bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of the Petroleum Industry Act on the Economics of Small and Stranded Gas Fields in Nigeria
Authors: Adegun Adedamola , Mdondo Justine , Agbo Chidozie , Samuel Pheeyedi
Published: August 2022

Nigeria's vast natural gas resources of over 600tcf are largely undeveloped and spread across the Niger Delta, Dahomey, Anambra and Benue basins. The Niger Delta basin is more developed but has been largely constrained by ‘oil-centric’ fiscal policies, ambiguous regulatory direction, and the preponderance of small (low reserves) and stranded (distant from infrastructure) fields. The gas (LNG) export business has flourished in Nigeria because it has benefitted from fiscal and legal incentives exogenous to the existing legal framework until the Petroleum Industry Act.

With the enactment of the Petroleum Industry Act in 2021, the legal, fiscal and regulatory landscape of the industry changed. An omnibus legislation, it encompasses new ‘gas-centric’ policies, introduces targeted incentives, provides improved regulatory clarity and commercial steer particularly for the gas industry. The act created a distinct regulator for the gas industry, revised the incentives in the Associated/Non-Associated Gas Framework, detailed domestic pricing methodologies, reinforced government commitment to domestic supply obligation amongst others.

In this paper, we analysed the Petroleum Industry Act, detailed the key drivers for gas business and assessed the impact on the viability of small and stranded gas fields. We highlighted the impact of the new fiscal terms on gas projects compared to the erstwhile terms and identified opportunities for government and investors to maximise value and meet gas production targets.
Hybridized Probabilistic Machine Learning Ranking System for Lithological Identification in Geothermal Resources
Authors: Ekeopara Praise , Odo Jude , Obah Boniface , Nwankwo Valerian
Published: August 2022

Geothermal resources are characterized by hard rocks with very high temperatures making it difficult to implement conventional tools for petrophysical analysis such as lithological identification. Several computation and artificial intelligence models such as K-means clustering algorithms have been applied, however, these algorithms are limited to certain applications due to the available data utilized and high computation time. It is hence pertinent to consider a robust model that can meet up with these requirements.

In this study, a proposed hybrid machine learning probabilistic ranking system was developed which considered the integration of several pattern recognition algorithms in the identification of formation lithology. The ranking system leverages on the large volume of drilling and log data collected from conventional oil and gas operation to develop five embedded lithology identification models: K-means clustering, Hierarchical clustering using ward linkage, K-mode clustering, Birch, Mini-batch kmeans. The analysis was carried out using gamma ray logs, density logs, neutron porosity logs and Spontaneous potential as input parameters in building the lithology identification models while rate of penetration, surface RPM, Flow in, surface torque and pump pressure were utilized to predict the different lithologies using the different pattern recognition models as outputs. The output derived from the respective lithology identification models are further ranked based on a probabilistic approach to predict the actual lithology of the encountered formation. The results show that the implementation of the ranking system was effective in identifying the lithology of the drilled formation.
Evaluation and Comparison of the Hydrate Inhibition Tendency of Hydrogen using Unisim and Towler and Mokhatab's Correlation
Authors: Ekeowa Collins Sopuruchukwu , Ajienka Joseph Atubokiki , Wachikwu-Elechi Virtue Urunwo , Ikiensikimama Sunday Sunday
Published: August 2022

Hydrate formation in pipelines has been a serious flow assurance issue in the oil and gas industry hence several methods have been developed to predict the conditions which favor its formation in order to develop a proper mitigation plan to control its formation. This study evaluated the inhibition effect of hydrogen gas on a methane / methane gas mixture using UNISIM software and Towler & Mokhatab correlation. After which the effectiveness of this prediction method was determined by comparing their predictions with experimentally obtained data. The predictions of hydrate formation temperature of the methane / methane gas mixtures by Unisim and correlation all showed a decrease in their hydrate formation temperatures when mixed with hydrogen gas. An error evaluation on the two prediction methods showed that the UNISIM prediction has a better prediction than the correlation for all the methane / methane gas mixture because it predicted hydrate formation temperature closer to the experimental predictions.
Torque and Drag Calculations in Multilateral Wells
Authors: Etaje Darlington , Shor Roman , Lashkari Reza
Published: August 2022

