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Standardized assessments, such as the Saber 11 test, play a crucial role in both individual and institutional decision-making in the educational field. In response to the constant changes of the pedagogical models, these tests are adapted to cover a broader range of cognitive skills and reflect the diverse capacities of students. Objective: This study aims to analyze the structural change in student performance in the Saber 11 test, in the period between 2014 and 2022, focusing on the year 2016 as a potential turning point at the General Santander Educational Institution in the municipality of Soacha, Cundinamarca. Methodology: The Chow test – a statistical tool that evaluates whether the coefficients in two linear regressions applied to two data sets are equivalent – was applied. Results: The analysis revealed that, on the one hand, the areas of critical reading and English experienced a significant structural change in 2016. On the other hand, mathematics, social studies, civic competencies and natural sciences did not present structural changes in the same period. Conclusion: Performance on the Saber 11 test showed an improvement between 2014 and 2016, followed by stagnation and even a decline in subsequent years. A slight upturn was anticipated in 2022, although still far from the levels reached in 2016. It is estimated that it will take at least five more years to return to the performance level of that year.

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Past investigations have assessed gender variations in thermal comfort and adaptation in different buildings. However, no reported study has evaluated differences in gender perceptions of the thermal environment in US Colonial Revival-style homes. As a result, this study aims to provide an understanding of variations in gender perceptions of summer comfort and adaptation in the buildings. The study evaluated data collected during the field studies of thermal comfort (FSTC), including physical measurements of environmental variables and subjects' votes. In this study, 67% females and 33% males participated in the field surveys in summertime. The mean neutral temperature of 25.5 °C and preferred temperature of 24.9 °C were noted for females. For males, the mean neutral and preferred temperatures of 25.8 °C and 25.6 °C were observed. Females feel neutral and prefer "no change" to the thermal environment at lower temperatures than males. A difference of 0.3 °C was noted between the average heat indices for females and males. Male respondents who feel warm perceive less air quality more than females. Females and males who perceive being thermally comfortable also perceive good air quality. Females and males who are thermally comfortable rated air humidity to be acceptable. There are similarities and differences between gender perceptions of the thermal environment. The investigation suggests interventions that can help users regulate their skin temperatures and use control measures that are sensitive to clothing insulation. The research also calls for policies that can promote building users' pivotal adaptive measures to improve their indoor thermal environment. The study enhances our understanding of sustainable indoor environments and how to improve living conditions and adaptive modifications among different groups of occupants in buildings. [ABSTRACT FROM AUTHOR]

In this study, two "on–off" probes (BF2-cur-Ben and BF2-cur-But) recognizing acetylcholinesterase (AChE) were designed and synthesized. The obtained probes can achieve recognition of AChE with good selectivity and pH-independence with a linear range of 0.5~7 U/mL and 0.5~25 U/mL respectively. BF2-cur-Ben has a lower limit of detection (LOD) (0.031 U/mL), higher enzyme affinity (Km = 16 ± 1.6 μM), and higher inhibitor sensitivity. A responsive mechanism of the probes for AChE was proposed based on HPLC and mass spectra (MS) experiments, as well as calculations. In molecular simulation, BF2-cur-Ben forms more hydrogen bonds (seven, while BF2-cur-But has only four) and thus has a more stable enzyme affinity, which is mirrored by the results of the comparison of Km values. These two probes could enable recognition of intracellular AChE and probe BF2-cur-Ben has superior cell membrane penetration due to its higher log p value. These probes can monitor the overexpression of AChE during apoptosis of lung cancer cells. The ability of BF2-cur-Ben to monitor AChE in vivo was confirmed by a zebrafish experiment. [ABSTRACT FROM AUTHOR]

Copyright of Revista Virtual Universidad Católica del Norte is the property of Revista Virtual Universidad Catolica del Norte and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Investigation & Surveying is the property of Railway Investigation & Surveying Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of this study was to prepare and conduct a comparative analysis on 5-fluorouracil (5FU) niosomes stabilized with or without Soluplus, utilizing, respectively, passive loading and active loading techniques. Cholesterol was combined with Span 40 and Tween 40 surfactants to prepare several niosomal formulations by either passive or active methods. Encapsulation efficiency (EE%), loading capacity (LC), mean particle sizes, polydispersity indices (PDI), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), pH stability, and in vitro drug release in bio relevant media of pH 1.2, 6.8, and 7.4 were employed to evaluate the formulations. Batches made using the active loading approach (B1-B5) demonstrated higher entrapment efficiency (25–40%) (p ˂ 0.05) than those made using the passive loading method (20%) (with the exception of batch A1). In comparison to niosomes made using the passive loading method, the active loading methodology produced niosomes with a lower PDI (˂ 0.25) and particle size (˂ 70 nm). SEM revealed spherical and discrete vesicles for the Soluplus stabilized niosomes. Both approaches produced niosomes with prominent hydrogen bonding as observed in the FTIR study. The formulations were stable and sustained drug release, but the active loading strategy imparted a more sustained release pattern than the passively loaded one. Soluplus stabilized niosomes demonstrated higher (p ˂ 0.05) drug release (60–70%) in all release media. Based on the production of smaller particle sizes and narrower size distribution, the active loading strategy may be a more effective way to load hydrophilic medicines such as 5FU into niosomes. [ABSTRACT FROM AUTHOR]

