Previous Models Research Articles

Partial Lyα\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} thermalization in an analytic nonlinear diffusion model

During recombination, the cosmic background radiation is disturbed, in particular, by Lyman-alpha emissions from neutral hydrogen. It is proposed to account for the subsequent time-dependent partial thermalization of the Lyα\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} energy content in an analytically solvable nonlinear diffusion model. The amplitude of the partially thermalized and redshifted Lyα\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} line is found to be too low to be visible in the cosmic microwave spectrum, in accordance with previous numerical models and Planck observations.

Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study

Previous deep learning models have been proposed to predict the pathological complete response (pCR) and axillary lymph node metastasis (ALNM) in breast cancer. Yet, the models often leveraged multiple frameworks, required manual annotation, and discarded low-quality images. We aimed to develop an automated and reusable deep learning (AutoRDL) framework for tumor detection and prediction of pCR and ALNM using ultrasound images with diverse qualities. The AutoRDL framework includes a You Only Look Once version 5 (YOLOv5) network for tumor detection and a progressive multi-granularity (PMG) network for pCR and ALNM prediction. The training cohort and the internal validation cohort were recruited from Guangdong Provincial People's Hospital (GPPH) between November 2012 and May 2021. The two external validation cohorts were recruited from the First Affiliated Hospital of Kunming Medical University (KMUH), between January 2016 and December 2019, and Shunde Hospital of Southern Medical University (SHSMU) between January 2014 and July 2015. Prior to model training, super-resolution via iterative refinement (SR3) was employed to improve the spatial resolution of low-quality images from the KMUH. We developed three models for predicting pCR and ALNM: a clinical model using multivariable logistic regression analysis, an image model utilizing the PMG network, and a combined model that integrates both clinical and image data using the PMG network. The YOLOv5 network demonstrated excellent accuracy in tumor detection, achieving average precisions of 0.880-0.921 during validation. In terms of pCR prediction, the combined modelpost-SR3 outperformed the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.833 vs 0.822 vs 0.806 vs 0.790 vs 0.712, all p<0.05) in the external validation cohort (KMUH). Consistently, the combined modelpost-SR3 exhibited the highest accuracy in ALNM prediction, surpassing the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.825 vs 0.806 vs 0.802 vs 0.787 vs 0.703, all p<0.05) in the external validation cohort 1 (KMUH). In the external validation cohort 2 (SHSMU), the combined model also showed superiority over the clinical and image models (0.819 vs 0.712 vs 0.806, both p<0.05). Our proposed AutoRDL framework is feasible in automatically predicting pCR and ALNM in real-world settings, which has the potential to assist clinicians in optimizing individualized treatment options for patients. National Key Research and Development Program of China (2023YFF1204600); National Natural Science Foundation of China (82227802, 82302306); Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (JNU1AF-CFTP-2022-a01201); Science and Technology Projects in Guangzhou (202201020022, 2023A03J1036, 2023A03J1038); Science and Technology Youth Talent Nurturing Program of Jinan University (21623209); and Postdoctoral Science Foundation of China (2022M721349).

Strawberry Maturity Recognition Based on Improved YOLOv5

Strawberry maturity detection plays an essential role in modern strawberry yield estimation and robot-assisted picking and sorting. Due to the small size and complex growth environment of strawberries, there are still problems with existing recognition systems’ accuracy and maturity classifications. This article proposes a strawberry maturity recognition algorithm based on an improved YOLOv5s model named YOLOv5s-BiCE. This algorithm model is a replacement of the upsampling algorithm with a CARAFE module structure. It is an improvement on the previous model in terms of its content-aware processing; it also widens the field of vision and maintains a high level of efficiency, resulting in improved object detection capabilities. This article also introduces a double attention mechanism named Biformed for small-target detection, optimizing computing allocation, and enhancing content perception flexibility. Via multi-scale feature fusion, we utilized double attention mechanisms to reduce the number of redundant computations. Additionally, the Focal_EIOU optimization method was introduced to improve its accuracy and address issues related to uneven sample classification in the loss function. The YOLOv5s-BiCE algorithm was better at recognizing strawberry maturity compared to the original YOLOv5s model. It achieved a 2.8% increase in the mean average precision and a 7.4% increase in accuracy for the strawberry maturity dataset. The improved algorithm outperformed other networks, like YOLOv4-tiny, YOLOv4-lite-e, YOLOv4-lite-s, YOLOv7, and Fast RCNN, with recognition accuracy improvements of 3.3%, 4.7%, 4.2%, 1.5%, and 2.2%, respectively. In addition, we developed a corresponding detection app and combined the algorithm with DeepSort to apply it to patrol robots. It was found that the detection algorithm exhibits a fast real-time detection speed, can support intelligent estimations of strawberry yield, and can assist picking robots.

