Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparation and Application of Cross-linked Alginate Nanoparticles as Drug Carrier: A Review

Nanotechnology also called as nanotech is referring to the science, technology and engineering to synthesis, manipulation and investigation of materials and devices at nanoscales. Nanoparticles (NPs) owns a special place in nanotechnology due to their extreme-small size, multifunctionality, and modifiable surface for wide range of applications especially in new drug delivery application as the stability, solubility, biocompatibility and drugs releasing of NPs are highly controllable. The variation of nanomaterials with different physiochemical and morphological properties will affect the materials-living cells interaction and determination of the route of clearance and potential toxic effects. Thus, it requires more cross-disciplinary research on the designing and developing of drugs delivery NPs based on the diagnosis and treatment of devastating diseases. Biopolymers-based NPs have gained great attention for drug delivery system over the past few decades compared to other materials-based NPs such as liposomes, ceramics and lipids as well as magnetic NPs. There are extensive studies and developments of biopolymers-based NPs for drug delivery and tissue engineering as it capable of controlling the release of drugs, stabilizing labile molecules from degradation, and site-specific drug targeting. Alginate is an anionic copolymer that extracted from algae. The NPs composed alginate have become one of the most extensively researched materials for biomedical applications such as tissue engineering, wound dressing, protein/enzyme carrier and drug delivery due to its biodegradability, biocompatibility, non-toxicity and mucoadhesive properties. Furthermore, by physiochemical crosslinking and cooperating with other polymers, the mechanical strength, cell affinity and drug release profile can be modified and improved. In this brief review paper, several aspects about using alginate or crosslinked alginate for drug delivery application will be discussed, mainly focus on the structure and properties of alginate such as biocompatibility, immunogenicity and biodegradability, as well as the effect of mucoadhesion and pH sensitivity of using alginate in drug delivery nanoparticles developments.
A Comparison between Sentinel-2 and Landsat 8 OLI Satellite Images for Soil Salinity Distribution Mapping Using a Deep Learning Convolutional Neural Network

Abstract In this paper, we aim to compare the suitability of Sentinel-2 and Landsat 8 OLI images for detecting and mapping soil salinity distribution (SSD) using a deep learning convolutional neural network (DL-CNN) approach. We first identified and selected six SSD predisposing variables to train the models. These variables are the normalized difference vegetation index (NDVI), land use, soil types, geomorphology, land surface temperature, and evaporation rate. Next, we collected 219 ground control points from the top 20 cm of the soil surface and randomly divided them into training (70%) and validation (30%) datasets. We then evaluated the different activation, loss/cost, and optimization functions and, finally, employed ReLu, Cross-Entropy, and Adam as the most effective activation function, loss/cost function, and optimizer, respectively. The results showed that the Sentinel-2 image (94.78% overall accuracy and a Kappa of 93.14%) is more suitable for detecting and mapping SSD than the Landsat 8 OLI image (91.45% overall accuracy and a Kappa of 90.45%). Our findings also demonstrated that the DL-CNN approach can support fast and reliable image analysis and classification. As such, this research is a promising step toward understanding, controlling, and managing the complex mechanisms of soil salinization.
Seismic design of solid waste containment facilities

Despite recent advances, seismic design of solid waste containment facilities must still be regarded as an emerging field of practice. However, sufficient data is available to provide a framework of knowledge for environmentally protective design. Observations of performance in earthquakes show that solid waste landfills can sustain very strong shaking with limited damage and without a harmful discharge of contaminants. The excellent record of seismic performance of landfills is tempered by limited experience with geosynthetic containment systems. The potential for amplification of ground motions by the waste mass and the low yield acceleration often associated with geosynthetic cover systems indicate that attention is warranted to this issue. The limited data from regions outside of California, limited experience with saturated waste, and evolutionary changes in waste streams and waste disposal practices provide continuing challenges to engineers designing solid waste disposal facilities subject to seismic loading.
Are Prior Smart Hotel Visiting Experience and Personal Innovativeness Critical to Future Visit Intention?

