Miracle-dz/UniFAR-FTUs huggingface dataset:
A review on weed management techniques in greengram

Greengram is the third important pulse crop of India. Among the various factors responsible for poor yield in greengram is an inadequate weed control measure. Weed infestation is one of the major constraints in greengram cultivation and causes 50 to 90% yield loss. Weeds have to be controlled for successful crop production. Significant crop losses due to weeds are simply not acceptable in a world where two billions more people will have to be fed in the next 40 years. Traditional practice of hand weeding requires dependence on increased number of labour during peak period of sowing and harvesting and becoming expensive However weeding through implements i.e., mechanical way is economical and time saving; it is not satisfactory in a broadcast or mixed cropped area. For effective and timely weed control in crop plants use of herbicides with proper liable techniques has become a common practice Based on the resource available to have adopting the best suitable weed control strategies like cultural control, mechanical methods, Herbicide adoption and integrated approaches will significantly decrease the weeds, which will lead to even greater yields. Finally, integrated weed management is the key to sustainable crop production throughout the world and will remain the mainstay for weed control for the foreseeable future.
The Late Halafian Lithic Industry of Tell Kashkashok I, the Upper Khabur, Syria

This paper presents the flaked flint and obsidian assemblages excavated in 1990 from Tell Kashkashok I, northeast Syria. Albeit a small collection selected from limited excavations, they represent one of the rare Halafian lithic assemblages that help define the lithic craft tradition of the Syrian Jazireh during this period. The analysis shows that the assemblages belong to the Late Halafian industry characterized by the common use of the following: imported raw materials, elaborate techniques for blank production including pressure debitage, snapped rectangular blades and crescent-shaped flake-blades with backed edges for sickle elements, bifacial knives made on tabular flint, and the rare manufacturing of burins. The literature survey reveals that these traits are recognized in the Early-Middle Halafian and the Late Pottery Neolithic industries of the Syrian Jazireh, suggesting that the Halafian lithic tradition of the region was established through indigenous cultural development. At the same time, the survey reveals that they do not occur in the neighboring regions as a package but in different combinations by regions. Future research into those regionally different patterns would provide a means to interpret the complex Halafian cultural dynamics from a perspective not examined in prior research that emphasized pottery analysis.
Leukocyte Protein Expressed in Transformed Human Fibroblasts

The phosphoprotein plastin was originally identified as an abundant transformation-induced polypeptide of chemically transformed neoplastic human fibroblasts. This abundant protein is normally expressed only in leukocytes, suggesting that it may play a role in hemopoietic cell differentiation. Protein microsequencing of plastin purified from leukemic T lymphocytes by high-resolution two-dimensional gel electrophoresis produced eight internal oligopeptide sequences. An oligodeoxynucleotide probe corresponding to one of the oligopeptides
The Relationship between Patents, Technology Transfer and Desorptive Capacity in Korean Universities

This study sought to examine the relationship between patent rights and technology transfer performances of Korean universities and to analyze the moderating effect of the desorptive capacity of industry-academic cooperation foundations. Through this, we study the impact of universities’ patents on both the number of licenses and the license incomes of the universities. We also examine the meaning of the desorptive capacity between them. Regression analysis and structural equation modeling are performed by using the number of patents registered as an independent variable, the number of licenses and license incomes as dependent variables and the desorptive capacity as a moderating variable. The analysis shows that the patent had a positive relationship with both the number of licenses and license incomes. Desorptive capacity had no significant effect between patents and the number of technology transfers. However, it is seen as having a moderating effect between patents and licensing fees from the technology transfers. This paper meaningfully illustrates the desorptive capacity of the industry-academic cooperation foundation from the perspective of outbound open innovation and analyzes the moderating effect of desorptive capacity in relation to patent rights and technology transfer performances.
Ranking Items in Large-Scale Item Search Engines with Reinforcement Learning

