Costing Billions Of Dollars Research Articles

Will It Rust? A Set of Simple Demonstrations Illustrating Iron Corrosion Prevention Strategies at Sea

Corrosion of metal, especially iron, is a natural electrochemical process that costs billions of dollars every year. Educating college and precollege students, our future STEM professionals, about the chemistry that governs corrosion and its prevention strategies is of paramount importance. In this manuscript, we share a few simple iron corrosion-related activities developed into a demonstration video that helped celebrate the 2021 National Chemistry Week and was used to accompany general chemistry lectures on electrochemistry. These easy-to-setup activities with clear and dramatic effects effectively demonstrated concepts of equilibria including Le Chatelier’s principle, buffers, oxidation and reduction potentials, and factors such as metal coupling, salinity, pH, and temperature that impact rusting and its inhibition. These demonstration activities, with an emphasis on maritime applications, may also be adapted for precollege science camps or chemistry laboratories.

Reliability of chemical osmosis as a practical method to extract water out of shale and promote shale’s mechanical stability

Wellbore instability in shale formations is one of the most bothersome problems leading to significant changes in shale’s petrophysical, mechanical, and chemical properties, and could cost billions of dollars annually. Drilling fluid’s ionic composition is one of the factors affecting wellbore stability. This paper investigates the impact of chemical osmosis, ionic diffusion, and diffusion osmosis on the stability of shale, identify the critical salt concentration, and examine the impact of ionic type and concentration on shale stability. Experimental methods include utilization of shale I and II through linear expansion and gravimetric measurement tests using different salts. Results show that the critical salt concentration in shale I and II is 8 w/w%. Chemical osmosis is found to be a reliable method for water extraction out of shale if critical concentration value is not surpassed. Beyond this value, the ionic diffusion and diffusion osmosis in shale I and II were adversely affecting shale’s mechanical and chemical stability. The swelling and gravimetric ions and water uptake tests showed that shale is a leaky semi-permeable membrane, and that chemical osmosis could be jeopardized by ionic transport into shale. However, the water transport impact on changes in the thickness of diffuse double layer of clay needs to be investigated. The impact of temperature on water and ions transportation into shale needs to be examined to reflect in-situ conditions. In this study, the cation type that makes up the salt was varied while the anion type was fixed. Since both cations and anions tend to diffuse into shale in the presence of a concentration gradient, it is recommended to vary the anion type to study the combined impact of cation and anion type on shale stability.

Tracking elusive and shifting identities of the global fishing fleet.

Illegal, unreported, and unregulated (IUU) fishing costs billions of dollars per year and is enabled by vessels obfuscating their identity. Here, we combine identities of ~35,000 vessels with a decade of GPS data to provide a global assessment of fishing compliance, reflagging patterns, and fishing by foreign-owned vessels. About 17% of high seas fishing is by potentially unauthorized or internationally unregulated vessels, with hot spots of this activity in the west Indian and the southwest Atlantic Oceans. In addition, reflagging, a tactic often used to obscure oversight, occurs in just a few ports primarily by fleets with high foreign ownership. Fishing by foreign-owned vessels is concentrated in parts of high seas and certain national waters, often flying flags of convenience. These findings can address the global scope of potential IUU fishing and enable authorities to improve oversight.

Nanofiber-coated, tacrolimus-eluting sutures inhibit post-operative neointimal hyperplasia in rats.

Post-operative complications of vascular anastomosis procedures remain a significant clinical challenge and health burden globally. Each year, millions of anastomosis procedures connect arteries and/or veins in vascular bypass, vascular access, organ transplant, and reconstructive surgeries, generally via suturing. Dysfunction of these anastomoses, primarily due to neointimal hyperplasia and the resulting narrowing of the vessel lumen, results in failure rates of up to 50% and billions of dollars in costs to the healthcare system. Non-absorbable sutures are the gold standard for vessel anastomosis; however, damage from the surgical procedure and closure itself causes an inflammatory cascade that leads to neointimal hyperplasia at the anastomosis site. Here, we demonstrate the development of a novel, scalable manufacturing system for fabrication of high strength sutures with nanofiber-based coatings composed of generally regarded as safe (GRAS) polymers and either sirolimus, tacrolimus, everolimus, or pimecrolimus. These sutures provided sufficient tensile strength for maintenance of the vascular anastomosis and sustained drug delivery at the site of the anastomosis. Tacrolimus-eluting sutures provided a significant reduction in neointimal hyperplasia in rats over a period of 14days with similar vessel endothelialization in comparison to conventional nylon sutures. In contrast, systemically delivered tacrolimus caused significant weight loss and mortality due to toxicity. Thus, drug-eluting sutures provide a promising platform to improve the outcomes of vascular interventions without modifying the clinical workflow and without the risks associated with systemic drug delivery.

