Miracle-dz/UniFAR-FTUs huggingface dataset:
Preoperative neoadjuvant chemotherapy favors advanced gastric cancer treated with laparoscopic radical surgery

Objective: Gastric cancer is a common clinical malignancy, and it is often treated with surgery plus chemotherapy. This study aimed to explore the efficacy of laparoscopic radical surgery plus NACT (neoadjuvant chemotherapy) for patients with advanced gastric cancer. Methods: A total of 131 eligible cases of advanced gastric cancer who received laparoscopic radical surgery were recruited and by random number table method divided into the observation group (n=68, patients treated with preoperative NACT) and the control group (n=63, patients treated without preoperative chemotherapy). Surgery related indicators, postoperative recovery status, KPS (Karnofsky Performance Status) score, efficacy and postoperative complications were observed. Results: There are statistical differences between the two groups in surgical margins after postoperative chemotherapy (all P<0.05). The KPS scores in the observation group were higher than those in the control group at three and six weeks after surgery (both P<0.05). Total effective rate and control rate of the observation group were significantly higher than those of the control group (P<0.05). The numbers of death, recurrence and metastasis and the incidence of adverse reaction in the observation group were significantly less than those in the control group (P<0.05). Conclusion: Laparoscopic radical surgery plus NACT can increase the resection rate of surgery with negative surgical margins (R0), reduce recurrence and metastasis of advanced gastric cancer, and improve patients’ quality of life.
Does Cooperation with Universities and KIBS Matter? Firm-level Evidence from Spain

This manuscript contributes to the literature on firm cooperation with universities and KIBS by framing the analysis according to the literature on causal effects, comparing the effect of the two different agents and exploring which firms benefit more from cooperation with a specific partner. Results shows that the lower bound for the effect is around 27-30% increase in sales from new products for both types of partners. After covariates and fixed effects are used, it is found that this effect is not likely driven by time-varying unobservable factors. Moreover, we show that firms that benefit the most from cooperation with universities are different from those firms that benefit the most from cooperation with KIBS.
A MANUAL FOR AMPS-1 D A One-Dimensional Device Simulation Program for the A nalysis of M icroelectronic and P hotonic S tructures

The objective of AMPS is to teach how material properties (e.g., band gap, affinity, doping, mobilities, doping, gap state defect distributions in the bulk and at interfaces, etc) and device design/structure together control device physics and thereby device response to light, impressed voltage, and temperature. AMPS allows users to learn the “whys” of device response to a given situation (i.e., light bias, voltage bias, and temperature) through exploring and comparing band diagram, current component, recombination, generation, and electric field plots available from AMPS as a function of light intensity, voltage, temperature, and position.
Yade DEM Formulation

: In this chapter, we mathematically describe general features of explicit DEM simulations, with some reference to Yade implementation of these algorithms. They are given roughly in the order as they appear in the simulation loop: collisions detection, creating new interactions and determining their properties, kinematics representation, contact law, forces applied on particles.
Extraction of dye from bark of Syzygium guineense for titanium dioxide based dye sensitized solar cell as photo sensitizer

This work was aimed to extract, prepare and use dyes from bark of Syzygium guineense as photosensitizer for dye sensitized solar cell. The extraction processes were takes place in presence of acetone, ethanol and water as solvent. The photoelectron-chemical performance of the DSSCs based on the acetone extract showed Voc of 0.36 V, FF of 0.546, Jsc of 1.57 mA cm -2 and a power conversion efficiency of 0.319% under the sunlight illumination of 100 mW cm -2 and active area of 1 cm 2 . The J-V curves were plotted according to collected data of DSSCs by using Extech Multimeter, and electrical circuit with resisters made from wood board. The acetone extract of day from bark of Syzygium guineense offered the highest conversion efficiency than ethanol and water extracts. Generally, the dye extracted from bark of Syzygium guineense as natural photosensitizer could be a possible alternative for the production of low cost and environment friendly DSSCs.
For Young Americans , Sustainable Is Not Organic

Millennials and Gen Z consumers are some of the most consumption-oriented groups in the United States. As these young Americans transition into higher paying jobs, their impact on the food industry is expected to compound. Data from a web-based survey of 1,351 young Americans were used to conduct a cluster analysis and an ordered probit used to investigate the impact of demographics and purchasing behavior on cluster membership. Four costumer segments were identified: committed, farm-to-fork, unattached, and skeptic. Results suggest that cluster membership is driven by personal motives, particularly the desire to contribute to the local food system and support local communities.
KNOWING WHAT YOU DON’T KNOW: UNCERTAINTY-BOOSTED ENSEMBLE FOR THE CLASSIFICATION OF BREAST CANCER STAGES

