Safety Integrity Research Articles

Integrated safety for sea and air transportation during the COVID-19 pandemic

Climate change and COVID-19 have disrupted food supply chains and exacerbated food security challenges (Rasul, 2021). The safety of sea and air transportation in food delivery is also a disruption that impacts logistics in the food value chain (Singh et al., 2021). Integration of sea and air transportation safety during the COVID-19 pandemic, with a focus on the impact of climate change on food supplies and the rapid spread of viruses through air travel, is essential to ensure that food distribution is not disrupted. The research investigates the response of countries and major international organizations such as the World Health Organization (WHO), the International Maritime Organization (IMO), and the International Civil Aviation Organization (ICAO). The methodology used in the study was a systematic literature review (SLR) by identifying 875 journals in the first stage. Then, through strict screening, 50 articles were selected and further analyzed. Covering continents, specific countries, and important ports, the research uses quantitative databases such as the Scopus journal index. The study results reveal the long-term impact of the COVID-19 pandemic on the aviation and maritime sectors and its influence on global passenger and goods transportation. SLR’s findings also bring together insights from diverse research on safety integration across different modes of transportation during the pandemic. Research provides recommendations — climate-related changes for safe transportation, applicable in normal and pandemic circumstances. In conclusion, this article comprehensively explains maintaining a safe and efficient transportation network amidst complex challenges.

Highly stretchable ionic hydrogels with enhanced thermoelectric performance and flame retardancy for intelligent fire protection

This work highlights the synergistic integration of mechanical flexibility, thermoelectric performance, and fire safety in the development of next-generation intelligent materials for fire protection.

Buyer Beware: Misinformation in the Healthcare Industry – What Can Clinicians Do?

In today's healthcare landscape, the exchange of medical information between physicians and industry is crucial for advancing knowledge and improving patient care. However, this collaboration also introduces challenges, including the potential for bias and misinformation. Amidst efforts to navigate the post-COVID-19 healthcare environment and maintain market competitiveness, the prevalence of misinformation has become increasingly evident (Ecker, 2022). This article explores the impact of misinformation in the medical industry, particularly concerning its drivers and persistence. Through an analysis of psychological and behavioral factors, the authors identify fear as a primary driver of bias, leading to the dissemination of inaccurate information (Tannenbaum, 2015). Additionally, deliberate efforts to manipulate narratives and facts contribute to the endurance of misinformation, posing significant risks to patient safety and healthcare integrity (Ecker, 2022). The authors highlight various methods employed to deliver misinformation, including social media, professional collaborations, and selective data sharing. These tactics obscure truth and hinder informed decision-making among healthcare practitioners (Rea, 2017). Despite the challenges posed by misinformation, the authors propose practical solutions for clinicians, emphasizing the importance of research, critical analysis, and collaboration with trusted sources to verify information and safeguard patient care (Wu, 2018). This comprehensive exploration of misinformation in the healthcare industry underscores the critical role of healthcare practitioners in combatting false narratives and ensuring the integrity of medical information. By equipping clinicians with the tools to discern truth from misinformation, this article aims to promote evidence-based practice and uphold the highest standards of patient care.

The landscape of decentralized clinical trials (DCTs): focusing on the FDA and EMA guidance.

Decentralized clinical trials (DCTs) consist of off-site trial-related procedures referred to as decentralized elements. We aimed to provide an overview of the landscape of DCTs by comparing regulatory guidance reports and analyzing decentralized elements from clinical trial registries. Two guidance reports on DCTs published by the U.S. Food and Drug Administration and the European Medicines Agencies were summarized and analyzed. Both guidance publications commonly emphasized an assessment of the appropriateness of decentralized elements along 2 axes: patient safety and data integrity. DCT cases were identified from ClinicalTrials.gov by searching with 6 keywords: decentralized, remote, mobile, digital, virtual, and hybrid. Cases where the keyword was used in a non-DCT context, such as digital flexor tendon, were excluded by means of natural language processing. A total of 4,874 trials were identified as DCT cases, with annual increases, especially after 2020. The most common keywords were 'mobile' and 'digital' (36.2% and 24.8%, respectively). Interventions in the DCT cases were analyzed by means of a network analysis. Behavioral and technological tokens were frequently combined, such as 'rehabilitation' and 'app.' Drugs were used in only 1.8% of the DCT cases. Of these, most drugs had been approved previously (96.8%) and were in oral formulation (67.2%). Most of the DCT cases identified in this study involved simple interventions and low-risk drugs. These characteristics were in accordance with the common recommendations in the DCT guidance publications.