Copyright of China Brewing is the property of China Brewing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Theoretical background: When making a decision on foreign investments, it is crucial for investors to have a clear understanding of both the potential of the host country and the risks awaiting them. To date, numerous approaches have been developed for assessing the business environment and investment climate (attractiveness) of countries. These developments are based on the research of international organizations, rating agencies, business schools, scientific and research institutions. Among them there are both universal (e.g. Harvard Business School, The Doing Business, Political Risk Services) and specialized (e.g. Method ology of the Bank of Austria, RAEX-Analytics) approaches. The abundance and diversity of methodologies have generated numerous studies aimed at their classification and comparative analysis. Purpose of the article: The aim of this article is to conduct a comparative assessment of selected specialized methodologies for evaluating the investment attractiveness of countries and classify them according to the Applicability Matrix. Research methods: The authors reviewed the literature in the field of investment theory and investment climate assessment, as well as materials containing a direct description of the methodologies for assessing investment climate themselves, algorithms for their application and the results of previous evaluations. The application of the system critical analysis method allowed for a comprehensive examination of the selected approaches and determination of their key characteristics, advantages, and disadvantages. The research procedure also necessitated the utilization of expert assessment method to conduct a weighted comparative analysis of the applicability of the methodologies. Main findings: Within the study, the characteristics, strengths, and weaknesses of the selected specialized methodologies for evaluating investment attractiveness were identified regarding to two key dimensions: information coverage and complexity. The main results include a comparative evaluation of the practical applicability of selected methodologies for assessing the investment climate, as well as their classification into groups with relevant qualitative characteristics according to Applicability Matrix. [ABSTRACT FROM AUTHOR]

Background: In the Visakhapatnam District, this study compares the quality management procedures used by public and private hospitals. Knowing how these practices are similar and different from one another can help inform policy decisions and improve the quality of health care. Materials and Methods: A cross-sectional study design was used, and 100 hospitals from both public (50 hospitals) and private (50 hospitals) were included in the sample size. A standardized questionnaire that evaluated different aspects of quality management practices was used to gather the data. Descriptive statistics and inferential tests were used in the quantitative analysis. Results: Significant variations in quality management procedures between public and private hospitals were found. In terms of patient happiness, service responsiveness, and technological use, private hospitals scored better. Regarding accessibility, cost, and equity of healthcare services, public hospitals fared better. Conclusion: The report emphasizes the necessity of focused initiatives to improve quality management procedures in both public and commercial institutions. Collaboration between the two sectors can make it easier to deploy evidence-based tactics and share best practices to raise overall healthcare quality in the Visakhapatnam area.

This study investigates the application of hyperspectral image space–spectral fusion technology in lithologic classification, using data from China's GF-5 and Europe's Sentinel-2A. The research focuses on the southern region of Tuanjie Peak in the Western Kunlun Range, comparing five space–spectral fusion methods: GSA, SFIM, CNMF, HySure, and NonRegSRNet. To comprehensively evaluate the effectiveness and applicability of these fusion methods, the study conducts a comprehensive assessment from three aspects: evaluation of fusion effects, lithologic classification experiments, and field validation. In the evaluation of fusion effects, the study uses an index analysis and comparison of spectral curves before and after fusion, concluding that the GSA fusion method performs the best. For lithologic classification, the Random Forest (RF) classification method is used, training with both area and point samples. The classification results from area sample training show significantly higher overall accuracy compared to point samples, aligning well with 1:50,000 scale geological maps. In field validation, the study employs on-site verification combined with microscopic identification and comparison of images with actual spectral fusion, finding that the classification results for the five lithologies are essentially consistent with field validation results. The "GSA+RF" method combination established in this paper, based on data from GF-5 and Sentinel-2A satellites, can provide technical support for lithological classification in similar high-altitude regions. [ABSTRACT FROM AUTHOR]

Background: In the Visakhapatnam District, this study compares the quality management procedures used by public and private hospitals. Knowing how these practices are similar and different from one another can help inform policy decisions and improve the quality of health care. Materials and Methods: A cross-sectional study design was used, and 100 hospitals from both public (50 hospitals) and private (50 hospitals) were included in the sample size. A standardized questionnaire that evaluated different aspects of quality management practices was used to gather the data. Descriptive statistics and inferential tests were used in the quantitative analysis. Results: Significant variations in quality management procedures between public and private hospitals were found. In terms of patient happiness, service responsiveness, and technological use, private hospitals scored better. Regarding accessibility, cost, and equity of healthcare services, public hospitals fared better. Conclusion: The report emphasizes the necessity of focused initiatives to improve quality management procedures in both public and commercial institutions. Collaboration between the two sectors can make it easier to deploy evidence-based tactics and share best practices to raise overall healthcare quality in the Visakhapatnam area. [ABSTRACT FROM AUTHOR]

