Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of main pollution matter volume into the sea from Yellow River and Xiaoqing River

The runoff volume of the Yellow River and the Xiaoqing River were tending to decrease from 1998 to 2002. The runoff volumes of the Yellow River were respectively 107.34×10 8 m 3 in 1998 and 41.57×10 8 m 3 in 2002. The runoff volume of the Xiaoqing River was decreasing from about 4×10 8 m 3 in 1998 to about 1×10 8m 3 in 2002. The total volumes of pollution matter (COD_ mn, NH_ 4-N, NO_ 2-N, NO_ 3-N, Phenol) of the Yellow River at mean annual into the sea was 44 322 t, maxmum 67 336 t, and the volumes of COD_ mn and NO_ 3-N which account for about 93.8% were a leading factor. The total volumes of pollution matter (COD_ mn, NH_ 4-N, NO_ 2-N, NO_ 3-N, Phenol) of the Xiaoqing River at mean annual into the sea was 17 017 t, maxmum 24 314 t, and the volumes of COD_ mn and NH_ 4-N which account for about 93.8% were a leading factor. Compared with the Xiaoqing River, the total volumes of pollution matter of the Yellow River at mean annual into the sea was 1.83 times of the Xiaoqing River, and the runoff volume at mean annual was 21.48 times of the Xiaoqing River. This shows the concen-tration of pollution matter from the Xiaoqing River was over 10 times higher than that from the Yellow River.
Preparing a Compound of New Nanoparticles of Lactam, PE to Improve Their Specifications

This research deals with the properties of polyethylene (PE) using lactams with metallic nano-oxide compounds extracted from (clove) plants, specifically the flower buds of the clove tree as stabilizers and reducing agents, where the nano-oxide stabilizes and covers the natural polymer. The study aims to show that the nano-oxide performs the best bonding of the prepared composites, due to the increased surface area, and thus can bind to the prepared polymer and ability of electronic stability is due to a large number of bonds, which allow the electronic charge to be distributed and then dispersed outside the surface of the polymer, which improves the properties of the polymer and makes it an environmentally friendly material with the easiest casting technique. So that it does not harm human health and other living organisms. In this study zinc oxide nanoparticles were prepared from natural plant sources high product to be used as a catalyst for the preparation of industrially important monomers, and a lactam polymer was added in the presence of zinc oxide extracted from plant (cloves). Poly lactam is a synthetic polymer that degrades when consumed by humans. Oxide nanoparticles, monomers, and polymers were characterized using spectroscopy methods. FTIR and TGA analysis indicated that using X-ray diffraction (XRD), scanning electron microscopy (SEM) and atomic force microscopy the resulting polymer is a high molecular weight homogeneous material grafted with high-quality nanoscales.
Adaptive spectral smoothening for development of robust keyword spotting system

It is well known that a keyword spotting (KWS) system provides significantly reduced performance in mismatched training and test conditions. In this work, an approach is proposed for reducing the mismatches between the training and test speech due to speaker-related variabilities and environmental noises. In the proposed approach, the variational-mode decomposition is first performed on the short-term magnitude spectra to decompose it into a number of variational mode functions (VMFs) in an adaptive manner. Then, a sufficiently smoothed spectra are reconstructed by selecting only two lower frequency VMFs. When the KWS system is developed by using Mel frequency cepstral coefficients (MFCCs) extracted from the smoothed spectra, a significantly improved performance is observed for pitch and noise mismatched test conditions. To further suppress the mismatches due to the pitch and speaking rate of the speakers, data-augmented training based on explicit prosody modification is performed. The experimental results presented in this study show that data-augmented training further enhances the performance of the developed KWS.
Deep placement and foliar fertilization of nitrogen for increased use efficiency An overview

Fertilizer nitrogen is the most important plant nutrient in agriculture all over the world. It accounts for about 68% of the total plant nutrients (NPK) consumed in India. About an 80% N is consumed as urea in India. Since the effi- ciency of fertilizer N is 50% or less, efforts are needed to increase it, because unutilized N is lost to atmosphere and groundwater creating pollution problems. Deep placement and foliar application of urea can significantly in- crease nitrogen-use efficiency, but adequate data on these aspects are not available and need to be created with- out further delay.
A Combination Method of Differential Evolution Algorithm and Neural Network for Automatic Identification Oil Spill at Vietnam East Sea