Financial distress prediction based on ensemble feature selection and improved stacking algorithm

PurposeWhile the Chinese securities market is booming, the phenomenon of listed companies falling into financial distress is also emerging, which affects the operation and development of enterprises and also jeopardizes the interests of investors. Therefore, it is important to understand how to accurately and reasonably predict the financial distress of enterprises.Design/methodology/approachIn the present study, ensemble feature selection (EFS) and improved stacking were used for financial distress prediction (FDP). Mutual information, analysis of variance (ANOVA), random forest (RF), genetic algorithms, and recursive feature elimination (RFE) were chosen for EFS to select features. Since there may be missing information when feeding the results of the base learner directly into the meta-learner, the features with high importance were fed into the meta-learner together. A screening layer was added to select the meta-learner with better performance. Finally, Optima hyperparameters were used for parameter tuning by the learners.FindingsAn empirical study was conducted with a sample of A-share listed companies in China. The F1-score of the model constructed using the features screened by EFS reached 84.55%, representing an improvement of 4.37% compared to the original features. To verify the effectiveness of improved stacking, benchmark model comparison experiments were conducted. Compared to the original stacking model, the accuracy of the improved stacking model was improved by 0.44%, and the F1-score was improved by 0.51%. In addition, the improved stacking model had the highest area under the curve (AUC) value (0.905) among all the compared models.Originality/valueCompared to previous models, the proposed FDP model has better performance, thus bridging the research gap of feature selection. The present study provides new ideas for stacking improvement research and a reference for subsequent research in this field.

Analysis of heat and mass transfer of a non-linear convective heat generating fluid flow in a porous medium with variable viscosity

The present study addresses the significance of analyzing the heat and mass transfer characteristics of a non-linear convective fluid flow featuring variable viscosity within a porous medium. Our focus lies on understanding the impact of varying viscosity on the hydromagnetic coupling stress, particularly in the presence of internal heat generation. The fluid is confined within a porous medium, and convection cooling takes place at both wall of the fluid. A key assumption in our investigation is the nonlinearity nature of the buoyancy force, specifically when there is a substantial difference in temperature and concentration within the fluid and its surroundings, the buoyancy force becomes quadratic. The exploration of couple stress fluids, alongside considerations of temperature-dependent internal heat generation and mass transfer, contributes to advancing our understanding of thermal system performance. By accounting for nonlinearity in density, temperature, and concentration, the research aims to provide more accurate results than previous linear models, crucial for optimizing energy consumption in practical applications such as nuclear reactors and heat exchangers. Dealing with this type of problem often pose significant challenges, as they often lack analytical solutions, necessitating the use of more advanced numerical methods or approximation techniques. We employ Spectral Quasilinearisation Method (SQLM) to solve the coupled non-linear boundary value problems (PDE) arising from the model, momentum equation, energy equation, concentration equation, and entropy generation. The obtained results are then validated using the Runge–Kutta method and a good agreement is achieved. The findings for the dimensionless velocity, temperature, mass concentration and entropy generation are presented in graphics and in table manner, and effects of controlling parameters are extensively explained. The results highlight that an increase in variable viscosity leads to an enhancement in the velocity profile, while simultaneously reducing the temperature in the system. Furthermore, increments in the non-linear convection parameter and coupling stress parameter result in an appreciation of the velocity profile, temperature profile, and entropy generation rate.