Hybrid Quantum-Classical Generative Adversarial Network for synthesizing chemically feasible molecules

Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. These generative models can extract salient features that characterize the molecules. However, they often suffer from increased time complexity. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules. There are two parts in the QGAN: a quantum generator that creates molecules based on the probability distributions of likely combinations and a classical discriminator that differentiates real molecules from the generated ones. We hypothesized that a quantum generator would be more impactful, offering a runtime speedup and increasing generated molecule possibilities, because we could use quantum mechanical phenomena superposition, entanglement, and interference to analyze more atom-bond combinations than a classical generator. The PyTorch-PennyLane implementation of the QGAN generated seven chemically stable molecules out of 300, a 2.3% success rate. Although the QGAN is a work in progress, it has demonstrated a path towards more efficient drug development, which would accelerate the development of medicines and reduce costs for the whole R&D process.

Randomized Trials Fit for the 21st Century: A Joint Opinion From the European Society of Cardiology, American Heart Association, American College of Cardiology, and the World Heart Federation.

Randomized Trials Fit for the 21st Century: A Joint Opinion From the European Society of Cardiology, American Heart Association, American College of Cardiology, and the World Heart Federation.

Prevention of seasonal influenza outbreak via healthcare insurance

The outbreak of seasonal flu costs billions of dollars in health care utilization and lost productivity. Despite the effectiveness of vaccination and antiviral medications to prevent serious flu-related complications and slow down the spread of an influenza epidemic, only 52% of the U.S. population aged 6 months and older received flu vaccines in the 2019-20 flu season. In addition, a costly out-of-pocket expense results in fewer patients seeking treatment, leading to potential hospitalizations and even flu-related deaths. In this study, we develop an integrated healthcare insurance mechanism that optimizes two incentive policies, vaccination reward and cost-sharing, to alleviate the medical cost and disease burden while preventing the outbreak of seasonal influenza. We model the dynamic interaction between a single insurer and multiple insureds as a Stackelberg vaccination game; we then embed the game into an agent-based simulation to model the spread of flu in a population under different policies. Finally, we apply machine learning and simulation optimization to optimize healthcare incentive policies in a large-scale flu transmission simulation. Simulation results indicate that the proposed methodology efficiently identifies a set of good incentive policies under different scenarios of flu vaccine efficacy and reproduction numbers.

Highly efficient and durable antimicrobial nanocomposite textiles

Healthcare associated infections cause millions of hospitalizations and cost billions of dollars every year. A potential solution to address this problem is to develop antimicrobial textile for healthcare fabrics (hospital bedding, gowns, lab coats, etc.). Metal nanoparticle-coated textile has been proven to possess antimicrobial properties but have not been adopted by healthcare facilities due to risks of leaching and subsequent loss of function, toxicity, and environmental pollution. This work presents the development and testing of antimicrobial zinc nanocomposite textiles, fabricated using a novel Crescoating process. In this process, zinc nanoparticles are grown in situ within the bulk of different natural and synthetic fabrics to form safe and durable nanocomposites. The zinc nanocomposite textiles show unprecedented microbial reduction of 99.99% (4 log10) to 99.9999% (6 log10) within 24 h on the most common Gram-positive and Gram-negative bacteria, and fungal pathogens. Furthermore, the antimicrobial activity remains intact even after 100 laundry cycles, demonstrating the high longevity and durability of the textile. Independent dermatological evaluation confirmed that the novel textile is non-irritating and hypoallergenic.

