Location Of Resources Research Articles

Rotten web citations cited in scholarly journals: use of time travel for retrieval

PurposeThe present study compares the use of web citations as references in leading scholarly journals in Library and Information Science (LIS) and Communication and Media Studies (CMS). A total of 20 journals (each 10 from LIS and CMS) were selected based on the publishing history and reputation published between 2008 and 2017.Design/methodology/approachThe present study compares the use of web citations as references in leading scholarly journals in LIS and CMS. A PHP script was used to crawl the Uniform Resource Locators (URLs) collected from the reference list. A total of 12,251 articles were downloaded and 555,428 references were extracted. Of the 555,428 references, 102,718 web citations were checked for their accessibility.FindingsThe research findings indicated that 76.90% URLs from LIS journals and 84.32% URLs from Communication and Media Studies journals were accessible and others were rotten. The majority of errors were due to HTTP 404 error code (not found) in both the disciplines. The study also tried to retrieve the rotten URLs through Time Travel, which revived 61.76% rotten URLs in LIS journal articles and 65.46% in CMS journal articles.Originality/valueThis is an in-depth and comprehensive comparative study on the availability of web citations in LIS and CMS journals articles spanning a period of 10 years. The findings of the study will be helpful to authors, publishers, and editorial staff to ensure that web citations will be accessible in the future.

Click Fraud Detection Approaches to analyze the Ad Clicks Performed by Malicious Code

Mobile PR is an important component of the mobile app ecosystem. A major threat to this ecosystem’s long-term health is click fraud, which involves clicking on ads while infected with malware or using an automated bot to do it for you. The methods used to identify click fraud now focus on looking at server requests. Although these methods have the potential to produce huge numbers of false negatives, they may easily be avoided if clicks are hidden behind proxies or distributed globally. AdSherlock is a customer-side (inside the app) efficient and deployable click fraud detection system for mobile applications that we provide in this work. AdSherlock separates the computationally expensive click request identification procedures into an offline and online approach. AdSherlock uses URL (Uniform Resource Locator) tokenization in the Offline phase to create accurate and probabilistic patterns. These models are used to identify click requests online, and an ad request tree model is used to detect click fraud after that. In order to develop and evaluate the AdSherlock prototype, we utilise actual applications. It injects the online detector directly into an executable software package using binary instrumentation technology (BIT). The findings show that AdSherlock outperforms current state-of-the-art methods for detecting click fraud with little false positives. Advertisement requests identification, mobile advertising fraud detection are some of the keywords used in this article.

Detecting Fake News Using Social Media Platforms

Abstract: Fake news detection is an interesting topic for computer scientists and social science. The recent growth of the online social media fake news has great impact to the society. There is a huge information from disparate sources among various users around the world. Social media platforms like Facebook, WhatsApp and Twitter are one of the most popular applications that are able to deliver appealing data in timely manner. Developing a technique that can detect fake news from these platforms is becoming a necessary and challenging task. This project proposes a machine learning method which can identify the credibility of an article that will be extracted from the Uniform Resource Locator (URL) entered by the user on the front end of a website. The project uses the five widely used machine learning methods: Long Short Term Memory (LSTM), Random Forest (random tree), Random Forest (decision tree), Decision Tree and Neural Network to give a response telling the user about the credibility of that news. Our initial definition of reliable and unreliable will rely on the human-curated data http://opensources.co. OpenSources.co has a list of about 20 credible news websites and a list of over 700 fake news websites. The proposed model is working well and defining the correctness of results upto 87.45% of accuracy. Keywords: Data Pre-processing, Fake news datasets, ML algorithms, Prediction.

