Space For Further Improvement Research Articles

Large Language Models and Genomics for Summarizing the Role of microRNA in Regulating mRNA Expression.

microRNA (miRNA)-messenger RNA (mRNA or gene) interactions are pivotal in various biological processes, including the regulation of gene expression, cellular differentiation, proliferation, apoptosis, and development, as well as the maintenance of cellular homeostasis and pathogenesis of numerous diseases, such as cancer, cardiovascular diseases, neurological disorders, and metabolic conditions. Understanding the mechanisms of miRNA-mRNA interactions can provide insights into disease mechanisms and potential therapeutic targets. However, extracting these interactions efficiently from a huge collection of published articles in PubMed is challenging. In the current study, we annotated a miRNA-mRNA Interaction Corpus (MMIC) and used it for evaluating the performance of a variety of machine learning (ML) models, deep learning-based transformer (DLT) models, and large language models (LLMs) in extracting the miRNA-mRNA interactions mentioned in PubMed. We used the genomics approaches for validating the extracted miRNA-mRNA interactions. Among the ML, DLT, and LLM models, PubMedBERT showed the highest precision, recall, and F-score, with all equal to 0.783. Among the LLM models, the performance of Llama-2 is better when compared to others. Llama 2 achieved 0.56 precision, 0.86 recall, and 0.68 F-score in a zero-shot experiment and 0.56 precision, 0.87 recall, and 0.68 F-score in a three-shot experiment. Our study shows that Llama 2 achieves better recall than ML and DLT models and leaves space for further improvement in terms of precision and F-score.

Universal Strategy with Structural and Chemical Crosslinking Interface for Efficient and Stable Perovskite Solar Cells

AbstractDue to the limited interface contact and weak interfacial interaction, planar heterojunction perovskite solar cells (PSCs) have space for further improvement. Herein, a structural and chemical crosslinking interface is proposed and constructed by introducing an extra layer, which blends tin dioxide (SnO2) nanoparticles with chloride salts. Since the incorporated materials can be dissolved during the fabrication of perovskite, the quality of perovskite films is improved, leading to larger grain size and reduced trap‐state density. Also, more chloride ions at the SnO2/perovskite interface are observed and the interaction between Cl− and Sn4+ is confirmed. It results in more pronounced n‐type SnO2 with better conductivity and deeper conduction bands, leading to preferable energy level alignment between SnO2 and perovskite. Consequently, the open‐circuit voltage and fill factor of the devices increase, and target cells present better stability, retaining 98% of initial efficiencies after >10 000 h storage in dry air (≈5% relative humidity) and maintaining 85.50% of the initial efficiency after 1000 h of operation under light. This strategy enables the achievement of 25.28% efficiency with a low bandgap (1.53 eV) perovskite composition, and it is confirmed to be universal when other related materials are utilized.

VLSI Placement Optimization Algorithms

Placement is a fundamental optimization step to compute cell locations. The quality of results in Clock Tree Synthesis (CTS) and routing stages is impacted by the placement solution. The placement optimization flow is split into (1)global placement, (2) legalization, and (3) detailed placement. In global placement, cell locations are computed to minimize total wire length subject to a maximum cell density threshold. The cell overlapping and cell alignment to site row boundaries are relaxed. In legalization, cells are placed in locations free of overlapping and aligned to site row boundaries. Legalization algorithms compute cell locations with minimized cell displacement. In detailed placement, objectives are optimized locally. Detailed placement algorithms iterate over one cell or a small set of cells. The traditional optimization objective is total wire length. Placement algorithms also address timing violations, routability, design rules, and so forth. The placement algorithms rely on heuristics and formal methods to compute optimized cell locations. Moreover, placement algorithms require models to address signal delay propagation, area density, routing congestion, hyper-edge nets, and so forth. In the literature, several algorithms have been presented to improve placement solutions. On the other hand, placement is a really challenging problem that continues to have space for further improvement and for innovative algorithms.