This paper proposes an automatic detection of oil spills on SAR (synthetic aperture radar) images using DE (differential evolution), neutral network and BP (back propagation) algorithm. Here, DE and BP are combined to train a multilayer perceptron (MLP) network for achieving the global extreme with a better convergence speed. The input data of neural networks are the geometrical characteristics of oil spills (e.g. area, perimeter, complexity) and the physical behavior of oil spills (e.g. mean or max backscatter value, standard deviation of the dark formation). The out data are oil spill or look-alike. We experiment ALOS/PALSAR and EnviSAT ASAR on East sea area of Viet Nam. The experimental results show that the combination algorithm converges faster and has significantly better capability of avoiding local optima.
Análisis bibliométrico de la producción científica sobre rehabilitación física en pacientes sometidos a diálisis en los últimos 24 años

Contexto: la enfermedad renal crónica (ERC) ha aumentado exponencialmente a nivel mundial, esta es una enfermedad que no solo afecta física y psicológicamente al paciente, sino que además altera su calidad de vida. El área renal es más bien nueva y poco explorada por los profesionales de la rehabilitación física, sobre todo a nivel latinoamericano, por lo que, se hace necesario obtener más información sobre el estado actual de ésta, para así permitir a los investigadores conocerla e interesarse en ella. Objetivo: el presente estudio tiene por objetivo realizar un análisis de cómo se ha comportado el campo científico en relación con la rehabilitación en pacientes sometidos a diálisis, explorar las bases que existen para nuevos estudios y conocer cómo ha variado entre los años de 1996 a 2020. Metodología: estudio bibliométrico en el que se identificó un total de 236 archivos, los cuales fueron analizados aplicando filtros según objetivo y tema, resultando un total de 191 publicaciones sobre rehabilitación en pacientes dializados entre los años 1996 y 2020. Para realizar los análisis se utilizaron los programas SciMAT y Bibliometrix Biblioshiny. Resultados: la tasa de publicaciones sobre rehabilitación en pacientes dializados ha aumentado con el tiempo, así como los autores que han publicado sobre el tema, las revistas en distintas categorizaciones y los estudios emergentes en el área. La red de colaboraciones que existen entre los autores de distintos países se ha expandido, siendo Estados Unidos pionero en la producción científica relacionada a este tema. Conclusiones: con este estudio bibliométrico se logró revelar que la investigación sobre rehabilitación en pacientes dializados es un tema que ha ido creciendo en el tiempo y se puede concluir que aún faltan más estudios por realizar, para así tener una base más sólida sobre el tema en cuestión. Los hallazgos obtenidos en el presente estudio proporcionan información muy útil para futuros investigadores que se interesen en comenzar a indagar en esta área.
Self-Management Competence in Depressed In-Patients: A Prospective Observational Study

Background Studies suggest that good self-management is associated with better coping with chronic mental conditions. However, an encompassing assessment of the relationship between depression and self-management competence is lacking. Methods This study assesses the relationship between depressiveness and self-management competence in a sample of 83 depressed in-patients. Beck Depression Inventory II (BDI-II) was used to assess depressiveness. The Self-Management Self-Test (SMST) was used to assess self-management competence. Patient surveys took place at the time of hospital admission (T1) and at the time of hospital discharge or approximately 4 to 6 weeks after (T2). Results Self-management competence correlated negatively with depressiveness at T1. Four out of five specific dimensions of self-management competence correlated inversely with depressiveness at T1. Self-management competence differed depending on the severity of the depressive syndrome and was higher the lower the severity of the depressive syndrome was. In the course of clinical treatment, self-management competence increased. Change of self-management competence during clinical treatment was not dependent on the sociodemographic variables gender or age. Change of self-management competence during clinical treatment predicted the change of depressiveness between hospital admission and discharge (T2 vs. T1) as well as depressiveness at T2. Limitations Sample size was relatively small. The SMST is a relatively new psychometric instrument that has not yet found widespread use in clinical research. Conclusion Our findings offer clinical evidence that in depressed in-patients, self-management competence and depressiveness are associated constructs. These results suggest that self-management competence may be a valuable resource in the treatment of depressive disorders.
Community level youth-led interventions to improve maternal-neonatal outcomes in low- and middle-income countries: A systematic review of randomised trials

