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How to successfully sell products through shopping apps has become an important topic in mobile commerce. This study recruited 34 Taobao App consumers to participate in simulated shopping tasks to explore differences in App functionality usage behaviors. We used clustering methods to identify five unique shopping groups and then applied lag sequential analysis (LSA) to analyze search paths. Additionally, we augmented the explanations of these approaches with lifestyle analysis to explore the characteristic of five unique shopping groups. The research findings indicate that time factors significantly influence shopping behaviors. Transactional-oriented group and recommendationadopting groups with shorter shopping times have simplified search moves. In contrast, price sensitive, information-consuming, and product-comparing in longer time groups demonstrate more diverse and unique search patterns and place emphasis on comparing prices, information, and brands, respectively. The lifestyle analysis in the study revealed significant differences in the information-seeking (IS) dimension among different shopping groups. This finding aligns with the research results obtained through segmentation analysis based on shopping App functionality usage in this study. The findings contribute to helping shopping App managers understand various consumer shopping behaviors for enhancing design of Apps functionalities and interfaces. [ABSTRACT FROM AUTHOR]

The impacts of insulin degludec U100 (Deg‐100) and insulin glargine U300 (Gla‐300) on glycemic variability (GV) in patients with type 1 diabetes, as well as the impact of major nutrient components on GV in these patients, remain unclear. This was an observational, cross‐sectional, retrospective study. Type 1 diabetes mellitus patients treated with either Deg‐100 or Gla‐300 as basal insulin were enrolled. After the participants underwent continuous glucose monitoring, GV indices and major nutrient components were analyzed. Forty patients with type 1 diabetes were enrolled, and 20 participants used Deg‐100, and 20 used Gla‐300. There was no significant difference in major nutrient components between the two groups. Better GV indices of standard deviation, coefficient of variation, mean amplitude of glycemic excursion, AUCn, M‐value, CONGA1, CONGA2, and CONGA4 were noted in the Gla‐300 group versus Deg‐100 group. Compared with patients who received once‐daily injection in the morning (QD), Deg‐100 administration once daily at bedtime (HS) yielded a higher low blood glucose index during both day and nocturnal periods, indicating a higher risk of hypoglycemic events. By contrast, there were significantly lower levels of CONGA1, CONGA2, and CONGA4 during insulin Gla‐300 QD administration than during HS administration, indicating a lower GV of a short interval. In this real‐world study involving type 1 diabetes patients, Gla‐300 appears to offer more stable glucose variability than Deg‐100. Administering once‐daily injections could lower the risk of hypoglycemia in the Deg‐100 group and minimize GV in the Gla‐300 group compared to bedtime injections. [ABSTRACT FROM AUTHOR]

Objectives: Patients with chronic limb-threatening ischemia (CLTI) have a high risk of lower limb amputation and loss of walking independence. Minor amputations play a key role in ensuring walking independence and they represent a challenge in terms of timing and level for vascular surgeons. A major cause of re-amputation is a defect in wound healing and a possible predictor of re-amputation for non-healing wounds could be the incorrect timing of minor amputation after revascularization. The lack of evidence in the literature leads to a wide variability of choices in clinical practice. The purpose of this study was to try to find the optimal timing analysing the risk of re-amputation in CLTI patients who have undergone successful revascularization and minor amputation focussing on timing of minor amputation. Methods: We conducted a single centre retrospective analysis on a cohort of 151 patients consecutively admitted to our hospital for CLTI (Rutherford 5) between January 2014 and April 2022. All the enrolled patients underwent successful revascularization of lower limbs and a minor amputation for dry acral necrosis. The characteristics of the patients and the revascularization procedures were collected and analysed. Patients were divided into two groups based on the timing of minor amputation performed before (group 1) or after the day (group 2) that best predicts the risk of re-amputation according to a Receiver Operating Characteristic (ROC) curve analysis. The primary outcome of this study was the risk of re-amputation during the first 60 days of follow-up after a primary minor amputation, with revascularization still effective. The impact of the timing of minor amputation after revascularization, the type of revascularization and the presence of risk factors known to prolong the wound healing process were evaluated in a uni- and multi-variable logistic regression model. Results: Systemic hypertension, and type of revascularization (i.e. open vs endovascular) were independent predictors of the risk of re-amputation at 60 days (HR 4.26, 95% CI 1.30–14.04, p =.017 and HR 2.35, 95% CI 1.16–4.78, p =.018, respectively). Moreover, time ≤14 days between revascularization and first amputation was associate with a clear, albeit not statistically significant, trend toward increased risk of re-amputation (HR 2.09, 95% CI 0.97–4.51, p =.06). Conclusions: In a cohort of patients who underwent a successful revascularization for CLTI and a minor amputation for dry gangrene in the first 14 days after revascularization, a higher -although not significant-risk of re-amputation was reported. In this cohort of patients, a delayed demolitive procedure should be considered to allow better tissue perfusion and to reduce the risk of re-amputation. [ABSTRACT FROM AUTHOR]

