Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanism Study of ZnO Nanorod-Bundle Sensors for H2S Gas Sensing

This work reports the H2S gas-sensing properties of ZnO nanorod bundles and an investigation of their gas sensing mechanism. A one-dimensional ZnO nanostructure was synthesized using the hydrothermal method; scanning electron microscopy (SEM); and X-ray diffraction (XRD) spectra confirmed that the structures were crystalline ZnO of hexagonal structure. A furnace-type gas sensing system was used to characterize the nanorod bundles’ sensing properties in air containing dilute H2S gas (50 ppm) at sensing temperatures Ts ≤ 500 °C. The response of ZnO nanorod-bundle sensors to H2S gas increased with Ts; this trend may be due to chemical reaction of nanorods with gas molecules. X-ray photoelectron spectroscopy results indicated that the sensing mechanism of the ZnO nanorod-bundle sensor was explained by both the well-known surface reaction between H2S and adsorbed oxygen on ZnO, and the formation of zinc−sulfur bonding in ZnO nanorods, which becomes a dominant sensing mechanism at high Ts above 300 °C.
Chaining algorithms and historical adjective extension

Natural language relies on a finite lexicon to express a potentially infinite set of ideas. This tension often results in the innovative reuse of existing words to describe emerging ideas. In this chapter, we take a computational perspective to examine how English adjectives extend their range over time to modify nouns and form previously unattested adjective-noun pairs. We hypothesize that how novel adjective-noun pairings emerge is non-arbitrary and follows a process of chaining, whereby novel noun referents for an adjective link to existing nouns modified by the same adjective that are close in semantic space. We test this proposal by exploring a set of probabilistic models that predict adjective-noun pairs from a historical text corpus (Google Books) that spans the past 150 years. Our findings across three diverse sets of adjectives support a chaining mechanism sensitive to local semantic neighbourhood -- formulated as an exemplar model of categorization similar to the Generalized Context Model. These findings mirror existing work on chaining in the historical growth of grammatical categories. We discuss the limitations and implications of our approach toward a general theory of word meaning extension in natural language.
Assessment of knowledge, attitude, practice on households related to e-waste management: a case study in DKI Jakarta

All countries of the globe pay attention very serious to the existence of e-waste considering the content of toxic and hazardous substances in electrical and electronic equipment. This is due to the lack of appropriate electronic waste management that could potentially trigger negative effects on the environment and human health. E-waste management in Indonesia has not provided significant information, especially regarding knowledge and public awareness related to e-waste management. In addition, the absence of government regulations on proper management and disposal practices may have other impacts. Thus, this study aims to analyze the level of knowledge, behavior and public attitudes in the management of e-waste, especially waste associated with washing machines, refrigerators, air conditioning, and television. A survey of questionnaires adapted from UNEP and modified has been distributed to households in DKI Jakarta to evaluate current e-waste recycling practices. Questionnaires were distributed randomly to approximately 400 households. From the results of the study, we conclude that the majority (60%) of the community do not know about e-waste and its problems; therefore, there is a strong requirement to spread awareness about the dangers that arise from e-waste.
Understanding Machine Learning Model Updates Based on Changes in Feature Attributions

: Machine learning operations (MLOps) are widely adopted in various real-world machine learning (ML) systems. To ensure ML model performance in such systems, it is crucial to minimize the inﬂuence of insu ﬃ cient data and concept drift. One simple and e ﬀ ective solution is to update models with newly added data. In MLOps, after updating a model, accuracy scores are usually used to validate the model. However, it is hard to obtain detailed information regarding the causes of performance changes. We therefore propose a method for understanding ML model updates by using a feature attribution method called Shapley additive explanations (SHAP), which explains the output of a ML model by assigning an importance value called a SHAP value to each feature. We calculate SHAP values using models before and after updates to investigate changes in SHAP values. By analyzing the extent of changes, we can identify the slight changes in models due to updates and the data related to the changes.
INPAINTING WITH FOKKER-PLANCK EQUATION

: In the present paper we employ the Fokker-Planck equation for completing an image with missing information. Using this equation has three advantages. The ﬁrst one is the fact that this equation has a mild solution. The second advantage is that the implicit approximation scheme provides a sequence of solutions which converges to the solution of the Fokker-Planck equation. The third quality of this equation is the low regularization effect on the initial data, thus preserving the edges from the inpainted image. The numerical experiments show that this equation provides a good solution for the inpainting problem
Ultra-Low Power ECG Hybrid Compression Architecture with R Peak detection for IoT Healthcare Devices