Multilateral drilling technology has advanced to the point that it is now feasible to explore and extract resources from previously unprofitable reservoirs. It may also help to enhance field development management by allowing for more efficient fluid flow from the formation. Despite its various benefits, a multilateral well has some drawbacks and requires a significant amount of technical work to optimize drilling parameters and depth and well trajectory rise. The most critical issues occur for a fish-bone lateral when the turns are formed for each lateral. The measurements of WOB are complex and may be inaccurate, leading to torque and drag values that are miscalculated or misread. Currently, the soft-string model and the intermittent contact due to drillstring stiffness are used in torque and drag models. But they also have some limitations, such as the neglection of dimensional changes in the string components when assuming clearance of contact and the inability to fully model the irregularity of the actual well path when assuming complete contact throughout the wellbore. The suggested model is unique in its capacity to estimate precisely anticipate drillstring-wellbore contact forces and solve torque and drag parameters from surface to total depth using a conditional alternating use of the assumption that there is continuous contact of the wellbore wall and the drill string while turning to each lateral and clearance between the drill string and wellbore-wall when drilling through straight sections. The model shows how well path design calculation is done for multilateral wells. A non-constant curvature trajectory is built into the model. The unique procedure for calculating torque and drag in multi-lateral wells is explained with several actual field data tests.
Artificial Neural Networks for Geothermal Reservoirs: Implications for Oil and Gas Reservoirs
Authors: Dikeh Calista , Ikeokwu Chinaza , Egbe ThankGod Itua , Ochuba Murphy Nnamdi , Adekanye Moromoke , Anifowose Emmanuel , Okoroafor Esuru Rita
Published: August 2022

Subsurface numerical models take a significant time to build and run. For this reason, the energy industry has been looking towards proxy models that could reduce model computational time. With the advancement of artificial neural network algorithms, building proxy models has become more efficient, and has enabled quick forecasting and quick reservoir management decision-making.

In this study, we used a geothermal reservoir to evaluate the suitability of two deep learning algorithms, feed forward neural network and convolutional neural network, for proxy modeling. We used metrics such as the mean square error, losses, number of parameters for the model, and time to run, to compare the two deep learning algorithms.

From our study, we determined that the convolutional neural network resulted in less error than the feed forward network and used less hyperparameters. However, the feed forward network was significantly faster than the convolutional neural network. The process of building the proxy model shows how a similar approach can be followed for oil and gas reservoir modeling and demonstrates the feasibility of neural networks in subsurface reservoir modeling and forecasting.
Outcome Based Contracting: An Effective Approach to Achieving Cost Competitiveness in Well Construction Projects
Authors: Reginald-Ugwuadu Onyinye , Akanegbu Stanley , Eze Nnadozie , Ocho Uchechukwu , Okoronkwo Chuma
Published: August 2022

In the last decade, the volatility in the price of oil and gas have caused the industry to seek innovative ways of delivering profitable projects. This has been further challenged by safety and security risks and other considerations such as societal and political demands for an accelerated transition to a net-zero energy system. These complexities increasingly challenge energy companies to find ways of delivering projects within acceptable Value Investment Ratio (VIR), thus requiring that all components of the capital and operating expenditure are cost efficient.

According to EIA 2016 report, drilling and completions cost (including tangible and intangible costs) account for 60 – 80% of the total well cost across the onshore and offshore plays. Most well projects in onshore and shallow offshore fields in Nigeria are in remote and challenging environments with most equipment sourced abroad. Therefore, deploying innovative approaches to managing supply chain risks and aligning these with the overarching business objective is critical to achieving the desired value for both the operator and supplier.

This paper describes how the Operator developed its Outcome Based Contracting (OBC) model for Drilling and Completions Services as a fundamental lever in its Supply Chain transformation journey; highlighting the technical and commercial value realised in terms of capital efficiency and cost competitiveness both to the operator and supplier. OBC is a contracting model where the Operator pays the supplier for the outcome agreed and not the activities leading to the outcome. This approach inherently drives competitiveness as both parties partner to provide enablers in project scoping, technology and innovation as well as related supply chain strategies towards realistic and achievable outcomes. It is expected that this will contribute to the body of knowledge and adopted by other operators to improve their contracting process for maximum benefits to all parties.
Flare Gas to Energy Using Hydrogen Fuel Cell Solid Oxide Fuel Cells: The Nigerian Perspective
Authors: Ezechi Chinenye , Ndulue Chukwuemeka
Published: August 2022

Energy must be available and affordable to attain energy security as it is fundamental to human and economic development which drives virtually every aspect of the world economy. Globally, the energy demand is increasing; however, the increase is more significant for the African continent due to increased population, industrialization, and economic development. As a proactive measure, technology to meet the demand is crucial to find, develop, process and produce this energy.

One of the challenges of the African continent is gas flaring due to gas management solutions and cost-related issues. Based on the African Energy Portal, (Africa Energy Portal, 2020) Nigeria loses about $2.5 billion yearly from 178 flare sites (predominantly onshore sites) nationwide, ranking Nigeria the 7th country on the list of the most flared gas countries in the world. While this is a massive loss in revenue for the major stakeholders, it is also detrimental to public health.

While there are other complementary technologies available to help utilize more flared gas, the hydrogen fuel cell has proven to be more instrumental in the quest for acleaner and sustainable energy. With Europe as a frontline adopter, the sustainable energy benefits of a hydrogen fuel cell can be seen in its continuous development, deployment, and utilization in most of its countries. Africa, especially Nigeria has the potential to reduce gas flaring by 70%, (Africa Energy Portal, 2020) via the deployment and usage of fuel cells technologies in the conversion and utilization of flared gas to clean hydrogen gas, which can serve as alternative energy used in a wide range of applications across multiple sectors.

