Excessive Time Research Articles

FLAIR MRI sequence synthesis using squeeze attention generative model for reliable brain tumor segmentation

Manual segmentation of brain tumors using structural magnetic resonance imaging (MRI) is an arduous and time-consuming task. Therefore, automatic and robust segmentation will considerably influence neuro-oncological clinical trials by reducing excessive manual annotation time. Herein, we propose a deep learning model that automatically segments brain tumors even in cases of missing MRI sequences, which are common in practical clinical settings. To address this issue, we enhance a generative adversarial network (GAN) by incorporating a squeeze-and-excitation (SE) attention module into its generator and a PatchGAN into its discriminator. The SE module recalibrates channel responses by explicitly modeling interdependencies, enabling the network to focus on critical regions such as tumor areas. Our proposed generative model is optimized using a combination of adversarial, structural similarity, and mean absolute error losses to synthesize missing MRI sequences more effectively. This enhancement allows our model to synthesize the missing MRI sequence (fluid attenuated inversion recovery [FLAIR]) by leveraging information from other available sequences (T1-weighted, T2-weighted, or contrast-enhanced T1-weighted [T1ce]). For the segmentation task, we employ an optimized nnU-Net model, which is trained using existing sequences and evaluated using both available and synthesized sequences (including missing ones), mimicking real-world scenarios where often only limited MRI sequences are available or usable. Our findings reveal a notable enhancement in brain tumor segmentation, as indicated by a significant increase in overall the Dice similarity coefficient (DSC) from 0.688% (when FLAIR is missing) to 0.873% (when using synthesized FLAIR derived from T2). This improvement brings the segmentation performance closer to what was achieved when real FLAIR was available, where the DSC reaches 0.901%. Moreover, our synthesizing model was also tested on two additional datasets: the BraTS 2020 validation set and BraTS Africa 2023 training set, which produces results comparable to those of BraTS 2021, thereby proving its robustness and generalizability. In addition, the resulting tumor segmentations are subsequently employed to assess the response to treatment in cases where all sequences were available and when synthesis was employed, according to response assessment in neuro-oncology criteria.

Association of screen-based sedentary behavior with executive function in school-aged children: A functional near-infrared spectroscopy study

Screen-based sedentary behavior (SSB) is a significant risk factor for the health of school-aged children, and guidelines recommend limiting SSB to 2 hr per day. This study aimed to examine association and potential mechanisms between SSB and executive function (EF) by comparing Stroop performance and frontal hemodynamic responses between children with and without excessive SSB. A total of 70 children aged 10 to 15 years were recruited and divided into two groups: excessive screen time (≥2 hr/day; n = 35; ES group) and normal screen time (<2 hr/day; n = 35; NS group). The Chinese version of the Adolescent Sedentary Activities Questionnaire was used to assess SSB, whereas EF was evaluated using the Stroop task. The frontal hemodynamic responses during the Stroop task were measured using functional near-infrared spectroscopy. The results indicated that the ES group had lower accuracy, longer reaction times, and greater activation in the bilateral dorsolateral prefrontal cortex (DLPFC) and left pre-supplementary motor area (Pre-SMA) compared with the NS group. Furthermore, significant correlations were observed between Stroop performance and cortical activation in the left DLPFC and Pre-SMA. These findings demonstrate that excessive SSB is associated with poor EF, which may be explained by a decrease in neural efficiency of the left DLPFC and Pre-SMA.

Application of CO2-Laser Micro-Perforation Technology to Freeze-Drying Whole Strawberry (Fragaria ananassa Duch.): Effect on Primary Drying Time and Fruit Quality.