Comparative analysis of growth, survival and sex proportion among tetraploid-based autotriploid (Crassostrea gigas and C. angulata) and their allotriploid oysters

Triploid is widely cultivated in China and many other oyster-producing countries for the advantages of fast growth, favorable infertility and better meat quality. However, triploid Crassostrea gigas is found to have disadvantages of slow growth in later grow-out stage and high summer mortality, while the triploid C. angulata has the disadvantages of fragile shells and a relatively high ratio of developed gonads in population, and therefore, both of them have space for further improvement. Due to the limitations of poor fertility and low survival of offspring, triploid genetic improvement cannot be carried out directly. Hybridization between species is a useful method for indirect genetic improvement of triploids, and natural crosses between tetraploid males and diploid females have proven to be the best method for commercial promotion of triploids. The growth, survival and gonad development of autotriploids (DGTG-diploid C. gigas ♀ × tetraploid C. gigas ♂, DATA-diploid C. angulata ♀ × tetraploid C. angulata ♂) and allotriploids (DATG-diploid C. angulata ♀ × tetraploid C. gigas ♂, DGTA-diploid C. gigas ♀ × tetraploid C. angulata ♂) were investigated in this study. It is showed that the fertilization rates of autotriploids were significantly higher than that of allotriploids, and DATG larvae grew significantly faster than the other three groups (P < 0.05). DGTA had significant shell height, whole weight and survival advantages at Laizhou site (P < 0.05), while DATG exhibited significantly fastest growth and highest survival at Zhangpu site (P < 0.05), but both DATG and DATA showed significantly faster growth and higher survival at Beihai site (P < 0.05), with no significant difference between them (P > 0.05). Sites, genotypes and their interaction always presented significant effects on shell height and whole weight from the 90th to 360th day, but not on survival with the 180th day as an exception. Gonad examination confirmed that the proportions of fertile individuals in the groups with maternal C. gigas (DGTG and DGTA) were lower than that in the groups with maternal C. angulata (DATA and DATG), and DATA always had the highest rate of developed gonad at three sites. These results indicate that DGTA has advantages in growth, survival and infertility in northern China while DATG performs best in southern China, which exhibits that there is a significant maternal effect of hybridization in allotriploids, and promise a guidance for their commercial production.

An edge feature aware heterogeneous graph neural network model to support tax evasion detection

Tax evasion is an illegal activity that causes severe losses of government revenues and disturbs the economic order. To alleviate this problem, decision support systems that enable tax authorities to detect tax evasion efficiently have been proposed. Recent researches tend to use graph to model the tax scenario and leverage graph mining techniques to conduct tax evasion detection, as so to make full use of the rich interactive information between taxpayers and improve the detection performance. However, a more favorable graph mining solution, graph neural networks, has not yet been thoroughly investigated in such settings, leaving space for further improvement. Therefore, in this paper, we propose a novel graph neural network model, named Eagle, to detect tax evasion under the heterogeneous graph. Specifically, based on the guidance of our designed metapaths, Eagle can extract more comprehensive features through a hierarchical attention mechanism that fully aggregates taxpayers’ features with their relations. We evaluate Eagle on real-world tax dataset. The extensive experimental results show that our model performs 15.71% better than state-of-the-art tax evasion detection methods in the classification scenario, while improves 5.22% in the anomaly detection scenario.

Accident Detection in Autonomous Vehicles Using Modified Restricted Boltzmann Machine

Accident detection in autonomous vehicles could save lives by reducing the time it takes for information to reach emergency responder. One of the most common reason for the death of humans is accident. Indeed, it was determined throughout the survey that road accidents are indeed the second greatest cause of death in the United States for people aged 30 to 44 years, representing for 1/3 among all deaths. The transportation industry is increasingly relying on mathematical methods and new data assets to detect injuries. Many machine learning and deep learning models have already been proposed for accident detection but still there is much space for further improvement to be done to save human lives in case of accident detection, if accidents are not identified well. In our present study, we proposed modified restricted Boltzmann machine for accident detection. Our proposed methodology consists of the following steps. In the first step, we took different accidental and nonaccidental images as an input. In the second step, we applied our proposed deep learning technique modified restricted Boltzmann machine. In the third step, when weight acceleration and coefficient adjustments are run as a generalization mechanism, then we check our model performance after applying through multiple procedures. As a result, multiple images are classified as accidental and nonaccidental images of vehicles. Proposed methodology has been applied for data set, and data have been divided into different training and testing ratios. The proposed MRBM model has an accuracy of 98% in classification of both accidental and nonaccidental images of vehicles. The proposed model outperforms the competition significantly than other in which they are compared like artificial neural network, support vector machine, and restricted Boltzmann machine techniques.