ABSTRACT This study explores the relationship between consumer perception of smart hotel technology amenities (TA) and consumer visit intention to smart hotels. The mediating role in this relationship is examined by technology acceptance model (TAM). The study also examines whether prior smart hotel visiting experience and personal innovativeness can moderate this relationship. An online survey was carried out with 617 Chinese consumers. Empirical results of structural equation modeling and PROCESS show that prior visiting experience has a moderating effect but PI is not shown to have any moderating effect. This study offers further implications to capitalize on consumer loyalty.
Angle-based Downlink Beam Selection and User Scheduling for Massive MIMO Systems

The next-generation cellular system operating in millimeter-wave (mm-Wave) frequencies requires different signal processing schemes from the legacy LTE system. Among these, beam management aligns the transmitter-receiver pairs with narrow beams and serves as an essential initial acquisition procedure. This paper introduces a novel angle-based downlink precoding strategy, including beam selection and user scheduling for a full dimension (FD) MIMO-OFDM system. The highly directional mm-Wave channel allows the system to perform user/beam scheduling rather than pure user scheduling during the precoding process. A beam-based receive matrix eliminates the remaining intra-cell interference caused by overlapped beams. The performance analysis proves the superiority of the proposed precoding and scheduling strategies over the conventional technique.
Utility of Red Cell Distribution Width (RDW) as a Noninvasive Biomarker for the Diagnosis of Acute Appendicitis: A Systematic Review and Meta-Analysis of 5222 Cases

Background: Despite great advances in medicine, numerous available laboratory markers, and radiological imaging, the diagnosis of acute appendicitis (AA) in some cases still remains controversial and challenging for clinicians. Because of that, clinicians are still looking for an ideal marker that would be specific to AA. The red blood cell distribution width (RDW) has been recently investigated in several studies as a potential biomarker for AA. The aim of this systematic review and meta-analysis was to systematically summarize and compare all relevant data on RDW as a diagnostic biomarker for AA. Methods: This systematic review and meta-analysis were performed as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Scientific databases (PubMed, Scopus, Web of Science, and Excerpta Medica database—EMBASE) were systematically searched for relevant comparative studies by two independent researches using keywords ((red cell distribution width) OR rdw) AND (appendicitis). An independent assessment of the methodological quality was performed by two authors using the Downs and Black scale. RevMan 5.4 software was used to perform the meta-analysis. Results: Fifteen studies were included in the final meta-analysis; the majority of the studies was retrospective. Nine studies compared the RDW values between AA and non-AA; four studies compared the same between AA and healthy controls, while two studies compared the RDW values among all three groups. The estimated heterogeneity among the studies for all outcome was statistically significant (I2 = 92–99%, p < 0.00001). The pooling the data demonstrated no statistically significant difference in the RDW values (weighted mean difference (WMD) = 0.03, 95% CI = (−0.46, 0.52), p = 0.91) between AA and healthy controls as well as between AA and non-AA cases (WMD = 0.23, 95%CI = (–0.19, 0.65), p = 0.28). A separate subanalysis was performed to evaluate the utility of this biomarker for the pediatric age group. Pooling the data demonstrated no significant difference among the AA and non-AA groups in terms of the RDW values (WMD = 0.99, 95% CI = (–0.35, 2.33), p = 0.15). Conclusion: The RDW value difference demonstrated no statistically significant difference in AA versus healthy individuals and AA versus non-AA individuals. At the moment, there is no evidence of RDW utility in diagnostic testing of AA. Further research with prospective, multicenter studies and studies targeting special patient groups with a large sample size are needed in this field.
A 20 Mfps high frame-depth CMOS burst-mode imager with low power in-pixel NMOS-only passive amplifier