Assessing tree species diversity is paramount for understanding ecosystem dynamics and guiding conservation efforts. In urban landscapes like Ilorin metropolis, where human activities exert significant pressure on ecosystems, accurate assessments of tree diversity are crucial for sustainable urban planning and conservation strategies. This study conducted a comparative analysis of Simpson’s and Shannon-Wiener’s indices to evaluate tree species diversity within Ilorin Metropolis, Nigeria. Data on tree species were gathered from the central areas of Ilorin. Data collection was from road networks and houses with trees using a systematic method. Data was analysed with descriptive and inferential statistics (t-test and correlation analysis). Tree species diversity was assessed using Shannon-Wiener’s and Simpson’s diversity index. The study identified 3225 individual trees belonging to 86 species and 23 taxonomic families. The most common species was Polyalthia longifolia (8.40%), while Ceiba pentadra, Ficus carica, and Strychnos spinosa (0.06% each) were the least common. The Simpson’s and Shannon-Wiener indices for the study area were 0.97 and 3.88, respectively. The results further revealed that Simpson’s had a mean, a standard error of the mean and a standard deviation of 0.00035, 0.00011 and 0.00098, respectively. In contrast, Shannon-Wiener’s had a mean, a standard error of the mean and a standard deviation of 0.045, 0.0045 and 0.041, respectively. The correlation coefficient between the indices was 0.83, while the t-test showed a significant relationship (t =10.189; p-value = 0.000). The study concludes that both diversity indices were highly suitable for assessing tree species diversity in the study area, but Simpson’s diversity is more suitable. [ABSTRACT FROM AUTHOR]

The nutritive value of mixtures of Megathyrsus maximus (MM) and leaf protein concentrates of Albizia lebbeck (AL) and Leucaena leucocephala (LL) the nitrogen-fixing multipurpose tree species (NFTS) were evaluated in this study. The treatment consists of M. maximus and each of the multipurpose tree species leaf protein concentrates - MM100, MM90:AL10, MM80:AL20, MM70:AL30, MM90:LL10, MM80:LL20 and MM70:LL30 respectively. Results revealed an increase (P<0.05) in CP with an increase in leaf protein concentrate inclusion while there was a decline (P<0.05) in NDF contents of the mixtures with a decrease in grass inclusion. The highest CP values (P<0.05) were recorded in treatment MM85:AL15 and MM85:LL15 respectively and the lowest value of CP was recorded in MM100. The highest significant (P<0.05) value of NDF was recorded for MM100 while treatments MM90:LL10 and MM100 had the highest values of ADF. The DMD values of all the mixtures of the grass and leaf protein concentrates had higher (P<0.05) values compared with sole grass (MM100) which recorded the least value. Treatment MM85:LL15 had the highest significant (P<0.05) mean value of OM. The DMI, RFV and CC were significantly (P<0.05) higher in treatments MM85:AL15, MM90:LL10 and M85:LL15 respectively. The study concluded that nutrients and digestibility of mixtures of grass and leaf protein concentrates of NFTS investigated were higher than in the sole grass with mixtures of M. maximus 85: A. lebbeck 15 and M. maximus 85: L. leucocephala 15 having the highest nutritive value. [ABSTRACT FROM AUTHOR]

Abstract Child health is an important public health issue in China and the Chinese government always attached great importance to child health care. With the implementation of a series of medical and health reforms in China in recent decades, the status of child health improved year by year. Objectives This study aims to comprehensively evaluate if the measures implemented in the medical and health reforms effectively promoted the development of Chinese child health care in recent years and provide theoretical support for future decision-making on the policies of child health care in China. Methods A total of six indicators were selected from the China Health Statistics Yearbook. Based on the multi-criteria decision analysis (MCDA) algorithm, three different evaluation methods were applied in the study, which are the weighted technique for order preference by similarity to an ideal solution (TOPSIS) method, the weighted rank-sum ratio (RSR) method, and the fuzzy comprehensive evaluation (FCE). Each indicator's weight was calculated by the entropy weight methods objectively. The sensitivity analysis was conducted to validate the stability and accuracy of the rank results. Results The results indicated that the rank values of each year’s child health care calculated by the different evaluation methods were not exactly the same, but the overall trend is consistent which is that child health care in China improved year by year from 2000 to 2020. The top 5 were ranked from 2016-2020 and the bottom 5 were ranked from 2000-2004. Conclusions The results indicated that the policies and measures implemented in the medical and health reforms, as well as improved sanitation conditions, availability of healthy food and water, etc., have jointly promoted the development of child health care in China in the past 20 years, providing a scientific theoretical basis for future policy-making to promote child health care.