Intelligent conflict detection of IoT services using high-level Petri nets

AbstractInternet of Things has become a common paradigm for various domains. To meet a user’s complex requirement, we should compose multiple IoT devices (IoT services) to provide comprehensive services to the user. However, these services usually coexist, which is likely to lead to conflicts. Moreover, each user may have different kinds of needs. Suppose that in a smart home there are more than one person, a conflict may occur when they request the same service in this environment. Actually, even though they request different IoT services, among which if there exists function impact or QoS impact, a conflict could still occur. In this paper, we propose to employ high-level Petri nets to detect conflicts among IoT services. We first model the formal methods for conflict policies. Then, we present a Petri nets-based mechanism for modeling and detecting conflicts. Finally, we expand the previous model and use high-level Petri nets for handling fuzzy IoT conflict policies modeling and reasoning. The experimental results show that the proposed approach performs well in accuracy.

RFEM: A framework for essential microRNA identification in mice based on rotation forest and multiple feature fusion

With the increasing number of microRNAs (miRNAs), identifying essential miRNAs has become an important task that needs to be solved urgently. However, there are few computational methods for essential miRNA identification. Here, we proposed a novel framework called Rotation Forest for Essential MicroRNA identification (RFEM) to predict the essentiality of miRNAs in mice. We first constructed 1,264 miRNA features of all miRNA samples by fusing 38 miRNA features obtained from the PESM paper and 1,226 miRNA functional features calculated based on miRNA-target gene interactions. Then, we employed 182 training samples with 1,264 features to train the rotation forest model, which was applied to compute the essentiality scores of the candidate samples. The main innovations of RFEM were as follows: 1) miRNA functional features were introduced to enrich the diversity of miRNA features; 2) the rotation forest model used decision tree as the base classifier and could increase the difference among base classifiers through feature transformation to achieve better ensemble results. Experimental results show that RFEM significantly outperformed two previous models with the AUC (AUPR) of 0.942 (0.944) in three comparison experiments under 5-fold cross validation, which proved the model's reliable performance. Moreover, ablation study was further conducted to demonstrate the effectiveness of the novel miRNA functional features. Additionally, in the case studies of assessing the essentiality of unlabeled miRNAs, experimental literature confirmed that 7 of the top 10 predicted miRNAs have crucial biological functions in mice. Therefore, RFEM would be a reliable tool for identifying essential miRNAs.

Machine learning models for predicting unscheduled return visits of patients with abdominal pain at emergency department and validation during COVID-19 pandemic: A retrospective cohort study.

Machine learning (ML) models for predicting 72-hour unscheduled return visits (URVs) for patients with abdominal pain in the emergency department (ED) were developed in a previous study. This study refined the data to adjust previous prediction models and evaluated the model performance in future data validation during the COVID-19 era. We aimed to evaluate the practicality of the ML models and compare the URVs before and during the COVID-19 pandemic. We used electronic health records from Chang Gung Memorial Hospital from 2018 to 2019 as a training dataset, and various machine learning models, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC) were developed and subsequently used to validate against the 2020 to 2021 data. The models highlighted several determinants for 72-hour URVs, including patient age, prior ER visits, specific vital signs, and medical interventions. The LR, XGB, and VC models exhibited the same AUC of 0.71 in the testing set, whereas the VC model displayed a higher F1 score (0.21). The XGB model demonstrated the highest specificity (0.99) and precision (0.64) but the lowest sensitivity (0.01). Among these models, the VC model showed the most favorable, balanced, and comprehensive performance. Despite the promising results, the study illuminated challenges in predictive modeling, such as the unforeseen influences of global events, such as the COVID-19 pandemic. These findings not only highlight the significant potential of machine learning in augmenting emergency care but also underline the importance of iterative refinement in response to changing real-world conditions.