Exact and heuristic solution approaches for the multi-objective AGV scheduling problem with battery constraints

Automated Guided Vehicles (AGVs) are self-driving vehicles used for transporting goods and materials throughout different areas, such as shipping and receiving areas, storage facilities, and workstations. The popularity of AGVs has grown rapidly due to their many benefits, including flexibility in processes, efficient use of space, product safety, and computer integration and control. The design and management of AGV-based systems present various tactical decisions, such as fleet sizing and flow path design, as well as operational decisions such as dispatching, routing, and scheduling.In this study, we focus on a scheduling problem faced by a manufacturing company that utilizes AGVs subject to battery constraints for the horizontal movement of materials. Unlike previous studies, we investigate a Multi-objective AGV scheduling problem where both the AGV fleet size and the makespan need to be minimized. Additionally, AGV charging time depends on battery depletion. To support the company decision-making system, we propose a Mixed Integer Linear Programming formulation and a genetic algorithm. We have tested and validated our proposed methods using real-world instances provided by a manufacturing company. The results show the efficacy and the scalability of the proposed solution methods.

Analytical Assessment of Composite L Angle Strength Under Internal Fuel Pressure in Composite Wing

The Interspar (IS) box stores the turbine fuel in the composite wing of an Aircraft. The filled fuel is under pressure that induces out-of-plane stresses on all the wing’s structural members around the fuel tank’s boundary. The wing is designed using carbon fiber reinforced plastic (CFRP) with the co-curing of the bottom skin and its stringers along with front and rear spars. A suitable number of cut-outs are made on the top and bottom sides of the IS ribs for the stringer to pass through them. The global finite element analysis showed that the stringer cut-out regions in the IS ribs are highly stressed, as they are subjected to out-of-plane load due to internal fuel pressure, which induces transverse tensile stress in the cut-out region. Resin is the weakest material in the composite laminate, but it has to resist the high-stress component due to out-of-plane loads. The top and bottom flanges of the ‘C’ section IS ribs geometrically form an L angle section with a stringer cut-out made in it. At the L angle location, the IS ribs are critical from both the strength and stiffness point of view. Strength and stiffness at the interface of the fastener joint are essential factors in maintaining the composite wing’s overall structural integrity under internal fuel pressure. The authors adopted the same standalone finite element (FE) modelling approach developed for the Bond Energy Method and carried out appropriate analytical studies to ascertain the safety and structural integrity of the composite wing under internal fuel pressure. This article presents the analytical study carried out on the standalone FE model of an L angle with the stringer cut-out introduced at its appropriate position. The static stress analysis is carried out by enforcing the global displacement on this standalone FE model and extracting three force components in CBAR beam elements representing the resin property in the transverse direction of the composite laminate. The stresses are calculated outside the analysis deck using Excel® spreadsheets. The maximum Normal Stress (σn) in the L angle flange is found to be 13.25 MPa, which is well within the resin or transverse tensile strength of the composite laminate 53 MPa, and the shear stress is found to be 11.10 MPa, which is less than the resin shear strength property of 23 MPa. Hence the study showed that the L angle is safe for carrying internal fuel pressure of 12.5 PSI. The study also showed that the strain and displacement values distribution in the standalone FE model are comparable with that of the global wing box model within the range of (8-10) % and (4-5) % difference, respectively.