Review of Toll payment apps for smartphons

The constant increase in motorization rate and passenger cars usage lead to rising traffic congestion, which causes traffic delays and unfavorable traffic regime. In the toll collection system, the use of modern devices, specifically smartphones, would allow vehicles to pass through the toll collection system without stopping and time delays, which would solve the problems of queuing but also reduce the impact of vehicles on environmental pollution. This paper describes the ways of working of various applications that are used today in countries around the world, mainly as a secondary way of paying the fee for the use of the road network. The analysis of applications showed that in order to pay the toll, in addition to downloading the application, the user needs to create a user account, enter the number of vehicle registration plates and the number of payment, credit or debit card through which payment will be made. In addition to the benefits offered by mobile devices, there are certain disadvantages and space for further improvement.

How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market

Understanding the customers’ perception of the value of constituent characteristics of a good is among the key questions in any pricing strategy. Hedonic pricing allows such an analysis and is frequently applied in economic fields. Although it is regarded as a benchmark in its original form, the availability of new data sources and the development of machine learning techniques created a space for further improvement. In this study, we propose a general framework for applying machine learning tools to enhance the hedonic pricing model in several directions. We do this, first, by adding image and text sources to conventional data and then by applying an advanced nonparametric prediction model. Lastly, we use model agnostic analysis to uncover new pricing factors and unravel complex relationships that could not be captured by conventional models.

C3N4/Cu/ZnFe2O4 Ternary Nanocomposites: Removal of Environmental Pollutants by the Synergy of Physical Adsorption and Photocatalysis

AbstractAlthough binary composites can enhance the separation efficiency of photo‐generated carriers to a certain extent, there is still space for further improvement. Here, we focus on using copper, a cheap transition metal, as an electron channel to improve the photo‐generated carrier separation efficiency of ZnFe2O4 (ZFO) and C3N4 (CN) composites. The kinetic constants of sample 40 %‐ZFO/CN for photodegradation reaction are 2.96 and 1.98 times than that of pure sample ZFO and CN under the same conditions. However, the kinetic constants of 3 %Cu@40 %‐ZFO/CN are 7.33 and 4.92 times than that of pure sample ZFO and CN, respectively. This is probably due to the physical adsorption performances of ternary composites for pollutants are significantly stronger than those of binary composites. The best photocatalysts achieved 90 % degradation efficiency within 15 minutes. A more reasonable photocatalytic mechanism model was proposed. Photo‐generated hole, superoxide anion, and hydroxyl radical all play an important role in the photocatalytic reaction. The importance of them is superoxide anion &gt; photo‐generated hole &gt; singlet oxygen &gt; hydroxyl radical from high to low.

A Study on Content Validity of Reading Comprehension in TEM-8 (2016-2021)

TEM-8 is an authoritative test to judge the professional level of English majors, in which reading comprehension occupies an important proportion. Based on Bachman & Palmer’s test task characteristics framework and combined with the requirements of teaching syllabus for English majors and the revised TEM-8 test syllabus in 2015, this paper, from the perspectives of language input characteristics and expected response characteristics, takes TEM-8 reading comprehension passages in the past five years as the research object to analyze the content validity of reading comprehension with the methods of qualitative and quantitative analysis. The results reveal that TEM-8 reading comprehension test in 2016-2021 has relatively high content validity on the whole, but there is space for further improvement in the length of reading text, the selection of text materials, the balance of text difficulty and the diversity of response types.

How accurate are Twitter and Facebook altmetrics data? A comparative content analysis

Data accuracy is essential for reliable and valid altmetrics analysis. Although Twitter and Facebook altmetrics data are widely used for scholarly communication and scientific evaluation, few studies have tapped into their accuracy issue. Based on content analysis of random sample records over two phases, this study has investigated and compared the accuracy of Twitter and Facebook altmetrics data. Major conclusions are drawn as follows. (1) Three error types were identified from the altmetric data provider and six error types were identified from the altmetric data aggregator. Twitter and Facebook have shared most of the error types except for minor differences in the sub-categories. (2) The overall error rate is substantially high, being 17% and 32% for Twitter and Facebook respectively in April, 2019. However, except for publication date error and posting date error, the percentage of the other error types is relatively low (being around 3%). (3) The percentage of error types related to the dynamic nature of Twitter and Facebook is increasing over time, while percentage of error types concerning the bibliographic data is decreasing over time. (4) The error types are either “high seriousness low percentage” or “low seriousness high percentage”, therefore, they would probably not bring significant negative influence. (5) Underlying reasons of these error types are various. They could be attributable to the Twitter (or Facebook) user, Twitter (or Facebook) platform, altmetric database, as well as the third-party data provider. These results suggest that Twitter and Facebook altmetrics data in the Altmetric database are reliable on the whole, although there is still space for further improvement.

