Billion In Losses Research Articles

Building the Human Firewall: Combating Phishing through Collective Action of Individuals Using Leaderboards

Phishing is an increasing organizational threat that causes billions in losses and damage to productivity, trade secrets, and reputation each year. This work explores how organizations can use gamification techniques to improve phishing detection efforts by individuals to create a human firewall. We build on cognitive evaluation theory to begin a new area of inquiry in gamification of IT security. With three experiments in a mock work setting, we test leaderboard components of validation, attribution, incentives, and public presentation for improvements in experiential (e.g., motivation) and instrumental outcomes (e.g., hits and false positives) in phishing reporting. Our findings suggest public attribution with rewards and punishments best balance the competing necessities of accuracy with widespread reporting. Further, our results demonstrate leaderboards’ unique benefits to phishing reporting over and above other phishing mitigation techniques (training and warnings). However, we noted that unintended consequences in false alarms may arise from shifts in motivation resulting from public display of incentives.

Detection of Anomalous Transactions in Mobile Payment Systems

Mobile payment systems are providing an opportunity for smartphone users for transferring money to each other with ease. This simple way of transferring through mobile payment systems has great potential for economic activity. However, fraudulent transactions may occur and can have a substantial impact on the economy of a country. Financial fraud and anomalous transactions can cause a loss of billions of dollars annually. Therefore, there is a need to detect anomalous transactions through mobile payment systems to prevent financial fraud. For this research study, a synthetic dataset is generated by using a PAYSIM simulator due to the lack of availability of a realistic dataset. This research study performed experiments on a financial transactional dataset using eight data mining classification algorithms. The performance of classification models was measured by using evaluation metrics: accuracy, precision, F-score, recall, and specificity. A comparative analysis of classification models was also performed based on their performance.

Credit Card Fraud Detection using Deep Learning based on Neural Network and Auto encoder

Credit card fraud is an event problem and fraud detecting techniques getting more sophisticated each day. Mainly internet is becoming more common in almost every domain. Online transactions, shopping, and e-commerce are expanding step by step. Due to which in the online payment system, fraudulent activities have also increased. It has cost banks and their customers a loss of billions of rupees. The techniques used now a day detects the anomaly only after the fraud transaction takes place. The intruders have found ways to crack the system loopholes and defeat the security. These frauds are not consistent in their actions, they constantly alter. Thus, Artificial Intelligent (AI) algorithms are used to detect the behavior of such activity by learning the past behavior of the transaction of the users. An unsupervised algorithm is used to detect online transactions, as fraudsters commit fraud once by online media and then move on to other techniques. This paper discusses the performance analysis and the comparative study of the two Deep Learning algorithms which include auto-encoder and the neural network. In this paper accuracy, precision, recall, and AUC curve are considered as a model evaluation factor.

The Challenges in Eradication of Iron Deficiency Anemia in Developing Countries

Anemia causes losses of billions of dollars annually in terms of productivity among working women of the reproductive age; it affects both their cognitive and physical performance. In developing countries where many manual labor jobs exist, an iron supplementation program is the first choice for eradicating anemia. However, this program cannot sufficiently meet reduction targets, and this created a polemic among health experts. The purpose of this study was to explore how iron deficiency anemia may be eradicated in developing countries. The results showed that the main challenges for iron anemia deficiency eradication are as follows: low adherence, infection, hidden symptoms, comorbidities from other non-communicable diseases, micronutrient deficiencies, and lack of program monitoring. In terms of low adherence, the side effects of iron supplements become a major factor in taking medication. Conversely, parasites and worms cause most of the high infection cases. Poor access to facilities of healthcare centers are becoming the main factor for the absence of anemia eradication program evaluation in remote areas of developing countries. Therefore, breakthroughs are expected to be developed to reduce anemia significantly. These include increasing patient visits to a healthcare center, diagnosing anemia accurately and intensively, prioritizing infection control, fortifying food, and monitoring iron side effects.

"Frozen evolution" of an RNA virus suggests accidental release as a potential cause of arbovirus re-emergence.