Background Evidence on the effectiveness of youth-led interventions for improving maternal-neonatal health and well-being of women and gender diverse childbearing people in low-income and middle-income countries (LMICs) is incomplete. We aimed to summarise the evidence on whether community level youth-led interventions can improve maternal and neonatal outcomes in LMICs. Methods We included experimental studies of youth-led interventions versus no intervention, standard care, or another intervention. Participants were women and gender diverse childbearing people during antepartum, intrapartum, and postpartum periods. MEDLINE, Embase, CINAHL, Global Health, Web of Science, and Cochrane Library, and grey literature were searched to January 2023. All interventions addressing and targeting maternal-neonatal health and well-being that were youth-led and community level were included. Primary outcomes of interest were maternal death and neonatal death. We excluded based on population, intervention, comparison, and outcome (PICO) and design. Two reviewers independently extracted key information from each included study and assessed risk of bias. Random-effects meta-analysis was performed where there were sufficient data. The certainty of evidence was assessed using Grading of Recommendations, Assessment, Development and Evaluation (GRADE). A narrative synthesis was done for results that could not be pooled. Results Of the 8054 records retrieved, four trials (21 813 enrolled participants) met the inclusion criteria. The Cooperative for Assistance and Relieve Everywhere, Inc. (CARE) Community Score Card intervention compared to standard reproductive health services control did not significantly improve Antenatal Care coverage (difference-in-differences estimate β = 0.04; 95% confidence interval (CI) = -0.11, 0.18, P = 0.610; one study, low certainty of evidence). The multi-component social mobilisation interventions compared to standard of care had no effect on adolescent/youth pregnancy (adjusted odds ratio estimate = 1.08; 95% CI = 0.87, 1.33; three studies; low certainty of evidence). Conclusions Youth-led interventions in LMICs did not show a significant improvement in maternal outcomes. More studies are required to make more precise conclusions. Registration PROSPERO: CRD42021288798.
The Validity of Using a Geographic Information System's Viewshed Function as a Predictor for the Reception of Line-of-Sight Radio Waves

A Geographic Information System (GIS) viewshed is the result of a function that determines, given a terrain model, which areas on a map can be seen from a given point(s), line or area. In the communications industry, this function has been used to model radio wave coverages and to site transceiver towers for cellular phones. However, there are errors involved with this function and, without the requisite data, it cannot account for building heights that may affect visibility in urban areas. This paper examines the ability to accurately show line-of-sight (LOS) radio wave coverages in order to establish the viability of replacing existing field methods with GIS viewshed analysis. An origin point capable of supporting a line-of-sight radio wave transmitter was chosen from within the Virginia Tech campus study area. A viewshed analysis was performed with ESRI's ArcView GIS, using this site as the observation point and a 30-meter resolution Digital Elevation Model (DEM) from the US Geological Survey. To check the accuracy of the viewshed, we transmitted at 27.5 GHz, a LOS frequency that has properties common in the wireless telecommunications industry. We also transmitted at 900 MHz from the same point to provide a comparison of the 27.5 GHz frequency to a non-line-of-sight radio wave. Data was then recorded using the following: a digital camera to capture the view from the data point to the transmitter, a Global Positioning System (GPS) receiver to pinpoint the test location, a 27.5 GHz receiver and a 900 MHz receiver to acquire the signals, and a spectrum analyzer to record the strength of both signals. The viewshed model was used as a reference map to define the area of data collection. The recorded data was then entered into the GIS where the iii visibility attribute of each point (the visual inspection recorded by the camera) was compared to the viewshed's prediction of visibility. After this comparison, footprints and heights of campus buildings were included in the model, by adding them to the ground heights of the DEM. Another viewshed analysis was performed using the same origin site and the new building height DEM as the elevation grid. This second viewshed was compared with the recorded visibility attribute and signal strength data. The use of more complete surface data was shown to have a more positive correlation with the recorded data than the previous model. The comparison of these two viewsheds demonstrated how …
Internal consistency and reliability of the lifetime and modified current cognitive activity questionnaires and their association with cognitive performance: a six-year follow up of the Brain in Motion study