Spatiotemporal variations in frost cracking measures in two dimensions: A case study for rock walls in Jotunheimen, southern Norway

The ground thermal regime has a profound impact on geomorphological processes and has been suggested to be particularly important for weathering processes in periglacial environments. Several frost-related damage indices have hitherto been developed to link climate and frost weathering potential in bedrock, although only for individual points or grid cells. Here, we model ground temperature and frost weathering potential in steep rock walls in the Jotunheimen Mountains, southern Norway, along a two-dimensional profile line for the Younger Dryas Stadial-Preboreal transition (c. 11.5 ka), the Holocene Thermal Maximum (c. 7.5 ka), the Little Ice Age (1750), and the 2010s. We use an established heat flow model and frost-cracking index based on the ice segregation theory. A central innovation of our model treatment is the implementation of ensemble simulations using distributions of automatically mapped crack radii in a rock wall, whereas previous frost damage models considered only a single characteristic crack radius. Our results allowed for the identification of sites with enhanced frost weathering. Such sites are typically found between rock walls and retreating glaciers, as well as in areas where snow depth changes abruptly, resulting in large thermal gradients. Hence, frost weathering may be highly active during glacier retreat, enhancing the damage to rock walls during deglaciation by adding to the damage from stress release. The coldest climates of the Younger Dryas Stadial-Preboreal transition and the Little Ice Age were generally most favorable for frost cracking. Such timing compares well with the knowledge about the timing of rockfall accumulations in Norway.

Model-based impact evaluation of new tuberculosis vaccines in aging populations under different modeling scenarios: the case of China.

The slow descent in TB burden, the COVID-19 pandemic, along with the rise of multidrug-resistant strains of Mycobacterium tuberculosis, seriously threaten TB control and the goals of the End TB strategy. To fight back, several vaccine candidates are under development, with some of them undergoing the phases 2B and 3 of the development pipeline. The impact of these vaccines on the general population needs to be addressed using disease-transmission models, and, in a country like China, which last year ranked third in number of cases worldwide, and where the population is aging at a fast pace, the impact of TB vaccination campaigns may depend heavily upon the age of targeted populations, the mechanistic descriptions of the TB vaccines and the coupling between TB dynamics and demographic evolution. In this work, we studied the potential impact of a new TB vaccine in China targeting adolescents (15-19 y.o.) or older adults (60-64 y.o.), according to varying vaccine descriptions that represent reasonable mechanisms of action leading to prevention of disease, or prevention of recurrence, each of them targetting specific routes to TB disease. To measure the influence of the description of the coupling between transmission dynamics and aging in TB transmission models, we explored two different approaches to compute the evolution of the contact matrices, which relate to the spreading among different age strata. Our findings highlight the dependence of model-based impact estimates on vaccine profiles and the chosen modeling approach for describing the evolution of contact matrices. Our results also show, in line with previous modeling works, that older adult vaccination is a suitable option in China to reduce the incidence of TB as long as the vaccine is able to protect already exposed individuals. This study underscores the importance of considering vaccine characteristics and demographic dynamics in shaping TB control strategies. In this sense, older adult vaccination emerges as a promising avenue for mitigating TB transmission in China but also remarks the need for tailored intervention strategies aligned with demographic trends.

In-silico techniques to inform and improve the personalized prescription of shoe insoles.

Background: Corrective shoe insoles are prescribed for a range of foot deformities and are typically designed based on a subjective assessment limiting personalization and potentially leading to sub optimal treatment outcomes. The incorporation of in silico techniques in the design and customization of insoles may improve personalized correction and hence insole efficiency. Methods: We developed an in silico workflow for insole design and customization using a combination of measured motion capture, inverse musculoskeletal modelling as well as forward simulation approaches to predict the kinematic response to specific insole designs. The developed workflow was tested on twenty-seven participants containing a combination of healthy participants (7) and patients with flatfoot deformity (20). Results: Average error between measured and simulated kinematics were 4.7 ± 3.1, 4.5 ± 3.1, 2.3 ± 2.3, and 2.3 ± 2.7° for the chopart obliquity, chopart anterior-posterior axis, tarsometatarsal first ray, and tarsometatarsal fifth ray joints respectively. Discussion: The developed workflow offers distinct advantages to previous modeling workflows such as speed of use, use of more accessible data, use of only open-source software, and is highly automated. It provides a solid basis for future work on improving predictive accuracy by adapting the currently implemented insole model and incorporating additional data such as plantar pressure.