— Data compression is done in sensors used in ECG healthcare devices for faster wireless transmission. The paper presents an ultra-low power R peak detector and an ECG dictionary based hybrid compression architecture for internet of things (IoT) healthcare devices. Data compression is done in sensors used in ECG IoT health care devices for faster wireless transmission. Ultra-low power electrocardiogram (ECG) processing hybrid architecture with an ample level of accuracy is a necessity in Internet of Things (IoT) medical wearable devices. An absolute-value curve length transform (A-CLT) is proposed that effectively intensify the QRS complex detection with minimized hardware resources. QRS detection is accomplished by using adaptive thresholds in the A-CLT transformed ECG signal. The proposed architecture requires adders, shifters and comparators only. No multipliers required. A lossless compression technique is included into the proposed architecture that use the ECG signal first derivative, variable length encoder and dictionary based code compressor. Dictionary code compression is done using bitmask algorithm. Compression architecture help IoT medical devices to achieve ultra-low power operation (in mW ranges) and minimize the data needed to be transmitted to minimize power consumption for devices equipped with wireless transmitters. The proposed architecture is synthesized using standard-cell-based flow. This technique is used in ECG based IoT healthcare devices such as implantable cardio-converter defibrillator
Can Ricardian Model Really Explain Trade?

This paper aims to analyse the international trade in the real world by applying the Ricardian trade theory. In doing this, simple comparative advantage assumptions are used to examine trading of palm oil and rice between Malaysia and Vietnam. By using this theory, it is proven that international trade takes place because of efficiency to produce exported product. A country will export products that use its abundant and cheap factors of production and import products that use its scarce factors. Various empirical evidences of previous studies are als o used to discuss the importance of the Ricardian model. However, it is also highlighted in the paper that the Ricardian model could be misleading as it has several limitations that restrict its usefulness.
Selective Harmonic Elimination in Multi-level Inverter with Equal-Power-Rating Series-Connected Transformers

This paper applies the Selective Harmonic Elimination (SHE) technique to design and operate a regulated AC/DC/AC power supply suitable for maritime military and underground trains' applications. The input is a single 50/60Hz AC Voltage and the output is a 400 Hz regulated voltage. The main contribution of the paper is that the switching angles for a multi-level inverter and the transformer turns ratio have been determined to operate with special equal-power-rate connected-transformers and to produce almost sinusoidal current. With the ability of directly controlling harmonics, SHE technique has the adaptability to associate with apparatus which are congenitally immune to specific harmonics, such as the series connection transformers, especially designed to equally share the total load power. In the presented work, a 50/60Hz input single phase source is rectified via a semi-controlled bridge rectifier to control the DC voltage level such as to regulate the output load voltage at a constant level. The DC rectified voltage is then supplying six single-phase quazi-square H-bridge inverters, each of which is supplying the primary of a single phase transformer. The secondary's of the six transformers are connected is series. Through off-line calculation, the switching angles of the six inverters and the turns' ratio of the six transformers are designed to ensure both equal power distribution for the transformers and a SHE technique to eliminate higher order harmonics of the output voltage. A digital implementation is used to implement the determination of the switching angles. Theoretical results are demonstrated, and a scaled-down experimental 600VA prototype is built to verify the validity of the proposed system.
Alpha Tocopherol Application as Seed Priming
Alters Antioxidative Defense System
of Okra Against Salinity Stress

Priming with antioxidants like α-tocopherol is a pragmatic method to alleviate salinity effects. In this study, seeds of two okra varieties (Noori and Sabzpari) were primed for 16 h before sowing with α-tocopherol levels [0 (distilled water), 100, 200 and 300 mg L-1] and salinity levels (0 and 100 mMNaCl) were maintained after sowing the seeds in plastic pots. Salt stress reduced the fresh biomass and yield while, increased the antioxidants activities(CAT, GPX, SOD and protease) total soluble sugars, reducing sugars, MDA, H2O2, total soluble proteins, glycinebetaine and free proline contents of tested okra varieties. Whereas non-significant effect of salinity was recorded for non-reducing sugars, ascorbic acid, total phenolics and activity of POD in tested okra varieties. Alpha tocopherol seed priming proved effective in improving root and shoot fresh biomass, yield, sugars, total soluble proteins, glycinebetaine content and activities/concentration of antioxidants (ascorbic acid, total phenolics, GPX, SOD and protease) in both tested varieties under saline regime. Though, no significant alteration was observed on the concentration of MDA, H2O2 and free proline content due to alpha tocopherol seed priming treatment. Among all α-tocopherol levels used inclusively 200 and 300 mg L-1 were more active. In addition, variety Noori performed better than Sabzpari in all
Austerity in 2009 – 13