This paper focuses on the impact of gas flaring, and the application of evolving hydrogen fuel cell technology as a means of flare gas reduction and gas recovery through steam reforming of methane. It also discusses the constraints of implementing hydrogen fuel cells in Africa, using Nigeria's Oil and Gas Sector as a case study.
Production Restoration Following Long Term Community Crisis – A Case Study of Well X in ABC Field, Onshore Nigeria
Authors: Adedokun Adewale Philip , Adeoye Olanike , Eleluwor Esta , Oke Martins Oluwatobi , Ibiyomi Christopher , Okenwa Odira , Ubendu Obisike N. , Obi Ikenna
Published: August 2022

Following multi-disciplinary reviews, an opportunity was identified to restore production and unlock incremental reserves from well X, a dual completion in two different reservoirs but subsequently deserted due to long term community crisis that led to over 25years of non-production with complete vandalization of well head and flowlines.

The method employed involved the strategic resolution of long-term crisis between two communities where well X is located, via a multi-disciplinary effort involving the operating company’s Community Relations, HSSE, Production Engineering, Operations Support and Portfolio Management functions. The installation of a retrofitted well head was done with first line and second line maintenance carried out. Wireline drifting and static bottom hole pressures were acquired for both strings using slickline equipment and a preliminary well test was conducted for both strings with production to a flowback tank.

The preliminary result for the long string (LS) indicated a high water cut (>80%), while the result from short string (SS) was in line with expectation (<57%). The test result from the short string informed the decision to construct a new flowline for restoring its production, while further subsurface evaluation is required for the long string (LS). The significance of the short string (SS) result is the unlocking of additional reserves ca. 1.0MMSTB from a reservoir with remaining oil in place estimated at ca. 18MMSTB, where the short string (SS) is the only drainage point currently completed on the reservoir.

This solution provides a cost effective and efficient way to increasing production and reserves at minimal expenditure leveraging on multi-disciplinary expertise, using existing infrastructures as well as resolving community crisis, where applicable.
Mapping Relevant Petroleum Engineering Skillsets for the Transition to Renewable Energy and Sustainable Energy
Authors: Okoroafor Esuru Rita , Offor Chinyere Princess , Prince Ekeoma Isaac
Published: August 2022

In the last century, the global energy mix has been primarily dominated by fossil fuels. However, commitments to tackling climate change have accelerated the conversations on transitioning to a lower-carbon future. This is beginning to influence governmental policies, investors' and shareholders interests, workforce preferences, academic curriculum adaptations, and student choices. Given the coordinated approach from government, industry, and academia necessary to facilitate this transition, it becomes increasingly essential to map the relevant skillsets for the energy transition. This paper aimed to identify the skill sets oil and gas students and professionals have that will be relevant in advancing a renewable and sustainable energy future. The study focused on students and professionals within Nigeria to address the conditions unique to the country and other fossil-fuel-dependent developing and emerging countries.

First, a survey was conducted on students and young professionals to identify the knowledge gap in clean energy technologies and digitalization technologies. Subsequently, the skills oil and gas professionals have were mapped with the different renewable and sustainable energy technologies to identify overlaps between the oil and gas industry and low-carbon energy technologies.

The results showed that the technical competencies of oil and gas industry professionals were most relevant in carbon storage, hydrogen storage, and geothermal energy. On the other hand, non-core oil and gas competencies such as project management, HSE (health, safety, and environment), and business development skills, cut across all low carbon technologies. These competencies appeared to be more relevant for renewable energy resources like solar, wind, and hydropower. Data science and digital skills cut across all the new energy technologies. The main deliverables of this study were a skillset map and a progressive curriculum that embraces digitalization, entrepreneurship, and clean energy technologies.

This study provides a skillset map where students and professionals can identify their competency gaps for renewable and sustainable energy technologies. This will enable them (and organizations) to know how to develop upskilling and reskilling strategies and provide academia with insights on how to modify the current oil and gas engineering curriculum in universities. Although the paper drew on data-driven insights within Nigeria, the findings apply to schools and organizations globally.
Cost Optimization by Designing an Ultra-Slim Horizontal Well in the Niger Delta – The Eremor Field Case Study
Authors: Okoli Ugo' , Okwa Hope , Adebayo Segun , Mkpong Ifiok
Published: August 2022

Oil price volatility is one of the major drivers, which drive the decision of Operators to drill oil wells to further develop oil fields. A more significant constraint, which deals a huge blow on the Marginal Field Operators in the Niger Delta is the huge and ‘unavailable’ CAPEX associated to the delivery of these wells.

This paper elucidates how ‘detailed’ well design and optimization were used to design and deliver two swamp wells for a Marginal Field operator in the Niger Delta. With the application of detailed engineering and optimization processes, the well costs were reduced by over 50%.