ABSTRACT Introduction Cognitive activity questionnaires could provide insight into neurocognitive reserve. The Lifetime Cognitive Activities Questionnaire (LCAQ) assesses cognitive activities at four stages of life. The Modified Current Cognitive Activities Questionnaire (CCAQ) assesses current cognitive activities. We examined the construct validity, internal consistency, test-retest reliability, and stability of these questionnaires throughout the Brain in Motion (BIM) study and their relationship with cognitive performance. Methods The LCAQ, Montreal Cognitive Assessment (MoCA), and neuropsychological battery were administered at the initial pre-intervention and six-year follow-up. The CCAQ was administered at five timepoints. Construct validity of the CCAQ/LCAQ was assessed using proxies of cognitive engagement (educational attainment and the North American Adult Reading Test [NAART]). Cronbach alpha analysis determined internal consistency. LCAQ reliability was established by comparing the pre-intervention and six-year follow-up. CCAQ reliability was determined by comparing both pre-intervention assessments, correlations throughout BIM determined stability. A multiple linear regression investigated the associations between cognitive engagement and cognitive domains derived from a principal component analysis. Results MoCA scores at the initial pre-intervention (27.49 ± 1.46) and six-year follow up (26.53 ± 2.08). The LCAQ and CCAQ correlated with educational attainment and the NAART. The LCAQ (n = 266) produced an alpha of 0.90 (20 items). The CCAQ (n = 261) resulted in an alpha of 0.71 (25 items). LCAQ scores (n = 94) at the initial pre-intervention and six-year follow-up were correlated. CCAQ (n = 94) scores at the initial pre-intervention correlated with scores at all five other timepoints. The multiple linear regression revealed associations between the CCAQ and verbal memory/attention. The NAART was associated with processing speed, concept formation, and verbal memory/attention. Conclusions In the absence of cognitive decline, these questionnaires exhibit significant construct validity, internal consistency, test-retest reliability, and the CCAQ displayed stability. The NAART and CCAQ were associated with neuropsychological performance. Our findings support future use of these questionnaires and exemplify the neuroprotective role of cognitive engagement.
Effect of Flecainide and Ibutilide Alone and in Combination to Terminate and Prevent Recurrence of Atrial Fibrillation

BACKGROUND: There is a need for improved approaches to rhythm control therapy of atrial fibrillation (AF). METHODS: The effectiveness of flecainide (1.5 µmol/L) and ibutilide (20 nmol/L), alone and in combination, to cardiovert and prevent AF recurrence was studied in canine-isolated coronary–perfused right atrioventricular preparations. We also examined the safety of the combination of flecainide (1.5 µmol/L) and ibutilide (50 nmol/L) using canine left ventricular wedge preparations. RESULTS: Sustained AF (>1 hour) was inducible in 100%, 60%, 20%, and 0% of atria in the presence of acetylcholine alone, acetylcholine+ibutilide, acetylcholine+flecainide, and acetylcholine+ibutilide+flecainide, respectively. When used alone, flecainide and ibutilide cardioverted sustained AF in 40% and 20% of atria, respectively, but in 100% of atria when used in combination. Ibutilide prolonged atrial and ventricular effective refractory period by 15% and 8%, respectively, at a cycle length of 500 ms (P<0.05 for both). Flecainide increased the effective refractory period in atria by 27% (P<0.01) but by only 2% in the ventricles. The combination of the 2 drugs lengthened the effective refractory period by 42% in atria (P<0.01) but by only 7% (P<0.05) in the ventricles. In left ventricular wedges, ibutilide prolonged QT and Tpeak-Tend intervals by 25 and 55%, respectively (P<0.05 for both; cycle length, 2000 ms). The addition of flecainide (1.5 µmol/L) partially reversed these effects (P<0.05 for both parameters versus ibutilide alone). Torsades de Pointes score was relatively high with ibutilide alone and low with the drug combination. CONCLUSIONS: In our experimental model, a combination of flecainide and ibutilide significantly improves cardioversion and prevents the recurrence of AF compared with monotherapies with little to no risk for the development of long-QT–mediated ventricular proarrhythmia.
Enhancing Computation Pushdown for Cloud OLAP Databases