Comparison of Three Measurement Principles on Water Triple Oxygen Isotopologues

The widespread method for measuring Δ17O (17O-excess) is an offline CoF3 (Cobalt tri-fluoride) conversion of water to molecular oxygen with subsequent isotope determination by dual inlet mass spectrometry. High precisions for Δ17O measurements, using CoF3 water conversion, are so far only possible with off-line methods. Here we report on an improved and modified online continuous flow method intended for high precision triple oxygen isotope analysis. This method is improved by optimizing the reactor (site for conversion of H2O into oxygen through the chemical reaction) compositions, size of the fused silica capillary, flow regulator, and data treatment. Our modified online continuous method was further compared with the recently developed cavity ring down measurement principle. The precision is significantly better for the commercially available laser-based system than our current version of improved online CoF3 conversion method using mass spectrometry. Factors identified for limiting precision in our continuous flow system are: (i) compaction of the reactor with time that leads to the restriction of flow rate of carrier gas, (ii) the CoF3 treatment, (iii) the amount of CoF3 inside the reactor, (iv) the pore size of the steel frit, and (v) the metallic tube. Changes in all of these items as well as the dimension of the fused silica capillary, the positioning of the fused silica capillary in the open split, and the memory effect can also lead to a declining precision. These limiting factors for precision still provide us enough space for further improvement of our improved online method which will be worthwhile for the measurement of smaller aliquot samples as fluid inclusions for palaeoclimatic applications. With present improvement, multiple injections (n = 15 or even more) should be applied to obtain a precision better than 10 per meg for Δ17O. Furthermore, a comparison of the laser-based system with an improved conventional equilibration method has been made on precipitation samples originating from Jungfraujoch.

3D urchin like V-doped CoP in situ grown on nickel foam as bifunctional electrocatalyst for efficient overall water-splitting

Cobalt phosphide (CoP) is considered to be a potential candidate in the field of electrocatalysis due to its low-cost, abundant resources and high electrochemical stability. However, there is a great space for further improvement of its electrocatalytic performance since its charge transfer rate and catalytic activity have not reached a satisfactory level. Herein, we design and fabricate a three dimensional urchins like V-doped CoP with different amounts of V-doping on nickel foam electrode. The V-doped CoP/NF electrode with optimized amounts of V-doping (10%) exhibits outstanding hydrogen evolution reaction (HER) performance under universal-pH conditions and preeminent oxygen evolution reaction (OER) performance in alkaline media. Notably, the assembled water-splitting cell displays a cell voltage of only 1.53 V at 10 mA·cm−2 and has excellent durability, much better than many reported related bifunctional catalysts. The experiment results and theoretical analysis revealed that vanadium atoms replace cobalt atoms in CoP lattice. Vanadium doping can not only raise the density of electronic states near the Fermi level enhancing the conductivity of the catalyst, but can also optimize the free energy of hydrogen and oxygen-containing intermediates adsorption over CoP, thus promoting its catalytic activity. Moreover, the unique nanostructure of the catalyst provides the various shortened channels for charge transfer and reactant/electrolyte diffusion, which accelerates the electrocatalytic process. Also, the in situ growth strategy can improve the conductivity and stability of the catalyst.