This article analyzes and compares conical curve creation methods using algorithms. The four methods investigated were linear interpolation, Bézier curve, geometric form approximation, and numerical differential equation solutions. Using mathematics, this research dissects these strategies to understand their underpinnings. Comparisons of accuracy, computing efficiency, and adaptability reveal each method's strengths and drawbacks. This article hopes to help practitioners and academics choose conical curve building techniques based on their construction applications. [ABSTRACT FROM AUTHOR]

In this study, two “on–off” probes (BF2-cur-Ben and BF2-cur-But) recognizing acetylcholinesterase (AChE) were designed and synthesized. The obtained probes can achieve recognition of AChE with good selectivity and pH-independence with a linear range of 0.5~7 U/mL and 0.5~25 U/mL respectively. BF2-cur-Ben has a lower limit of detection (LOD) (0.031 U/mL), higher enzyme affinity (Km = 16 ± 1.6 μM), and higher inhibitor sensitivity. A responsive mechanism of the probes for AChE was proposed based on HPLC and mass spectra (MS) experiments, as well as calculations. In molecular simulation, BF2-cur-Ben forms more hydrogen bonds (seven, while BF2-cur-But has only four) and thus has a more stable enzyme affinity, which is mirrored by the results of the comparison of Km values. These two probes could enable recognition of intracellular AChE and probe BF2-cur-Ben has superior cell membrane penetration due to its higher log p value. These probes can monitor the overexpression of AChE during apoptosis of lung cancer cells. The ability of BF2-cur-Ben to monitor AChE in vivo was confirmed by a zebrafish experiment.

The capability of partially penetrating vegetation canopy and efficiently collecting high-precision point clouds over large areas makes airborne laser scanning (ALS) a valuable tool for various geospatial applications. However, automated ground filtering (GF), one fundamental and challenging step for most ALS applications, has remained a widely researched yet unsolved problem for decades. The recent breakthroughs in supervised deep learning (DL) techniques, which rely on sufficient and high-quality labeled datasets, provide a new solution to better solve this problem. Unfortunately, public 3D geospatial datasets are scarce, especially for those tailored for the landform-scale GF task. Moreover, whether advanced deep neural networks (DNNs) can be well-scaled to the problem of GF remains an open question. To comprehensively advance the development of effective DL-based GF pipelines, we first publish an ultra-large-scale GF dataset built upon open-access ALS point clouds of four different countries worldwide, which covers over 47 km 2 and nine different terrain scenes. Then, multiple attractive advantages of DL techniques in GF are evaluated through extensive experimental comparisons with traditional GF methods on the presented dataset. Furthermore, we reveal several issues faced by generalizing existing advanced 3D DNNs into GF tasks with a series of in-depth experimental analyses. Finally, some promising directions for future research are suggested in response to the identified challenges. Our dataset, named OpenGF, is available at https://github.com/Nathan-UW/OpenGF. • A new ultra-large-scale ground filtering dataset is published. • Attractive advantages of deep learning in ground filtering are evaluated. • Key issues faced by generalizing deep learning into ground filtering are revealed. • A new strategy for the rapid construction of landform-scale datasets is provided. [ABSTRACT FROM AUTHOR]

Accurate quantification of reference crop evapotranspiration (ETo) plays a significant role in determining crop water requirements in irrigated agriculture. A plethora of methods for the estimation of ETo are available. However, the regional suitability of these methods needs to be assessed given the limited availability of meteorological data. In this study, daily estimates of 11 ETo models were selected and compared with the FAO-Penman-Monteith equation (FAO-PM). The selected methods were Blaney-Criddle (BC), Jaisen-Haise (JH), Hargreaves method (HM), McGuinness-Borndne (MB), Chapman (CM), Abtew model (AM), Turc method (TM), FAO-PM equation, Penman equation (PM), Priestley-Taylor (PT) and Matt-Shuttleworth (MS). Evaluation of these models was carried out during 2016-20 in the New Bhupania Minor Command of the Dulhera distributary, Western Yamuna Canal Command (WYCC), Haryana, India. The selected models were evaluated to find a substitute for the FAO-PM equation based on different statistical indices. It was observed that the PT method performed best and was in line with the FAOPM equation with correlation coefficient, root mean square error, mean absolute error, Nash-Sutcliffe coefficient and mean bias error as 0.92, 0.74, 0.48, 0.83, 0.171 respectively. Based on this study and statistical error indices values, the models can be ranked as PT > CM > TM > JH > AM > PM > MS > HM > BC > MB. Thus, we recommend using the PT model for the estimation of ETo in the study area with available meteorological parameters for irrigation scheduling. [ABSTRACT FROM AUTHOR]

Deep learning methods are recognised as state-of-the-art for many applications of machine learning. Recently, deep learning methods have emerged as a solution to the task of automatic music generation (AMG) using symbolic tokens in a target style, but their superiority over non-deep learning methods has not been demonstrated. Here, we conduct a listening study to comparatively evaluate several music generation systems along six musical dimensions: stylistic success, aesthetic pleasure, repetition or self-reference, melody, harmony, and rhythm. A range of models, both deep learning algorithms and other methods, are used to generate 30-s excerpts in the style of Classical string quartets and classical piano improvisations. Fifty participants with relatively high musical knowledge rate unlabelled samples of computer-generated and human-composed excerpts for the six musical dimensions. We use non-parametric Bayesian hypothesis testing to interpret the results, allowing the possibility of finding meaningful non-differences between systems' performance. We find that the strongest deep learning method, a reimplemented version of Music Transformer, has equivalent performance to a non-deep learning method, MAIA Markov, demonstrating that to date, deep learning does not outperform other methods for AMG. We also find there still remains a significant gap between any algorithmic method and human-composed excerpts. [ABSTRACT FROM AUTHOR]