The mechanisms underlying virus emergence are rarely well understood, making the appearance of outbreaks largely unpredictable. Bluetongue virus serotype 8 (BTV-8), an arthropod-borne virus of ruminants, emerged in livestock in northern Europe in 2006, spreading to most European countries by 2009 and causing losses of billions of euros. Although the outbreak was successfully controlled through vaccination by early 2010, puzzlingly, a closely related BTV-8 strain re-emerged in France in 2015, triggering a second outbreak that is still ongoing. The origin of this virus and the mechanisms underlying its re-emergence are unknown. Here, we performed phylogenetic analyses of 164 whole BTV-8 genomes sampled throughout the two outbreaks. We demonstrate consistent clock-like virus evolution during both epizootics but found negligible evolutionary change between them. We estimate that the ancestor of the second outbreak dates from the height of the first outbreak in 2008. This implies that the virus had not been replicating for multiple years prior to its re-emergence in 2015. Given the absence of any known natural mechanism that could explain BTV-8 persistence over this long period without replication, we hypothesise that the second outbreak could have been initiated by accidental exposure of livestock to frozen material contaminated with virus from approximately 2008. Our work highlights new targets for pathogen surveillance programmes in livestock and illustrates the power of genomic epidemiology to identify pathways of infectious disease emergence.

A Survey of Analytical Techniques for Noroviruses.

As the leading cause of acute gastroenteritis worldwide, human noroviruses (HuNoVs) have caused around 685 million cases of infection and nearly $60 billion in losses every year. Despite their highly contagious nature, an effective vaccine for HuNoVs has yet to become commercially available. Therefore, rapid detection and subtyping of noroviruses is crucial for preventing viral spread. Over the past half century, there has been monumental progress in the development of techniques for the detection and analysis of noroviruses. However, currently no rapid, portable assays are available to detect and subtype infectious HuNoVs. The purpose of this review is to survey and present different analytical techniques for the detection and characterization of noroviruses.

Gene expression during development and overexpression after Cercospora kikuchii and salicylic acid challenging indicate defensive roles of the soybean toxin.

SBTX has defensive role against C. kikuchii, and therefore, its constituent genes SBTX17 and SBTX27 are promising candidates to engineer pathogen resistant plants. Soybean (Glycine max [L.] Merr.) is economically the most important legume crop in the world. Its productivity is strongly affected by fungal diseases, which reduce soybean production and seed quality and cause losses of billions of dollars worldwide. SBTX is a protein that apparently takes part in the defensive chemical arsenal of soybean against pathogens. This current study provides data that reinforce this hypothesis. Indeed, SBTX inhibited in vitro the mycelial growth of Cercospora kikuchii, it is constitutively located in the epidermal region of the soybean seed cotyledons, and it is exuded from mature imbibed seeds. Moreover, RT-qPCR analysis of the SBTX associated genes, SBTX17 and SBTX27, which encode for the 17 and 27kDa polypeptide chains, showed that both genes are expressed in all studied plant tissues during the soybean development, with the highest levels found in the mature seeds and unifoliate leaves. In addition, to assess a local response of the soybean secondary leaves from 35-day-old plants, they were inoculated with C. kikuchii and treated with salicylic acid. It was verified using RT-qPCR that SBTX17 and SBTX27 genes overexpressed in leaves compared to controls. These findings strongly suggest that SBTX has defensive roles against C. kikuchii. Therefore, SBTX17 and SBTX27 genes are promising candidates to engineer pathogen resistant plants.

Preparation and characterization of amphiphilic polymer coating for marine biofouling control

Biofouling is a major issue prevalent in marine industry resulting in losses in billions of dollars for repairing and maintenance. Coatings developed to tackle marine biofouling are either toxic towards environment or have shorter lifespan. Amphiphilic block copolymer coatings have gained wide attention recently to mitigate biofouling. In this work, performance of synthesized coating towards biofouling was studied. Hydroxyl terminated polydimethylsiloxane as hydrophobic and chitosan as hydrophilic moiety was used to synthesize the amphiphilic coating. Characterization of the prepared coating using Scanning Electron Microscopy (SEM) showed heterogeneous microstructure. Antifouling performance using egg white as the protein testing probe was done as it simulates the adhesives used by biofouling organism. Resistance to protein adhesion was seen on the surface of the coating. So, the developed environmental-friendly coating can be used effectively for the mitigation of marine biofouling.