In this paper we present the algorithm which we developed for our entry to the 2017 Camelyon Challenge of the International Symposium on Biomedical Imaging (ISBI). The challenge asked participants to automatically detect breast cancer metastases in whole-slide images of histological lymph node sections and produce a per-patient cancer stage assessment that reﬂects the size and abundancies of the detected metastases. This score is to be predicted on the basis of ﬁve gigapixel whole-slide images per patient, a task that current hardware limitations require to be disentangled in the form of sub-tasks. For this reason, we ﬁrst segment patches from the whole-slide images producing a tumor probability map for each whole-slide and then combine this information in a patient-level score. For the segmentation task we introduce a novel ensemble method that explicitly factors in the pixel-wise model uncertainties of the ensemble members. The ensemble combines both coarse and dense predictions of different convolutional neural networks (CNNs) and leverages the pixel-wise and instance-dependent model uncertainties in order to optimally produce a low-resolution tumor probability map.
A Survey on Assistance System for Visually Impaired People for Indoor Navigation

: In the past few years, the variety of wearable devices and remote processing systems developed for assisting visually impaired people for indoor navigation as well as for outdoor navigation. The main aim of such assisting wearable devices, smart phone applications and remote processing units is to find out a plausible path in the real-time environment for secure navigation. To sense the surrounding environment by applying techniques of computer vision domain such as visual information processing by using image processing methodology is one of the challenging task. This paper reviews various types of assisting systems developed for vision less people for indoor navigation.
36 IJAEMS-APR-2016-55

A deterministic inventory model has been developed for deteriorating items and Genetic Algorithms (GA) having a ramp type demands with the effects of inflation with two-storage facilities. The owned warehouse (OW) has a fixed capacity of W units; the rented warehouse (RW) has unlimited capacity. Here, we assumed that the inventory holding cost in RW is higher than those in OW. Shortages in inventory are allowed and partially backlogged and Genetic Algorithms (GA) it is assumed that the inventory deteriorates over time at a variable deterioration rate. The effect of inflation has also been considered for various costs associated with the inventory system and Genetic Algorithms (GA). Numerical example is also used to study the behaviour of the model. Cost minimization technique is used to get the expressions for total cost and other parameters. Keywords— Genetic Algorithms, EOQ model, Rented warehouse (RW), Owned warehouse (OW).
Rock the Registration: Same Day Registration Increases Turnout of Young Voters

Studies find that same day registration (SDR) laws increase turnout, but less is known about which kinds of voters are most affected. Young people are disproportionately burdened by traditional registration laws because they frequently change addresses and infrequently interact with government agencies providing registration services. SDR laws, which lower the cost of registration, should increase turnout most among young people. Laws that lower the cost of voting but not the cost of registration should be less effective at increasing youth turnout. Difference-in-differences estimates suggest that SDR disproportionately increases turnout among individuals aged 18–24 (an effect between 3.1 and 7.3 percentage points). The effect of SDR on young voters is especially pronounced in presidential elections. By contrast, the effects of early voting and other reforms are smaller and do not consistently vary by age. The results suggest that expanded SDR may produce a younger electorate.
Motivating Bureaucrats: Group incentives and Organizational Performance in Local Governments

This report looks at the development and results of the pilot project of a largescale field experiment in Benin aimed at enhancing public service provision by means of group and individual incentives tied to on-the-job productivity of local level bureaucrats. Extensive preliminary qualitative work was conducted in the summer of 2016 to assess the legitimacy, the straightforwardness, and the usability of potential performance measures in this context. In early 2017, two types of incentive schemes were implemented among 6 town-halls (communes), as the lowest level of administration in Benin. Accordingly, three communes were assigned to a group tournament amongst the teams (services) of their local governments, in which the best performing service received monetary and non-monetary awards as measured by the degree of completion of predetermined performance targets. In the remaining three communes, the group tournament was further complemented by an individual tournament, where the best performing worker of each commune – measured by comprehensive peer assessments – was rewarded similarly. We analyze the detailed administrative and survey data collected in each commune throughout the pilot intervention to document behavioral responses to the incentive schemes, and to evaluate the extent to which the intervention affects the delivery of public works to their local communities. In sum, we find some suggestive evidence that both group and individual incentives have stronger and more consistent effects on improving outcomes.
The Importance of Business Travel for Trade: Evidence from the Liberalization of the Soviet Airspace