Transfer learning model for estimating site amplification factors from limited microtremor H/V spectral ratios

Summary Site amplification factors (SAFs) of seismic ground motions are essential in evaluating and estimating seismic hazards. In our previous study, the authors proposed a simple and cost-effective method to estimate a SAF based on a deep neural network (DNN) model and microtremor horizontal-to-vertical spectral ratio (MHVR). Since the previous DNN model was based on the observed SAFs and MHVRs within a limited district in Japan, the applicability of the previous model to non-source regions with different site conditions was limited. This study explored the application of a transfer learning (TL) technique to adapt an existing (pre-trained) DNN model to new regions and a different database. The SAFs obtained through generalized spectral inversion technique (GIT) at the seismic observation stations (K-NET and KiK-net) in Japan were collated as the ground truth for site effects. MHVRs recorded at the stations in several districts of Japan were collected to construct a dataset for the development of the TL model. Subsequently, a TL model was constructed, leveraging the neural network layers and their weights from the pre-trained model while incorporating additional neural network layers to enhance the performance. During the training process, a total data set of 112 sites was divided into training set, validation set, and external test set by 1:1:5. Utilizing a cross-validation strategy, the residuals between pSAFs (pseudo-SAFs) estimated by the TL model and the observed SAFs were analyzed for the external test set containing 80 sites. The results showed that the TL model outperformed the pre-trained DNN model. The cross-validation results demonstrated that almost consistent prediction results were obtained regardless of any combination of 16 sites selected as the training set. Furthermore, by contrasting the influence of varying training set sizes on the performance of the TL model and comparing the TL model to a DNN model with an extended training set, the effectiveness of constructing the model with the limited number of data (16 sites) was ascertained. Finally, the effectiveness and limitations of the TL model were evaluated using MHVRs with peak frequencies falling outside the training set's range.

Optical Analysis of Perovskite/Silicon Tandem Solar Cells: Effect of Rear Side Grating and TOPCon Tunnel Junction

In the focus of the presented work is the analysis of a rear side reflection grating in context of a perovskite/silicon tandem solar cell. The typical configuration of a perovskite/Si tandem device requires a hole transport layer at the rear side of the silicon solar cell (p-i-n bottom cell structure). As such a poly-Si passivating contact like TOPCon is an attractive candidate. Until now, research faces the challenge to deposit p-TOPCon layers with good surface passivation properties on textured surfaces. A planar surface would avoid this issue and due to its smaller surface area intrinsically allowing for a better passivation. The optical disadvantage of the planar rear side can be eliminated by an appropriate optical grating at the rear side which enables ideal light trapping. In this work a new approach is developed to describe the optical properties of a diffraction grating as structured rear side reflector in a silicon bottom cell. The light distribution and the parasitic absorption per grating interaction are fitted to reflectance and absorptance measurements of a III-V on silicon triple-junction device with a rear grating structure. Compared to previous models, this new approach makes it possible to reliably quantify the different loss mechanisms in the spectral region above 1000 nm. An application of the simulation model to a perovskite/silicon tandem device shows the potential of the system with rear side grating. In addition, an integration of a TOPCon tunnel junction is evaluated and a process chain for the integration of a structured rear side reflector into the tandem system is discussed.

Analytical model of compressive arch action for one-way RC beams and two-way framed substructures under column removal scenarios

This paper proposes a direct analytical model to evaluate compressive arch action (CAA) of reinforced concrete (RC) beam-column sub-assemblages under middle column removal scenario (MCRS), incorporating bending curvature of the double-span beam, and different horizontal and rotational restraint conditions at the beam ends. Based on the proposed approach, the structural engineer can easily obtain the CAA peak capacity and the corresponding CAA displacement by hand calculations. Unlike the previous models in which the accuracy highly relies on pre-assumed CAA displacement, this prediction approach directly derives the CAA displacement and CAA capacity through force equilibrium, geometric relationship, compatibility and boundary conditions, incorporating various horizontal restraint conditions, span-to-depth ratio and section profile of the bridging beam. Comparison with existing test results in available literature indicates that this model not only gives good predictions of peak capacity, displacement and maximum axial beam force in CAA, but also incorporates the effect of horizontal restraint stiffness and working stress of compression reinforcement. Besides, a further simplified explicit model is proposed to enhance usability in practical design, and sensitive studies are conducted to study how the simplification affects the prediction accuracy of vertical CAA capacity and displacement. Thereafter, the proposed model is extended to substructures with orthogonal beams in X- and Y- directions to predict CAA capacity under different column removal scenarios such as interior, penultimate, edge and corner column removal cases.