New York state BH community upset over loss of billions for Medicaid reform

Mental health advocates and providers across New York state have expressed disappointment and concerns over the Trump administration's denial last week to extend its Med‐icaid waiver program to improve care and reduce avoidable hospitalizations. State officials were seeking $8 billion more in federal funding for the program's extension over five years.

Opportunities and Challenges for Hurricane Resilience on Agricultural and Forest Land in the U.S. Southeast and Caribbean

Three storms in the 2017 hurricane season caused $265 billion in damages in the U.S. Southeast and Caribbean, including billions in losses in the agriculture and forestry sector. Climate change projections indicate that such disastrous hurricane seasons are becoming more normal. Working land management sectors need to prepare for this future. However, few studies evaluate hurricane resilience strategies, or challenges faced by land managers surrounding hurricane events. Boundary organizations are critical to hurricane preparedness and recovery, advising land managers before hurricanes, and often supporting recovery efforts. Here, we rely on public advisors’ experiences to understand how land managers pursue hurricane resilience. Using focus groups and an online survey of three agencies in the Southeast U.S. and U.S. Caribbean (n = 607), we identify challenges faced by land managers before and after hurricanes, and the strategies they implement to minimize damage. We learn that land managers are faced with many diverse and unique challenges related to hurricanes, but that long-term planning for hurricane events is uncommon compared to shorter-term preparedness and recovery activities. Efforts towards hurricane resilience should incorporate local needs, align with other land management goals, and increase overall resilience to climate change and related stressors. The results of this research can guide state/territorial and national-level prioritizations regarding hurricane resilience, as well as identify research needs on hurricane resilience strategies.

Detecting malicious URLs using binary classification through adaboost algorithm

Malicious Uniform Resource Locator (URL) is a frequent and severe menace to cybersecurity. Malicious URLs are used to extract unsolicited information and trick inexperienced end users as a sufferer of scams and create losses of billions of money each year. It is crucial to identify and appropriately respond to such URLs. Usually, this discovery is made by the practice and use of blacklists in the cyber world. However, blacklists cannot be exhaustive, and cannot recognize zero-day malicious URLs. So to increase the observation of malicious URL indicators, machine learning procedures should be incorporated. This study aims to discuss the exposure of malicious URLs as a binary classification problem using machine learning through an AdaBoost algorithm.

Black Wave: How Networks and Governance Shaped Japan’s 3/11 Disasters

Reviewer: James R. Elliott, Rice University Imagine being violently shook, drowned, and nuked in rapid succession. That in essence is what happened to Japanese communities at the epicenter of the triple disasters of March 11, 2011. It started with a nearby earthquake that thrust the ocean floor upward 52 feet. That violent energy then produced a tsunami 65 feet high, enough to devastate retaining walls and turn the sea’s surging waters black with debris as they pushed 6 miles inland, up local waterways. In the Fukushima Prefecture, these events triggered an automatic shutdown of nuclear reactors at the local power plant, as planned. The problem was that the huge batteries designed to cool the reactors by powering water through their cores were in the basement—a location intended to avoid outages from tornadoes in the United States, where the reactors were designed, but not from tsunamis where they were actually installed. With that backup power neutralized, radioactive fuel inside the reactors reached temperatures of 5,000 degrees Fahrenheit, melting through the 7-inch steel barriers and eventually spreading radioactive cesium and iodine over more than 5,000 sq. mi. The result was one of the costliest disasters in human history, with more than $235 billion in losses and an expected $200 billion more in costs of active decontamination and victim compensation, which is not yet complete.

Towards a better understanding of the full impact of the left digit effect on individual trading behaviour: unearthing a trading profit effect

ABSTRACTInvestors’ perceptions of price have been shown to be disproportionately affected by the left-most digit(s). However, a similar left digit effect (LDE) in relation to another important determinant of investors’ behaviour (i.e. trading profit) has not been explored. We examine over 7,314,570 trades made by 25,766 individuals and find a LDE in profit that is 1.71 times stronger than that related to closing price; suggesting that individuals focus more on left digits in profit than price when deciding when to close a trade. In addition, we observe a positive synergistic relationship between the LDE related to profit and price, suggesting that its total influence may result in losses of billions of dollars per financial year for investors. We suggest that these results make a strong case for educating investors against this bias.