The wells were initially designed, and to be delivered for circa $13MM per well, which is the P50 cost of drilling Swamp wells in the Niger Delta. However, post design optimization, the wells were designed and delivered for circa 6.5MM per Well.

The paper also details the drilling execution methods put in place to ensure that the wells designed were delivered efficiently.
Condensate Well Production Optimisation in Oredo Field Using Simulated Surface Proportional Integral Derivative Controller, Downhole Transmitter and Wellhead/Bottomhole Chokes
Authors: Dala Joshua , Akanji Lateef , Bello Kelani , Olafuyi Olalekan , Jadhawar Prashant
Published: August 2022

A method of optimising gas production from condensate well in Oredo field by simulating surface proportional integral derivative controller, downhole transmitter, wellhead and bottomhole chokes is presented. This method overcomes the potential risk of high backpressure imposed on the production tubing by manual choking or other control solutions using wellhead valve. Firstly, a model of Oredo well O7 is constructed with a closed node constituting the reservoir unit and a surface pressure node on the wellhead. An automated pressure integral derivative controller that senses and controls the bottomhole flowing pressure by actuating the wellhead choke is then installed at the wellhead. Measurement input to the auto-controller is delivered via an insitu transmitter. This design approach is successfully applied to the well O7 model through a commercial multiphase simulator on well models and provides a condensate banking monitoring mechanisms with improved production output.
The habit, choice, intention, and perception of raw beef consumers on raw beef-eating: the health risk management perspective
Authors: Gebeyehu Daniel Teshome , Alemu Biruk , Belete Gemechu
Published: August 2022

Apart from its nutritive value, meat is one of the substances for the transmission of pathogenic micro-organisms to consumers and the raw beef eating habit of Ethiopians can create a favourable condition for the transmission of pathogens from contaminated meat to raw beef consumers. The face-to-face interview of raw beef consumers was done using a structured questionnaire and 570 total samples were collected. A considerable number (74%) of raw beef consumers had favourable food choice; 85% of the raw beef consumers had favourable intentions to stop their raw beef eating habit, and 67% of them had an unfavourable perception of the safety of raw beef-eating. In conclusion, the study showed that raw beef consumers were not aware of the health risks of raw beef-eating. As a result, urgent sensitization intervention is required to shift the raw beef consumers from unhealthy eating habits to prudent (processed) eating practices.
Adapting to Climate Risks through Cross-Border Investments: Industrial Vulnerability and Smart City Resilience
Authors: An Yao , Liu Ning , Zheng Huanhuan , Zhang Lin
Published: August 2022

Climate change entails potential risks for investors, and its effects on investment has spread beyond physical borders. This study investigates how multinational corporations (MNCs) incorporate climate risks into their decisions regarding foreign direct investments (FDIs). We find that large differences in the climate risks of home and host cities discourages FDI by increasing cross-border adaptation costs. Such impacts are particularly pronounced among environmentally sensitive industries that are more exposed to climate risks. Further analysis reveals that city-based smartness factors mitigate the negative impacts of climate risk differences on FDI by reducing adaptation costs and engendering new business opportunities. This study provides new evidence on the profound effects of climate risks on FDI and how smart cities can increase their resilience to climate risks in the context of international business.
General Improvements versus Interruptive or Non-interruptive Alerts in the Blood Order Set: study protocol for a randomized control trial to improve packed red blood cell utilization
Authors: Mistry Neelam , Richardson Vanessa , Carey Evan , Porter Samuel , Pincus Sharon , Novins-Montague Sylvie , Elmer Megan , Lin Chen-Tan , Ho P Michael , Anstett Tyler
Published: August 2022

BackgroundBlood transfusions can serve as a life-saving treatment for patients, though inappropriate blood product transfusions can result in patient harm and excess costs for health systems. Despite published evidence supporting restricted packed red blood cell (pRBC) usage, many providers transfuse outside of guidelines. Here we report a novel prospective, randomized control trial to increase guideline-concordant pRBC transfusions comparing three variations of clinical decision support (CDS) in the electronic health record (EHR). MethodsAll inpatient providers at University of Colorado Hospital (UCH) who order blood transfusions are randomized in a 1:1:1 fashion to the three arms of the study: (1) general order set improvements; (2) general order set improvements plus non-interruptive in-line help text alert; (3) general order set improvements plus interruptive alert. Transfusing providers receive their same randomized order set changes for 18 months. The primary outcome of this study is a comparison is between the group using the new interface (arm 1) versus the two groups using the new interface with interruptive or non-interruptive alerts (arms 2 and 3, combined). The secondary outcomes compare guideline-concordant transfusion rate between arm 2 and arm 3 as well as comparing all of arms of the study in aggregate to historical controls. This trial is ongoing.DiscussionCDS tools can increase guideline concordant behavior. This trial will examine three different CDS tools to determine which type is most effective at increasing guideline concordant blood transfusions. Trial registration: Registered on ClinicalTrials.gov 3/20/21, NCT04823273, https://clinicaltrials.gov/ct2/show/NCT04823273. Approved by University of Colorado Institutional Review Board (19-0918).
An investigation of the creation of localized silicon quantum dots deposited on etched Si substrate by aggressive laser –assisted etching pathway
Authors: Rashid Rasha B , Alwan Alwan M. , Mohammed Mohammed S.
Published: August 2022