Data Augmentation to Support Biopharmaceutical Process Development through Digital Models—A Proof of Concept

In recent years, monoclonal antibodies (mAbs) are gaining a wide market share as the most impactful bioproducts. The development of mAbs requires extensive experimental campaigns which may last several years and cost billions of dollars. Following the paradigm of Industry 4.0 digitalization, data-driven methodologies are now used to accelerate the development of new biopharmaceutical products. For instance, predictive models can be built to forecast the productivity of the cell lines in the culture in such a way as to anticipate the identification of the cell lines to be progressed in the scale-up exercise. However, the number of experiments that can be performed decreases dramatically as the process scale increases, due to the resources required for each experimental run. This limits the availability of experimental data and, accordingly, the applicability of data-driven methodologies to support the process development. To address this issue in this work we propose the use of digital models to generate in silico data and augment the amount of data available from real (i.e., in vivo) experimental runs, accordingly. In particular, we propose two strategies for in silico data generation to estimate the endpoint product titer in mAbs manufacturing: one based on a first principles model and one on a hybrid semi-parametric model. As a proof of concept, the effect of in silico data generation was investigated on a simulated biopharmaceutical process for the production of mAbs. We obtained very promising results: the digital model effectively supports the identification of high-productive cell lines (i.e., high mAb titer) even when a very low number of real experimental batches (two or three) is available.

Environmental drivers of seasonal shifts in abundance of wild pigs (Sus scrofa) in a tropical island environment

BackgroundNon-native wild pigs (Sus scrofa) threaten sensitive flora and fauna, cost billions of dollars in economic damage, and pose a significant human–wildlife conflict risk. Despite growing interest in wild pig research, basic life history information is often lacking throughout their introduced range and particularly in tropical environments. Similar to other large terrestrial mammals, pigs possess the ability to shift their range based on local climatic conditions or resource availability, further complicating management decisions. The objectives of this study were to (i) model the distribution and abundance of wild pigs across two seasons within a single calendar year; (ii) determine the most important environmental variables driving changes in pig distribution and abundance; and (iii) highlight key differences between seasonal models and their potential management implications. These study objectives were achieved using zero-inflated models constructed from abundance data obtained from extensive field surveys and remotely sensed environmental variables.ResultsOur models demonstrate a considerable change in distribution and abundance of wild pigs throughout a single calendar year. Rainfall and vegetation height were among the most influential variables for pig distribution during the spring, and distance to adjacent forest and vegetation density were among the most significant for the fall. Further, our seasonal models show that areas of high conservation value may be more vulnerable to threats from wild pigs at certain times throughout the year, which was not captured by more traditional modeling approaches using aggregated data.ConclusionsOur results suggest that (i) wild pigs can considerably shift their range throughout the calendar year, even in tropical environments; (ii) pigs prefer dense forested areas in the presence of either hunting pressure or an abundance of frugivorous plants, but may shift to adjacent areas in the absence of either of these conditions; and (iii) seasonal models provide valuable biological information that would otherwise be missed by common modeling approaches that use aggregated data over many years. These findings highlight the importance of considering biologically relevant time scales that provide key information to better inform management strategies, particularly for species whose ranges include both temperate and tropical environments and thrive in both large continental and small island ecosystems.

THE EFFECT OF SUEZ CANAL BLOCKAGE ON SUPPLY CHAINS

The purpose of supply chain risk management is to understand the risks and their impacts, to take preventive actions and to determine plans to be implemented when risk events occur. Even if supply chain risks appear to be a minor risk event that was initially ignored, they can cause billions of dollars in costs and long-term supply chain disruptions at the macro level. The ship named Ever Given, which blocked the canal by accident during transportation in the Suez Canal on March 29, 2021, caused significant adverse effects in the supply chains, but also caused a break in the supply chain. In this study, the occurrence of the event is examined in terms of past marine accidents and supply chain risk management. In addition, alternative scenarios that occurred during the accident were examined, their effects on supply chains were determined, and suggestions were made against such accidents in the future. Furthermore, in the conclusion part of the study, the changes it will bring about in the supply chain approaches are included.

Imposing a health insurance surcharge on the unvaccinated.

COVID-19 hospitalizations among unvaccinated individuals cost billions of dollars. More employers are considering imposing a premium surcharge on employees participating in the company's health plan who are not vaccinated against COVID-19. These employers see this approach as similar to health premium surcharges for tobacco use, justifying the higher premiums on the basis that unvaccinated individuals could cause the plan to experience higher hospitalization and related costs. However, imposing a surcharge on unvaccinated employees will require employers to think through legal and policy implications and the interests of their employees and their businesses. Employers should weigh their vaccination goals against these interests and consider whether a legally compliant surcharge would further their goals. Employers should carefully consider the prevailing culture among their employees and assess whether the policy would be effective and noncoercive. Premium surcharges may be effective for some but not all employers.