This study investigates the application of hyperspectral image space–spectral fusion technology in lithologic classification, using data from China’s GF-5 and Europe’s Sentinel-2A. The research focuses on the southern region of Tuanjie Peak in the Western Kunlun Range, comparing five space–spectral fusion methods: GSA, SFIM, CNMF, HySure, and NonRegSRNet. To comprehensively evaluate the effectiveness and applicability of these fusion methods, the study conducts a comprehensive assessment from three aspects: evaluation of fusion effects, lithologic classification experiments, and field validation. In the evaluation of fusion effects, the study uses an index analysis and comparison of spectral curves before and after fusion, concluding that the GSA fusion method performs the best. For lithologic classification, the Random Forest (RF) classification method is used, training with both area and point samples. The classification results from area sample training show significantly higher overall accuracy compared to point samples, aligning well with 1:50,000 scale geological maps. In field validation, the study employs on-site verification combined with microscopic identification and comparison of images with actual spectral fusion, finding that the classification results for the five lithologies are essentially consistent with field validation results. The “GSA+RF” method combination established in this paper, based on data from GF-5 and Sentinel-2A satellites, can provide technical support for lithological classification in similar high-altitude regions.

As decarbonisation agendas mature, macro-energy systems modelling studies have increasingly focused on enhanced decision support methods that move beyond least-cost modelling to improve consideration of additional objectives and tradeoffs. One candidate is modelling to generate alternatives (MGA), which systematically explores new objectives without explicit stakeholder elicitation. This paper provides comparative testing of four existing MGA methodologies and proposes a new Combination vector selection approach. We examine each existing method’s runtime, parallelizability, new solution discovery efficiency, and spatial exploration in lower dimensional ( N ⩽ 100) spaces, as well as spatial exploration for all methods in a three-zone, 8760 h capacity expansion model case. To measure convex hull volume expansion, this paper formalizes a computationally tractable high-dimensional volume estimation algorithm. We find random vector provides the broadest exploration of the near-optimal feasible region and variable Min/Max provides the most extreme results, while the two tie on computational speed. The new Combination method provides an advantageous mix of the two. Additional analysis is provided on MGA variable selection, in which we demonstrate MGA problems formulated over generation variables fail to retain cost-optimal dispatch and are thus not reflective of real operations of equivalent hypothetical capacity choices. As such, we recommend future studies utilize a parallelized combined vector approach over the set of capacity variables for best results in computational speed and spatial exploration while retaining optimal dispatch.

The management of the production processes in an optimal manner involves the usage of knowledge about past jobs as reference for current decisions. During a manufacturing flow in every process step the process engineers could be in situations that request quick decisions based on comparison of different potential manufacturing paths. The Method for Holistic Optimization was developed in order to be used as support for decisions. The method was validated thru different studies. For the mentioned studies there were used artificial and real instances databases. The approach of the optimal management of the manufacturing processes was developed in the current study in order to estimate the consequences of a decision, are used known methods, such as: NN modeling, big data analysis, statistics, etc. In all these cases, the database size plays an essential role in terms of estimation quality. The main purpose of the study is to analyze and validate that the Method for Holistic Optimization is feasible to be used in case a decision-maker uses a reduced database. This can be a significant advantage compared with other methods. The study it is performed using an instance database which was artificially generated in case of a turning process. The obtained results are consistent and promising.

Stakeholders in Environmental Education (EE) often face difficulties identifying and selecting programs that best suit their needs. This is due, in part, to the lack of expertise in evaluation knowledge and practice, as well as to the absence of a unified database of Environmental Education Programs (EEPs) with a defined structure. This article presents the design and development of a web application for evaluating and selecting EEPs. The certified users of the application can insert, view, and evaluate the registered EEPs. At the same time, the application creates and maintains for each user an individual and dynamic user model reflecting their personal preferences. Finally, using all the above information and applying a combination of Multi-Criteria Decision-Making Methods (MCDM), the application provides a comparative and adaptive evaluation in order to help each user to select the EEPs that best suit his/her needs. The personalized recommendations are based on the information about the user stored in the user model and the results of the EEPs evaluations by the users that have applied them. As a case study, we used the EEPs from the Greek Educational System. [ABSTRACT FROM AUTHOR]