The current work discusses the deposition of localized silicon quantum dots (SiQDs) layer on etched Si substrate by aggressive laser– assisted etching pathway. The effect of elevated laser power density of (200, 300, 400 and 500 mW/cm 2 ) was investigated for SiQDs substrate by employing a short wavelength (530 nm) laser diode and a current density of 60mA/cm 2 . The structural and photoluminescence PL properties of the localized SiQDs and the horizontally oriented Si nano pillars-like structures of the synthesized layer were studied via the analysis of (FESEM) images, (XRD) patterns and PL spectra. The achieved results revealed a simple, easy and well-organized pathway for creating localized SiQDs layer by controlling the heating effect of the elevated laser power density. The histogram of dimensions and surface density of SiQDs within the deposited layer synthesized with 400 and 500 mW/cm 2 laser power density showed unique characteristics of high SiQDs surface density; owing to the maximum heat accumulation inside the laser substrate interaction area and the low tendency of probability of collapsing the dots. PL properties showed that laser power density has, specifically, modified the PL properties of the localized SiQDs by narrowing and increasing the PL intensity.
Comparative study of rainfall measurement by optical disdrometer, tipping-bucket rain gauge, and weighing precipitation gauge
Authors: Ro Yonghun , Chang Ki-Ho , Hwang Hyunjun , Kim Minhoo , Cha Joo-Wan , Lee Chulkyu
Published: August 2022

In this study, the rainfall measurement characteristics of an optical Particle Size Velocity (Parsivel) disdrometer, tipping-bucket rain gauge (TBG), and Pluvio weighing precipitation gauge (WPG) were analyzed and compared. Correlation analysis was performed between the 10-min and 1-h rainfall data observed with the Parsivel, TBG, and Pluvio from 2010 to 2019 at the Cloud Physics Observation Site which is located in north-east area of South Korea (N37.6869, E128.7586). At higher rainfall intensities, the Parsivel observed more rainfall; however, the TBG lost more rainfall during observation. The correlation between the Pluvio and Parsivel data was higher than that between the TBG and Parsivel data. Additionally, the Pluvio showed reduced loss in the rainfall observation than that by the TBG. The correlation between the Pluvio and TBG data was the highest, and the coefficient of determination increased by a maximum of 42.08% for 1-h rainfall compared to that for 10-min rainfall. Therefore, the Pluvio can generate relatively accurate rainfall data for water resource utilization.
Empirical evaluation of internal validation methods for prediction in large-scale clinical data with rare-event outcomes: a case study in suicide risk prediction
Authors: Coley Yates , Liao Qinqing , Simon Noah , Shortreed Susan M
Published: August 2022

Background: There is increasing interest in clinical prediction models for rare outcomes such as suicide, psychiatric hospitalizations, and opioid overdose. Accurate model validation is needed to guide model selection and decisions about whether and how prediction models should be used. Split-sample estimation and validation of clinical prediction models, in which data are divided into training and testing sets, may reduce predictive accuracy and precision of validation. Using all data for estimation and validation increases sample size for both procedures, but validation must account for overfitting, or optimism. Our study compared split-sample and entire-sample methods for estimating and validating a suicide prediction model. Methods: We compared performance of random forest models estimated in a sample of 9,610,318 mental health visits (“entire-sample”) and in a 50% subset (“split-sample”) as evaluated in a prospective validation sample of 3,754,137 visits. We assessed optimism of three internal validation approaches: for the split-sample prediction model, validation in the held-out testing set and, for the entire-sample model, cross-validation and bootstrap optimism correction.Results: The split-sample and entire-sample prediction models showed similar prospective performance; the area under the curve, AUC, and 95% confidence interval was 0.81 (0.77-0.85) for both. Performance estimates evaluated in the testing set for the split-sample model (AUC=0.85 [0.82-0.87]) and via cross-validation for the entire-sample model (AUC=0.83 [0.81-0.85]) accurately reflected prospective performance. Validation of the entire-sample model with bootstrap optimism correction overestimated prospective performance (AUC=0.88 [0.86-0.89]). Measures of classification accuracy, including sensitivity and positive predictive value at the 99th, 95th, 90th, and 75th percentiles of the risk score distribution, indicated similar conclusions: bootstrap optimism correction overestimated classification accuracy in the prospective validation set.Conclusions: While previous literature demonstrated the validity of bootstrap optimism correction for parametric models in small samples, this approach did not accurately validate performance of a rare-event prediction model estimated with random forests in a large clinical dataset. Cross-validation of prediction models estimated with all available data provides accurate independent validation while maximizing sample size.
Badland erosion susceptibility mapping using machine-learning data mining techniques, Firozkuh watershed, Iran
Authors: Mohammady Majid
Published: August 2022