CO2 utilization: Turning greenhouse gas into fuels and valuable products

This study critically reviews the recent developments and future opportunities pertinent to the conversion of CO2 as a potent greenhouse gas (GHG) to fuels and valuable products. CO2 emissions have reached an alarming level of around 410 ppm and have become the primary driver of global warming and climate change leading to devastating events such as droughts, hurricanes, torrential rains, floods, tornados and wildfires across the world. These events are responsible for thousands of deaths and have adversely affected the economic development of many countries, loss of billions of dollars, across the globe. One of the promising choices to tackle this issue is carbon sequestration by pre- and post-combustion processes and oxyfuel combustion. The captured CO2 can be converted into fuels and valuable products, including methanol, dimethyl ether (DME), and methane (CH4). The efficient use of the sequestered CO2 for the desalinization might be critical in overcoming water scarcity and energy issues in developing countries. Using the sequestered CO2 to produce algae in combination with wastewater, and producing biofuels is among the promising strategies. Many methods, like direct combustion, fermentation, transesterification, pyrolysis, anaerobic digestion (AD), and gasification, can be used for the conversion of algae into biofuel. Direct air capturing (DAC) is another productive technique for absorbing CO2 from the atmosphere and converting it into various useful energy resources like CH4. These methods can effectively tackle the issues of climate change, water security, and energy crises. However, future research is required to make these conversion methods cost-effective and commercially applicable.

Performance analysis of machine learning classifiers for non-technical loss detection

Power companies are responsible for producing and transferring the required amount of electricity from grid stations to individual households. Many countries suffer huge losses in billions of dollars due to non-technical loss (NTL) in power supply companies. To deal with NTL, many machine learning classifiers have been employed in recent time. However, few has been studied about the performance evaluation metrics that are used in NTL detection to evaluate how good or bad the classifier is in predicting the non-technical loss. This paper first uses three classifiers: random forest, K-nearest neighbors and linear support vector machine to predict the occurrence of NTL in a real dataset of an electric supply company containing approximately 80,000 monthly consumption records. Then, it computes 14 performance evaluation metrics across the three classifiers and identify the key scientific relationships between them. These relationships provide insights into deciding which classifier can be more useful under given scenarios for NTL detection. This work can be proved to be a baseline not only for the NTL detection in power industry but also for the selection of appropriate performance evaluation metrics for NTL detection.

Test for Anticorrosive Properties of Packing Paper under the Conditions of Increased Humidity and Presence of Chlorides

During transportation, long-term storage and operation metal products are exposed to atmospheric corrosion that leads to billions of losses every year. The urgency of the problem being investigated is due to the fact that export of metal products is mainly carried out by sea through regions with humid tropical climate. The search for new packaging materials is a significant trend in corrosion control. The research of effective corrosion inhibitors that are capable to protect all kinds of metals, such as steel, zinc, copper, aluminum, is actually carried out, as well as the process of development of getting the packaging materials containing the corrosion inhibitors. The purpose of this paper was a research of anticorrosive properties of experimental packaging papers under increased humidity and in the presence of chloride of sodium. In this paper the test conditions which, correspond to the handling conditions of steel products through the zones with tropical climate, were simulated. The studies research found out that the experimental patented corrosion inhibitors do not take protective effect on galvanized steel. The generated corrosion products are incoherent, weakly coupled with metal and easily fall off (crumble), that results in metal loss.