How to Choose VLSI IC from E-Commerce Sites?: Sentiment Analysis with the help of Python Tools

Very Large Scale Integration (VLSI) dominates the digital technology in the present era. The VLSI based products have strong computing power in one hand & small in physical dimension due to its space minimization feature. Unfortunately, the people who use these ICs frequently are still not in a position to frequently purchase such VLSI IC in online mode & rely more on offline shopping from electronics shop in his or her locality. Due to the emergence of cashless economy, there is a paradigm shift in purchasing behavior of customers across the globe but it is an established fact that the ecommerce sites are still not matured for VLSI IC. This study is an attempt to use Natural Language Processing Tool Kit (NLTK) in Python & augment it with Valence Aware Dictionary and Sentiment Reasoner (VADER) analysis for development of a newly proposed App supposed to be used in the smart mobile phones for Sentiment Analysis of online feedbacks & reviews of customers who use VLSI IC frequently in their profession. The purpose is to create a confidence & prepare a convenient platform for those people towards their online purchasing behavior of VLSI IC. Uniqueness of this research is the use of word cloud for the textual review along with the star rating in the sentiment analysis through an automated system which is supposed to accept the Uniform Resource Locator (URL) of the concerned product only. The study demonstrates the validation on a set of specific product reviews from the amazon.in. & unfold the challenge of said App development successfully. The newly developed tools will be helpful for a customer to select such a VLSI IC before purchase.

RESOURCES OF RAW MATERIAL SPECIES OF MEDICINAL PLANTS OF RADIUS PHYTOCENOSES OF PEREYASLAVSCHINA

The article presents the results of the resource assessment and location of individual raw material species of medicinal plants of meadow and meadow-steppe phytocenoses of Pereyaslav region (Kyiv region, Boryspil district). It is established that the participation of wild medicinal plants of Pereyaslav region in meadow phytocenoses takes the second (145 species out of the total number – 582), and in meadow-steppe third (114 species) places. The meadow type of floristic complexes in the study area is represented by floodplain meadows of the Dnieper, Supa, Trubizh, Alta, Brovarka rivers, and a number of small rivers. They are presented in four types of formations: steppe, real, swampy and saline meadows.During the field research conducted in 2015-2020, the resources of wild raw medicinal plants of official medicine were taken into account: Achillea submillefolium Klok. et Krytzka, Hypericum perforatum L., Helichrysum arenarium (L.) Moench, Thymus serpyllum L. and their raw material productivity (biological and operational reserves) in meadow and meadow-steppe ecotopes of Pereyaslav region was determined. The location of raw materials of the studied species of medicinal plants of the natural flora of the specified area is determined.As a result of resource surveys, it was found that the stocks of Achillea submillefolium are significant and fully sufficient to meet the needs of the pharmaceutical industry; stocks of Thymus serpyllum, Hypericum perforatum are sufficient for industrial procurement; reserves of Helichrysum arenarium are insufficient and need to be limited.

Designing a sustainable biofuel supply chain by considering carbon policies: a case study in Iran

BackgroundCarbon emissions and global warming have increased as a result of population growth and greater usage of fossil fuels. Finding a long-term replacement for fossil fuels, such as biofuels, has become a major problem for energy supply management in recent years. Sustainability must be addressed as a key problem in building biofuel supply chains (BSCs), given the pressing need for societies to limit environmental consequences and promote social responsibility of company activities. Various modeling frameworks have been established so far to design a BSC. At the same time, no research exists that examines both the sustainable development paradigm and the influence of various carbon regulatory policies on the strategic and operational decisions made by BSCs.MethodsThis study develops a multi-objective, multi-period, multi-echelon BSC from switch grass regarding the economic, environmental and social aspects of sustainability. Four carbon policies are taken into account when assessing the environmental aspect: the carbon cap, the carbon tax, the carbon trade, and the carbon offset. To solve the multi-objective model, the fuzzy interactive programming method is used, and the fuzzy best–worst method is used to weight the social objective components.ResultsAn actual case study in Iran is studied to demonstrate the model’s applicability. Under various carbon policies, different network configurations are obtained based on the location of switch grass resources and installed facilities. Biofuel production and transportation activities account for approximately 28% and 51% of total carbon emissions, respectively, according to numerical results. Furthermore, these activities account for roughly 62% of overall expenses. In the suggested case study, implementing the carbon trade policy reduces carbon emissions by more than 30% while increasing total profit by about 27%. In comparison to other policies, the carbon trade policy has a substantial impact on enhancing social considerations. Overall, the carbon trade policy can greatly improve the economic and environmental components of sustainability without significantly decreasing in the social sustainability.ConclusionsThe proposed model can assist policymakers and governments in simultaneously optimizing BSC profitability, carbon emission reduction, and social concern.