Hyperspectral identification of oil adulteration using machine learning techniques

Food adulteration is a global concern, drawing attention from safety authorities due to its potential health risks. Detecting and categorizing oil adulteration is crucial for consumer safety and food industry integrity. This research explores hyperspectral imaging (HSI) analysis to identify substandard oil adulteration at different stages. Using the non-destructive HSI Specim Fx 10 system, a method for precise and easy imaging-based fraud detection and classification was proposed. The 670 oil samples, including pure (Almond, Mustard, Coconut, Olive) and adulterated (Sunflower, Castor, Liquid Paraffin), were analyzed. The Savitzky-Golay filter preprocessed the images to remove noise and smooth spectral signatures. The oils were identified using various machine learning approaches, including Support Vector Machines, Logistic Regression, Linear Discriminant Analysis, Random Forests, Decision Trees, K-Nearest Neighbors, and Naïve Bayes with Linear Discriminant Analysis excelling in identification. Performance parameters, including precision, recall, F1-score, and overall accuracy, were calculated. The proposed method achieved a validation accuracy of 100%, outperforming numerous state-of-the-art approaches. This study introduces a robust pipeline for effective oil adulteration detection, offering a significant advancement in food safety and quality control.

Energy-Efficient Functional Safety Design Methodology Using ASIL Decomposition for Automotive Cyber-Physical Systems

Automotive cyber-physical systems (ACPS) are typical cyber-physical systems because of the joint interaction between the cyber part and physical part. Functional safety requirement (including response time and reliability requirements) for an ACPS function must be assured for safe driving. Auto industry is cost-sensitive, power-sensitive, and environment-friendly. Energy consumption affects the development efficiency of the ACPS and the living environment of people. This paper solves the problem of optimizing the energy consumption for an ACPS function while assuring its functional safety requirement (i.e., energy-efficient functional safety for ACPS). However, implementing minimum response time, maximum reliability, and minimum energy consumption is a conflicting problem. Consequently, solving the problem is a challenge. In this paper, we propose a three-stage design process toward energy-efficient functional safety for ACPS. The topic problem is divided into three sub-problems, namely, response time requirement verification (first stage), functional safety requirement verification (second stage), and functional safety-critical energy consumption optimization (third stage). The proposed energy-efficient functional safety design methodology is implemented by using automotive safety integrity level decomposition, which is defined in the ACPS functional safety standard ISO 26262. Experiments with real-life and synthetic ACPS functions reveal the advantages of the proposed design methodology toward energy-efficient functional safety for ACPS compared with state-of-the-art algorithms.

Influence of Different Construction Methods on Lateral Displacement of Diaphragm Walls in Large-Scale Unsupported Deep Excavation

This study examines the influence of different construction methods on the lateral displacement of diaphragm walls in large-scale, unsupported deep excavations. Using the three-dimensional finite element method (FEM) with PLAXIS 3D 2017 software, the research assesses how varying construction techniques impact wall stability, particularly in proximity to sensitive structures like metro systems. The project uniquely integrates peripheral top-down and central bottom-up approaches to minimize environmental disruption. Key focus areas include the roles of back-pull slabs, zoned excavation, and cross walls in reducing wall deformation. Findings reveal that zoned excavation significantly controls lateral displacement on longer site sides, enhancing adjacent structure safety and overall construction integrity. Back-pull slabs are shown to effectively decrease top wall deformation, thereby increasing structural stiffness. Moreover, despite their considerable length (nearly 60 m), cross walls play a crucial role in controlling lateral deformation along the excavation’s length. These insights offer valuable guidance for future projects, especially in regions like Taiwan, where experience with such large-scale, unsupported excavations is limited.

A mathematical optimization model for determining safety integrity levels in process facilities

In process industries, Safety Instrumented Functions (SIFs) are implemented to meet government-mandated risk tolerance criteria, which aim to ensure the safety of both individuals and society. The level of risk reduction achieved by a SIF is quantified by its Safety Integrity Level (SIL). Determining the target SILs is a challenging task for facility owners as they must balance the costs and benefits of risk reduction while complying with regulatory requirements. Furthermore, governments define risk tolerance criteria for an overall facility rather than for single scenarios. Therefore, determining a set of SILs for all SIFs becomes imperative to collectively reduce both individual and societal risks to tolerable levels intended for the overall facility. In this paper, we propose a mathematical optimization model to determine the most beneficial collection of SILs that guarantees meeting the risk tolerance criteria. Our model considers both individual and societal risk perspectives and effectively models an overall facility. The proposed model is applied to a process facility and the results are compared with the results from the existing methods for SIL determination. The results demonstrate the model's superior effectiveness in helping owners to select the most beneficial target SILs while ensuring compliance with government requirements.