Financial Hazard Assessment for Electricity Suppliers Due to Power Outages: The Revenue Loss Perspective

The electrical power infrastructure of the modern world is advanced, efficient, and robust, yet power outages still occur. In addition to affecting millions of people around the world, these outage events cost billions of dollars to the global economy. In this paper, the revenue loss borne by electricity-supplying companies in the United States due to power outage events is estimated and predicted. Various factors responsible for power outages are considered in order to present an exploratory data analysis at the U.S. level, followed by the top ten affected states, which bear over 85% of the total revenue loss. The loss is computed using historic observational data of electricity usage patterns and the tariff offered by the energy suppliers. The study is supplemented with reliable and publicly available records, including electricity usage patterns, the consumer category distribution, climatological annotations, population density, socio-economic indicators and land area. Machine learning techniques are used to predict the revenue loss for future outage events, as well as to characterize the key parameters for efficient prediction and their partial dependence. The results show that the revenue loss is a function of several parameters, including residential sales, percentage of industrial customer, time-period of the year, and economic indicators. This study may help energy suppliers make risk-informed decisions, while developing revenue generation strategies as well as identifying safer investment avenues for long-term returns.

A DNA Extraction Method for Insects From Sticky Traps: Targeting a Low Abundance Pest, Phthorimaea absoluta (Lepidoptera: Gelechiidae), in Mixed Species Communities.

Invasive insects can cause catastrophic damage to ecosystems and cost billions of dollars each year due to management expenses and lost revenue. Rapid detection is an important step to prevent invasive insects from spreading, but improvements in detection capabilities are needed for bulk collections like those from sticky traps. Here we present a bulk DNA extraction method designed for the detection of Phthorimaea absoluta Meyrick (Lepidoptera: Gelechiidae), an invasive moth that can decimate tomato crops. We test the extraction method for insect specimens on sticky traps, subjected to different temperature and humidity conditions, and among mock insect communities left in the field for up to 21 d. We find that the extraction method yielded high success (>92%) in recovering target DNA across field and lab trials, without a decline in recovery after three weeks, across all treatments. These results may have a large impact on tomato growing regions where P. absoluta is in the early stages of invasion or not yet present. The extraction method can also be used to improve detection capabilities for other bulk insect collections, especially those using sticky traps, to the benefit of pest surveys and biodiversity studies.

Recent Innovations in Nano Container-Based Self-Healing Coatings in the Construction Industry

:Globally, the maintenance and repair of infrastructure cost billions of dollars and impact the day-to-day life of people. Corrosion of infrastructure and metals used in the manufacture of goods and supplies is a major cause of deterioration in the construction industry. Nanocontainerbased self-healing coatings attract enormous scientific attention as they offer a wide range of applications in conjunction with long-lasting inhibition performance. These coatings prevent the rate of crack progression by releasing active agents from micro/nanocontainers in a controllable manner and heal crack, thereby mitigating corrosion. The potential of such coatings to heal local damage induced by climatic causes or by mechanical damage is a significant contributing factor to their desirability. This review is a comprehensive analysis of nanocontainers used to manufacture self-healing anticorrosive coatings as well as explains their self-healing mechanism. The technique used to develop nanocontainers such as layer-by-layer assembly of layered double hydroxide has been clarified. An attempt has also been made to cover the latest developments in the manufacture of nanocontainermediated self-healing corrosion coatings used in several construction industries.

An improved model of type 2 diabetes with effects on glucose tolerance, neuropathy and retinopathy with and without obesity

RationaleType 2 diabetes (T2D) costs billions of dollars annually, is also associated with pain (diabetic neuropathy), as well as retinopathy, lower urinary tract/urinary bladder dysfunction, depression, and systemic inflammation, affecting quality of life for patients. To that end, animal models are utilized to explore potential treatments, but may not reflect the complexity of the condition. ObjectiveWe aimed to test an improved model of T2D that more closely mimics the clinical mechanisms and symptoms in an outbred strain of mouse. FindingsMale and female CD-1 mice (n = 72) were fed one of four diets: regular chow (REG), our Standard American Diet (SAD), a revised SAD (SAD2), or the commonly-used high-fat diet (HFD). Overall, HFD- and SAD-fed mice had significant weight gain and increased fat mass. Following injury, the SAD- and SAD2-fed mice showed protracted recovery, but the HFD-fed mice did not. Similarly, SAD- and SAD2-fed mice showed impaired retinal function compared to REG-fed mice, but the HFD-fed mice did not. ConclusionsThe SAD and SAD2 more closely model the problematic dietary intake and subsequent clinical symptoms associated with T2D. Potential Impact of studyThe adjusted SAD2 may be a better representation of a human-translatable diet than the SAD and HFD, and may allow for increased advances in the investigation of T2D-related symptoms.

