Miracle-dz/UniFAR-FTUs huggingface dataset:
Stabilised Control of Converter Interfaced DERs for Reliable Operation of Microgrid and Microgrid Clusters

The demand for power electronic converter interfaced Distributed Energy Resources (DERs) are rapidly growing in present grid systems. In the near future, this trend will replace dominance of fossil-fuel based energy sources in electrical power generation systems. The prime objective of this thesis is to achieve the stabilised control for converter interfaced DER dominated MicroGrid (MG) and MG clusters so that the reliability and resiliency of power generation can be ensured. With the increasing number of Voltage Source Converter (VSC) fed DERs, the stability and harmonic issues in grid interfaced systems can be aggravated, which necessitates advanced control measures to rectify them. This thesis evaluates the suitability of voltage and current control mode operation for VSCs that have different types of output filters. Voltage control is preferred in an LC filter fed VSC, where a closed loop space vector modulation scheme is proposed to facilitate the effective DC link voltage utilization, in addition to the reduced switching frequency and losses. Furthermore, discrete quadratic regulator control designed considering the filter dynamics is more robust to parameter variations and can provide stable system operation. On the other hand, the current control mode is preferred in L or LCL filter fed VSCs, where a proportional resonant controller is used for regulating the converter current or grid current. The stability issues in these two configurations have been analysed and the need for suitable damping techniques for grid current regulated systems are identified. It has been demonstrated that
Correlation between CXCR5 expression and prognosis in stage T1 non-small cell lung cancer

Background: Lung cancer is the leading cause of cancer-related death. Even if early detection has been applied and proved to be effective, the survival outcomes are still poor. Thus, to find out novel prognostic factors is of great significance to identify high-risk patients and guide individualized precise treatment in order to reduce recurrence and improve survival outcomes.Methods: Tissue samples and clinicopathologic data of 244 stage T1 NSCLC patients were collected. We investigated CXCR5 expression using immunochemical method and analyzed its correlation with pathologic subtypes and prognosis.Results: Elevated level of positive CXCR5 expression was found in tumor tissues (p<0.0001) and patients with positive CXCR5 expression are more likely to have LVIs (p=0.030) and recurrences (p<0.043). Besides, CXCR5 expression is also correlated to histological type and differentiation. Lepidic predominant tumors have significantly lower expression of CXCR5 (p<0.001). Survival analyses showed that patients with positive CXCR5 had a significantly lower DFS (p=0.038) and patients with solid or micropapillary predominant NSCLC had a significantly worse prognosis. CXCR5 expression was proved to be an independent prognostic factor for DFS through multivariate analysis.Conclusions: CXCR5 expression has been proved to be an independent prognostic factor for stage T1 NSCLC patients. In addition, CXCR5 was also found to be relevant to cancer recurrence, and this indicated that CXCR5 may play an important role in lymph node and distant metastasis.
An Approach to Reduce the Data Duplication using Simple Map Reduce Algorithm

Data de duplication is used for reducing the storage space in an organization. In many organizations storage system contains many duplicate copies of data. Information gathered from the user is taken for finding out the duplicate data. Labeled set is one of the critical tasks, which is used to reduce the duplicate data by comparing two sets of data. Two stage sampling selection strategy (T3S) is used for identifying the duplicate data from the large dataset. It consists of three steps. In first step, collect all the candidate pairs for labeling. Block the data according to the alphabetical order. Second step consists of two stages. In its the first step is sample selection strategy. Compare the data of the candidate pairs and produce the small balanced data. The second step consists of fuzzy region identification and classification. Fuzzy region is used to identify the exact match of the candidate data. Classification step classifies the duplicate data from the original data. SIM_MR algorithm is proposed in the paper to improve the accuracy. Checking and conforming the matching pairs of the de duplication in large datasets. Keywords— Data de duplication; Two stage sampling selection strategy; Simple Map Reduce.
Peer-To-Peer Accommodation Service Process: A Framework of Service Blueprint

—Sharing economy is the result of technological developments that create an innovative new business model and create a revolution in how business entities interact. Peer-to-peer accommodation (P2P) creates an online marketplace where peer providers and consumers meet each other’s needs and can exchange roles. Without ownership of assets, P2P accommodation becomes a strong competitor of hotels. This phenomenon creates opportunities for Indonesia in improving competitiveness among other ASEAN countries. This preliminary study becomes important and relevant to the phenomenon of sharing economy, considering that not much research discusses the uniqueness and dynamics of interactions that occurs in the perspective of service-dominant logic (SDL). This study uses the Soft System Methodology (SSM) approach, precisely the ﬁrst stage which is deﬁning an unstructured situation. Approaches of gathering data include using participant observation, online observation, and in-depth interview. Interactions between actors can be explored using a service blueprint framework. In the perspective of SDL, the ability to identify operant resources (people and process) is crucial for value co-creation.
Remote Proxy V2V Messaging Using IPv6 and GeoNetworking