The efficiency of control of the number of Helicoverpa armigera Hbn. in sunflower sowings

The insect Helicoverpa armigera Hbn. (Lepidoptera: Noctuidae) is a widespread species in the Palearctic. It is considered one of the main pests of many agricultural crops, annually causing more than $ 2 billion of losses to agriculture in the world. Therefore, insecticide treatment of sowings is crucial in pest control. The frequent application of pesticides increases resistance in insects, resulting in an increase in dosage and (or) in the usage of new highly toxic chemicals. Consequently, the search for reliable means of pest control remains a problem. In 2017-2019, we studied the efficiency of insecticides against H. armigera in sunflower sowings at V.S. Pustovoit All-Russian Research Institute of Oil Crops (Krasnodar). In 2017, the colonization of sunflower heads with H. armigera caterpillars was at the level of 3.2 specimen/plant, in 2018 – 4.5 specimen/plant, in 2019 it reached 6.0 specimen/plant. Bifetrin + Chlorpyrifos, EC (87 %) and Chlorantraniliprole, SC (91 %) showed the highest biological efficiency. Fitoverm-M, EC was the best organic insecticide – 56 %. The treatment of sunflower plants with Beta-cypermethrin + Dimethoate, EC (standard) and Chlorantraniliprole, SC in a production experiment contributed to a significant decrease in the number of caterpillars – 71.7 and 97.0 %, respectively. The yield reliably exceeded the control one, the kept yield was 0.23-0.39 t/ha.

КОРПОРАТИВНОЕ МОШЕННИЧЕСТВО. К ВОПРОСУ О ВОЗРАСТАЮЩИХ УГРОЗАХ И ИХ КОРНЯХ

As more and more institutions appear in the financial sector that are considered “too big to fail,” the threat of corporate fraud and its consequences for banks and the economy today has a greater impact than ever before. Fraud trading (speculation) is one of the types of corporate fraud that banks have not succeeded in fighting since it appeared in the spotlight in 1995. Speculators engage in unauthorized trading on behalf of their bank, and the most serious cases have led to the loss of billions and also to the collapse of the Barings Bank. Despite the obvious threat posed by speculation, it seems that banks have failed to establish internal control over it. Cases of unauthorized trading, which led to huge losses for banks, have been repeated for many years. At the same time, the numbers are steadily increasing to frightening amounts. The reason speculation is so controversial in corporate fraud is that, in theory, it does not begin with neglecting the interests of the bank. On the contrary, most of these scammers begin to conduct unauthorized transactions, wanting benefits for the company. But at the same time, the fact remains that after a turning point, when fraudsters try to hide their losses, they already consciously begin to act against the bank. And even then, most of them believe that they will be able to recover their losses and achieve profit and profit for the bank. Thus, the purpose of this article is to analyze the reasons why fraudulent trade has continued over the past two decades and the measures that have been taken to combat it. And you also need to understand how effective these measures are and what can be done in the future to change the situation for the better.

ANALYSIS AND OPTIMIZATION OF TRAFFIC CONGESTION AT SINGLE INTERSECTION USING MATLAB AND ARENA SIMULATION

Traffic jam is one of the most widespread problems commonly seen all over the world that causes loss of billions of dollars and useful hours per annum. Traffic lights are used at the intersections to manage the traffic flow, but one problem of those traffic lights is that the color changes at constant intervals irrespective of the traffic density or time of the day. As a result, the measure fails to keep the optimal traffic flow throughout the day at every convergence. This study describes a possible and effective method to systemize traffic lights by considering the traffic density for a specific time of the day. The range of idle time or waiting time within the queue is identified as a function of cycle time, effective green time and traffic arrival rate. After plotting the values within the graph, the trend is observed for idle time with reference to these variables. The lower and upper bound for these independent variables like cycle time, effective green time and traffic arrival rate are decided from observed data sets. As the idle time comes in the form of a range, an estimation of traffic congestion at a particular time is yielded from the method. This traffic flow is analyzed using Arena. The result of idle time using Arena is analogous with the previously analyzed model using MATLAB. Also, at the end of the study, a simulation video is generated that gives practical visual experience.

An Xgboost based system for financial fraud detection

Credit card fraud leads to billions of losses in online transaction. Many corporations like Alibaba, Amazon and Paypal invest billions of dollars to build a safe transaction system. There are some studies in this area having tried to use machine learning or data mining to solve these problems. This paper proposed our fraud detection system for e- commerce merchant. Unlike many other works, this system combines manual and automatic classifications. This paper can inspire researchers and engineers to design and deploy online transaction systems.