Abstract The impacts of insulin degludec U100 (Deg‐100) and insulin glargine U300 (Gla‐300) on glycemic variability (GV) in patients with type 1 diabetes, as well as the impact of major nutrient components on GV in these patients, remain unclear. This was an observational, cross‐sectional, retrospective study. Type 1 diabetes mellitus patients treated with either Deg‐100 or Gla‐300 as basal insulin were enrolled. After the participants underwent continuous glucose monitoring, GV indices and major nutrient components were analyzed. Forty patients with type 1 diabetes were enrolled, and 20 participants used Deg‐100, and 20 used Gla‐300. There was no significant difference in major nutrient components between the two groups. Better GV indices of standard deviation, coefficient of variation, mean amplitude of glycemic excursion, AUCn, M‐value, CONGA1, CONGA2, and CONGA4 were noted in the Gla‐300 group versus Deg‐100 group. Compared with patients who received once‐daily injection in the morning (QD), Deg‐100 administration once daily at bedtime (HS) yielded a higher low blood glucose index during both day and nocturnal periods, indicating a higher risk of hypoglycemic events. By contrast, there were significantly lower levels of CONGA1, CONGA2, and CONGA4 during insulin Gla‐300 QD administration than during HS administration, indicating a lower GV of a short interval. In this real‐world study involving type 1 diabetes patients, Gla‐300 appears to offer more stable glucose variability than Deg‐100. Administering once‐daily injections could lower the risk of hypoglycemia in the Deg‐100 group and minimize GV in the Gla‐300 group compared to bedtime injections.