Vehicle-to-vehicle (V2V) messaging plays an important role in cooperative intelligent transportation systems (CITS), which are advanced applications addressing the problems of road transport management. In this regard, the Cooperative Awareness Message (CAM) protocol is standardized in EU for V2V messaging. However, V2V messages work well only when all vehicles have transmitters and work well in short range and no obstacle. To solve the former problem, the existance of the non-ITS road users, in the early deployment phase, we previously proposed a system called Proxy CAM, wherein roadside sensors detect target vehicles and transmit V2V messages on behalf of the vehicles. However, the V2V transmission range is still limited to the wireless range of IEEE802.11p. Therefore, in this study, we propose a system that delivers CAMs over the Internet using UDP/IPv6 and LTE in addition to the standard specification (i.e., the Basic Transportation Protocol/GeoNetworking and IEEE802.11p). Moreover, we implement the cellular network component of the system and evaluate its performance in terms of the packet delivery ratio and packet delay for various distances and packet frequencies. In our evaluation, we define the average Proxy CAM update delay and calculate this parameter for both IEEE802.11p and LTE. We find that using LTE over long distances is more efficient than using IEEE802.11p.
A perspective on knowledge sharing and lean management: an empirical investigation

ABSTRACT We conducted a single-case study analysis of a global leading manufacturer of carton packaging for food and beverages, to reveal linkages between knowledge sharing (KS) and lean management (LM) and examine the moderating role of corporate culture. Such linkages, ensure that lean knowledge acquired during a company’s transformation from conventional management to lean, is appropriately shared to all interested parties. In this context, the study findings revealed that there are enablers (e.g., employee involvement in decision making; common targets and goals) and disablers (e.g., time constraints; extreme workload; complicated KMS) of KS in a lean context. Firms need to take into account both enablers and disablers in order to minimize “knowledge waste” and achieve successful lean outcomes. These KS-LM relationships highlight a need for organizations to depart from the mere operational view of lean to a holistic one by drawing attention to a “soft” paradigm into the maximization of lean.
APPLICATION OF ARTIFICIAL NEURAL NETWORK IN OCCURRENCE OF BREAKING WAVES

This study seeks to explore all characteristics that influence the occurrence of breaking waves, with these parameters chosen based on physical phenomenon behavior and the Buckingham theory process for Appling the ANN approach to forecast breaking wave performance. When compared to the other parameters of fluid properties and wave characteristics, the results of mathematical dimension analysis with non-dimensional groups employed in ANN approach to predict the occurrence of breaking wave, the results shows a reasonable agreement that the ANN can be applied with R equal to 97%.
Impersonal reference in Russian Sign Language (RSL)

This paper contains the first description of impersonal reference in Russian Sign Language (RSL). Impersonal reference has been investigated using a variety of elicitation techniques. It has been found that RSL uses a variety of strategies, namely pro-drop, an indefinite pronoun someone, a plural pronoun ix pl, and probably a second-person pronoun ix 2 in impersonal contexts. The impersonal strategies in RSL follow the general typological tendencies previously identified for spoken languages (Gast & Van der Auwera 2013), and do not show obvious modality effects (such as described by Barberà & Quer 2013). Some impersonal strategies show evidence of influence of spoken/written Russian in the form of borrowing and/or code-switching.
Reliability of Cryptographic Information on Fiscal Data

The relevance of the study is due to the fact that modern data exchange systems transport information that stores elements of personal information. The operator of fiscal data is interested in secure transport of information. The degree of security in modern realities is very important and is designed to ensure confidentiality on the one hand, and on the other hand, full transparency of information for state monitoring bodies. In this regard, this article aims to identify the weaknesses of the adopted system of transport of cryptographic information by the operator of fiscal data and to systematize the theoretical data in relation to the theory on the question of part of the FTS data. The leading approach to the study of the problem is the analysis of the existing system in conjunction with the synthesis of data through the compilation of information, which allows a comprehensive review and systematization of data on the economic phenomenon. The article substantiates the need to reduce the state regulation of restrictions on the formation of information security barriers. The materials of the article are of practical value for the organization of big data stream transportation protection.
ALGORITHM OF USER’S PERSONAL DATA PROTECTION AGAINST DATA LEAKS IN WINDOWS 10 OS

In the European Union, in the first half of 2018, the General Data Protection Regulation came into force, which established the new rules for processing users’ personal data for IT companies. The operating systems (OS) are the dominant software that is responsible for collecting and processing data in computer systems. The most common OS is the Windows OS family. The authors identified Windows 10 operating systems, that collect and accumulate user's personal data; developed and tested practically an algorithm, the application of which localizes and blocks the transfer of user's personal data to official servers of the Microsoft company.
Image-based CAPTCHAs based on neural style transfer