Optimization of safety instrumented system performance and maintenance costs in Algerian oil and gas facilities

Gas processing industry is associated with high risks which have the potential to cause catastrophic accidents. Safety Instrumented Systems (SISs) are considered the most effective safety barriers in this sector aiming to prevent undesired events and mitigate their consequences. However, several factors can affect their performance including maintenance strategy in place. In this paper, a Stochastic Petri Net (SPN) model is proposed for evaluating maintenance strategies related to SIS. The model is applied to analyze the performance of an Emergency Shutdown System (ESD) in a Flared Gases Recovery Unit located at south Algerian field. Furthermore, the paper investigates the financial impact of proof tests, including direct costs (such as manpower, equipment, and transportation) and indirect costs (such as production losses and gas flaring tax). These costs can be effectively managed and reduced by optimizing proof test intervals and scheduling tests during planned plant shutdowns. The results demonstrate that the proposed Stochastic Petri Net (SPN) model successfully analyzes the impact of imperfect full and partial proof tests on PFD average values of Safety Instrumented Functions (SIFs). In addition, it has been shown through reliability analysis that the proposed model is able to minimize spare parts expenses leading to significant cost savings while maintaining the required safety integrity levels (SILs), about 60% benefit achieved within two years compared to the actual procurement process over same period.

Parametric Automated Design and Virtual Simulation of Building Machine Using BIM

With the continuous development of the construction technology of the main structure of high-rise buildings, traditional construction techniques have been widely used in high-rise building projects. However, these technologies have problems such as low safety, low intelligence, and poor integrity. In order to solve these challenges, the Third Engineering Bureau of China Construction independently developed a new type of construction technology—a high-rise building integrated work construction platform (referred to as a building machine). Building machines has been gradually applied to high-rise building projects due to its intelligence, integration, safety, and other advantages. However, with the increase in the number of high-rise building projects, the traditional way of designing building machine layout programs is inefficient and the design program is complicated to change. To solve these problems, this paper proposes a building machine parametric design and layout and virtual simulation method. This method uses Blender open-source software to model the building machine parametrically and quickly using its visual programming tool Geometry Nodes (GN) and simulates the building machine in the virtual scene through the Unity3D platform. The results show that, compared with the traditional design mode, the method proposed in this paper can quickly complete the scheme design in the design phase of the building machine and display it through the 3D model, which has a better visualization effect, improves the design efficiency, and reduces the design cost, and the Unity simulation platform can also provide the construction personnel with pre-shift education and simulation of the operation of the building machine. This method provides a theoretical basis and guidance for the digital construction of the building machine.

Accelerating the Adoption of New and Emerging Therapies for DLBCL: A Community-Based Quality Improvement Initiative