Freeze-drying (FD) processing preserves foods by combining the most effective traditional technologies. FD conserves the structure, shape, freshness, nutritional/bioactive value, color, and aroma at levels similar to or better than those of refrigerated and frozen foods while delivering the shelf-stable convenience of canned/hot-air-dehydrated foods. The mass transfer rate is the essential factor that can slow down the FD process, resulting in an excessive primary drying time and high energy consumption. The objective of this study was to reduce the FD processing time using CO2 laser technology to improve product competitiveness in the preservation of whole strawberries. The research process consisted of the selection and characterization of fresh strawberries, followed by preparation, pre-treatment, freeze-drying, a primary drying time assessment, and a quality comparison. Experiments were carried out using strawberries without micro-perforation and with five and eight micro-perforations. Quality parameters were determined for fresh, frozen/thawed, and freeze-dried/rehydrated strawberries. It was found that the primary drying time can be significantly reduced by 20% (95% CI) from 26.7 h for non-perforated fruits to 22.3 h when five micro-perforations are made on each strawberry. The quality parameters used to evaluate the strawberries did not show significant differences when comparing frozen/thawed fruits with freeze-dried/rehydrated fruits. The experiments conducted in this study showed that freeze-drying may efficiently compete with freezing technology when processing whole strawberries.

Indian fathers’ perceptions of young childcare and feeding – A qualitative study

The role of parents in fostering children's healthy habits is a robust area of research. However, most of the existing literature predominantly focuses on mothers' parenting practices. Given the emergence of nuclear, dual earning families and the recent surge in maternal employment in urban India, fathers' engagement in child rearing and feeding warrants attention. The purpose of this research was to document the views of Indian fathers about paternal parenting practices, with an emphasis on children's diet among other health behaviors. Thirty-three fathers of children aged 6–59 months from Kolkata, India took part in semi-structured interviews conducted either face-to-face or over Zoom/telephone in Hindi, Bengali, and English. The interviews were audio recorded, transcribed verbatim, and translated to English. The transcribed data were subjected to thematic analysis informed by the Template Analysis technique. Themes were detected using the NVivo software program. Overall, these Indian fathers played an important role in early childhood care and feeding as reflected in the following seven themes: (i) Involvement of fathers in various childcare activities; (ii) Implementation of responsive and non-responsive feeding practices; (iii) Concerns regarding fussy and slow eating; (iv) Concerns regarding excessive screen time; (v) Paternal vs maternal parenting; (vi) Barriers to routine engagement of fathers in childcare; (vii) Desire for more nutrition and health knowledge. This enhanced understanding of paternal parenting behavior supports the inclusion of fathers in future family-focused lifestyle interventions aimed at improving children's health outcomes, including their dietary habits.

Experiences of Gifted Mathematics Students in Mainstream Classrooms

The purpose of this study was to investigate the daily educational experiences of gifted mathematics students in mainstream classrooms in Malta. Gifted students have their own special educational needs, mostly the need to be provided with an intellectually challenging environment commensurate with their abilities. However, little is known about whether the intellectual needs of gifted students are met in Maltese mainstream classrooms. Online interviews were conducted with 9 gifted mathematics students in Year 8 and Year 9 attending various schools in Malta. Thematic analysis was used to analyse the data. The findings of this study indicate that the experiences of gifted mathematics students are enhanced when they are actively involved in learning. Secondly, it appears that participants lack relevant and meaningful experiences, and a challenging learning environment. Finally, evidence suggests that differentiated teaching strategies are underutilised or not appropriately implemented, resulting in participants having excessive extra time at hand. This study highlights the need for the teaching of mathematics to move away from a whole-class approach. Thus, it is suggested that both on a national level and at a school level there is commitment to provide training for educators on student-centred approaches that include active learning and differentiation.

A cross-sectional study of sleep quality in urban and rural adolescents with reference to their digital exposure

Abstract Background: With the widespread use of digital devices and increased screen time among adolescents, concerns have arisen regarding its potential impact on sleep quality. This study aims to investigate the correlation between excessive screen time and Pittsburgh Sleep Quality Index (PSQI) global scores in adolescents. Materials and Methods: A cross-sectional study was conducted among 167 adolescents aged 10–19 years residing in rural and urban areas. Participants completed self-report questionnaires evaluating their screen time habits and sleep quality using the PSQI. Excessive screen time was defined as more than 2 h per day of exposure to digital screens, and an abnormal PSQI global score was defined as more than 5. Statistical analyses, including unpaired t test, Spearman’s correlation, and chi-square test, were employed to examine the association between excessive screen time and PSQI global scores. Results: The results revealed a significant positive correlation between excessive screen time and abnormal PSQI scores in adolescents (r = 0.623, P &lt; 0.001). Adolescents reporting excessive screen time were significantly more prone to abnormal PSQI scores compared to those with lower screen time (P &lt; 0.0001, t = 9.63, degree of freedom = 165). Conclusion: This study highlights a correlation between excessive screen time and abnormal sleep quality in adolescents, as assessed by the PSQI. These findings underscore the importance of promoting healthy screen time habits among adolescents to mitigate potential adverse effects on their sleep quality and overall health. Further research is needed to explore the mechanisms underlying this relationship and to develop effective interventions to address this issue.