This paper presents a 20 Mfps 32 × 84 pixels CMOS burst-mode imager featuring high frame depth with a passive in-pixel amplifier. Compared to the CCD alternatives, CMOS burst-mode imagers are attractive for their low power consumption and integration of circuitry such as ADCs. Due to storage capacitor size and its noise limitations, CMOS burst-mode imagers usually suffer from a lower frame depth than CCD implementations. In order to capture fast transitions over a longer time span, an in-pixel CDS technique has been adopted to reduce the required memory cells for each frame by half. Moreover, integrated with in-pixel CDS, an in-pixel NMOS-only passive amplifier alleviates the kTC noise requirements of the memory bank allowing the usage of smaller capacitors. Specifically, a dense 108-cell MOS memory bank (10fF/cell) has been implemented inside a 30μm pitch pixel, with an area of 25 × 30μm2 occupied by the memory bank. There is an improvement of about 4x in terms of frame depth per pixel area by applying in-pixel CDS and amplification. With the amplifier’s gain of 3.3, an FD input-referred RMS noise of 1mV is achieved at 20 Mfps operation. While the amplification is done without burning DC current, including the pixel source follower biasing, the full pixel consumes 10μA at 3.3V supply voltage at full speed. The chip has been fabricated in imec’s 130nm CMOS CIS technology.
Analyzing Active Fund Managers' Commitment to ESG: Evidence from the United Nations Principles for Responsible Investment

The United Nations Principles for Responsible Investment (PRI) is the largest global environmental, social, and governance (ESG) initiative in the asset-management industry to date. We analyze what happens after active U.S. mutual funds sign the PRI to assess whether they exhibit ESG implementation. We find that PRI signatories attract a large fund inflow, but we do not observe improvements in fund-level ESG scores or fund returns. We consider a battery of ways to proxy for funds’ ESG incorporation (e.g., entry/exit, screening, engagement, voting for pro-ESG proposals), but fail to observe evidence of meaningful on average follow-through. Next, we explore cross-sectional fund characteristics and find that only quant funds exhibit small improvements in ESG performance versus other funds, mainly through buying high-ESG-performing stocks. Furthermore, we note that signatories are not superior performers in ESG issues prior to joining the PRI relative to non-PRI funds, but PRI affiliation tends to be widely advertised on company websites, marketing materials, and fund documents. Overall, a reasonable reader may perceive our findings as consistent with PRI funds’ greenwashing. We note, however, that what we uncover is based only on outcome-based measures and may miss some actual efforts of signatories. This paper was accepted by Brian Bushee, accounting.
Effects of withholding answers coupled with physical manipulation on students’ learning of magnetism-related science content

ABSTRACT Background The question of the efficiency of withholding answers and the physical manipulation of material in science education has become the target of a large number of researchers (proponents of the Cognitive Load Theory) in recent years. However, no research has been found examining the contribution of these elements of teaching to the development of a deeper conceptual understanding and the retention of what has been learned in the scientific education of lower primary school students. Purpose This research aims to examine whether withholding answers coupled with physical manipulation of material contribute to developing a deeper conceptual understanding and retention of learning about magnetism-related content in a hands-on (HO) learning environment on higher-level cognitive skills. Sample The research involved N = 80 third graders (aged 9–10 years) from four classes of two primary schools from a smaller urban environment. Design and methods The research uses a quasi-experimental design. The convenience sampling method was used in the research. Students were assigned into the following groups: DIHO (direct instruction + experimenter demonstration of HO – no withholding answers + no physical manipulation) and IIHO (implicit instruction + independent student performance of HO – withholding answers + physical manipulation). Results The results of ANCOVA analysis show that the students in the IIHO group achieved a much deeper conceptual understanding and retention of what they learned about magnetism-related content at higher-level cognitive skill: I create when compared to the students in the DIHO group. Conclusion It is assumed that withholding answers coupled with physical manipulation of material, contribute to the higher motivation and additional engagement of students on a mental level, which triggers a germane cognitive load, which is essential for productive learning.
Tessellations on bidimensional materials based on Phthalocyanine and applications: a review