Background Remarkable progress has been made in the treatment and management of DLBCL. With a rapidly evolving landscape and novel treatment options, support is needed for community oncologists to guide integration of the latest safety and efficacy data into existing treatment planning. In this quality improvement (QI) initiative, we undertook an innovative practice transformation model bringing together providers from 4 community-based oncology clinics to assess current practice patterns and develop strategies to overcome barriers to optimal care for patients with relapsed/refractory DLBCL. Methods Between 8/2022-11/2022, 75 hematology/oncology clinicians completed surveys designed to evaluate practice patterns, challenges, and barriers to patient-centered care (Table 1). HCP cohorts within each oncology clinic participated in 1-hour live small-group audit-feedback (AF) sessions in which the teams reviewed the survey data, reflected on their own practice patterns and developed action plans to close identified DLBCL care gaps. Additional surveys conducted before and after the AF sessions evaluated changes in participants' knowledge and confidence in delivering quality care. Results Baseline Provider Surveys: HCPs reported identifying when patients have shifted to a more progressive disease course (31%) and selecting optimal therapies for individual patients (20%) as the biggest challenges in selecting therapies for DLBCL patients. The most challenging issues in managing DLBCL patients were supportive care counseling (28%) and recognizing and managing adverse events (20%). In addition to clinical guidelines, the other factors HCPs identified as most important for treatment decision-making for patients with DLBCL were the effects on quality of life (53%) and treatment effectiveness (48%). In terms of confidence, 23% reported very high confidence in the team's ability to stay up-to-date with new data on DLBCL treatment advances, and 29% reported very high confidence in aligning their clinical practices with evidence-based DLBCL guidelines. When asked about discussing novel treatment options with patients, 8% do not discuss, while 36% briefly discussed, and 56% discussed in detail. Moreover, 36% of HCPs reported low/very low confidence in identifying patients eligible for treatment with novel therapies, and 43% had low/very low confidence in navigating access to incorporate novel therapies into individual DLBCL treatment plans. To improve DLBCL care, only 3% of HCPs felt completely educated on the management of DLBCL, with 44% wanting to improve efficiency and ease of obtaining insurance approval for novel therapies and 44% seeking education on identifying patients eligible for newer therapies. Small-Group AF Sessions: In assessing HCPs' knowledge impact, only 35% correctly selected T-cell as the other targeted cell, in addition to tumor cells, for the mechanism of bispecific antibodies in DLBCL (Figure 1). Although this was improved compared to the 26% correct prior to the activity, more education is needed to close this knowledge gap. HCPs plan to address the following challenges: employing strategies to better identify and manage adverse events of novel therapies for DLBCL (79%), enhancing knowledge of clinical evidence for new and emerging therapies for DLBCL (71%), and developing personalized treatment plans incorporating patient-specific factors and preferences (68%). To achieve these goals, HCPs committed to improve patient communication and education, expand treatment access, engage patients by working to understand patient-specific considerations related to cellular therapies, and to educate and train staff in managing adverse events. Conclusions Barriers to providing patient-centered care in DLBCL identified in this QI initiative include staying up-to-date with novel therapies, recognizing and managing adverse events, and identifying patients eligible for novel therapies. After participating in this QI initiative, HCPs developed action plans to address the identified gaps and reported meaningful confidence gains in delivering DLBCL care. Practice gaps uncovered in this initiative can inform future QI programs. Study Sponsor Statement The study reported in this abstract was funded by an independent educational grant from Genmab US, Inc. and Kite Pharma, Inc. The grantors had no role in the study design, execution, analysis, or reporting.

Finding a Needle in a Haystack: Faint and Small Space Object Detection in 16-Bit Astronomical Images Using a Deep Learning-Based Approach

With the increasing interest in space science exploration, the number of spacecraft in Earth’s orbit has been steadily increasing. To ensure the safety and operational integrity of active satellites, advanced surveillance and early warning of unknown space objects such as space debris are crucial. The traditional threshold-based filter for space object detection heavily relies on manual settings, leading to limitations such as poor flexibility, high false alarm rates, and weak target detection capability in low signal-to-noise ratios. Therefore, detecting faint and small objects against a complex starry background remains a formidable challenge. To address this challenge, we propose a novel, intelligent, and accurate detection method called You Only Look Once for Space Object Detection (SOD-YOLO). Our method includes the following novel modules: Multi-Channel Histogram Truncation (MHT) enhances feature representation, CD-ELAN based on Central Differential Convolution (CDC) facilitates learning contrast information, the Space-to-Depth (SPD) module replaces pooling layer to prevent small object feature loss, a simple and parameter-free attention module (SimAM) expands receptive field for Global Contextual Information, and Alpha-EIoU optimizes the loss function for efficient training. Experiments on our SSOD dataset show SOD-YOLO has the ability to detect objects with a minimum signal-to-noise ratio of 2.08, improves AP by 11.2% compared to YOLOv7, and enhances detection speed by 42.7%. Evaluation on the Spot the Geosynchronous Orbit Satellites (SpotGEO) dataset demonstrates SOD-YOLO’s comparable performance to state-of-the-art methods, affirming its generalization and precision.

Application of Smart Overpressure Protection Devices to batch and semi-batch processes: The zero-emission mode

Chemical reactors in batch- and semi-batch processes are mainly protected by mechanical safety devices or safety integrity systems against critical runaway reactions. Both devices have in common, to limit with their fixed set conditions and discharge areas the productivity and flexibility of chemical plants. Besides, the trend in the industry is to reduce/avoid toxic and explosives to be discharges trough safety devices due to the increased requirements for climate- and environmental protection. A new idea in that regard is to connect the advantages of mechanical safety devices and safety integrated systems in so called smart overpressure protection devices (SmOP). Here, a predictive CSE conversion model (PreCoM) is online tracking the hazard potential of a chemical reaction and defines individual shutoff limits for the reaction. Its zero-emission mode ensures flexible, reaction independent application without fluid discharge. The smart relief mode is maximizing the plant utilization with individual shutoff limits and is minimizing the pollution with controlled discharge areas. In this article, the SmOP concept and the utilization of the PreCoM model for two different reactions and three industrial use cases for zero-emission application is being presented.