Study on the heat treatment of multispectral ZnS for residual stress reduction

Multispectral ZnS (M − ZnS) will inevitably introduce stresses during the chemical vapor deposition (CVD) polycrystalline growth and hot isostatic pressing (HIPping) stages. In this paper, the generation mechanism and elimination method of residual stresses in M − ZnS are analyzed by means of SEM, transmittance, XRD analysis and mechanical property characterization, and the effects of heat treatment temperature, time and material annealing structure on the apparent morphology, optical and mechanical properties of M − ZnS materials are investigated. The results show that the internal stresses of M − ZnS mainly originate from the CVD ZnS growth and HIPping stages, and the most effective method to reduce the stresses is heat treatment. The key factors affecting the effect of heat treatment include temperature, time, and annealing structure. Selecting a GZ(G) annealing structure combined with a suitable cooling rate for heat treatment of M − ZnS materials will significantly improve the heat treatment effect. More annealing time has a significant effect on the transmittance in the 0.38–3 μm band, and insignificant effect on the transmittance in the 3–5 μm and 8–10 μm bands. Heat treatment will bring some degree of structural changes to the material, often manifested including a trace of hexagonal phase structure, which will affect the optical imaging quality of the window material. Heat treatment contributes to secondary grain growth, with an increase in grain size and a decrease in grain boundaries. Excessive heat treatment accelerates the loss of hardness and bending strength, and also leads to the generation of micro-cracks and macro-cracks in the material, reducing the mechanical properties of the material. Heat treatment holding 200 h and 100 h compared to the unannealed, Vickers hardness decreased by 13.518 and 7.96, respectively, bending strength decreased by 4.77 MPa and 1.73 MPa, respectively. Therefore, in actual production, should not be selected excessive heat treatment time.

Silicon-based optical phased array with a reconfigurable aperture for “gaze” scanning of LiDAR

Light detection and ranging (LiDAR) serves as one of the key components in the fields of autonomous driving, surveying mapping, and environment detection. Conventionally, dense points clouds are pursued by LiDAR systems to provide high-definition 3D images. However, the LiDAR is typically used to produce abundant yet redundant data for scanning the homogeneous background of scenes, resulting in power waste and excessive processing time. Hence, it is highly desirable for a LiDAR system to “gaze” at the target of interest by dense scanning and rough sparse scans on the uninteresting areas. Here, we propose a LiDAR structure based on an optical phased array (OPA) with reconfigurable apertures to achieve such a gaze scanning function. By virtue of the cascaded optical switch integrated on the OPA chip, a 64-, 128-, 192-, or 256-channel antenna can be selected discretionarily to construct an aperture with variable size. The corresponding divergence angles for the far-field beam are 0.32°, 0.15°, 0.10°, and 0.08°, respectively. The reconfigurable-aperture OPA enables the LiDAR system to perform rough scans via the large beam spots prior to fine scans of the target by using the tiny beam spots. In this way, the OPA-based LiDAR can perform the “gaze” function and achieve full-range scanning efficiently. The scanning time and power consumption can be reduced by 1/4 while precise details of the target are maintained. Finally, we embed the OPA into a frequency-modulated continuous-wave (FMCW) system to demonstrate the “gaze” function in beam scanning. Experiment results show that the number of precise scanning points can be reduced by 2/3 yet can obtain the reasonable outline of the target. The reconfigurable-aperture OPA (RA-OPA) can be a promising candidate for the applications of rapid recognition, like car navigation and robot vision.