Ranking items in large-scale item search engines such as Amazon and Taobao is a typical multi-step decision-making problem. Due to the interactive nature between the human user and the search engine, reinforcement learning is a natural solution to this problem. In this project, we use Virtual-Taobao as the environment and some effective methods to solve this problem. Experimental results show that TD3 performs best on this problem. The video link is https://cuhk.zoom. us/rec/share/2XwILIEaNmJ0k_i_ujmtBqOZebJdz761-o1T3dlwe1uqgbvfUIohYKV
Dynamic Land Use Change Prediction and Analysis of Its Impacts on Streamflow for Dabus Watershed, Upper Blue Nile Basin

Based on the recorded watershed characteristics, the future conditions on the basin system can be predicted using a different method. In this study, dynamic land-use change and its impacts on the streamflow for the Dabus watershed were predicted using ANN-CA based method. The model performance for accurate prediction of the future land-use change on the Dabus River watershed has been checked by validation of the simulated value with the actual value, hence the overall kappa value (k) = 0.83 for the simulated 2016-LULC validated with actual 2016-LULC. Then, 2026-LULC was predicted based on the 2004 and 2009-LULC. The streamflow for the case of 2004 and 2009-LULC has been simulated using the SWAT model. The value of NSE = 0.87 and 0.90 was attained during validation of simulated streamflow for 2004 and 2009-LULC data cases, respectively. The agreement of simulated value of streamflow with the observed data is indicated as R = 0.91 and 0.96 for 2004LULC and 2009-LULC. The effects of the dynamic land-use change on streamflow for the predicted land use(2026-LULC) catchment were evaluated by T-test analysis. Hence, T-stat =0.04 and -0.002 in the case of simulated streamflow used 2004LULC and 2009-LULC, respectively compared with simulated value using 2026-LULC. Key Ward: streamflow, dynamic land-use change, ANN, SWAT, Dabus river ____________________________________________________________________________________
Time-series sales forecasting for an Enterprise Resource Planning system

: Resale businesses and product suppliers rely on enterprise resource planning (ERP) systems to manage their inventory. One of these systems is Microsoft Business Central (BC). BC can create purchase orders for sold products, in order to keep stock at a constant level. The aim of this thesis is to implement a model that can forecast the sales before they happen and thus create the purchase orders before the product runs out of stock or is sold. The dataset used in the thesis is from a product supplier consisting of product sales data for about a thousand different products on a time period of three years. The thesis presents three different forecasting algorithms: autoregressive integrated moving average (ARIMA), long short-term memory (LSTM) and neural networks. The different models were compared using root mean squared error (RMSE) values based on the datasets values against the predicted values. The results show that the best suited model for most of the product sub-datasets is ARIMA. Many of the products however had too little data to be reliably modeled. Further study and development is proposed to be done in testing the presented architecture with another dataset as well as optimizing the LSTM and neural network models.
The Impact of Fund Holdings on the Real Activities Earnings Management

After 20 years of development, the impact of Fund holdings in corporate governance has also received more and more attention. This paper analyzes the governance effect of fund holdings from the perspective of real activities earnings management, and considers the nature of the firm and the impact of the fund industry reform in 2012. This paper took the data of a-share listed companies from 2006 to 2017 as the sample, referred to the model Roychowdhury used to measure the level of real activity earnings management, and used multiple linear regression method for empirical analysis. The findings are as follows: fund holdings have a significant inhibitory effect on real earnings management; when considering the nature of enterprises, the inhibiting effect is more significant in non-state-owned enterprises, but not in state-owned enterprises; when considering the fund industry changes in 2012, this inhibition is more pronounced after the change. The above results show that the fund can exert its earnings management governance effect by supervising the management and restricting the major shareholders. The state-owned enterprise nature will limit its governance effect, and the fund industry standard development and active guidance will strengthen its governance effect.
Similarity study of clinical data