Oil Price Shocks and Conflict Escalation: Onshore versus Offshore

We reconsider the relationship between oil and conflict, focusing on the location of oil resources. In a panel of 132 countries over the period 1962-2009, we show that oil windfalls escalate conflict in onshore-rich countries, while they de-escalate conflict in offshore-rich countries. We use a model to illustrate how these opposite effects can be explained by a fighting capacity mechanism, whereby the government can use offshore oil income to increase its fighting capacity, while onshore oil may be looted by oppositional groups to finance a rebellion. We provide empirical evidence supporting this interpretation: we find that oil price windfalls increase both the number and strength of active rebel groups in onshore-rich countries, while they strengthen the government in offshore-rich ones.

Do Resource-Rich Provinces Perform Lower Democratization Progress? Estimating Oil Impact on Democracy Indicators in Indonesia

This study examined whether oil dependence affected democracy quality by referring to provincial-level data in Indonesia. While physical quantity production was used to measure oil dependence, democracy was measured based on the Indonesian Democracy Index (IDI). Static panel data analysis was employed to control unobserved time-invariant characteristics of each province, including a robust Driscoll and Kraay (D-K) standard error model for the general forms of cross-sectional dependence when dealing with panel observation. The results showed that oil-dependent provinces tended not to have weak democratic qualities. The effects were also robust when democracy was separated into three main elements of the IDI: political rights, civil liberties, and democratic institutions, or when an alternative measure of dependency was used. Interestingly, this study found that oil dependence had a stronger effect on democracy in provinces with a lack of oil than in Sumatra and Kalimantan, globally known as dominant resource locations.

Augmenting Traditional Support Groups for Adolescents With Type 1 Diabetes Using Instagram: Mixed Methods Feasibility Study.

BackgroundIn-person support groups have been shown to benefit adolescents with type 1 diabetes (T1D) by helping to decrease perceived diabetes burden and improving knowledge related to chronic disease management. However, barriers exist to participation in traditional support groups, including the timing and location of meetings and resources needed to attend. Adolescents are increasingly utilizing online support groups, which may provide solutions to some of the challenges faced when implementing in-person support groups.ObjectiveThe purpose of this study was to assess the feasibility and acceptability of a hybrid support group model where traditional in-person support groups were augmented with Instagram participation between monthly support group sessions for adolescents with T1D.MethodsParticipants (13-18 years old with T1D for ≥6 months) were asked to post photos each week for 3 months based on predetermined topics related to diabetes management. At the end of each month, participants attended an in-person support group to discuss their photos using the Photovoice method. Feasibility was assessed through enrollment and retention, number of Instagram posts, poststudy questionnaire, and a template analysis of the focus groups.ResultsOf 24 eligible participants, 16 (67%) enrolled in the study, with 3 dropping out prior to support group participation. The number of photos posted over 3 months ranged from 14 to 41. Among the 11 participants who completed a follow-up questionnaire, the majority of participants (6/11, 55%) reported that they very much enjoyed participating in the hybrid support group, and more than three-quarters (9/11, 82%) of participants reported that they “related to the photos posted.” Over half of participants (8/11, 73%) reported “learning something new from the photos posted,” which arose from sharing knowledge and experiences related to navigating the common challenges of diabetes management. Additionally, the use of Instagram posts helped facilitate peer discussions during the in-person support groups.ConclusionsThe novel combination of using Instagram to augment traditional in-person support groups was feasible and acceptable to adolescents with T1D. The overall satisfaction with the hybrid support group model, combined with the observed engagement with peers between support group sessions over social media, suggests that a hybrid support group model may have the potential to provide more pronounced benefits to adolescents than in-person meetings alone. Future research should investigate the use of social media as part of the support group model and examine the potential improvement of self-esteem, benefit-finding, and social support using validated tools in adolescents with diabetes.

Detecting phishing websites using machine learning technique.

