoretic operations, such as complement, union, intersection, and-product, and or-product. Then, we propose the information measures of DIVNSSs, including the distance, similarity, and entropy measures. And we develop three corresponding decision making methods. In the decision making process, we employ a nonlinear programming model to weight every single time objectively, considering that the importance degrees of every single time are quite different. Further, we put forward a dynamic interval-valued neutrosophic soft aggregation rule to combine the parameter weights evaluated by all experts under every single time. Moreover, we give a numerical example to display the application of the proposed methods in decision making. Finally, we present a sensitivity analysis of the parameter time-degree and a comparative analysis with the methods of IVNSSs and interval-valued neutrosophic sets (IVNSs). The results show the effectiveness and superiority of the proposed method in solving the problem with dynamic inconsistent information. [ABSTRACT FROM AUTHOR]

The traditional fractional-order particle swarm optimization (FOPSO) algorithm depends on the fractional order α , and it is easy to fall into local optimum. To overcome these disadvantages, a novel perspective with PID gains tuning procedure is proposed by combining the time factor with FOPSO, i.e. a new fractional-order particle swarm optimization called TFFV-PSO, which reduces the dependence on the fractional order to enhance the ability of particles to escape from local optimums. According to its influence on the performance of the algorithm, the time factor is varied with population diversity parameters to balance the exploration and exploitation capabilities of the particle swarm, so as to adjust the convergence speed of the algorithm, then it follows that a better convergence performance will be obtained. The improved method is tested on several benchmark functions and applied to tune the PID controller parameters. The experimental results and the comparison with previous other methods show that our proposed TFFV-PSO provides an adequate velocity of convergence and a satisfying accuracy, as well as even better robustness. [ABSTRACT FROM AUTHOR]