Badlands are usually fluvial, with dissected V-shaped valleys, short steep slopes, and high drainage density, and results from a very important type of erosion that develops due to a complex interaction of conditioning factors, including climatic, hydrologic, geologic and soil properties, topographic characteristics, and land use. The main goals of this study were 1) create badland susceptibility maps of the Firozkuh watershed and five machine learning algorithms (models)—functional discriminant analysis (FDA), generalized linear model (GLM), mixture discriminant analysis (MDA), multivariate adaptive regression spline (MARS) and support vector machine (SVM); and 2) compare the accuracy of these models. Sixteen conditioning factors were chosen to model and classify badland susceptibility based on a literature review, data availability, and field surveys. Model accuracy was assessed using ROC curve and AUC analyses. The analyses showed that SVM was "excellent", MARS was "very good", MDA was "good", and GLM and FDA were "moderate" in classification accuracy. The land area of the very high and high classes ranged from 31–51% of the Firozkuh watershed for the GLM and SVM models, respectively. This indicates that badland erosion is a very important problem in the study area. Climatic, hydrologic, geologic, topographic and soil conditions as well as land use changes renders the Firozkuh watershed prone to badland formation and soil erosion which results in substantial socio-economic losses. Badland susceptibility mapping is an important tool that can be used to improve managing future badland erosion in the Firozkuh watershed and other areas affected by badland erosion.
Effect of prebiotic oligosaccharides on bowel habit and the gut microbiota in children with functional constipation (Inside study): study protocol for a randomised, placebo-controlled, multi-centre trial.
Authors: Wegh Carrie A.M. , Schoterman Margriet H.C. , Vaughan Elaine E. , Smidt Hauke , Belzer Clara , Benninga Marc A.
Published: August 2022

Background
Functional constipation (FC) in children is a common gastrointestinal disorder with a worldwide pooled prevalence of 9.5%. Complaints include infrequent bowel movements, painful defecation due to hard and/or large stools, faecal incontinence, and abdominal pain. Prebiotic oligosaccharides have been shown to relieve constipation symptoms in young adults and elderly. However, sufficient evidence is lacking linking additional prebiotic intake to improve symptoms in children with FC. We hypothesise that prebiotic oligosaccharides are able to relieve symptoms of constipation in young children as well.
Methods
In the present randomised, double-blind, placebo-controlled, multi-centre study, we will study the effects of two prebiotic oligosaccharides in comparison to a placebo on constipation symptoms in children of 1–5 years (12 to 60 months) of age diagnosed with FC according to the Rome IV criteria for functional gastrointestinal disorders. The primary outcome measure will be change in stool consistency. Secondary outcomes include stool frequency and stool consistency in number of cases (%). Tertiary outcomes include among others painful defecation, use of rescue medication, and quality of life; In addition the impact on gut microbiome outcomes such as faecal microbiota composition and metabolites will be investigated. Participants start with a run-in period, after which they will receive supplements delivered in tins with scoops for eight weeks, containing one of the two prebiotic oligosaccharides or placebo, followed by a 4-week wash-out period.
Discussion
This randomised double-blind, placebo-controlled multi-centre study will investigate the effectiveness of prebiotic oligosaccharides in children aged 1–5 years with FC.
Trial registration
: ClinicalTrials.gov NCT04282551.
Efficacy and safety of neostigmine on treating gastrointestinal dysmotility in severe acute pancreatitis patients: study protocol for a randomized controlled trial.
Authors: Sun Han , Sheng Yaqi , Du Tiekuan , Zhu Huadong
Published: August 2022

BackgroundAcute pancreatitis is a serious threat to human health and gastrointestinal dysmotility is a common complication for acute pancreatitis patients, resulting in delayed feeding, oral feeding intolerance, paralytic ileus and abdominal compartment syndrome. Currently, there are limited treatment for this complication.Neostigmine is known to increase gastrointestinal motility and has been used to treat gastrointestinal dysmotility after surgery. However, research in treating acute pancreatitis with neostigmine is currently limited.MethodsThis trial is a randomized, placebo-controlled, mono-centric trial that will test the hypothesis that neostigmine can improve gastrointestinal motility in patients with severe acute pancreatitis. Up to 56 patients will be randomized in this study receiving 0.5mg/1ml of neostigmine methylsulfate injection twice per day or 1ml of saline injection twice per day. Defection time (aim 1), borborygmus and intra-abdominal pressure (aim 2), and length of ICU and hospital stay (aim 3) will be assessed.DiscussionFindings from this study will provide data supporting the usage of neostigmine for treating severe acute pancreatitis patients with gastrointestinal dysmotility.Trial registrationThis study is registered on chictr.org.cn with the identifier as ChiCTR2200058305. Registered on April 5, 2022.
4 Fitness Dependent Optimizer for IoT Healthcare using Adapted Parameters: A Case Study Implementation
Authors: Aladdin Aso M. , Abdullah Jaza M. , Salih Kazhan Othman Mohammed , Rashid Tarik A. , Sagban Rafid , Alsaddon Abeer , Bacanin Nebojsa , Chhabra Amit , Vimal S. , Banerjee Indradip
Published: August 2022