In this review, we explore the bidimensional materials based on phthalocyanine molecules. The phthalocyanine molecule is used as a brick for the construction of new bidimensional materials. In particular, the phthalocyanine molecules can be placed at each vertex or sharing edges to form tessellations. The tessellations available are constrained to the four-fold type of the phthalocyanine molecules and can be a mix of several polygons to increase the number of possibilities. Computationally, the popular tessellations used are the Archimedean tiling, but many others expect to be discovered and well-studied. Different tessellations will provide new symmetric systems to explore. Each new symmetry pattern will modify the physical and chemical properties of the new bi-dimensional material. These new materials present many exciting applications as capture and storage of greenhouse gases and molecular electronic devices. In the present review, we summarized some of these tessellations and the many applications that they can have.
Codebook-based Beam Tracking for RIS Assisted Mobile MmWave Networks

The emerging reconfigurable intelligent surfaces (RIS) technique can effectively compensate the propagation loss in millimeter wave communications. Considering the mobility of users in realistic environment, it is urgent to design efficient beam tracking algorithms for RIS to fast align the target users. In this work, we propose a practical codebook-based RIS passive configuration based on the updating channel parameters, i.e., angle-of-departures (AoDs) of RIS-users links, to avoid complex channel estimation procedure. Simulation results show that the proposed algorithm has high tracking accuracy, and yields better sum rate performance compared with the conventional channel estimation method.
Efficient Wireless Traffic Prediction at the Edge: A Federated Meta-Learning Approach

Wireless traffic prediction plays a vital role in managing high dynamic and low latency communication networks, especially in 6G wireless networks. Regarding data and computing resources constraints in edge devices, federated wireless traffic prediction has attracted considerable interest. However, federated learning is limited to dealing with heterogeneous scenarios and unbalanced data availability. Along this line, we propose an efficient federated meta-learning approach to learn a sensitive global model with knowledge collected from different regions. The global model can efficiently adapt to the heterogeneous local scenarios by processing only one or a few steps of fine-tuning on the local data sets. Additionally, distance-based weighted model aggregation is designed to capture the dependencies among different regions for better spatial-temporal prediction. We evaluate the performance of the proposed scheme by comparing it with the conventional federated learning approaches and other commonly used benchmarks for traffic prediction. The extensive simulation results reveal that the proposed scheme outperforms the benchmarks.
Perancangan Aplikasi Usaha Kecil Mikro Dan Menengah Berbasis Mobile Android (Studi Kasus : Sentra UMKM Tingkir Lor-Salatiga)

Abstract — Empowerment SME (Small and Medium Enterprise) was a step for Dinas Perindustrian Perdagangan dan Koperasi (Disperindagkop) Salatiga to answer one vision to create empowerment public economics to increase productivity to head a independent and prosperous public in Salatiga. According to data Dinas Perindustrian Perdagangan dan Koperasi (Disperindagkop) Salatiga, in 2016 there are 2730 units SME (Small and Medium Enterprise) registered. There are one of many problems in SMEs there was no media to promote SMEs for public to know and find out the SMEs product in Salatiga. This situation basically happen whole in most region in Indonesia. This research is to design and implement android based applications for promote SMEs in Salatiga. This application is usefull for promote SMEs product in Salatiga with support of LBS (Location Based Service) and Google Cloud Messaging (GCM).
Influence of Genotypic and Environmental Factors on Tobacco Leaves Based on Metabolomics