The classification of patients is very important for accurately determining the patient’s disease and subsequent drug use. More and more clinical data provide a clue for the classification of various diseases. We use related software to generate simulation data based on ICD10 and propose a specific algorithm to classify patients, and find patients belonging to other categories in the classified patient group, which provides a theoretical basis for the improvement of various classification algorithms for diseases.
Iterative State Estimation With Weight Tuning and Pseudo-Measurement Generation

The proliferation of distributed generation (DG) in the power system at low and medium voltage levels is creating challenges to the operation and control of the network and its protection system. Recent studies focus on using smart methods to deal with these challenges. These methods require access to information that can be provided by distribution network state estimation. However, state estimation in the distribution network is hindered by limited network observability and increased uncertainty due to the presence of DGs. With the threat of false data injection attacks, the security of state estimation is also a concern. To this end, this article proposes an iterative state estimation that uses: 1) an extended state vector that includes DG outputs; 2) pseudo-measurement generation based on DG characteristics and static voltage control modes; and 3) a weight updating algorithm that incorporates bad data detection. Simulations on a 44-node test distribution network show that the proposed iterative method improves the estimation of the status and outputs of the DGs and subsequently increases the accuracy of the fault current calculation.
Fault Diagnosis of Rotating Machinery Based on Wasserstein Distance and Feature Selection

This article presents a fault diagnosis algorithm for rotating machinery based on the Wasserstein distance. Recently, the Wasserstein distance has been proposed as a new research direction to find better distribution mapping when compared with other popular statistical distances and divergences. In this work, first, frequency- and time-based features are extracted by vibration signals, and second, the Wasserstein distance is considered for the learning phase to discriminate the different machine operating conditions. Specifically, the 1-D Wasserstein distance is considered due to its low computational burden because it can be evaluated directly by the order statistics of the extracted features. Furthermore, a distance weighting stage based on neighborhood component features selection (NCFS) is exploited to achieve robust fault diagnosis at low signal-to-noise ratio (SNR) conditions and with high-dimensional features. In detail, the NCFS framework is here adapted to weight 1-D Wasserstein distances evaluated from time/frequency features. Experiments are conducted on two benchmark data sets to verify the effectiveness of the proposed fault diagnosis method at different SNR conditions. The comparison with state-of-the-art fault diagnosis algorithms shows promising results. Note to Practitioners—This article was motivated by the problem of fault diagnosis of rotating machinery under low SNR and different machine operating conditions. The algorithm employs a statistical distance-based fault diagnosis technique, which permits to obtain an estimation of the fault signature without the need for training a classifier. The algorithm is computationally efficient during the training and testing stages, and thus, it can be used in embedded hardware. Finally, the proposed methodology can be applied to other application domains such as system monitoring and prognostics, which can help to schedule the maintenance of rotating machinery.
A narrative review of tumor-associated macrophages in lung cancer: regulation of macrophage polarization and therapeutic implications

Lung cancer is the deadliest malignancy worldwide. An inflammatory microenvironment is a key factor contributing to lung tumor progression. Tumor-Associated Macrophages (TAMs) are prominent components of the cancer immune microenvironment with diverse supportive and inhibitory effects on growth, progression, and metastasis of lung tumors. Two main macrophage phenotypes with different functions have been identified. They include inflammatory or classically activated (M1) and anti-inflammatory or alternatively activated (M2) macrophages. The contrasting functions of TAMs in relation to lung neoplasm progression stem from the presence of TAMs with varying tumor-promoting or anti-tumor activities. This wide spectrum of functions is governed by a network of cytokines and chemokines, cell-cell interactions, and signaling pathways. TAMs are promising therapeutic targets for non-small cell lung cancer (NSCLC) treatment. There are several strategies for TAM targeting and utilizing them for therapeutic purposes including limiting monocyte recruitment and localization through various pathways such as CCL2-CCR2, CSF1-CSF1R, and CXCL12-CXCR4, targeting the activation of TAMs, genetic and epigenetic reprogramming of TAMs to antitumor phenotype, and utilizing TAMs as the carrier for anti-cancer drugs. In this review, we will outline the role of macrophages in the lung cancer initiation and progression, pathways regulating their function in lung cancer microenvironment as well as the role of these immune cells in the development of future therapeutic strategies.
Orthogonal projections for image quality analyses applied to MRI