In recent years, advancements in Internet and cloud technologies have led to a significant increase in electronic trading in which consumers make online purchases and transactions. This growth leads to unauthorized access to users’ sensitive information and damages the resources of an enterprise. Phishing is one of the familiar attacks that trick users to access malicious content and gain their information. In terms of website interface and uniform resource locator (URL), most phishing webpages look identical to the actual webpages. Various strategies for detecting phishing websites, such as blacklist, heuristic, Etc., have been suggested. However, due to inefficient security technologies, there is an exponential increase in the number of victims. The anonymous and uncontrollable framework of the Internet is more vulnerable to phishing attacks. Existing research works show that the performance of the phishing detection system is limited. There is a demand for an intelligent technique to protect users from the cyber-attacks. In this study, the author proposed a URL detection technique based on machine learning approaches. A recurrent neural network method is employed to detect phishing URL. Researcher evaluated the proposed method with 7900 malicious and 5800 legitimate sites, respectively. The experiments’ outcome shows that the proposed method’s performance is better than the recent approaches in malicious URL detection.

Machine learning approach for identifying suspicious uniform resource locators (URLs) on Reddit social network

The applications and advantages of the Internet for real-time information sharing can never be over-emphasized. These great benefits are too numerous to mention but they are being seriously hampered and made vulnerable due to phishing that is ravaging cyberspace. This development is, undoubtedly, frustrating the efforts of the Global Cyber Alliance – an agency with a singular purpose of reducing cyber risk. Consequently, various researchers have attempted to proffer solutions to phishing. These solutions are considered inefficient and unreliable as evident in the conflicting claims by the authors. Against this backdrop, this work has attempted to find the best approach to solving the challenge of identifying suspicious uniform resource locators (URLs) on Reddit social networks. In an effort to handle this challenge, attempts have been made to address two major problems. The first is how can the suspicious URLs be identified on Reddit social networks with machine learning techniques? And the second is how can internet users be safeguarded from unreliable and fake URLs on the Reddit social network? This work adopted six machine learning algorithms – AdaBoost, Gradient Boost, Random Forest, Linear SVM, Decision Tree, and Naïve Bayes Classifier – for training using features obtained from Reddit social network and for additional processing. A total sum of 532,403 posts were analyzed. At the end of the analysis, only 87,083 posts were considered suitable for training the models. After the experimentation, the best performing algorithm was AdaBoost with an accuracy level of 95.5% and a precision of 97.57%.

Preferential Attraction of Oviposition-Ready Oriental Fruit Flies to Host Fruit Odor over Protein Food Odor.

Simple SummaryOriental fruit fly, Bactrocera dorsalis, is one of the most destructive invasive pests of tropical fruit and vegetable crops worldwide. Current oriental fruit fly quarantine programs focus heavily on the control and surveillance of male flies, which is less effective for mitigating the impact caused by female populations. We investigated the relationship between olfactory preference and oviposition outcome of oriental fruit flies. In laboratory bioassays using similarly aged (14–16 day old) cohorts of mated females, some females preferred host fruit odors over protein food odor (torula yeast), while some preferred protein odor. The females that preferred host fruit odor had 2.1 times greater egg load and laid 2.4 times more eggs than females that preferred protein odor. Our results suggest that mated female oriental fruit flies with a preference for host fruit odor are likely to be in an imminent oviposition-ready physiological status, while mated females that prefer torula yeast odor are likely more protein-hungry and need more protein to produce the critical egg load necessary for oviposition.Olfaction plays a key role in the location of food and oviposition resources by tephritid fruit flies. Adult females, including oriental fruit fly, Bactrocera dorsalis, can sustain egg production throughout their lives provided they obtain sufficient protein. Thus, preferential attraction to food or oviposition sites (host fruit) will depend on a fly’s particular physiological state. In this study, laboratory bioassays were conducted with mature, mated B. dorsalis (provisioned protein and sugar ad libitum) to evaluate attraction to traps baited with torula yeast versus six host fruit sources (guava, guava juice, mango, orange, Surinam cherry, or white sapote). Females that preferred fruit laid a significant number of eggs around the trap entrance (average 405 eggs/fly), while almost no eggs were laid by females that preferred yeast (0.5 and 1.3 eggs/fly on two occasions). Similar results were observed in a bioassay using headspace extracts of guava juice and torula yeast, supporting olfactory-mediated responses. When individual females were allowed to oviposit in guava juice traps 0–24 h after a choice test, 45.8% of females that chose guava juice laid eggs (average 14.7 eggs/fly), compared with 27.5% that chose yeast (average 6.5 eggs/fly). Dissections indicated that females with a preference for guava juice had an egg load 2.4 times greater than females that preferred yeast. These results suggest there is an olfactory-based behavioral switch in preference from protein to host odors when female B. dorsalis are oviposition-ready. We discuss the implications of our findings for practical behavioral management and detection programs for B. dorsalis.