Over the last few years, completely automated public turing test to tell computers and humans apart (CAPTCHA) has been used as an effective method to prevent websites from malicious attacks, however, CAPTCHA designers failed to reach a balance between good usability and high security. In this study, the authors apply neural style transfer to enhance the security for CAPTCHA design. Two image-based CAPTCHAs, Grid-CAPTCHA and Font-CAPTCHA, based on neural style transfer are proposed. Grid-CAPTCHA offers nine stylized images to users and requires users to select all corresponding images according to a short description, and Font-CAPTCHA asks users to click Chinese characters presented in the image in sequence according to the description. To evaluate the effectiveness of this techniques on enhancing CAPTCHA security, they conducted a comprehensive field study and compared them to similar mechanisms. The comparison results demonstrated that the neural style transfer decreased the success rate of automated attacks. Human beings have achieved a successful solving rate of 75.04 and 84.49% on the Grid-CAPTCHA and Font-CAPTCHA schemes, respectively, indicating good usability. The results prove deep learning can have a positive effect on enhancing CAPTCHA security and provides a promising direction for future CAPTCHA study.
Soft Computing Technique for Categorization of Unstructured Web Data

The World Wide Web has huge amount of information that is retrieved using information retrieval tool like Search Engine. It becomes tedious for the user to manually extract real required information. The detection of common and distinctive topics within a document set, together with the generation of multi-document summaries, can greatly ease the burden of information management. In the present work, a technique is proposed called Soft Computing Technique for Categorization ofUnstructured Web Data that creates the clusters of web documents using fuzzy clustering which focuses on this problem of mining the useful information from the collected web document. We have used FCM (Fuzzy C mean) clustering algorithm and HCM (Hard C Mean) algorithm. FCM clustering is a clustering technique which is separated from hard C mean that employs hard partitioning. Such that a point can belong to all groups with different membership grades between 0 and 1. The evaluation of the performance is done by validation measures and it is evaluated by F-measure, entropy, and purity measure and time complexity. It is found that fuzzy clustering algorithms yields better results than hard clustering algorithms.
Understanding how and when workplace ostracism jeopardizes work effort

PurposeWhile the performance consequences of workplace ostracism (WO) have been examined in many studies, relatively little is known about WO's relationship with work effort (WE), which is a vital part of the performance domain. Moreover, the literature is largely silent regarding how WO translates into reduced effort and when such effects are less likely. The purpose of the study is to bridge these gaps. Specifically, the paper examines the relationship between WO and WE, taking into account the mediating role of emotional exhaustion (EE) and the moderating role of work centrality (WC). Design/methodology/approachData for the study came from an online survey of 310 full-time employees of service-sector organizations in Pakistan. The PROCESS macro, a robust computational tool for research models involving both mediating and moderating mechanisms, was used for analysis.FindingsWO was found to be a risk factor for organizations in that it not only induces/aggravates strain in employees, but also hampers them in expending effort in given roles. Findings further highlight that the negative relationship between WO and WE is mediated by EE and moderated by WC.Research limitations/implicationsOwing to the cross-sectional data and correlational research design, the study has limited power to make causal inferences about the relationships between the constructs (e.g. WO and WE). Further, the study is conducted in a collectivist culture where people are particularly sensitive to WO; it is, therefore, possible that the strength of relationships between the constructs might differ in individualistic cultures.Practical implicationsApart from informing management practices in relation to minimizing the occurrence of WO, the study also offers valuable insights into how employees can protect themselves from the negative effects of WO.Originality/valueThe study is among the very few empirical works that simultaneously explicate how and when WO jeopardizes employees' WE.
Broiler feed formulated with phosphorus, calcium, and energy deficiencies supplemented with exogenous enzymes