The conventional wisdom is (i) that fiscal austerity was the main culprit for the recessions experienced by many countries, especially in Europe, since 2010 and (ii) that this round of fiscal consolidation was much more costly than past ones. The contribution of this paper is a clarification of the first point and, if not a clear rejection, at least it raises doubts on the second. In order to obtain these results we construct a new detailed “narrative” dataset which documents the size and composition of the fiscal plans implemented by several countries over 2009–13. Out of sample simulations, that project output growth conditional only upon the fiscal plans implemented since 2009, do reasonably well in predicting the total output fluctuations of the countries in our sample over the years 2010–13 and are also capable of explaining some of the cross-country heterogeneity in this variable. Fiscal adjustments based on cuts in spending appear to have been much less costly, in terms of output losses, than those based on tax increases. Our results, however, are mute on the question whether the countries we study did the right thing implementing fiscal austerity at the time they did, that is 2009–13.
MST4 negatively regulates the EMT, invasion and metastasis of HCC cells by inactivating PI3K/AKT/Snail1 axis

Background: Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and has a poor prognosis due to the high incidence of invasion and metastasis-related progression. However, the underlying mechanism remains elusive, and valuable biomarkers for predicting invasion, metastasis, and poor prognosis of HCC patients are still lacking. Methods: Immunohistochemistry (IHC) was performed on HCC tissues (n = 325), and the correlations between MST4 expression of the clinical HCC tissues, the clinicopathologic features, and survival were further evaluated. The effects of MST4 on HCC cell migratory and invasive properties in vitro were evaluated by Transwell and Boyden assays. The intrahepatic metastasis mouse model was established to evaluate the HCC metastasis in vivo. The PI3K inhibitor, LY294002, and a specific siRNA against Snail1 were used to investigate the roles of PI3K/AKT pathway and Snail1 in MST4-regulated EMT, migration, and invasion of HCC cells, respectively. Results: In this study, by comprehensively analyzing our clinical data, we discovered that low MST4 expression is highly associated with the advanced progression of HCC and serves as a prognostic biomarker for HCC patients of clinical-stage III-IV. Functional studies indicate that MST4 inactivation induces epithelial-to-mesenchymal transition (EMT) of HCC cells, promotes their migratory and invasive potential in vitro, and facilitates their intrahepatic metastasis in vivo, whereas MST4 overexpression exhibits the opposite phenotypes. Mechanistically, MST4 inactivation elevates the expression and nuclear translocation of Snail1, a key EMT transcription factor (EMT-TF), through the PI3K/AKT signaling pathway, thus inducing the EMT phenotype of HCC cells, and enhancing their invasive and metastatic potential. Moreover, a negative correlation between MST4 and p-AKT, Snail1, and Ki67 and a positive correlation between MST4 and E-cadherin were determined in clinical HCC samples. Conclusions: Our findings indicate that MST4 suppresses EMT, invasion, and metastasis of HCC cells by modulating the PI3K/AKT/Snail1 axis, suggesting that MST4 may be a potential prognostic biomarker for aggressive and metastatic HCC.
CHARACTERIZATION OF TEXAS TORTOISE (GOPHERUS BERLANDIERI) HOME RANGES, HABITAT USE, AND LANDSCAPE-SCALE HABITAT CONNECTIVITY IN CAMERON COUNTY, TEXAS

ed animal movement. Circuitscape performs habitat connectivity simulations by running an electrical current between pairs of nodes. During a Circuitscape simulation, I designated nodes of a certain landcover characteristic and the program ran simulations for every possible pair of nodes before aggregating all simulations into a cumulative map. I constructed and analyzed three resistance maps: a low resistance, medium resistance, and high resistance scenario. Each 90 x 90 m pixel in a map was then assigned a resistance value based on the given resistance of the landcover and roads within the pixel. Resistance values were assigned by ordinally ranking how resistant each landcover or road type would presumably be to a dispersing G. berlandieri individual. The lowest resistance value of 1 was assigned to lomas, scrublands and herbaceous grasslands, and the highest resistance value for each resistance category was assigned to primary roads such as multilane highways. All resistance values are listed in Table 1. Many pixels contained both landcover information from the NLCD and road information from TIGER. In these situations, the higher of the two resistances was assigned to the pixel. For the Circuitscape simulations, I placed focal nodes at the centroid of each loma at PABNHP where each loma was represented as a polygon in ArcGIS. The loma
Emergence and Constraints of Enterprise Platform Silos for Online Interactions: A Case Study from Ghana Before and During Covid-19

The purpose of this study is to understand the emergence and constraints of enterprise platform silos for online interactions at the organizational level. Information systems (IS) research has focused more on external platforms without silos. As a result, not much is known about internal platforms and silos at the organizational level and how they constrain online interactions, especially in the developing economic context of Africa. This study follows an interpretive, qualitative case study methodology and the encounter-episode process model to investigate the case of using enterprise platform silos in an organization in Ghana, before and during Covid-19. The findings show how failure to modernize the installed-based platforms, integrate platform portfolios, use enterprise architecture, and build internal competence for platform integration leads to silos. Identified constraints include challenges with information sharing and transfer between platforms, manual intervention with delays and errors, and multiple sign-ins with different password formats per user.
THE SHORT-TERM EFFECT OF PRE IPO EARNINGS MANAGEMENT ON POST IPO OWNERSHIP STRUCTURE