Principal Component Analysis as a novel method for the assessment of the enclosure use patterns of captive Livingstone’s fruit bats (Pteropus livingstonii)

The Spread of Participation Index (SPI) is a standard tool for assessing the suitability of enclosure design by measuring how captive animals access space. This metric, however, lacks the precision to quantify individual-level space utilization or to determine how the distribution of resources and physical features within an enclosure might influence space use. Here we demonstrate how Principal Component Analysis (PCA) can be employed to address these aims and to therefore facilitate both individual-level welfare assessment and the fine-scale evaluation of enclosure design across a range of captive settings. We illustrate the application of this methodology by investigating enclosure use patterns of the Livingstone’s fruit bat (Pteropus livingstonii) population housed at Jersey Zoo. Focal sampling was used to estimate the time each of 44 individuals in the first data collection period and 50 individuals in the second period spent in each of 42 theoretical enclosure sections. PCA was then applied to reduce the 42 sections to five and seven ecologically relevant “enclosure dimensions” for the first and second data collection periods respectively. Individuals were then assigned to the dimension that most accurately represented their enclosure use patterns based on their highest dimensional eigenvalue. This assigned dimension is hereafter referred to as the individual’s Enclosure Use Style (EUS). Sex was found to be significantly correlated with an individual’s EUS in the second period, whilst age was found to significantly influence individual fidelity to assigned EUS. When assessing the effect of resource location on group-level preference for certain sections, the presence of feeders and proximity to public viewing areas in period one, and feeders and heaters in period two, were positively correlated with space use. Finally, individual EUS remained consistent between both data collection periods. We interpret these results for this species in the context of its observed behavioural ecology in the wild and evaluate the degree to which the current captive enclosure for this population allows for optimal individual welfare through the facilitation of spatial choice. We then explore how these methods could be applied to safeguard captive animal welfare across a range of other scenarios.

RESEARCH OF THE PROCEDURE OF INITIAL ACCESS TO THE FIFTH GENERATION COMMUNICATION NETWORK

Traditional mobile networks have bandwidth and infrastructure limitations. Each base station has a limited number of users and data capacity. 5G technology uses advanced technologies, such as massive MIMO and beamforming, to increase data rates and spectrum efficiency. These features, and the large number of small cells, allow many 5G users to receive low latency and high traffic without overloading the base station. Small cells work the same way as regular cells, but use new technology and millimetre waves (5G New Radio). Low-power base stations can be easily deployed. Cell search is a procedure by which the user equipment (UE) obtains time and frequency synchronization with the base station and decodes the cell ID of the mobile device. System information is broadcast information that is periodically transmitted by the base station on the downlink. This is very important information for the mobile device (UE) needed to connect to the base station (gNB). The UE reads system information to determine the location of mobile devices when turned on, to select and re-select a cell. System information provides the following information: system frame number, system bandwidth, PLMN, mobile subscriber selection and redial thresholds, etc., required to access the network. Physical Cell ID (PCI) is used to distinguish mobile devices in the network radio communication. PLMN (Public land Mobile Network) – mobile network service area. Is a set of all mobile devices of one cellular network operator. The UE scans the bandwidth in the sync raster. The synchronization raster indicates the frequency positions of the synchronization unit that can be used by the UE to detect the system when there is no explicit indication of the position of the synchronization unit. In the next step, the UE decodes the primary and secondary synchronization signals to obtain the Physical Cell ID (PCI) and the DMRS PBCH. The base station transmits SSB packets that contain one SSB per beam. The beam direction changes for each SSB transmission. The following steps are performed to complete the synchronization procedure. The UE selects the preferred beam and decodes the PBCH MIB information, such as SFN, SSB index, raster offset, default DL numerology, RMSI configuration, DM-RS location, and cell prohibition information. If the cell is not locked, the UE attempts to decode the physical downlink control channels (PDCCH) to obtain the frequency location of the PDSCH resources. A study of the model of restoring the synchronization of symbols and frames in the QPSK system, caused by a variable synchronization error. A study of the formation of the radio channel at the physical level.