A possible way to explicitly account for different molecular geometries with an equation of state

Different molecular geometries are determinant to condensed phases structures, and to the thermodynamic behavior. To take into consideration the nonsphericity in the development of equations of state, molecules have been taken either as chains of spherical segments or sets of nonspherical segments. In this work, we model molecules as single anisotropic particles, introducing a general Hard Nonspherical Segment (HNS) reference term, which can, in principle, be used within the framework of the Statistical Associating Fluid Theory (SAFT), once coupled with an appropriate association term. The resulting model explicitly accounts for different molecular geometries. Variants of the HNS reference term for ellipsoids, cylinders, and spherocylinders were applied to calculate vapor-liquid equilibrium of carbon dioxide, benzene, toluene, n-perfluoroalkanes (CF4, C2F6, C3F8), and n-alkanes (from methane to octane). We have shown that, in general, our approach of modeling molecules as nonspherical particles yield better results than a comparable equation of state for which molecules are taken as chains of spherical segments. Furthermore, the fitted parameters lead to physically sound geometrical models; for instance, the oblate geometry of the aromatic rings was inferred from vapor-liquid equilibrium data upon fitting the model parameters. We show that the adjusted aspect ratios of the ellipsoidal and spherocylindrical particles can be correlated with the acentric factor of their respective molecules, adding evidence that the degree of nonsphericity of molecules is indeed somehow captured by the acentric factor. State-of-the-art equations of state such as PC-SAFT and SAFT-VR Mie still give better results, and the level of complexity in their formulation may justify it. Therefore, we believe that the proposed methodology with nonspherical segments is promising, but has space for further improvement.

China’s energy cooperation within the 17+1

The paper analyses Chinese energy cooperation within the 17+1 Cooperation Framework. In order to present the broader political context of this collaboration, special attention is given to Chinese energy interests and EU energy policy. Since the existing databases about Chinese energy projects in 17+1 were incomplete, the authors created a new dedicated database. The authors address key questions about the principal projects involved such as: what are the countries and energy subsectors in which China invests the most; what are the main obstacles in existing energy cooperation; does this kind of energy cooperation have a positive impact on the development of 17+1 members and is China successful in fulfilling its geo-economic strategy in 17+1 in regards to its overall energy policy. The authors find that China is primarily interested in building coalfired power plants, but results remain below expectations, with performance affected by a combination of EU opposition, project costs, and internal political issues in the 17+1 countries. The nuclear energy subsector is where Chinese enterprises have experienced some of their greatest failures, while the hydro energy subsector still has potential for future development. In addition, China is strongly investing in green energy and slowly but surely achieving its energy policy goals as part of its broader geo-economic strategy. The authors conclude that the overall effects of cooperation in the energy field are promising, but there is still space for further improvement.