Pavement vibration monitoring under vehicle loads can be used to acquire traffic information and assess the health of pavement structures, which contributes to smart road construction. However, the effectiveness of monitoring is closely related to sensor performance. In order to select the suitable acceleration sensor for pavement vibration monitoring, a printed circuit board (PCB) with three MEMS (micro-electromechanical) accelerometer chips (VS1002, MS9001, and ADXL355) is developed in this paper, and the circuit design and software development of the PCB are completed. The experimental design and comparative testing of the sensing performance of the three MEMS accelerometer chips, in terms of sensitivity, linearity, noise, resolution, frequency response, and temperature drift, were conducted. The results show that the dynamic and static calibration methods of the sensitivity test had similar results. The influence of gravitational acceleration should be considered when selecting the range of the accelerometer to avoid the phenomenon of over-range. The VS1002 has the highest sensitivity and resolution under 3.3 V standard voltage supply, as well as the best overall performance. The ADXL355 is virtually temperature-independent in the temperature range from −20 °C to 60 °C, while the voltage reference values output by the VS1002 and MS9001 vary linearly with temperature. This research contributes to the development of acceleration sensors with high precision and long life for pavement vibration monitoring. [ABSTRACT FROM AUTHOR]

Environmental DNA (eDNA) metabarcoding is an emerging tool for monitoring biological communities in aquatic ecosystems. The selection of bioinformatic pipelines significantly impacts the results of biodiversity assessments. However, there is currently no consensus on the appropriate bioinformatic pipelines for fish community analysis in eDNA metabarcoding. In this study, we compared three bioinformatic pipelines (Uparse, DADA2, and UNOISE3) using real and mock (constructed with 15/30 known fish) communities to investigate the differences in biological interpretation during the data analysis process in eDNA metabarcoding. Performance evaluation and diversity analyses revealed that the choice of bioinformatic pipeline could impact the biological results of metabarcoding experiments. Among the three pipelines, the operational taxonomic units (OTU)-based pipeline (Uparse) showed the best performance (sensitivity: 0.6250 ± 0.0166; compositional similarity: 0.4000 ± 0.0571), the highest richness (25–102) and minimal inter-group differences in alpha diversity. It suggested the OTU-based pipeline possessed superior capability in fish diversity monitoring compared to ASV/ZOTU-based pipeline. Additionally, the Bray-Curtis distance matrix achieved the highest discriminative effect in the PCoA (43.3%–53.89%) and inter-group analysis (P < 0.01), indicating it was better at distinguishing compositional differences or specific genera of fish community at different sampling sites than other distance matrices. These findings provide new insights into fish community monitoring through eDNA metabarcoding in estuarine environments. [Display omitted] • Three bioinformatic pipelines were compared based on real and mock community. • Bioinformatic pipelines significantly impacted the results of fish diversity. • OTU-based pipeline showed superior capability for fish eDNA metabarcoding monitoring. • Bray-Curtis distance matrix was more suitable for fish community differentiating. [ABSTRACT FROM AUTHOR]

Various factors related to growing conditions can influence the nutritional quality of plants, including vegetable crops, especially the contents of health-promoting phytochemicals. In this study, the phytochemical contents of spinach (Spinacia oleracea) cultivated under greenhouse and open field conditions were comparatively analyzed using a metabolomic approach with Mass Profiler Professional (MPP) software. S. oleracea, which is one of the well-known leafy vegetables belonging to the family Chenopodiaceae, is cultivated worldwide. Although the nutritional value of spinach is high, the phytochemical contents of spinach cultivated under greenhouse and open field conditions have not been comparatively analyzed. Metabolomic analysis of the methanol (MeOH) extracts of greenhouse-cultivated spinach (GS) and open field-cultivated spinach (OFS) using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), followed by principal component analysis (PCA) with MPP demonstrated the differential metabolite profiles of GS and OFS. The active compounds 1–3 were isolated and identified using LC-Q-TOF-MS-guided fractionation. Among these, 5,3',4'-trihydroxy-3-methoxy-6,7-methylenedioxyflavone 4'-glucuronide (2) exhibited growth-inhibitory activities against Helicobacter pylori 51. Distribution analysis of compound 2 revealed that the anti-H. pylori compound 2 is an OFS-specific bioactive phytochemical. This indicates that the phytochemical quality of OFS is better than that of GS. These findings will aid in providing vital data for vegetable processors, dieticians, nutritionists, and consumers to select optimal green leafy vegetables. [ABSTRACT FROM AUTHOR]

Debris flows, triggered by dual interferences extrinsically and intrinsically, have been widespread in China. The debris-flow susceptibility (DFS) assessment is acknowledged as the benchmark for the mitigation and prevention of debris flow risks, but DFS assessments at the national level are lacking. The role of human activities in the DFS assessment has always been overlooked. On the basis of a detailed inventory of debris-flow sites and a large set of environmental and human-related characteristics, this research presents the comparative performance of the well-known information value (IV), logistic regression (LR) and random forest (RF) models for DFS assessments in China. Twelve causative factors, namely, elevation, slope, aspect, rainfall, the normalized difference vegetation index (NDVI), land use, landform, geology, distance to faults, density of villages, distance to rivers and distance to roads, were considered. Debris-flow susceptibility maps were then generated after the nonlinear relationship between the debris-flow occurrence and the causative factors was captured. Finally, the predictive performance of the three maps was evaluated through receiver operating characteristic (ROC) curves, and the validation results showed that areas under the ROC curves were 81.98%, 79.96% and 97.38% for the IV, LR and RF models, respectively, indicating that the RF model outperformed the other two traditional statistical methods. The importance ranking of the RF model also revealed that distance to roads, slope and rainfall dominated the spatial distribution of debris flows. This is the first experiment to compare between the traditional statistical and machine learning methods in DFS studies for the whole of China. Our results could provide some empirical support for China's policymakers and local practitioners in their efforts to enable residents to be less vulnerable to disasters. [ABSTRACT FROM AUTHOR]