Despite decades of intense globalization, empirical research provides robust evidence that the distance elasticity of trade is significant, not declining and largely unaccounted for by conventional explanations such as transport costs. One hypothesis is that face-to-face interaction through business travel is important for trade, and that transporting people is costly. I use the liberalization of Soviet airspace for civil aviation to test this hypothesis. The opening of Soviet airspace radically reduced travel time between Europe and East Asia. Using a difference-in-difference approach, I show that shorter flight routes were associated with a rapid and substantial increase in trade volumes. I also show that the increase in trade was proportional to the reduction in flight distance, that results hold for goods not typically transported by air, and that the impact was larger for differentiated goods.
3D Prostate Segmentation from MR Images using FCNN

This paper presents a novel architecture using a fully convolutional neural network (FCNN) for prostate segmentation has been proposed. The FCNN model presented here is a modiﬁed version of V-net with smart factorization of convolutional layers for reducing the number of parameters, and Batch Normalization for better and faster convergence. Unlike previous approaches which focus on segmentation using 2D slices, whole volume in 3D has been considered which saves time during inference and additionally, the algorithm can take advantage of the 3D spatial information. Various on-the-ﬂy augmentation methods have been applied to prevent overﬁtting of the network. The model achieved a Dice Similarity Coeﬃcient (DSC) of 0.92 and 0.80 on training and validation set respectively.
Region-Speciﬁc Hierarchical Segmentation of MR Prostate Using Discriminative Learning

We demonstrate the eﬀectiveness of learning-based methods and hierarchical boundary deformation for eﬃcient, accurate segmentation of prostate in T2 weighted MRI data. After normalizing intra-and inter-image intensity variation, Marginal Space Learning (MSL) is used to align a statistical mesh model on the image. This mesh is then hierarchically reﬁned to the image boundary using spatially varying surface classiﬁers. Using 10-fold cross validation on 50 cases, we achieve accurate delineations (0.89 dice coeﬀ., 1.91mm surface error) in under 3s.
Performance Analysis of Power Quality Improvement in Distribution Network using an Inductively Active Filter

With the widespread use of power electronics devices such as rectifier, inverter etc. in power system causes serious problem relating to power quality. One of such problem is generation of current and voltage harmonics causing distortion of load waveform, voltage fluctuation, voltage dip, heating of equipment etc. Also presence of non-linear loads such as UPS, SMPS, speed drives etc. causes the generation of current harmonics in power system. They draw reactive power components of current from the AC mains, hence causing disturbance in supply current waveform. Thus to avoid the consequences of harmonics we have to compensate the harmonic component in power utility system. Among various method used, one of the effective method to reduce harmonic in power system is the use of Inductively Active Filtering (IAF). This method comprehensively improves the power quality (PQ) for not only the distribution network (public grid) but also the powersupply system (nonlinear load) connected to the network. At first, a new main-circuit topology for implementing the IAF method is presented. It consists of an inductively filtered transformer and a fully tuned branch controlled by an inverter. The control strategy of the FT branch and the impedance coordination for the inductively filtered transformer are designed based on theoretical analysis. A case study is investigated in detail to illustrate the operating characteristics of the IAF method. Both the theoretical and the case studies show that the IAF method can effectively prevent harmonic components from flowing into the primary (grid) winding of the transformer. Since the harmonic components are suppressed near the harmonic source, it is good for the power-supply system and especially good for the converter transformer. Besides, since the harmonic flow is limited to near the harmonic source, the PQ of the public network can be guaranteed completely.
Clinical effect of paclitaxel combined with cisplatin in oral cancer patients and the influence on immune function of patients

Objective: We aimed to discuss the therapeutic effect of paclitaxel combined with cisplatin in oral cancer patients and the influence on immune function and prognosis. Methods: Eighty oral cancer patients were selected as subjects of study and divided into an observation group (n=40) and a control group (n=40) according to a random number table method. The patients in the observation group were treated with paclitaxel combined with cisplatin and those in the control group were treated with 5-fluorouracil combined with cisplatin, so as to respectively observe and compare the total effective rates, immune function indexes, serum cytokine indexes, quality of life scores, adverse reaction rates, and 1and 2-year cumulative survival rates in the two groups. Results: The total effective rate was assessed as 87.50% in the observation group and 62.50% in the control group, which indicated that the difference had statistical significance (P<0.05). The tested values of CD3+, CD4+ and CD4+/CD8+ increased after treatment and the tested value of CD8+, VEGF and HGF decreased after treatment. The degree of increase and decrease in the observation group were much more apparent than those in the control group, which indicated that the difference had greater significance (P<0.05). The adverse reaction rate in the observation group was lower than that in the control group, and the 1and 2-year cumulative survival rates in the observation group were higher than those in the control group, with statistical significance (P<0.05). Conclusion: The application of paclitaxel combined with cisplatin in oral cancer patients who were admitted to hospital, clearly improves the therapeutic effect, increases the level of immune function and enhances the quality of life. Besides, paclitaxel combined with cisplatin, has higher safety, and can achieve a relatively ideal cumulative survival rate, so it has very important promotional value clinically.
HIERARCHICAL FRAMEWORK FOR BREAST CANCER STAGE CLASSIFICATION WITH HISTOPATHOLOGY IMAGES