Ultrahigh thermal gradient granulites in the Narryer Terrane, Yilgarn Craton, Western Australia, provide a window into the composition and formation of Archean lower crust

AbstractGranulites from the Narryer Terrane in the northern Yilgarn Craton, Australia, record evidence for high to ultrahigh thermal gradients during the Meso–Neoarchean. U–Pb zircon ages reflect a complex history of high‐grade, prolonged and poly‐phase metamorphism, with evidence for several thermal pulses at ca. 2745–2725, ca. 2690–2660, and ca. 2650–2610 Ma. Forward phase equilibrium modeling on rocks with varying bulk compositions and mineral assemblages suggests that peak temperatures reached 880–920°C at pressures of 5.5–6 kbar at ca. 2690–2665 Ma, followed by near‐isobaric cooling. These new P–T results also indicate that these rocks experienced some of the hottest thermal gradient regimes in the metamorphic record (≥150°C/kbar). Based on P–T models, U–Pb ages, and geochemical constraints, our data suggest that the geodynamic setting for the formation of this unusual thermal regime is ultimately tied to cratonization of the Yilgarn Craton. Previous models have inferred that ultrahigh thermal gradients and coeval large‐scale anatexis in the Narryer Terrane were primarily generated by mantle‐driven processes, despite most of the lithological, isotopic, and geochemical observations being at odds with the expected geological expression of large‐scale mantle upwelling. We re‐evaluate the mechanisms for high‐grade metamorphism in the Narryer terrane and propose that long‐lived high crustal temperatures between ca. 2690 Ma and 2610 Ma were instead facilitated by elevated radiogenic heat production in thickened, highly differentiated ancient crust. Mantle‐derived magma input and new crustal addition may not be the only drivers for high‐ to ultrahigh‐temperature metamorphism and stabilization of ancient crustal blocks.

Design model of a twisted and folded Clos network with multi-step grouped intermediate switches guaranteeing admissible blocking probability

A future data center network is expected to be constructed by a Clos network consisting of optical-circuit switches to deal with traffic growth. A previous model addressed a Clos-network design problem that divides the set of intermediate switches by each role for request routing to guarantee an admissible blocking probability to maximize the switching capacity. However, the previous model divides the set of intermediate switches into at most only two groups, and there is room for a more flexible design by increasing the number of divisions. This paper proposes a design model that generalizes the number of divisions to increase the switching capacity while guaranteeing an admissible blocking probability. We formulate the design model as an optimization problem. We introduce two algorithms to obtain a feasible solution that satisfies the constraints of the optimization problem. Numerical results showed that the proposed model can increase the switching capacity as the number of divisions increases.

Effect of lean-green practice and green human resource on supply chain performance: a resource-based view

PurposeThe study examined a wide range of proactive supply chain practices to demonstrate a cross-linkage among them and to understand their effects on both practitioners of previous decision-making models, frameworks, strategies and policies. Here, six supply chain practices are empirically evaluated based on 28 constructs to investigate a comprehensive model and confirm the connections for achieving performance and competence. The study presents a conceptual model and examines the influence of many crucial factors, i.e. supply chain collaboration, knowledge, information sharing, green human resources (GHR) management and lean-green (LG) practices on supply chain performance.Design/methodology/approachStructural equation modeling (SEM) examines the conceptual model and allied relationship. A sample of 175 respondents' data was collected to test the hypothesized relations. A resource based view (RBV) was adopted, and the questionnaires-based survey was conducted on the Indian supply chain professionals to explore the effect of LG and green human resource management (GHRM) practices on supply chain performance.FindingsThe study presented five constructs for supply chain capabilities (SCCA), five constructs for supply chain collaboration and integration (SCIN), four constructs for supply chain knowledge and information sharing (SCKI), five constructs for GHR, five constructs for LG practices (LGPR) and four constructs for lean-green SCM (LG-SCM) firm performance to be utilized for validation by the specific industry, company size and operational boundaries for attaining sustainability. The outcome emphasizes that SCCA positively influence GHRM, LG practices and LG supply chain firm performance. However, LG practices do not influence LG-SCM firm performance, particularly in India.Originality/valueThe study exploited multiple practices in a conceptual model to provide a widespread understanding of decision-making to assist in developing a holistic approach based on different practices for attaining organizational sustainability. The study stimulates the cross-pollination of ideas between many supply chain practices to better understand SCCA, SCIN, SCKI, GHRM and LG-SCM under a single roof for retaining organization performance.