Measuring digital image quality is of importance in numerous applied research fields, such as image acquisition, image processing or image reconstruction. Subjective evaluation, i.e. assessment by human beings, is often too time‐consuming, expensive and inconsistent in complicated tasks such as medical imaging. In order to better understand the relation between objective image quality assessment measures, we compare a selection in an experimental setup. It is well known that L2 error estimation, i.e. computing the mean squared error (MSE) between two images, does not correspond well to the perceptual evaluation by the human visual system. Here, we illustrate on the example of MRI data the interaction of the MSE with the signal to noise ratio (SNR), the variance and subjective evaluation. We use random coverings of the space of orthogonal projections for coil combination to enable a comprehensive correlation analysis.
MULTIBAND ELLIPTICAL PATCH FRACTAL AND DEFECTED GROUND STRUCTURES MICROSTRIP PATCH ANTENNA FOR WIRELESS APPLICATIONS

A multiband microstrip antenna is designed for wireless communication application with fractal and defected ground structures. Antenna prototype is fabricated using Rogers RT Duroid 5880 dielectric material on a double layer PCB with dielectric constant 2.2 and thickness 0.8 mm. Through implementing the concept of elliptical shape fractal geometry to microstrip patch antenna, more miniaturization is achieved. Further with defected ground structures, wide impedance bandwidth and gain are achieved. A compact microstrip feedline is used to couple electromagnetic energy to radiator through lumped port. Proposed antenna shows multiband characteristics. Antenna resonates at 2.6 GHz, 6 GHz, and 8.2 GHz frequency bands with impedance bandwidth of 410 MHz, 1070 MHz, and 4840 MHz. Experimental validation is done to validate simulation results. Antenna operates on different wireless standards like Wi-Fi (2.4 GHz), WLAN (2.4/5.2/5.8 GHz), Wireless Body Area Network (2.3/2.4 GHz), which falls under ISM (Industrial Scientific and Medical) band applications. It also covers communication bands, X-band (8–12 GHz) and S-band (2.3–2.4 GHz).
CLIMATIC NICHE DETERMINES THE GEOGRAPHIC DISTRIBUTION OF MYRTACEAE SPECIES IN BRAZILIAN SUBTROPICAL ATLANTIC FOREST

ABSTRACT The understanding of factors determining species geographic distribution is a fundamental aim of ecology. We investigated the environmental niche for three Myrtaceae species in the Brazilian Subtropical Atlantic Forest (BSAF), part of a global conservation hotspot. Based on a literature review, we selected one representative Myrtaceae species in three important forest types in this region: Evergreen Rain Forest (coastal plains and associated mountains ranges); Araucaria Forest, and Seasonal Deciduous Forest (continental upland areas). Geographical coordinates of their distribution were obtained from the BIEN database. As explanatory variables, we considered altitude, climate, cloud cover, and soil classes. We summarized the environmental space occupied by each pair of species using Principal Components Analysis, determined niche overlaps, and applied statistical tests to verify niche equivalences and similarities. The selected species in Evergreen Rain Forest, Araucaria Forest, and Seasonal Deciduous Forest were Myrcia splendens (Sw.) DC. , Myrcia guianensis (Aubl.) DC. , and Campomanesia xanthocarpa O.Berg. , respectively. C. xanthocarpa showed a more restricted geographic distribution than the two Myrcia species that occur from central America to southern Brazil. Species’ geographic distribution were fundamentally determined by temperature and rainfall regimes. Only C. xanthocarpa and M. guianensis, from uplands forest formations, showed environmental niche equivalence. In conclusion, we found that both species of Myrcia showed high climatic niche amplitudes occurring throughout the climatic gradient, while C. xanthocarpa was more subtropical, distributed mostly in the south and southeast of Brazil.
Synoptic aspects associated with pervasive dust storms in the southwestern regions of Iran