PROPHETA: A NEW MORPHOMETRIC ANALYSIS SOFTWARE FOR INDUSTRIAL PRODUCT DESIGN

ABSTRACT: This paper introduces ProPheTA (Product Phenetics-based Typology Assessment) as a new morphometric analysis software for industrial, phenetics-based product design (PP). Phenetics is used in Biology to determine similarities between species by comparing their formal external attributes. This way, PP replicates this procedure and, by doing a morphological analysis of different models from the same product family, makes it possible to relate their morphology with the user's emotional, perceptive, hedonic and buying intention response. All this requires working with CAD models, aligning, scaling, selecting them along the X, Y and Z axes, editing images, obtaining dissimilarity indexes, and correlating them with the user's response. Various CAD and morphometric computation tools not explicitly developed for these procedures are used to fulfil these tasks. File compatibility problems and excessive manual work and working time must be circumvented. ProPheTA has been developed in Matlab to solve these problems using a single platform. Its performance in a previous PP study, which related shape to buying intention, has been evaluated to demonstrate its reliability and speed. Results show a ten-time speed improvement in the computation with identical dissimilarity results. In addition, ProPheTA offers several advantages, such as ease of use, robust batch-processing capabilities, multiple output formats and improved compatibility. These functionalities, together with its ability to analyze the outline of 3D shapes without manual selection of landmark points, make it a versatile software application which is flexible, reliable and much faster. Thus, ProPheta is arguably valuable for helping speed up the application of PP in industrial design. Keywords: phenetics, product design, morphology, computer-based assessment

SCHOOL COUNSELORS’ SELF-EFFICACY IN ADDRESSING ADOLESCENTS’ MENTAL ISSUES IN THE AFTER-COVID-19 ERA

The unanticipated arrival of the COVID-19 pandemic has upset thousands of adolescents worldwide, whereas shifts to remote schooling have minimized their access to school-based support. This study focused on three research questions: How do School Counselors (a) evaluate the impact of COVID-19 restrictive measures on adolescents' mental health? (b) appraise their self-efficacy to help adolescents cope with the challenges? (c) Assess post-pandemic pupil behavior and their self-efficacy beliefs. It aimed to resource creative school-counseling approaches to improve youth resilience. Findings showed that counselors regarded generalized worry, concern about academic studies and job development, and increasing internet use as adolescents' most important psychological/behavioral issues. Most counselors felt efficient in their professional development, teamwork, cross-cultural sensitivity, leadership, assessment, personal and social growth, and development. Counselors who evaluated pupils as high/medium in generalized anxiety, depressive symptoms, academic/career development, and excessive internet time evaluated themselves significantly higher on all factors of the self-efficacy scale compared to those who assessed students as medium/low on the aforementioned factors, who scored significantly lower in self-efficacy.

Effectiveness of self-instructional module on knowledge regarding doom scrolling among undergraduate nursing students in selected nursing colleges: A pre-experimental

Doom Scrolling is Spending excessive time online surfing and reading negative or unfavorable news makes a person unhealthy. The primary objectives of the study is To assess the effectiveness of Self-instructional Module on Knowledge regarding Doom scrolling among undergraduate Nursing students. And the secondary objectives are, To assess the pre-test knowledge regarding Doom scrolling among undergraduate Nursing students. To assess the post-test knowledge regarding Doom scrolling among undergraduate Nursing students. To assess the effectiveness of Self-instructional Module on Knowledge regarding Doom scrolling among undergraduate Nursing students. Pre-Experimental one group pre-test post-test research design is used in this study and included 60 undergraduate students as a sample by using Non-Probability Convenient sampling. Assessment is done by using Self Structured Questionnaire on Demographic Variables and Structured Questionnaire on Doom Scrolling. The finding reveals that in pre test 41% students got poor score, 37% students have average and 22% students got good knowledge score while after administrating Self Instructional Module in post test 12% students have poor, 24% students got average and 64% students got good score. It is interpreted that after administrating Structure Instructional Module the knowledge regarding ‘Doomscrolling’ has been improved for undergraduate students.