Network is a major bottleneck in modern cloud databases that adopt a storage-disaggregation architecture. Computation pushdown is a promising solution to tackle this issue, which offloads some computation tasks to the storage layer to reduce network traffic. Existing cloud OLAP systems statically decide whether to push down computation during the query optimization phase and do not consider the storage layer's computational capacity and load. Besides, there is a lack of a general principle that determines which operators are amenable for pushdown. Existing systems design and implement pushdown features empirically, which ends up picking a limited set of pushdown operators respectively. In this paper, we first design Adaptive pushdown as a new mechanism to avoid throttling the storage-layer computation during pushdown, which pushes the request back to the computation layer at runtime if the storage-layer computational resource is insufficient. Moreover, we derive a general principle to identify pushdown-amenable computational tasks, by summarizing common patterns of pushdown capabilities in existing systems. We propose two new pushdown operators, namely, selection bitmap and distributed data shuffle. Evaluation results on TPC-H show that Adaptive pushdown can achieve up to 1.9x speedup over both No pushdown and Eager pushdown baselines, and the new pushdown operators can further accelerate query execution by up to 3.0x.
Range Entropy Queries and Partitioning

Data partitioning that maximizes or minimizes Shannon entropy is a crucial subroutine in data compression, columnar storage, and cardinality estimation algorithms. These partition algorithms can be accelerated if we have a data structure to find the entropy in different subsets of data when the algorithm needs to decide what block to construct. While it is generally known how to compute the entropy of a discrete distribution efficiently, we want to efficiently derive the entropy among the data items that lie in a specific area. We solve this problem in a typical setting when we deal with real data, where data items are geometric points and each requested area is a query (hyper)rectangle. More specifically, we consider a set $P$ of $n$ weighted and colored points in $\mathbb{R}^d$. The goal is to construct a low space data structure, such that given a query (hyper)rectangle $R$, it computes the entropy based on the colors of the points in $P\cap R$, in sublinear time. We show a conditional lower bound for this problem proving that we cannot hope for data structures with near-linear space and near-constant query time. Then, we propose exact data structures for $d=1$ and $d>1$ with $o(n^{2d})$ space and $o(n)$ query time. We also provide a tune parameter $t$ that the user can choose to bound the asymptotic space and query time of the new data structures. Next, we propose near linear space data structures for returning either an additive or a multiplicative approximation of the entropy. Finally, we show how we can use the new data structures to efficiently partition time series and histograms with respect to entropy.
Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems

Adapting Large Language Models for recommendation (LLM4Rec)has garnered substantial attention and demonstrated promising results. However, the challenges of practically deploying LLM4Rec are largely unexplored, with the need for incremental adaptation to evolving user preferences being a critical concern. Nevertheless, the suitability of traditional incremental learning within LLM4Rec remains ambiguous, given the unique characteristics of LLMs. In this study, we empirically evaluate the commonly used incremental learning strategies (full retraining and fine-tuning) for LLM4Rec. Surprisingly, neither approach leads to evident improvements in LLM4Rec's performance. Rather than directly dismissing the role of incremental learning, we ascribe this lack of anticipated performance improvement to the mismatch between the LLM4Recarchitecture and incremental learning: LLM4Rec employs a single adaptation module for learning recommendation, hampering its ability to simultaneously capture long-term and short-term user preferences in the incremental learning context. To validate this speculation, we develop a Long- and Short-term Adaptation-aware Tuning (LSAT) framework for LLM4Rec incremental learning. Instead of relying on a single adaptation module, LSAT utilizes two adaptation modules to separately learn long-term and short-term user preferences. Empirical results demonstrate that LSAT could enhance performance, validating our speculation.