This discusses a case study on Fitness Dependent Optimizer or so-called FDO and adapting its parameters to the Internet of Things (IoT) healthcare. The reproductive way is sparked by the bee swarm and the collaborative decision-making of FDO. As opposed to the honey bee or artificial bee colony algorithms, this algorithm has no connection to them. In FDO, the search agent's position is updated using speed or velocity, but it's done differently. It creates weights based on the fitness function value of the problem, which assists lead the agents through the exploration and exploitation processes. Other algorithms are evaluated and compared to FDO as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the original work. The key current algorithms—The Salp-Swarm Algorithms (SSA), Dragonfly Algorithm (DA), and Whale Optimization Algorithm (WOA) have been evaluated against FDO in terms of their results. Using these FDO experimental findings, we may conclude that FDO outperforms the other techniques stated. There are two primary goals for this chapter: first, the implementation of FDO will be shown step-by-step so that readers can better comprehend the algorithm method and apply FDO to solve real-world applications quickly. The second issue deals with how to tweak the FDO settings to make the meta-heuristic evolutionary algorithm better in the IoT health service system at evaluating big quantities of information. Ultimately, The target of this chapter's enhancement is to adapt the IoT healthcare framework based on FDO to spawn effective IoT healthcare applications for reasoning out real-world optimization, aggregation, prediction, segmentation, and other technological problems.
Shelter identification for shelter-transporting AGV based on improved target detection model yolov5
Authors: Yang Dian , Su Chen , Wu Hang , Xu Xinxi , Zhao Xiuguo
Published: August 2022

Shelter identification is the fundamental issue to make shelter-transporting automated guided vehicle (AGV) effectively detect and transport shelter. Actively identifying shelter has an important problem of high accuracy but slow speed for a complex model, and fast speed but low accuracy for a simple model. However, all kinds of target detection algorithms available have low detection accuracy and speed. In this paper, the model yolov5n6* is developed based on the modified yolov5 model by selecting different model structures, introducing an attention mechanism, and improving loss function and non-maximum suppression (NMS). Then, the experiments for shelter recognition were carried out using the model yolov5n6*. The experimental results show that the box_loss is reduced by 1.2%, the mAP_0.5:0.95 is improved by 2%, and the detection accuracy is improved by 0.87% for the improved model yolov5n6* compared with the yolov5n6. However, the yolov5n6* size is only 7.2M, and the detection time is increased by 0.2ms. So it is proved that the modified model yolov5n6* not only has a significant improvement in the shelter detection ability but also has strong robustness, which meets both the requirements of the recognition accuracy and the detection speed.
Multicriteria seismic risk assessment in Puerto Vallarta metropolitan area, Mexico
Authors: Jaimes Diana L. , Escudero Christian R , Flores Karen L. , Zamora-Camacho Araceli
Published: August 2022

The destructive effects of an earthquake are enhanced when the population is not prepared. The experiences from the past highlighted the importance of proper planning based on adequate scientific data. The Puerto Vallarta metropolitan area (PVMA) comprises medium-sized, rapid-growing urban areas of independent administrative and political entities that maintain a constant, direct socioeconomic interrelation. The PVMA is located in a region with a complex tectonic setting that produces important seismic activity and is affected by beaches, rivers, and estuary systems that produce complex soils and subsoil conditions. Moreover, the PVMA constantly undergoes anthropogenic processes that modify the local geomorphology and produce improper urbanization. Therefore, the PVMA constitutes an ideal natural laboratory to implement and test techniques to estimate seismic hazard, social vulnerability, and seismic risk. These techniques can be later implemented in other similar cities around the world. To determine the seismic hazard, social vulnerability, and seismic risk of the PVMA, we implemented the Multicriteria Evaluation Method within a Graphical Information System considering geomorphological (i.e., bedrock, soil, slope, curvature, flow accumulation), geophysical (peak ground acceleration, shear velocity, vibration frequency), and social information (population density, age, disabilities, health access, housing, and economic activity). We estimate the seismic hazard, social vulnerability, and seismic risk by considering three possible earthquakes. The results indicate a heterogeneous distribution of seismic risk with levels between moderate to high. Finally, a seismic risk microzonation in terms of the percentages of the seismic risk levels is proposed for the Puerto Vallarta metropolitan area.
Susceptibility evaluation of highway landslide disaster s based on SBAS-InSAR: A case study of S211 Highway in Lanping County
Authors: Li Yimin , Ji Peikun , Liu Shiyi , Zhao Juanzhen , Yang Yiming
Published: August 2022