Environmental factors affect plant metabolites, different climates, cultivation conditions, and biotic stresses and genotypes strongly affect their chemical composition and contents. Our aim is to examine the environmental and genetic interaction effects on tobacco metabolite composition. UPLC-QTOF MS/MS coupled with multivariate data analyses were applied for the metabolomics analysis of three tobacco cultivars from different planting regions in China. Principal component analysis (PCA) revealed that environmental factors have a greater effect on tobacco metabolism compared to genotypes. Twelve biomarkers were screened by orthogonal partial least squares discrimination analysis (OPLS-DA). Univariate analysis indicated that Malate, conjugated chlorogenic acid, chlorogenic acid, quercetin 3-rutinoside-7-glucoside, and unknown compound 5 were only influenced by environmental factors (independent of genotype). Quinate, neochlorogenic acid, and ouabagenin, taxezopidine K1, taxezopidine K2, and taxezopidine K3 in tobacco were influenced by the interaction of environmental factors and the genotype. Our results suggest that metabolomics based on UPLC-QTOF MS/MS could be used to analyze the ecological functions of biomarker metabolites and understand the mechanisms of plant adaption to the environment.
Transcriptomic and Metabolomic Responses in Cotton Plant to Apolygus lucorum Infestation

Simple Summary To elucidate the cotton plant’s defense mechanism against Apolygus lucorum, the transcriptomic and metabolomic differences in cotton plants after infestation with A. lucorum were investigated. The results showed that A. lucorum feeding elicits a rapid and strong defense response involved in many primary and secondary metabolic processes during the whole infestation process. The jasmonic acid pathway was shown to mediate the defense mechanisms in cotton plants, and the salicylic acid pathway was attenuated after damage from A. lucorum. The accumulation of condensed tannins was also promoted in cotton plants as a defense mechanism against A. lucorum. These results provided comprehensive insights into the defense system and will help us to discover unknown defensive genes and improve the integrated pest management of A. lucorum. Abstract With the wide-scale adoption of transgenic Bacillus thuringiensis (Bt) cotton, Apolygus lucorum (Meyer-Dür) has become the most serious pest and has caused extensive yield loss in cotton production. However, little is known about the defense responses of cotton at the seedling stage to A. lucorum feeding. In this study, to elucidate the cotton defense mechanism, cotton leaves were damaged by A. lucorum for 0, 4, 12 and 24 h. The transcriptomic results showed that A. lucorum feeding elicits a rapid and strong defense response in gene expression during the whole infestation process in cotton plants. Further analysis revealed that at each assessment time, more differentially expressed genes were up-regulated than down-regulated. The integrated analysis of transcriptomic and metabolic data showed that most of the genes involved in jasmonic acid (JA) biosynthesis were initially up-regulated, and this trend continued during an infestation. Meanwhile, the content levels of JA and its intermediate products were also significantly increased throughout the whole infestation process. The similar trend was displayed in condensed tannins biosynthesis. This research proved that, after plants are damaged by A. lucorum, the JA pathway mediates the defense mechanisms in cotton plants by promoting the accumulation of condensed tannins as a defense mechanism against A. lucorum. These results will help us to discover unknown defensive genes and improve the integrated pest management of A. lucorum.
Bi-LSTM-Based Two-Stream Network for Machine Remaining Useful Life Prediction

In industry, prognostics and health management (PHM) is used to improve the system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failure and reducing operation cost. Recently, with the development of deep learning technology, long short-term memory (LSTM) and convolutional neural networks (CNNs) are adopted into many RUL prediction approaches, which shows impressive performances. However, existing deep learning-based methods directly utilize raw signals. Since noise widely exists in raw signals, the quality of these approaches’ feature representation is degraded, which degenerates their RUL prediction accuracy. To address this issue, we first propose a series of new handcrafted feature flows (HFFs), which can suppress the raw signal noise and thus improve the encoded sequential information for the RUL prediction. In addition, to effectively integrate our proposed HFFs with the raw input signals, a novel bidirectional LSTM (Bi-LSTM)-based two-stream network is proposed. In this novel two-stream network, three different fusion methods are designed to investigate how to combine both streams’ feature representations in a reasonable way. To verify our proposed Bi-LSTM-based two-stream network, extensive experiments are carried out on the commercial modular aero propulsion system simulation (C-MAPSS) dataset, showing superior performances over state-of-the-art approaches.