The purpose of the research is to present the realised forecasts of Ukraine's economic power in order to find reserves for the recovery of the national economy and opportunities for the formation of digital entrepreneurship on the basis of innovative functioning, which will lead to economic growth in the medium and long term. The object of scientific research is the expenditure on SRD and S&T development by types of work in Ukraine from 2010 to 2022 and the analysis of indicators of GDP the influence of time, employment and inflation factors; expansion of digital capabilities of entrepreneurship due to the conducted R&D, which will become a guarantee of the emergence of breakthrough innovations as one of the key reserves of innovative and digital development of the country during the war and post-war reconstruction of Ukraine. Methodology. A study was conducted utilising dialectical, systematic, mathematical and statistical methods to investigate the R&D expenses by types of work in Ukraine from 2010 to 2022 and the GDP from 2011 to 2023. This analysis determined the prospective existing reserve of economic strength with positive dynamics and highlighted the necessity for a strategic format of digital business work based on innovation. A CRA was conducted to determine the strength of the relationship between SRD expenditures and time (i.e., a set of factors that consistently influence SRD funding and drive its growth trend). Fisher's test was calculated, and an econometric analysis was performed based on GDP indicators over 56 quarters, establishing the dependence of the GDP volume on the time factor and the cyclicality of seasonal fluctuations. Resluts. The conditions for accelerating the digitisation of business processes at domestic enterprises are, in particular, the presence of highly qualified S&R personnel, innovators and researchers in the country, the development of new institutes of innovative and digital development, and the transformation and adaptation of old institutes of development to the existing conditions. The obtained data of CRA show that there is a close relationship between the GDP of Ukraine and the time factor, and the direction of the relationship is direct, i.e., linear, which in this case is a positive fact. It is determined that in pursuit of the goal of restoring the innovative potential of the national economy in the post-war period and further active development of digital entrepreneurship in Ukraine, it is necessary to continue financial support for scientific research and scientific and technical developments carried out in various sectors of the economy. Practical implications. The analysed statistical data had a positive impact on the professionalism of the forecast calculations and allowed to state that in 2027, with a probability of error of 6.29%, the volume of expenditures on research and development is projected to range from 20,202.74 to 29,201.18 million UAH. The results of the CRA show that the multiple correlation coefficient (R) is 0.94, which indicates a close overall relationship between the country's GDP and three independent variables (inflation rate, unemployment rate, time factor). The linear regression equation fits the sample data well and the model is qualitative. The results of the forecast are as follows: Ukraine has the potential for post-war recovery and can develop models for post-war economic reconstruction and changes in its structure. Government officials can develop institutional instruments to attract investment and provide effective mechanisms for the future transformation of the existing labour market and human capital institution. Value / Originality. Having conducted a thorough analysis of the statistical data on the dynamics of spending on S&T development and implementation of the SRD in Ukraine in 2010-2022, the authors managed to determine the relative error of approximation – a criterion for assessing the reliability of the forecast, which amounted to 8.74% and considered the approximation to be qualitative, and the forecast for 2027 is reliable. It is determined that the regression equation is most accurate when R2 approaches its maximum value, that is, 1, and in this case, it is 0.9096, which is a good result and means that the linear regression equation fits the sample data well and the model is of high quality. And for Ukraine's economy, which is under martial law, such positive expectations for GDP and the possibility of increasing R&D spending give hope that Ukraine's economic strength is real, as confirmed by the forecast calculations.

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]

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.

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

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.

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.

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.

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]

“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.

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.

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.

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. [ABSTRACT FROM AUTHOR]

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]

Decision-makers consult systematic reviews and clinical guidelines to make informed coverage decisions based on the current state of evidence. Outdated recommendations in rapidly evolving areas such as lung cancer treatment, are challenging. The COVID-19 pandemic highlighted the need for good decision-making under uncertainty. The descriptive analysis of two samples of evidence bases for evidence synopses to prepare the decision on appropriate comparators shows that living systematic reviews and living clinical guidelines are rare (41/5,463; 0.75%) but present, with COVID-19 being the most common indication. We also describe some characteristics and quality issues of these living formats in the German context. We note an overlap between living and rapid formats, where updates may not adhere to methodological standards in evidence selection, appraisal and formulation of recommendations, or may lack transparency in their methodological processes. The need for critical appraisal of living formats is highlighted as crucial aspect. The production of living systematic reviews and clinical guidelines requires considerable resources and expertise. While there is a need for timeliness in decision making, especially in situations of high uncertainty such as the COVID-19 pandemic, the trade-off between time and quality needs to be balanced. The focus should therefore be on how best to select and process recommendations that are relevant for updating and those that are not. Regularly updated systematic reviews and clinical guidelines that adhere to recommended standards are important for decision-making bodies such as the Federal Joint Committee (G-BA). Transparent documentation of the process and methods used increases confidence in decision-making, even when the evidence base is not perfect., (Copyright © 2024. Published by Elsevier GmbH.)

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.

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.

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.

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]

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.

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.

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.

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]

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.

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.

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.

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]