Cangshan Glacier is one of the southernmost mountains in the Northern Hemisphere (Asian Continent) during the last Quaternary glaciation, and is a standard area with well-preserved and abundant traces of Quaternary Dali glaciation. In this study, the geological heritage resources of Cangshan Mountain are evaluated in terms of condition indicator and value indicator. Through the determination of quantitative evaluation indicators and evaluation factor weights of geological heritage landscapes, the geological heritage resources of Cangshan Mountain are identified and classified. The results show that there are two world-class geological heritage landscapes, 14 national geological heritage landscapes, 54 provincial geological heritage landscapes and 55 geological relic landscapes below the provincial level in the park. Cangshan Mountain's geological heritage resources are compared with glacial heritage resources, Quaternary glaciation resources and tectonic geomorphic heritage resources both at home and abroad, so as to form a complete evaluation of the geological heritage landscape resources of Cangshan Mountain. [ABSTRACT FROM AUTHOR]

To compare and evaluate the quality and accuracy of soil moisture data of SMOS (soil moisture and ocean salinity) and SMAP (soil moisture active passive) remote sensing products, using the actual measurement of 261 stations from three types of stations in the United States, i. e., CRN (climate reference network), SCAN (soil climate analysis network), and SNOTEL (snowpack telemetry network), the different influencing factors that affect the performance were explored from the perspectives of the whole and the individual sites. The results show that: 1) When the two satellite remote sensing moisture products are evaluated as a whole or according to sites, SMAP performs better than SMOS. 2) The accuracy of the same soil moisture product varies greatly under different land cover types; different soil moisture products have different responses to the same land cover type. 3) The temperature and precipitation changes under seasonal changes, which affect the accuracy of satellite inversion. 4) The terrain and altitude changes also cause changes in the inversion level of satellite products. As the altitude increases, the accuracy of satellite observations decreases. In general, there is still some difference between the soil moisture directly obtained from satellite observation and the station data. In the future, we should further improve the accuracy of satellite observation data. [ABSTRACT FROM AUTHOR]

Drinking water halogenated disinfection byproducts (DBPs) have become an increasing health concern. However, the endocrine-disrupting effects of DBPs have not been well evaluated, and the limited available data have inhibited a comprehensive understanding of their health risks. In this study, a total of 43 DBPs were evaluated for their estro-androgenic effects using two types of human breast cancer cells. Among the tested DBPs, 16 exhibited estrogenic/antiestrogenic/androgenic/antiandrogenic effects, and the effects could be observed even at concentrations typically detected in drinking water. Iodinated and polyhalogenated DBPs generally showed higher effects than other species. For a broader comparison, DBP endocrine-disrupting effect data from this study and previous studies were summarized. It was found that the endocrine disruption efficacy of DBPs followed the rank order of iodinated > brominated > chlorinated species, and halophenolic DBPs were potential endocrine-disrupting compounds. Moreover, molecular docking results demonstrated that the binding of DBPs to estro-androgenic receptors was dominated by hydrophobic bonding, hydrogen bonding, halogen bonding, and van der Waals forces. The force strength and molecular volume were related to the magnitude of the estro-androgenic effects. Iodinated DBPs and polyhalogenated DBPs tended to have larger binding forces than other analogues and thus exhibited stronger effects.

Cangshan Glacier is one of the southernmost mountains in the Northern Hemisphere (Asian Continent) during the last Quaternary glaciation, and is a standard area with well-preserved and abundant traces of Quaternary Dali glaciation. In this study, the geological heritage resources of Cangshan Mountain are evaluated in terms of condition indicator and value indicator. Through the determination of quantitative evaluation indicators and evaluation factor weights of geological heritage landscapes, the geological heritage resources of Cangshan Mountain are identified and classified. The results show that there are two world-class geological heritage landscapes, 14 national geological heritage landscapes, 54 provincial geological heritage landscapes and 55 geological relic landscapes below the provincial level in the park. Cangshan Mountain’s geological heritage resources are compared with glacial heritage resources, Quaternary glaciation resources and tectonic geomorphic heritage resources both at home and abroad, so as to form a complete evaluation of the geological heritage landscape resources of Cangshan Mountain.