Natural Resources as a Factor in Production of a Regional Tourist Product and in Creation of Tourist Destinations

Introduction. Geographical location of natural resources is a determinant of localization of tourism. Based on the results of the research conducted, the article studies the theoretical and practical aspects of transformation of natural resources into a factor in production and consumption of a tourist product, as well as those of formation of directions for the flow of tourists. Materials and Methods. Materials of surveys of potential tourists conducted by independent research firms, Russian and foreign scientific publications and expert assessments, as well as the results of research carried out by the author of the article were used as the information base of the study. To assess the role and place of natural resources in the production and consumption of a tourist product, content analysis, as well as structural and functional analysis were employed. Using the methods of logical interpretation, statistical data processing, tabular and graphical methods, a scheme of transformation of natural resources into a factor in production and consumption of a tourist product has been devised and the degree of consumer preference for a tourist destination has been identified. Results. The study has identified problematic aspects of the use of natural resources in tourism, one of which is the underestimation of such resources as a basic factor in production and consumption of a tourist product, which negatively affects the level of economic feasibility of tourism development programs. The role of natural resources in formation of tourist needs and directions of the flow of tourists has been revealed. Conclusions have been drawn about the growing need for tourism with the predominance of the natural component of the tourist product and about the gradual formation of preconditions for local manifestations of overtourism and tourism phobia, in particular in the resorts in the South of Russia. The author has formulated proposals aimed at improving the economic feasibility of domestic tourism development programs. Discussion and Conclusion. Availability of natural resources is the primary basic condition for production and consumption of a tourist product. Significant costs for their transformation into tourist goods increase the requirements for the level of reasonableness of managerial decisions. The research results can be applied in strategic and medium-term planning of the development of domestic tourism in Russian regions on the basis of their natural potential.

COVIDium: a COVID-19 resource compendium.

The severe acute respiratory syndrome coronavirus 2 that causes coronavirus disease 2019 (COVID-19) disrupted the normal functioning throughout the world since early 2020 and it continues to do so. Nonetheless, the global pandemic was taken up as a challenge by researchers across the globe to discover an effective cure, either in the form of a drug or vaccine. This resulted in an unprecedented surge of experimental and computational data and publications, which often translated their findings in the form of databases (DBs) and tools. Over 160 such DBs and more than 80 software tools were developed, which are uncharacterized, unannotated, deployed at different universal resource locators and are challenging to reach out through a normal web search. Besides, most of the DBs/tools are present on preprints and are either underutilized or unrecognized because of their inability to make it to top Google search hits. Henceforth, there was a need to crawl and characterize these DBs and create a compendium for easy referencing. The current article is one such concerted effort in this direction to create a COVID-19 resource compendium (COVIDium) that would facilitate the researchers to find suitable DBs and tools for their research studies. COVIDium tries to classify the DBs and tools into 11 broad categories for quick navigation. It also provides end-users some generic hit terms to filter the DB entries for quick access to the resources. Additionally, the DB provides Tracker Dashboard, Neuro Resources, references to COVID-19 datasets and protein–protein interactions. This compendium will be periodically updated to accommodate new resources. Database URL: The COVIDium is accessible through http://kraza.in/covidium/

Capturing the Information-seeking of Students Employing a Wearable Camera: Discussions of Application in School Libraries

Utilizing wayfinding, think-aloud protocol (TAP), retrospective think-aloud protocol (RTAP), and a wearable camera, the actions, thoughts and feelings of an autistic college freshman and a neurotypical peer are recorded as they navigate their campus library in search of materials. The library website, virtual maps to resource locations, and library workers served equally as enablers and barriers to both students in their information seeking. This proof of concept study demonstrates that participant viewpoint ethnography is a viable research methodology that could be applied in school libraries for both neurotypical and autistic students.