The purpose of this study is to investigate whether IPO firms engage in earnings management before IPO to increase institutional ownerships after IPO. Using a sample of 302 IPO’s, we find that IPO firms with high discretionary accruals, a measure of aggressive earnings managements, have greater institutional ownerships one quarter after IPO than IPO firms with low discretionary accruals. This result holds after controlling for other influencing factors such as initial offer price, underwriter reputation, and offer fraction. This result is robust across different testing methods.
Invariant Extended Kalman Filtering for Robot Localization using IMU and GPS

—This paper derives an IMU-GPS-fused inertial navigation observer for a mobile robot using the theory of invariant observer design. One of the main features of invariant observers for invariant systems on Lie groups is that the estimation error is autonomous, hence the observable state variables can be rendered convergent within a domain of attraction that is independent of the system’s trajectory. The Invariant Extended Kalman Filter (In-EKF) which is an extension of the Extended Kalman Filter (EKF) is supposed to be more efﬁcient given that the system converges to constant values on a larger set of trajectories as opposed to the equilibrium points that an EKF is based on. This paper explores the implementation of the In-EKF for robot localization and is compared against an implementation of the EKF. The localization is performed on the University of Michigan north campus long-term vision and LIDAR dataset.
Adaptive Generative Modeling in Resource-Constrained Environments

Modern generative techniques, deriving realistic data from incomplete or noisy inputs, require massive computation for rigorous results. These limitations hinder generative techniques from being incorporated in systems in resource-constrained environment, thus motivating methods that grant users control over the time-quality trade-offs for a reasonable “payoff” of execution cost. Hence, as a new paradigm for adaptively organizing and employing recurrent networks, we propose an architectural design for generative modeling achieving flexible quality. We boost the overall efficiency by introducing non-recurrent layers into stacked recurrent architectures. Accordingly, we design the architecture with no redundant recurrent cells so we avoid unnecessary overhead.
Flexible Updating of Attractors in Virtual Network Topology Control with Bayesian Attractor Model

Network virtualization is expected to handle various forms of network traffic induced by Internet of Things applications and other Internet-based services. Because traffic patterns change with time, virtual networks should be dynamically reconstructed to accommodate increasing traffic and to free unused resources. However, collecting all traffic information is difficult when applications are deployed on a wide-area network. It is therefore necessary to consider uncertainty of information due to data incompleteness or traffic dynamics. Our research group has proposed a virtual network reconstruction method based on a Bayesian attractor model that deals with uncertain information in decision-making. However, this method requires advance knowledge of the assumed environment as an attractor. When the environment changes, attractors must also be changed. In this study, we use control feedback to automatically update attractors when the environment changes. Simulation-based evaluations demonstrate that the proposed method deals with unknown situations while maintaining noise tolerance.
Electronic Structure of a Spin Crossover Molecular Adsorbate

We have investigated the occupied and unoccupied electronic structure of ultrahigh vacuum (UHV) evaporated molecular thin films of the spin crossover [Fe(H2B(pz)2)2(bipy)] complex (with H2B(pz)2 = bis(hydrido)bis(1H-pyrazol-1-yl)borate and bipy = 2,2′-bipyridine) by ultraviolet photoelectron spectroscopy (UPS), inverse photoemission (IPES), and X-ray absorption spectroscopy (XAS). A bandgap of 2–3 eV is deduced from combined UPS and IPES measurements of the molecular films on Au substrates. The matching Fe XAS and IPES spectra indicate that the electronic unoccupied states have a significant Fe weight. The shift of the unoccupied density of states seen in inverse photoemission is consistent with the thermally induced spin crossover transition for [Fe(H2B(pz)2)2(bipy)] deposited on the organic ferroelectric copolymer poly(vinylidene fluoride) with trifluoroethylene (PVDF–TrFE).
A COMPARATIVE STUDY ON LOGISTIC REGRESSION AND SVM BASED MACHINE LEARNING APPROACH FOR ANALYZING USER REVIEWS

Sentiment Analysis is a technique for classifying the user review about the product or services into positive, negative or neutral. In the era of internet technology and big data the advancement in technology such as machine learning and Deep learning makes organization to know the feedback about their products or service in a much efficient and accurate manner with the help of opinion mining or sentimental analysis. In this paper linear regression and support vector machine MLtechniques are applied to the IMDB data sets obtained from kaggle.The dataset has 50000 movie reviews comments given by various users with positive and negative polarity comments. Two machine learning algorithm such as logistic regression and SVM classifier were applied. The results were analyzed through accuracy,precision and recall. It is found that using logisticregression an accuracy of 90% was achieved compared with SVM with 81% accuracy. It is found that the performance of logistic regression is more effective when compared with SVM.