Dust storm is a natural hazardous phenomenon that affects arid and semi-arid regions of the world the same as Iran. The present research aims to investigate the formation of synoptic patterns of pervasive dust storms (PDSs) in the southwestern regions of Iran. For this purpose, daily data of visibility less than 1000m in 16 synoptic stations (Ilam and Khuzestan provinces) were reviewed during 2004–2017, and 59 PDSs with more than 2 days of duration (overlapped: 70% of the region) were extracted. In practice, mid-level atmospheric data (500, 700, 850 hPa, and sea level pressure (SLP)) with 2.5*2.5 degree resolution (domain: 0-80°E and 10-70°N) were obtained from NCEP/NCAR reanalysis dataset, and the matrix 825*59 of 500 hPa data was performed. Principal component analysis (PCA) with S-mod, were used for extracting synoptic patterns that make PDSs. PCA showed that the first four components ensured more than 86.45% of the data variance. PDSs classification based on output components showed that the patterns had seasonal structures. Synoptically, the north wind blowing in the first pattern is the most dominant structure in the formation of PDSs in the Middle East. The second and third patterns showed postfrontal structures. The fourth pattern with prefrontal structure was the reason for PDSs in the cold seasons of the year. From the four final patterns, the first three patterns showed the dominance of the Persian trough in the SLP maps. Mean values map analysis of the aerosol optical depth suggests that each of the most consistent synoptic patterns stimulates special dust centers.
Current Ripple Prediction and DPWM Based Variable Switching Frequency Control for Full ZVS Range Two Parallel Interleaved Three-Phase Inverters

In this article, an improved variable switching frequency zero-voltage switching (ZVS) control strategy is proposed for two parallel interleaved three-phase inverters. Discontinuous pulsewidth modulation is adopted and full-range ZVS can be achieved at any load or modulation ratio. The critical switching frequency can be easily calculated in a digital controller based on current ripple prediction instead of current zero-crossing detection. No additional high-frequency sensor or auxiliary circuit is needed. Compared with the continuous pulsewidth modulation, the switching frequency variation range is narrower and the frequency calculation equation is simplified. In addition, lower peak current ripple leads to lower current stress. Owing to the high switching frequency and the low inductance, the size and the cost of the inverter can be significantly reduced. A 6.6-kW experimental prototype with SiC MOSFETs interfacing 400-V dc with three-phase 110-V ac grid is developed to verify the effectiveness of the proposed control strategy.
A Scalable and Efficient Multi-Agent Architecture for Malware Protection in Data Sharing Over Mobile Cloud

Sensitive Data requires encryption before uploading to a public cloud. Access control based on Attribute-Based Encryption (ABE) is an effective technique to ensure data shared security and privacy in the public cloud. Cipher-Text Policy of ABE may suffer from scalability and performance issues as they do not permit for addition or removal of computing nodes at run time. Furthermore, another problem is existing approaches suffer from single-point-of-failure (SPoF). Therefore, we introduce a scalable multi-agent system architecture based on CP-ABE to ensure data sharing on public cloud storage and reliability in our proposed work. We proposed a cloud host as an inter-mediator between the user and the authorized agents without violating the system’s privacy and security. We have also proposed a novel methodology to protect the cloud from malware by exploiting the state’s efficient power of the art Gemini approach. Gemini is an efficient methodology for binary code-based graph embedding similarity detection. Our proposed study overcomes the deficiencies of scalability and efficiency along with providing the mechanism for malware detection in the cloud. It covers three aspects: scalability, the efficiency with multi-agents, and malware detection capability. Our contributed work is scalable, efficient for cloud data sharing, and protects from malware. Results reveal that our work provides better performance with preserving the security, privacy, and fine granularity features of CP-ABE and malware screening using regress analysis by the graph embedding technique.