Systematic review on contract-based safety assurance and guidance for future research

The safety requirements are often described via specifications called contracts. To verify that the system fulfils certain safety requirements, for instance, in the assume-guarantee contract specification, the key safety indicators are organized, so that if certain assumptions hold then the respective behaviour is guaranteed. Safety contracts provide a means of exposing potential incompatibilities early in the development process, selecting components to reuse, certifying systems, and identifying uncertainty sources during the operational phase. There exist several studies on contract-based safety assurance, however, there is not any systematic study in this field. For this, a first Systematic Literature Review (SLR) is carried out to obtain an overview of the various contract-based safety assurance concepts, problems, proposed solutions, and their usefulness. In our study, the identification and selection of the primary studies were based on a well-planned search strategy. The search process identified a total of 2881 studies published between 1969 and 2021, out of which 66 studies were selected through a multi-stage process according to our predefined SLR protocol. This SLR aims to highlight the state-of-the-art of contract-based safety assurance and identify potential gaps for future research. Based on research topics in selected studies, we identified the following main categories: contract type, analysis techniques for system safety, compliance with standards, development stage, domain, level of automation, type of study and evaluation, and tool support. The findings of the systematic review not only highlight that the contracts are even more important for advanced safety-critical systems but also strategies to exploit their full potential should be considered in future studies. The suggestions revealed for future research include the usage of contracts for adapting new behaviour, defining system boundaries, interacting with other systems, managing risk during operation, dynamic/runtime safety assurance, and integration of safety with security.

Impact of ground surface subsidence caused by underground coal mining on natural gas pipeline

Underground mining of minerals is accompanied by a change in the rock mass geomechanical situation. This leads to the redistribution of stresses in it and the occurrence of unexpected displacements and deformations of the earth's surface. A significant part of the civil and industrial infrastructure facilities are located within the mine sites, where mining and tunneling operations are constantly conducted. Irrational planning of mining operations can lead to loss of stability and destruction of undermined facilities. Therefore, it is important to study the earth’s surface deformation processes during mining operations, which ensures safe and sustainable operating conditions. The research objective of this paper is to analyse the behaviour of a natural gas pipeline under the influence of underground mining activities, with a particular focus on understanding the effects of horizontal surface deformations and their potential impact on pipeline safety and structural integrity. Its performance and safety are determined on the basis of the found parameters of the earth's surface horizontal deformations and their comparison with permissible parameters characterizing the conditions for laying pipelines, depending on the mining-geological conditions and the degree of their undermining. Based on determined conditions for the safe undermining of the natural gas pipeline, it has been revealed that in its section between the PK212+40 and PK213+80 (140 m) pickets, the estimated parameters of the earth's surface horizontal deformations exceed their permissible values. This can cause deformation and damage to the pipeline. For the safe operation of the pipeline during the period of its undermining, in order to eliminate the hazardous impact of mining the longwall face, additional protection measures must be applied. It is therefore recommended that the gas pipeline between the PK212 and PK214+20 pickets be opened prior to the displacement process (200 m from the stoping face), thus reducing the density of the gas pipeline-soil system. Recommendations for controlling the earth’s surface deformations within the natural gas pipeline route are also proposed, which will ensure premature detection of the negative impact of mining operations.