The impact of digitalization on poverty alleviation in Africa

ABSTRACT & RÉSUMÉ & ZUSAMMENFASSUNG : Digitalization in Sub-Saharan Africa enhanced the accessibility of communications by the majority of the poor who had been excluded among others from social media, independent information channels, mobile banking and e-commerce. The creation of new economic opportunities, e.g. the pay-as-you-go business, and increased flow of information also boosted people’s self-esteem, sense of belonging and citizenship. The smartphone became the main source of internet access which also bridged the divide between urban and rural communities. Thus, mobile telecommunications contributed positively to economic growth even in less developed regions, and there is apparently still ample space for further improvement. Yet, Africans were also confronted with new forms of the digital divide between the poor and the rich, between advanced and less advanced African countries, as well as between Africa and the rest of the world. Moreover, the digitalization of the public sphere became a double-edged sword. Autocratic governments like Sudan and Togo shut down the internet during elections to facilitate the rigging of the polls. The lack of transparency and objectivity fuelled fake news which rapidly spread in social media, notably in times of the Corona crisis. Last, but not least, not everybody surfing in the internet had the same access to quality information. For example, disinformation was supported clandestinely by foreign powers to destabilize political regimes, or spy software was provided to governments to control the opposition. Both false news in social media and spy-software impeded poverty relieve in Africa significantly. ----------------------------------------------------------------------------------------------------------------------------------------------------- RÉSUMÉ : La numérisation en Afrique subsaharienne a amélioré l'accessibilité des communications par la majorité des pauvres qui avaient été exclus, entre autres, des médias sociaux, des canaux d'information indépendants, des services bancaires mobiles et du commerce électronique. La création de nouvelles opportunités économiques, par ex. l'activité par répartition et l'augmentation du flux d'informations, ont également renforcé l'estime de soi des personnes, leur sentiment d'appartenance et de citoyenneté. Le smartphone est devenu la principale source d'accès à Internet, ce qui a également permis de combler le fossé entre les communautés urbaines et rurales. Ainsi, les télécommunications mobiles ont contribué positivement à la croissance économique, même dans les régions les moins développées, et il y a apparemment encore amplement de place pour de nouvelles améliorations. Pourtant, les Africains ont également été confrontés à de nouvelles formes fossé digital entre les pauvres et les riches, entre les pays africains avancés et moins avancés, ainsi qu'entre l'Afrique et le reste du monde. De plus, la numérisation de la sphère publique est devenue une arme à double tranchant. Des gouvernements autocratiques, comme le Soudan et le Togo, ont fermé Internet pendant les élections pour faciliter le trucage des élections. De plus, le manque de transparence et d'objectivité a alimenté les fausses informations qui se sont rapidement propagées sur les réseaux sociaux, notamment en période de crise de COVID-19. Enfin, tout le monde qui navigue sur Internet n'a pas le même accès à des informations de qualité. La désinformation était soutenue clandestinement par des puissances étrangères pour déstabiliser les régimes politiques, ou des logiciels espions fournis aux gouvernements pour contrôler l'opposition. Les fausses nouvelles dans les médias sociaux et les logiciels espions ont entravé la pauvreté en Afrique de manière significative. ----------------------------------------------------------------------------------------------------------------------------------------------------------- ZUSAMMENFASSUNG : Die Digitalisierung in Subsahara-Afrika verbesserte Kommunikations-Zugang für die Mehrheit der Armen, die bis dahin unter anderem von sozialen Medien, unabhängigen Informationskanälen, Mobile Banking und E-Commerce ausgeschlossen waren. Die Schaffung neuer wirtschaftlicher Möglichkeiten, z.B. das Umlageverfahren und der verstärkte Informationsfluss, stärkten auch das Selbstwertgefühl, das Zugehörigkeitsgefühl und das Nationalbewußtsein der Menschen. Das Smartphone wurde zur Hauptquelle des Internetzugangs, der auch die Kluft zwischen urbanen und ländlichen Gebieten überbrückte. Damit trug der Mobilfunk auch in weniger entwickelten Regionen positiv zum Wirtschaftswachstum bei, und es gibt offenbar noch viel Raum für weitere Verbesserungen. Afrikaner wurden aber auch mit neuen Formen der digitalen Kluft zwischen Arm und Reich, zwischen entwickelten und weniger entwickelten afrikanischen Ländern sowie zwischen Afrika und dem Rest der Welt konfrontiert. Zudem wurde die Digitalisierung der Öffentlichkeit zu einem zweischneidigen Schwert. Autokratische Regierungen wie der Sudan und Togo haben das Internet beispielsweise während der Wahlen abgeschaltet, um Wahlfälschung zu erleichtern. Zudem befeuerte der Mangel an Transparenz und Objektivität insbesondere in Zeiten der Corona-Krise fake news, die sich in den sozialen Medien rasant verbreiteten. Nicht zuletzt hatte nicht jeder, der im Internet surfte, den gleichen Zugang zu qualitativ hochwertigen Informationen. Desinformation wurde zudem oft heimlich von ausländischen Mächten unterstützt, um politische Regime zu destabilisieren, oder den Regierungen Spionagesoftware zur Verfügung gestellt, um die Opposition zu kontrollieren. Sowohl Desinformation in sozialen Medien als auch Spionagesoftware haben die Armutsbekämpfung in Afrika erheblich behindert.

Evaluation of the TRMM Product for Spatio-temporal Characteristics of Precipitation over Nepal (1998-2018)

Precipitation is a fundamental component of the water cycle and integral to the society and the ecosystem. Further, continuous monitoring of precipitation is essential for predicting severe weather, monitoring droughts, and high-intensity related extremes. The present study evaluated the spatio-temporal distribution of precipitation and trends between 1998– 2018 using Tropical Rainfall Measuring Mission (TRMM) (3B43-V7) with reference to 142-gauge observations over Nepal. TRMM moderately captured precipitation patterns' overall characteristics, although underestimated the mean annual precipitation during the study period. TRMM precipitation product well captured the seasonal variation of the observed precipitation with the highest correlation in the winter season. The decreasing seasonal and annual trend was found in both observed and TRMM products, with the highest (lowest) decreasing trend observed during the monsoon (winter) season. It was also noted that the TRMM product showed a smaller bias before 2007, while a large error was found after 2007, especially in the monsoon months. In general, the TRMM product is a good alternative to observe rain gauge measurement in Nepal. However, there is still space for further improvement in rainfall retrieval algorithms, especially in high-elevation areas during the winter season.