Stochastic pre-event preparation for enhancing resilience of distribution systems

Extreme weather events are the common causes for power supply interruptions and power outages in electrical distribution systems. Improving the distribution system and enhancing its resilience is becoming crucial due to the increased frequency of extreme weather events. Preparation and allocation of multiple flexible resources, such as mobile resources, fuel resources, and labor resources before extreme weather events can mitigate the effects of extreme weather events and enhance the resilience of power distribution systems. In this paper, a two-stage stochastic mixed-integer linear programming (SMILP) is proposed to optimize the preparation and resource allocation process for upcoming extreme weather events, which leads to faster and more efficient post-event restoration. The objective of the proposed two-stage SMILP is to maximize the served load and minimize the operating cost of flexible resources. The first stage in the optimization problem selects the amounts and locations of different resources. The second stage considers the operational constraints of the distribution system and repair crew scheduling constraints. The proposed stochastic pre-event preparation model is solved by a scenario decomposition method, Progressive Hedging (PH), to ease the computational complexity introduced by a large number of scenarios. Furthermore, to show the impact of solar photovoltaic (PV) generation on system resilience, three types of PV systems are considered during a power outage and the resilience improvements with different PV penetration levels are compared. Numerical results from simulations on a large-scale (more than 10,000 nodes) distribution feeder have been used to validate the effectiveness and scalability of the proposed method.

Semi-supervised learning approach for malicious URL detection via adversarial learning1

Uniform Resource Location (URL) is the network unified resource location system that specifies the location and access method of resources on the Internet. At present, malicious URL has become one of the main means of network attack. How to detect malicious URL timely and accurately has become an engaging research topic. The recent proposed deep learning-based detection models can achieve high accuracy in simulations, but several problems are exposed when they are used in real applications. These models need a balanced labeled dataset for training, while collecting large numbers of the latest labeled URL samples is difficult due to the rapid generation of URL in the real application environment. In addition, in most randomly collected datasets, the number of benign URL samples and malicious URL samples is extremely unbalanced, as malicious URL samples are often rare. This paper proposes a semi-supervised learning malicious URL detection method based on generative adversarial network (GAN) to solve the above two problems. By utilizing the unlabeled URLs for model training in a semi-supervised way, the requirement of large numbers of labeled samples is weakened. And the imbalance problem can be relieved with the synthetic malicious URL generated by adversarial learning. Experimental results show that the proposed method outperforms the classic SVM and LSTM based methods. Specially, the proposed method can obtain high accuracy with insufficient labeled samples and unbalanced dataset. e.g., the proposed method can achieve 87.8% /91.9% detection accuracy when the number of labeled samples is reduced to 20% /40% of that of conventional methods.

Interactive framework development for microgrid expansion strategy and distribution network expansion planning

Microgrids provide a good platform to improve the efficiency of distribution networks. This platform, which is owned by the private sector, needs coordination with the distribution network owner in order to carry out its mission in the fields of operation and expansion planning. The distribution network expansion decisions are influenced by the expansion of microgrids. Therefore, the main issue that needs to be addressed is how expansion decisions interact with distribution networks and microgrids. This paper proposes a new framework for studying the interactions between resources and lines expansion in microgrids, and distribution network expansion. The proposed model is designed as a bi-level optimization problem. In the upper-level, with the aim of maximizing the profit of the microgrid owner, optimal decisions on distributed resources (location and sizes) and lines expansion are adopted. The lower-level problem involves the distribution network problem, in which, with the aim of minimizing the expansion and operation costs, energy exchanged with the microgrid and the upstream network, and the lines expansion of distribution network, are determined. Due to the non-convexity of both the upper and lower-level problems, a combination of evolutionary and mathematical algorithms is used to solve the proposed model. The study results on the IEEE 33-bus network using proposed framework show that the presence of a microgrid structure in the distribution network expansion planning helps to improve operation conditions and encourage investor participation in the power system. • A new framework for the interaction between DGs and lines expansion in MGs, and DN. • A bi-level framework for interactive simultaneous expansion planning of DN and MG. • Considering the DG expansion and supply lines expansion in the MG, simultaneously.