Carbon fiber comparison to traditional materials for the automotive industry

As automotive technology continues to advance; the industry is confronted with the ongoing challenge of improving car performance while maintaining safety and structural integrity. This long-standing challenge in the field of automotive technology may soon find a solution through the emergence of carbon fiber composite materials, potentially ushering in a new era of material dominance. Carbon fiber composites possess exceptional strength and extreme stiffness compared to metals, yet they exhibit a much lower density 80% less than steel. This unique combination of properties makes carbon fiber a possible alternative for automobile materials. Moreover, carbon fiber is a more sustainable material compared to traditional metals, offering environmental benefits. It also enables more efficient manufacturing techniques, contributing to reduced energy consumption and waste production. The present study investigates the feasibility of utilizing carbon fiber as the primary material for future automobiles, exploring the intricate aspects of its manufacturing process and examining the wide range of applications associated with this new material. By investigating carbon fiber's potential, the automotive industry could unlock many new opportunities for improved vehicle performance, fuel efficiency, and sustainability.

Gully erosion susceptibility mapping and prioritization of gully-dominant sub-watersheds using machine learning algorithms: Evidence from the Silabati River (tropical river, India)

Gully erosion is a severe environmental issue that poses threats to agriculture, human safety, habitats, infrastructure, and soil integrity. Selecting the right machine learning model is vital for accurate Gully Erosion Susceptibility Mapping (GESM) due to varying environmental hazard performance. This study employed a comparative analysis of two machine learning techniques, Random Forest (RF) and Extreme Gradient Boost (XGBoost), to develop a highly precise GESM for the Silabati watershed (India). The analysis incorporated 24 controlling factors and examined a dataset of 460 sample points, with equal representation of gullies and non-gullies. Variance Inflation Factors (VIF) and Information Gain Ratio (IGR) techniques were applied to assess multicollinearity test among the controlling factors. Lithology, elevation, distance from the road, LULC, geomorphology, rainfall, drainage density, and coarse fragments emerged as crucial factors in determining GESM. Statistical tests, including the Kappa index, Root Mean Square Error (RMSE), Accuracy (ACC), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Receiver Operating Characteristic (ROC), were employed to evaluate the RF and XGB models on training and testing data. Both models demonstrated strong performance, with the XGBoost and RF models achieving ROC values of 84.1% and 83.2%, respectively. The applied quantile classification method resulted in the creation of five distinct GESMs, categorized as very high (VH), high (H), moderate (M), low (L), and very low (VL). We found that the very high GESM areas of the watershed were 4.10% in the XGBoost model and 4.61%. in the RF model. Out of the 26 sub-watersheds, the results have identified five sub-watersheds as highly prioritized for sustainable management. Therefore, the present study provides an accurate gully erosion identification using advanced models, offering valuable insights for policymakers to proper management implementation.

Valorization of calcium hypochlorite precipitate as a new source of heterogeneous catalyst development for biodiesel production: A preliminary experiment

One of the main problem related with liquid bleach production from calcium hypochlorite is the amount of precipitates generated and its consequent management. As a result, academic and industrial communities have been challenged with searching of a means for its valorization. Therefore, this research explores the application of the precipitate as a viable source of Ca-based heterogeneous catalyst development for the production of waste cooking oil methyl esters for the first-time. The catalyst was prepared by dividing the precipitates into three forms, viz. raw untreated (RC), heat treated (RC-TB), and NaOH impregnated plus thermally activated (RC-ITB). The prepared catalysts were efficiently characterized by XRF, XRD, FTIR, SEM, and BET techniques. The characterization results indicated that the catalysts are mainly composed of calcium metal in the form of oxides (CaO), calcite (CaCO3) and Portlandite (Ca(OH)2), which are the promising constituents of basic catalysts. The BET inspection of RC, RC-TB, and RC-ITB revealed the specific surface area of 8.509, 9.089, and 9.312 m2/g, respectively. At the same reaction conditions, the maximum biodiesel yield of 76.05 % was achieved by RC-ITB compared to RC-TB (62.57 %) and RC (19.74 %), because it's larger specific surface area and highest basic nature (pH = 12.65 at 1:5 w/v) improves the reaction catalysis through better catalyst-substrates interactions. The lower biodiesel yield was attained through the RC catalyst due to its untreated surface, lower specific area, and weak alkaline nature (pH value = 10.66 at 1:5 w/v). Furthermore, regardless of the amount of yield, almost similar fuel properties and functional groups of the products over the coded catalysts were observed. Generally, the possibility of calcium hypochlorite precipitate as a precursor of Ca-based heterogeneous catalyst has been effectively proven in this research, which could be very important for environmental safety and industrial resource integration.