The pathologic N-stage (pN-stage) prediction can tell whether cancer has spread to the regional lymph nodes. However, this prediction needs analysis on whole-slide images which usually are very huge and consume too much time to detect the tumor for pathologist. In this paper, we introduce the deep learning methods recent years in computer vision into tumor detection and propose an automated hierarchical and ensemble framework to predict patience’s pN-stage.
A Survey on Semantic Image Segmentation

: The ﬁeld of digital image processing has experienced continuous and notable expansion in recent years. Nowadays, the advancement and wide availability of image processing hardwares has further enhanced the usefulness of image processing. Image Segmentation is an important technique in this area and is essential for image analysis tasks. Here, we are considering the concept of semantic image segmentation which describes the process of associating each pixel of an image with a class label. Also, the aim of semantic segmentation is that it clusters parts of images which belong to same object class. This paper deals with detailed survey on different semantic image segmentation techniques.
Deduplication Mechanism for Storing Data over Cloud

—Since cloud computing has been driving attention of large number of users for storing their important data, data compression technique managing data stored over cloud had become huge necessity. Task of data deduplication technique is to remove duplicate copies of data on a server and keep only one physical copy. Duplicate copies over cloud are identified by generating tags from content of data copy and matching them with tags of files already residing over cloud. Duplicate copies will correspond to same tags generated using MD5 hashing technique. To maintain security of data, it is encrypted using linear congruentiality algorithm before uploading over cloud which employs convergent encryption concept. To encrypt data, convergent keys are derived from content of data using SHA-512 hashing technique and data is encrypted using this key according to convergent encryption concept which requires key derived from content of data.
Review of Various Text Categorization Methods

: Measuring the similarity between documents is an important operation in the text processing field. Text categorization (also known as text classification, or topic spotting) is the task of automatically sorting a set of documents into categories from a predefine set [1]. TEXT categorization (TC) is the task of automatically classifying unlabeled natural language documents into a predefined set of semantic categories [2]. The term weighting methods assign appropriate weights to the terms to improve the performance of text categorization [1]. The traditional term weighting methods borrowed from information retrieval(IR), such as binary, term frequency (tf), tf:idf, and its various variants, belong to the unsupervised term weighting methods as the calculation of these weighting methods do not make use of the information on the category membership of training documents. Generally, the supervised term weighting methods adopt this known information in several ways. Therefore, the fundamental question arise here, “Does the difference between supervised and unsupervised term weighting methods have any relationship with different learning algorithms? ”, and if we consider normalized term frequency instead of term frequency along with relevant frequency the new method will be ntf.rf but will this new method is effective for text categorization? So we would like to answer these questions by implementing new supervised and unsupervised term weighing method (ntf.rf). The proposed TC method will use a number of experiments on two benchmark text collections 20NewsGroups and Reuters.
Detection of Packet Dropping Attacks in MANET

: Remote Adhoc system is raising quick because of its remarkable component, for example, simple to send. There are different security issues. These security issues are because of the parcel drops in the system. The hubs in the system co-work each other to forward the bundles from source to goal. The adver zry may misuse this agreeable nature by dropping the parcels. The parcel dropping might be either by the nearness of malevolent hub or connection blunder. There are different philosophies to distinguish and disconnect the reason for the parcel drop. In this paper the study is completed for the identification and seclusion of bundle drop.
Image Retrieval System Using Three-Level Searching

: To retrieve crucial pictures from a varied collection through the usage of visible queries as seek arguments are the onerous and large open problems. In this paper the writers have referred to the designs and implementations of a easy yet very effective Content-Based Image Retrieval (CBIR) device. The colorings, textures and the shapes capabilities are the essential components of this machine. With the three important consequent looking steps the looking will become multilevel. Such propounded structures are very precise as they recollect one characteristic at every step and use the consequences of the previous step as the input for the subsequent coming step in multilevel sample while in the sooner techniques all the capabilities are blended right away for the unmarried-level seek of a mean CBIR system. The propounded approach is very simple and secure to adopt. The retrieval grade of the propounded technique is valuated the usage of bi-benchmark datasets for a photograph category. The above gadget of strategies suggests very good results in phrases of amelioration in retrieval characteristics, when in comparison with the literature. In proposed paintings we get accuracy like between 65.58 % to 91.25%. In used distinctive capabilities.