Covid-19 And Cardiovascular Disease In The Global Chronic Disease Epidemic

The COVID-19 epidemic and global chronic disease epidemic is expected to cost billions of dollars in the next 20 years. The role of various chronic diseases such as NAFLD, diabetes, cardiovascular disease and neurodegenerative disease research may now be relevant to the COVID-19 pandemic with the anti-aging gene repression connected to mitophagy [1,2] and the severity of the COVID-19 and heart disease.

A Review on Current Technologies and Future Direction of Water Leakage Detection in Water Distribution Network

Water leakage in the supply system is a silent problem that costs billions of dollars yearly. As these supply pipes are mostly underground, this leakage remains undetected for a long time. In 2019, Liemberger and Wyatt estimated an annual loss of thirty-nine billion dollars due to water leakage in the supply pipe. In this systematic review, we have analyzed forty-seven articles about water leakage detection and location research. The aim is to find the new technology, trends, and possible direction in this research field. We have derived four research questions. The first question was about how the research evolved over time, and we have observed that researchers focus more on experimental data collection, ML algorithms, and IoT technology. The second question was about the sensor the researcher was using. The most popular sensors researchers have used are: vibration, acoustic, and flow sensors, as they are cheap and easy to install. We can also see some novel applications of image and optical fiber sensors. The third question was about the trend in the algorithm. ML and threshold-based algorithms are dominating the field. The fourth question was about the communication technology trend, and WIFI, cellular IoT, and LoRa technology are leading the space capturing 80% of the research.

Multi-Objective Genetic Algorithm and CNN-Based Deep Learning Architectural Scheme for effective spam detection

E-mail has traditionally been regarded as the most powerful medium in online social networks, where users can discuss, connect, and share links with other online social media users. In particular, Twitter, in particular, has been determined to be the most popular social network that serves as the best communication channel for its users to share current news, ideas, thoughts, comments, and beliefs with other online social media users. Despite the efforts put in to combat spam operations on the online social network, Twitter spam has a new type of functionality that is limited to 140 characters. It is not only the major cause of annoyance for day-to-day users, but also responsible for the majority of computer security issues that cost billions of dollars in terms of productivity losses. In this paper, we propose a Multi-Objective Genetic Algorithm and a CNN-based Deep Learning Architectural Scheme (MOGA–CNN–DLAS) for the predominant Twitter spam detection process. The experimental details and results discussions of the proposed MOGA-CNN-DLAS are evaluated in terms of accuracy, precision, recall, F-Score, RMSE, and MAE by varying the ratio of training data under the utilization of three real datasets, such as the Twitter 100k dataset and the ASU dataset.

The Influence Of Traffic Control Devices And Road Geometric Characteristics On Traffic Crushes Of Rular Two Lane Road

Traffic accidents worldwide are among the most alarming phenomena because they cost billions of dollars due to death rates and property damage. In Ethiopia, the accident fatality rate is becoming one of the most serious problems. Specifically, in rural highways where in there are problems with traffic control device enforcement and geometric deficiencies. Gedeo zone faces a similar problem that is considered a hot issue on-road crash. This study focused on the influence of traffic control devices and geometric characteristic related to road crashes. The data collection method was a purposive sampling technique considering both primary and secondary data collection system. Direct field observations were conducted, such as field survey and recording of the existing road geometric elements to figure out which geometric element contributory to traffic crashes. The data collected from the police traffic was categorized by clustering the road into different road segments composed of the same geometric characteristics. The severity of the accident analyzed and identified the hazardous road sections (black spot area). The relationships of accident crash established between the influence of traffic control access devices and geometric elements on the crash reduction at the identified accident-prone areas. Further, the study used ANN modeling through engineering software MATLAB to analyze the weight age of crashes on specified road segments concerning geometric road characteristics. Hence, the gradient carriageway width, super-elevation, cross slope, gradient, sight distance number of the horizontal curve, number of vertical curves, and AADT are the major factors for the occurrence of both fatal and injury at the blackspot segment along the rural highway.