Understanding Drivers’ Behavioral attitudes and Intentions to Use Guidance Systems in Urban Complex Parking Lots Based on the C-TAM-TPB Framework

Multi-function services characteristic of complexes, complicated spatial structure, and inaccurate parking guidance in complex parking lots create many issues for drivers, including orientation problems, excessive parking time, and difficultly finding a parking space close to the desired destination. Complex parking lot guidance systems (CPLGSs), if accurately designed, can potentially make a substantial contribution to alleviate these problems and reduce drivers’ travel time while improving the parking experience. However, the extent of this contribution depends on drivers’ acceptance of this technology. This paper reports on the intention of complex visitors to use CPLGSs. Specifically, an integrated model combining the technology acceptance model and the theory of planned behavior was developed and tested to analyze the factors that can affect drivers’ behavioral attitudes and intentions for CPLGSs. Perceived risk is included as an antecedent on behavioral attitude. Data were collected through an online questionnaire, and 810 valid questionnaires were obtained. Results from a structural equation model show that perceived usefulness, subjective norm, behavioral attitude, and perceived behavioral control are positively connected with behavioral intention, while perceived risk is negatively related to behavioral attitude. Findings on mediators are also discussed. In addition, multi-group differences are analyzed, revealing that the use intention of the age ≤ 40 group is more positively influenced by subjective norms compared with the age &gt; 40 group. Moreover, perceived ease of use has a strong influence on behavioral attitude, especially among individuals who travel to a complex frequently. The theoretical implications in this paper are intended to guide the service improvement of CPLGSs and other parking guidance systems.

IMPACT OF SOCIAL MEDIA ON STUDENTS

In the contemporary digital age, social media has become an integral part of daily life, profoundly influencing various aspects of society, including education. This research paper delves into the multifaceted impact of social media on students, encompassing both the positive and negative ramifications. The positive effects of social media on students include enhanced communication and collaboration, access to vast educational resources, and opportunities for self-expression and creativity. Additionally, social media platforms can facilitate peer support networks, fostering a sense of belonging and community among students. Moreover, social media can serve as a platform for academic and career networking, enabling students to connect with mentors, professionals, and potential employers. However, the pervasive use of social media also poses significant challenges and risks for students. Excessive screen time and engagement with social media have been linked to decreased academic performance, attention deficits, and sleep disturbances. Furthermore, social media platforms often expose students to cyberbullying, online harassment, and negative social comparison, leading to psychological distress and decreased self-esteem. Moreover, the dissemination of misinformation and fake news on social media can hinder students' critical thinking skills and undermine their ability to discern credible sources of information. This research paper synthesizes existing literature to provide a comprehensive understanding of the impact of social media on students, highlighting the complexities and nuances of this phenomenon. By elucidating the various dimensions of social media's influence on students, this paper aims to inform educators, parents, policymakers, and students themselves about the opportunities and challenges inherent in navigating the digital landscape. Additionally, the paper proposes recommendations for promoting responsible social media usage and mitigating its adverse effects on students' well-being and academic success.

Prediction of Inland Excess Water Inundations Using Machine Learning Algorithms

Regularly, large parts of the agricultural areas of the Great Hungarian Plain are inundated due to excessive rainfall and insufficient evaporation and infiltration. Climate change is expected to lead to increasingly extreme weather conditions, which may even increase the frequency and extent of these inundations. Shallow “floods”, also defined as inland excess water, are phenomena that occur due to a complex set of interrelated factors. Our research presents a workflow based on active and passive satellite data from Sentinel-1 and -2, combined with a large auxiliary data set to detect and predict these floods. The workflow uses convolutional neural networks to classify water bodies based on Sentinel-1 and Sentinel-2 satellite data. The inundation data were complimented with meteorological, soil, land use, and GIS data to form 24 features that were used to train an XGBoost model and a deep neural network to predict future inundations, with a daily interval. The best prediction was reached with the XGBoost model, with an overall accuracy of 86%, a Kappa value of 0.71, and an F1 score of 0.86. The SHAP explainable AI method showed that the most important input features were the amount of water detected in the satellite imagery during the week before the forecast and during the period two weeks earlier, the number of water pixels in the surroundings on the day before the forecast, and the potential evapotranspiration on the day of the forecast. The resulting inland excess water inundation time series can be used for operational action, planning, and prevention.