Spatial Distribution of Extreme Precipitation Events and Its Trend in Nepal

The mountainous country with complex topography and climatic conditions over Nepal, the Tropical Rainfall Measuring Mission (TRMM-3B42), were evaluated for monitoring extreme precipitation events using 142-gauge observation from January 1998 to December 2018. Several extreme precipitation indices based on daily timescale data were selected for evaluation. Detection skills, spatial distribution, and trends in extreme precipitation were also investigated. TRMM product moderately capture (POD>70%) the true precipitation events (Probability of Detection) at most of the station. Although, TRMM product shows higher (FAR>40%) false precipitation events (False Alarm Ratio) however, it accurately captures (ACC>80%) the precipitation and no-precipitation events (Accuracy) over the country. Based on five different extreme precipitation indices; heavy precipitation events (R10mm), extreme precipitation events (R25mm), 7 Consecutive Dry Days (CDD), 7 Consecutive Wet Days (CWD), and one-day maximum precipitation (Rx1day), it was observed that the TRMM product can reproduce the spatial distribution of heavy and extreme precipitation events however, it tends to underestimate (overestimate) the frequency of R25mm and CDD (R10mm and CWD spells). Further, increasing (decreasing) trend of dry (wet) spells are observed in both datasets (observed and TRMM) during the study period. The highest Rx1day (about 500 mm/day) was observed during 2014 and 2017; differently, TRMM product shows during 2008 with the highest Rx1day about 300 mm/day. This study provides accuracy of TRMM product to represent the spatio-temporal distribution of extreme precipitation events and its recent trends in Nepal, which is of great significance to hydro-meteorological application. In general, the TRMM product is a good alternative to monitoring extreme precipitation events in Nepal; however, there is still space for further improvement in rainfall retrieval algorithms, especially in high-elevation areas.

Denoising method of motion capture data based on neural network

Optical motion capture systems are prone to data loss or errors during the capture process, which can cause noise in the data. In order to suppress the influence of noise, it is necessary to denoise the captured data to improve the quality of the data and make the denoised motion data reflect the actual motion of the actor as accurately and completely as possible. Aiming at how to effectively denoising motion capture data, researchers have proposed many effective approaches. These approaches are mainly divided into the following three categories: based on filtering strategies, algorithms based on matrix low-rank theory, and data-driven algorithms. Among them, the data-driven denoising method has gradually become the mainstream technology. At present, many studies in this area have achieved certain results, but there is still space for further improvement. Based on the data-driven method, this paper proposes a neural network-based machine learning algorithm to implement denoising processing for motion capture data. Experiments show that this algorithm can significantly recover noisy data.

How Accurately Can Satellite Products (TMPA and IMERG) Detect Precipitation Patterns, Extremities, and Drought Across the Nepalese Himalaya?

AbstractThis study aims to assess the accuracy of two satellite‐based precipitation products (SBPPs), that is, Tropical Rainfall Measurement Mission (TRMM)‐based Multi‐satellite Precipitation Analysis (TMPA) and its upgraded version Integrated Multi‐Satellite Retrievals for Global Precipitation Measurement (IMERG), in capturing spatial and temporal variation of precipitation and their application for extreme events (high‐intensity precipitation and drought). They were evaluated against 142‐gauge stations from Nepal during 2001–2018. The results show that, in general, both SBPPs show the overall characteristics of precipitation patterns, although underestimated the mean annual precipitation during the study period. It was also noted that IMERG product yields better performance to detect precipitation events (probability of detection) and no‐precipitation events (false alarm ratio) than TMPA. Based on four different extreme precipitation indices: heavy precipitation events (R10mm), extreme precipitation events (R25mm), five consecutive dry days (CDD), and five consecutive wet days (CWD), it was observed that the SBPPs underestimated the frequency of R25mm and CDD spells while overestimated R10mm and CWD spells. Additionally, both SBPPs exhibited considerable capabilities in capturing the drought events during the study period. Overall, the drought event, bias, and frequency show that the IMERG product has slightly better capabilities to capture drought than the TMPA product. In general, IMERG was found to be superior at a daily timescale, while TMPA shows consistent performance on a monthly scale during the study period. Furthermore, there is still space for further improvement of IMERG rainfall retrieval algorithms.