Evaluation of landslide susceptibility along highways is critical for risk management in engineering development, construction, and operation and maintenance. The research target is the S211 Highway in Lanping County, Nujiang Prefecture, Yunnan Province, with its buffer zone extending 10 km as the research area. Eight evaluation factors are selected for the study, including slope, slope aspect, vegetation coverage, distance from the water system, rock group, rainfall, distance from the fault, and elevation. The findings of the susceptibility evaluation were classified into five categories, and the susceptibility grades of landslide disasters in the study area were evaluated using the information value and logistic regression coupling model. The accuracy of the coupling model was evaluated by the ROC curve and AUC value. The deformation rate in the study area was estimated by processing 28 Sentinel-1A satellite images captured from January to December 2019 using the SBAS-InSAR technology and was used to optimize the landslide susceptibility grade. The results show that the extremely high and high-risk areas of the information value-logistic regression coupling model account for 28.33% of the total area of the study area, which constitutes nearly 83.82% of the historical landslide disaster sites, mainly occupying areas along highways with low vegetation coverage and within 2000 m from rivers. The AUC values in the accuracy verification reach 0.843, indicating that the evaluation model can accurately predict the landslide susceptibility. The vulnerability grade of landslide geological disaster in the entire evaluation unit is significantly increased by optimizing the result of the surface deformation obtained by SBAS-InSAR technology. A total of 79587 grid units were added to the area, resulting in an extremely high vulnerability grade. This technique may optimize the evaluation results of landslide hazard susceptibility and provide decision support for disaster prevention and maintenance along highways.
New catalytic mechanism replaces serine protease mechanism in antigen cleavage by catalytic antibody
Authors: Hifumi Emi , Nonaka Tamami , Taguchi Hiroaki , Uda Taizo
Published: August 2022

The cleavage reactions of catalytic antibodies are mediated by a serine protease mechanism involving a catalytic triad composed of His, Ser, and Asp residues, which reside in the variable region. Recently, we discovered a catalytic antibody, H34 wild type (H34wt), that is capable of enzymatically cleaving an immune-check point PD-1 peptide and recombinant PD-1; however, H34wt does not contain His residues in the variable region. To clarify the reason behind the catalytic features of H34wt and the amino acid residues involved in the catalytic reaction, we performed site-directed mutagenesis focusing on the amino acid residues involved in the cleavage reaction, followed by catalytic activity tests, immunological reactivity evaluation, and molecular modeling. The results revealed that the cleavage reaction by H34wt proceeds through the action of a new catalytic triad composed of Arg, Thr, and Gln. This new mechanism replaces the serine protease mechanism of catalytic antibodies.
Preoperative umeclidinium/vilanterol and tiotropium improves postoperative FEV1 in lung cancer patients with complicated untreated chronic obstructive pulmonary disease
Authors: Homma Takahiro
Published: August 2022

Background: This study aimed to examine the effects of preoperative interventions in lung cancer patients with untreated chronic obstructive pulmonary disease (COPD). This study evaluated the efficiency of preoperative interventions using tiotropium or umeclidinium/vilanterol (UMEC/VI) and included more patients than in previous studies on this topic.Methods: We conducted a two-center retrospective study. Perioperative forced expiratory volume in 1 second (FEV1) was compared between a preoperative COPD intervention group and an untreated group. COPD therapeutic drugs were started 2 weeks before surgery and were continued until 3 months after surgery. Radical lobectomy was performed in patients with an FEV1 of ≥1.5 L. Results: A total of 92 patients were enrolled (untreated, 31 patients; intervention, 61 patients). In the intervention group, 45 (73.8%) patients were prescribed the UMEC/VI intervention and 16 (26.2%) received TIO. The intervention group showed a greater increase in FEV1 than the untreated group (FEV1: 120 vs. 0 mL, P = 0.014). In the intervention group, the UMEC/VI group showed a greater increase in FEV1 than the TIO group (FEV1: 160 vs. 7 mL, P = 0.0005). In 9 of 15 (60.0%) patients with an FEV1 of <1.5 L before intervention, FEV1 increased to ≥1.5 L after intervention. Postoperative FEV1 in the intervention group was similar to that before intervention, unlike in the untreated group (−0.05 vs. −0.25 mL, P = 0.0026). Moreover, the FEV1 in the untreated group was similar to the preoperative predicted value, whereas that in the intervention group was significantly higher than the predicted value (+0.33 vs. +0.04 mL, P < 0.001)Conclusions: In lung cancer patients with untreated COPD, aggressive preoperative intervention improved respiratory function, expanded treatment options, and maintained respiratory function to a degree that exceeded preoperative predictions. Trial registration: retrospective registered