Stress and energy release rate analysis of cross-ply carbon fiber-reinforced plastic laminate with transverse cracks subjected to ultrasonic vibration using a variational approach

Application of carbon fiber reinforced plastics (CFRP) to structures that are subjected to increasingly high numbers of cyclic loads is expected to expand, so evaluation of fatigue properties up to the very-high cycle regime is essential to prove their long-term reliability. Conventional fatigue testing is not practical for such conditions owing to the excessive time required; ultrasonic fatigue testing, which can be performed at a frequency of 20 kHz, is an effective alternative. Stress distribution in ultrasonic fatigue testing differs from that in conventional fatigue testing because the specimen is resonated. The stress distribution of cross-ply laminates with transverse cracks subjected to resonance was calculated using a variational approach. The energy release rate was calculated based on the results of a previous study on transverse crack initiation. The results of the stress analysis are in good agreement with those of finite-element analysis. Quantitative evaluation of transverse crack initiation was also conducted.

Taylor Approximation of Inventory Policies for One-Warehouse, Multi-Retailer Systems with Demand Feature Information

We consider a distribution system in which retailers replenish perishable goods from a warehouse, which, in turn, replenishes from an outside source. Demand at each retailer depends on exogenous features and a random shock, and unfulfilled demand is lost. The objective is to obtain a data-driven replenishment and allocation policy that minimizes the average inventory cost per time period. The extant data-driven methods either cannot guarantee a feasible solution for out-of-sample feature observations or generate one with excessive computational time. We propose a policy that resolves these issues in two steps. In the first step, we assume that the distributions of features and random shocks are known. We develop an effective heuristic policy by using Taylor expansion to approximate the retailer’s inventory cost. The resulting solution is closed-form, referred to as Taylor Approximation (TA) policy. We show that the TA policy is asymptotically optimal in the number of retailers. In the second step, we apply the linear quantile regression and kernel density estimation to the TA solution to obtain the data-driven policy called Data-Driven Taylor Approximation (DDTA) policy. We prove that the DDTA policy is consistent with the TA policy. A numerical study shows that the DDTA policy is very effective. Using a real data set provided by Fresh Hema, we show that the DDTA policy reduces the average cost by 11.0% compared with Hema’s policy. Finally, we show that the main results still hold in the cases of correlated demand features, positive lead times, and censored demand. This paper was accepted by J. George Shanthikumar, data science. Funding: Y. Yang acknowledges financial support from the NSFC [Grants 72125004, 71821002]. W. Zhou acknowledges financial support from the NSFC [Grants 72192823, 71821002]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.04241 .

Longitudinal relations of screen time duration and content with executive function difficulties in South Korean children

ABSTRACT Research has consistently shown that excessive screen time (ST) is harmful to children’s cognitive development including executive function (EF). Most studies have investigated the long-term consequences of excessive ST and have not explored how content impacts cognitive development. Thus, the current study aimed to examine (1) the associations among the duration and content of ST and EF difficulties and (2) the interaction effect of ST duration and content on EF difficulties. A total of 1,484 third-grade children participated in the study and were followed up one year later. ST duration was measured by the average daily hours on smart devices and computers, and ST content was assessed by the extent to which children engaged in educational or recreational ST. EF difficulties were measured by parental reports of their child’s EF-related behaviors. The results showed ST duration did not predict EF difficulties one year later. However, higher levels of educational ST predicted fewer EF difficulties. A significant interaction effect between ST duration and educational ST demonstrated the benefits of educational ST were not present in children with higher levels of duration. These findings provide a foundation to develop more practical, efficient, and specific ST guidelines considering both ST content and duration.

Impact of homework time on adolescent mental health: Evidence from China

Fierce competition in China's basic education system results in students spending too much time on homework. However, few studies have explored the mental health effects of homework time. Using data from the China Education Panel Survey (CEPS), this paper empirically examines the impact of homework time on adolescent mental health through the fixed-effects model and instrumental variables regression. Additionally, this study explores the moderating effects of teacher support and parent involvement. The results indicate that homework time has a negative effect on adolescent mental health, but only when the amount of time spent on homework exceeds about 1 hour and 15 minutes. Overall, there is a non-linear relationship between homework time and adolescent mental health. Teacher support, particularly emotional support, can mitigate the adverse mental health effects of excessive homework time, whereas parental involvement does not show the same positive effect. The analysis of heterogeneity reveals that adolescents from rural schools or medium family economic backgrounds experience a more pronounced negative impact from excessive homework time compared to those from urban schools, poorer or richer family backgrounds. Furthermore, students with outstanding academic performance are affected more significantly than their peers with poor academic performance. The empirical results echo the targets of the Chinese "double reduction policy" which requires strict control of homework time. Education policymakers should reasonably regulate homework time and systematically explore education concepts and teaching methods that are compatible with the reduction of homework time.