Based on the hourly precipitation data from the short-term 3 km resolution hourly updated CMA-GD(R3) model simulation, the present study examined the hourly data of precipitation during MarchSeptember in 2020, evaluated observational data and data from the CMA-GD(R3) model simulation and compared these data in terms of overall correction and categorized precipitation correction using the optimal threat score (OTS) method. The results are as follows: In terms of overall correction, the CMA-GD (R3) data of precipitation with rain rate ≥ 1 mm/ h is well corrected by using the OTS method, and the improvement becomes greater with the increase of rainfall intensity. Using the OTS method, we find the decrease of omission rate is more than that of false alarm rate, and the significant bias is either greater than or equal to 1. As for the correction of hourly precipitation forecast of three types of heavy rainfall (frontal rain, monsoon rain, and typhoon-induced rain), the OTS method can help produce positive correction for precipitation with rain rate ≥ 1 mm/h. The hourly precipitation forecast of monsoon rain is the best on the rain rate categories of 0.1 mm/h, 1 mm/h, 10 mm/h, 20 mm/h, 35 mm/h and 50 mm/h, and their respective threat scores are 0.403, 0.232, 0.053, 0.023, 0.009 and 0.004; on the rain rate category of 5 mm/h, the hourly precipitation forecast of frontal rain is the best, and its threat score is 0.102. Moreover, on the rain rate category of 1 mm / h, the improvement of typhoon-induced rain forecast is the most significant; on the rain rate categories of 5 mm/h and 10 mm/h, the improvement of frontal rain forecast is the largest; on the rain rate categories of 20 mm/h, 35 mm/h, and 50 mm/h, the improvement of monsoon rain forecast is the greatest. [ABSTRACT FROM AUTHOR]

The dissolving of cellulose under harsh and environmentally unfavorable circumstances is the basis of traditional chemical methods for nanocellulose (NC) or derivatization. Due to the limitations of present methods for dissolving and processing NC, more efficient and ecologically acceptable solvents are required. Because of their excellent thermal and chemical stability, non-flammability, and miscibility with many other solvent systems, ionic liquids (ILs) have emerged as useful and environmentally friendly solvents. Meanwhile, another procedure for producing NC with homogeneous and extremely crystalline characteristics is ultra-sonification. Ultrasound energy is delivered to cellulose chains during ultrasonication by a process known as cavitation, which refers to the development, growth, and collapse of cavities in a liquid medium. Cavitation provides 10–100 kJ/mol of energy in this so-called sonochemistry, which is within the hydrogen bond energy scale. As a result, both catalytic IL treatments and ultrasonication influence the progressive disintegration of NC synthesis. [ABSTRACT FROM AUTHOR]

THE SOCIAL RESPONSIBILITY OF ORGANIZATIONS IS RELATED TO ISLAM'S EMPHASIS ON ISLAMIC VALUES SUCH AS SOCIAL JUSTICE AND LIFE BALANCE, BY WHICH THE BASIC REQUIREMENTS FOR HUMAN LIFE ARE PROVIDED. ACCORDING TO JURISPRUDENCE AND SHARIAH (ISLAMIC LAW) TEXTS, ISLAM'S MAIN GOAL OF SOCIO-ECONOMIC JUSTICE IS TO ESTABLISH FAIRNESS IN SOCIETY, SO IT IS SAID THAT IN ISLAM THERE IS NO DISCRIMINATION BETWEEN MEMBERS OF SOCIETY, AND JUSTICE AND SECURITY FOR THE LIVES OF MEMBERS OF SOCIETY, THEIR PROPERTY AND WEALTH MUST ALWAYS BE ESTABLISHED. BE SOCIAL JUSTICE PLAYS A VITAL ROLE IN THE DEVELOPMENT OF SOCIETY, BUT IT SHOULD BE SEEN WHETHER THESE PROMISES HAVE BEEN FULFILLED OR IGNORED. IN THIS REGARD, IN THIS ARTICLE, FIRST, THE LEVEL OF SOCIAL-ISLAMIC RESPONSIBILITY OF BANKS IN IRAN IS EXAMINED COMPARED TO WHAT IS EXPECTED AT THE INTERNATIONAL LEVEL, AND THEN, USING THE METHOD OF VARIANCE ANALYSIS, THE RELATIONSHIP BETWEEN THE LEVEL OF DISCLOSURE OF SOCIAL-ISLAMIC RESPONSIBILITY AND FINANCIAL PERFORMANCE BANKS ARE STUDIED. THE RESULTS SHOWED THAT THE BANKS IN IRAN DO NOT ADEQUATELY FULFILL THEIR SOCIAL-ISLAMIC RESPONSIBILITIES, AND THE FULFILLMENT OF THESE RESPONSIBILITIES DOES NOT AFFECT THEIR FINANCIAL PERFORMANCE. [ABSTRACT FROM AUTHOR]

Based on a mixed methodology employing data envelopment analysis, this article evaluates the relative efficiency of 19 Colombian National Army research groups to create collaborative improvement plans that produce synergies and generate better future results based on the indicators and characteristics evaluated. The results show a wide margin for improvement for the groups evaluated. However, six of these groups have a relative efficiency of 100 %, indicating a higher level of maturation. Thus, we propose a sponsorship program by these six groups for the group-sets resulting from an allocation model with clear purposes. This design would allow significant advances in the Army's research system results. [ABSTRACT FROM AUTHOR]