Three experiments were carried out to verify the effects of the enzyme phytase, alone or combined with an enzyme complex, in diets deficient in available phosphorus (AP), calcium (Ca), and metabolizable energy (ME) on broiler performance, ME, and dietary amino acid digestibility. A total of 1,538 male Cobb 500 broilers were allocated to the three experiments, each of which consisted of five treatments: positive control (PC; basal ration); negative control 1 (NC1; PC minus 0.15% of AP, 0.16% of Ca, and 68 kcal kg-1 ME); negative control 2 (NC2; PC minus 0.15% of AP, 0.16% of Ca, and 101 kcal kg-1 of ME); NC1 plus phytase; and NC2 plus phytase plus enzymatic complex. Body weight gain (WG) and feed intake were measured from 1-21 days and from 1-42 days, and the corrected feed conversion rate (FCR) for mortality was calculated. In the second and third experiments, the apparent ME corrected for nitrogen balance (AMEn) and standardized digestibility of amino acids, respectively, were determined, for the diets supplemented with phytase and the enzymatic complex. In the first experiment, enzyme supplementation increased (p < 0.05) WG at 21 days and 42 days relative to the negative controls. Phytase inclusion improved (p < 0.05) FCR at the initial phase compared to the NC1 diet. In the second experiment, enzyme supplementation did not affect (p > 0.05) AMEn. In the third experiment, both enzyme treatments improved (p < 0.05) the digestibility of amino acids in the supplemented diets compared to the deficient diets. Supplementation with phytase and carbohydrases preserves the performance of broilers fed diets deficient in AP, Ca, and ME and improves amino acid digestibility.
Obscure: Information-Theoretically Secure, Oblivious, and Verifiable Aggregation Queries

We develop a secret-sharing-based prototype, entitled Obscure that provides communication-efficient and information-theoretically secure algorithms for aggregation queries using multi-party computation (MPC). The query execution algorithms over secret-shared data are developed to deal with an honest but curious, as well as, a malicious server by providing result verification algorithms. Obscure prevents an adversary to know the data, the query, and the tuple-identity satisfying the query.
Electrical Discharge Machining of SiC Reinforced 6061-T6 Aluminum Alloy Surface Composite Fabricated by Friction Stir Processing

Engineered materials with high hardness, great wear tolerance, high high-temperature power, and a low thermal expansion coefficient are aluminum-based composites. These materials are widely used in the automotive and aerospace industries. Friction stir processing (FSP) method used to prepare SiC reinforced aluminium alloy surface composite. Material removal rate (MRR) and surface roughness (SR) are measured with the impact of pulse on time, discharge current, and pulse off time (add one or two outcomes remark at SR and MRR optimal condition) is examined. For each of the three machining parameters, L9 orthogonal arrays (OA) of three levels were used in conducting the experiments. The validity of the Aluminum Surface Composite experiment programme is determined using MINITAB.
A spam filtering method

The invention discloses a method for filtering spam short messages, comprising following steps: setting a user credit degree to each short message user; according the credit degree of the short message user, sampling and filtering short messages in different intensity, sending short messages which are not sampled as normal short messages, the sampled short messages are used as distrustful short messages; for the distrustful short messages, according to character information of the short messages, determining that the short messages are whether or not spam short messages, if yes, filtering directly, if no, affirming the messages as normal short messages and sending; according to short message sending conditions of short message users, amending the user credit degree of the short message user. Based on traditional methods for filtering spam short messages, the invention samples short messages in different intensity according to differnt user credit degrees, affirms the short message whether or not being a spam short message according to character content of the short message, does not analyze each short message one by one, improves short message processing efficiency, solves jam of the short message service center network at a certain degree.
Energy Efficient Perturb and Observe Maximum Power Point Algorithm with Moving Average Filter for Photovoltaic Systems

Utilization of renewable energy resources is gaining more importance due to improved rate of returns on investment and the technology. Solar energy is the main resource at present to receive wide spread acceptance due to the maturity of the technology among all renewable energy resources. The power production from the solar source largely depends upon the irradiance and the temperature and hence requires complex control strategies for better utilization of energy. Many researchers have been working on improving the solar power extraction by way of maximum power point tracking and other means. This paper presents the modified Perturb and Observe (P&O) algorithm with Moving Average Filter (MAF) to extract the maximum power from the solar for photovoltaic systems. The paper also presents a buck converter controlled by the modified algorithm to charge the battery of a photovoltaic system. The proposed system is simulated and implemented in hardware. The simulation and experimental results show a good performance of the modified algorithm and its suitability for photovoltaic applications.
Automatic authorship attribution based on character n-grams in Swiss German

Automatic authorship attribution aims to train computers to identify the author of a disputed text based on idiolectal language features. When confronted with nonstandard data – in the present study Swiss German instant messages – languagespecific NLP toolkits are often unavailable, limiting the availability of features to classify texts. Thus, the approach I propose for Swiss German is based on character ngrams, which not only avoids the problem of a lack of available NLP tools, but – in addition to being a proven successful feature for authorship attribution – allows the capturing of orthographical idiosyncrasies. It thus allows the exploitation of Swiss German’s lack of standardised spelling rules, turning the challenge that Swiss German presents as non-standard data into an advantage. Different lengths of n-grams as features of a Na¨ive Bayes classifier combined with varying sizes of training and test corpora were tested, and 6- and 7-grams were found to faultlessly identify authors for all combinations considered. The number of distinctive n-grams in an author’s data set was found to be a determining factor for the classifier’s success, highlighting the benefits of exploiting Swiss German’s non-standard nature for authorship identification.