Characterization of Bioactive Metabolites and Antioxidant Activities in Solid and Liquid Fractions of Fresh Duckweed (Wolffia globosa) Subjected to Different Cell Wall Rupture Methods.

Fresh Wolffia globosa, the smallest flowering plant well-known for its favorable nutrient composition and rich content of bioactive compounds, was subjected to boiling, freeze-thawing, and mechanical crushing to reduce its excessive (95-96%) moisture level and consequent drying time. The resultant three wolffia matrixes were filtered through a plankton net to fractionate into the residue and the filtrate. The proximate composition, bioactive metabolites, antioxidant activity, and characterization of bioactive metabolites by LC-ESI-QTOF-MS/MS and Fourier transform infrared spectroscopy were made from oven-dried residues and filtrates. Among residues, crude protein (29.84%), crude lipid (5.77%), total carotenoids (TCC; 722.8 μg/g), and vitamin C (70.02 mg/100 g) were the highest (p < 0.05) for freeze-thawing against higher ash (7.99%), total phenolic content (TPC; 191.47 mg GAE g-1 dry weight), total flavonoid content (TFC; 91.54 mg QE g-1 dry weight), DPPH activity (47.46%), and ferric reducing antioxidant power (FRAP) activity (570.19 μmol FeSO4 equiv/mg) for the crushed counterpart and Chl-b in residues from boiling. No significant variation was evident in the total tannin content (TTC). Among filtrates, higher total phenolic content (773.29 mg GAE g-1 dry weight), TFC (392.77 mg QE g-1 dry weight), TTC (22.51 mg TAE g-1), and antioxidant activity as DPPH activity (66.46%) and FRAP (891.62 μmol FeSO4 equiv/mg) were evident for boiling, while that from crushing exhibited the highest TCC (1997.38 μg/g DM). LC-ESI-QTOF-MS/MS analysis identified 72 phenolic compounds with the maximum in residue (33) and filtrate (33) from freeze-thawing, followed by crushing (18 and 19) and boiling (14 and 13) in order, respectively. The results indicated that the predrying cell rupturing method significantly impacted quantitative, as well as qualitative compositions of residues and filtrates from fresh wolffia.

Optimization of short-term hydropower scheduling with dynamic reservoir capacity based on improved genetic algorithm and parallel computing

The optimal scheduling of hydropower plants is important for the efficient operation and management of hydropower energy systems. In the short-term optimization scheduling of river-type reservoirs, the dynamic reservoir capacity is a key factor. Currently, the short-term power generation scheduling model considering dynamic reservoir capacity was solved by the enumeration method, providing a more accurate simulation of the water flow process compared to the model considering static reservoir capacity. However, the solving process had the disadvantage of a large computational burden and excessive time consumption, which restricted application in the actual scheduling of reservoirs. Therefore, to solve the time-consuming problem of repeated calculation of hydrodynamics model in optimization scheduling, an improved genetic algorithm including adaptive and elitist-selection strategies combined with the parallel computing technique was proposed. The optimal scheduling plan generated by this model were compared with the optimization results of the enumeration method and the genetic algorithm scheduling model. The results showed that the optimal power generation selected by the three models were extremely similar and the optimization model could find the optimal value for the scheduling period. In terms of computing speed, the improved genetic algorithm parallel technique took approximately 0.8 h, around 246 times faster than the enumeration method and 20 times faster than the model without using parallel computing. It demonstrates that the present algorithm is practically usable, significantly enhancing the optimization efficiency of scheduling solutions. Furthermore, it satisfies the engineering application efficiency requirements and can provide technical support for the subsequent power generation scheduling of the Three Gorges Reservoir.