PSHA: Does It Deal with What It Is or What We Want It to Be?

Abstract In a recent opinion piece Albarello and Paolucci (2023; hereafter, AP23) provide their view as members of the past Seismic Group of the Commissione Grandi Rischi (CGR-SRS) in Italy, which represents the main scientific consultant for Italian Civil Protection, about the difficulty using probabilistic seismic hazard analysis (PSHA) models for building code purposes. Here, we refer to this specific kind of PSHA modeling as National Seismic Hazard Model (NSHM). We agree with AP23 that the topic is of great and general importance, and here we aim at contributing to this discussion by offering our perspective on two points that are at the heart of the matter, concluding that AP23 is misguided in how to deal with them. First, we assert that the credibility of an NSHM has to be rooted only in the use of the best available science, which includes a rigorous testing phase with observations, independent from the consequences in terms of risk. (PSHA deals with what it is.) Second, we claim that the difficulties in accepting a new NSHM with some major changes with respect to the previous model are mostly due to too rigid building code procedures that do not account for the epistemic uncertainty in the hazard estimates.

POPs identification using simple low-code machine learning

Effectively identifying persistent organic pollutants (POPs) with extensive organic chemical datasets poses a formidable challenge but is of utmost importance. Leveraging machine learning techniques can enhance this process, but previous models often demanded advanced programming skills and high-end computing resources. In this study, we harnessed the simplicity of PyCaret, a Python-based package, to construct machine-learning models for POP screening based on 2D molecular descriptors. We compared the performance of these models against a deep convolutional neural network (DCNN) model. Utilising minimal Python code, we generated several models that exhibited superior or comparable performance to the DCNN. The most outstanding performer, the Light Gradient Boosting Machine (LGBM), achieved an accuracy of 96.20 %, an AUC of 97.70 %, and an F1 score of 82.58 %. This model outshone the DCNN model. Furthermore, it excelled in identifying POPs within the REACH PBT and compiled industrial chemical lists. Our findings highlight the accessibility and simplicity of PyCaret, requiring only a few lines of code, rendering it suitable for non-computing professionals in environmental sciences. The ability of low code machine learning tools (e.g. PyCaret) to facilitate model comparison and interpretation holds promise, encouraging prompt assessment and management of chemical substances.

An Integrated Model for Acid Fracturing without Prepad Considering Wormhole Growth

Acid fracturing is an effective stimulation technology that is widely applied in carbonate reservoirs. An integrated model for acid fracturing without prepad treatment has been established. Compared with the previous models which use prepad for generating hydraulic fractures, this model can simultaneously simulate the fracture propagation and the acid etching of fracture surfaces, as well as the wormhole growth during acid fracturing. The influences of some essential factors have been studied through a series of numerical simulations, and the main conclusions are as follows. First, increasing the injected acid volume can expand the size of the formed hydraulic fractures and extend the propagation distance of the wormhole. Increasing the injected acid volume can also expand the etched width and extend the effective distance of the injected acid. Second, a high injection rate impels more acid to flow into the depth of a fracture before infiltration and reaction, resulting in the augmentation of a hydraulic fracture’s geometric size and the extension of the effective distance. But the maximum etched width decreases as the injection rate rises. A high injection rate can also enable wormholes to grow in the natural fracture area farther away from the hydraulic fracture inlet, but shorten the length of the original wormhole near the hydraulic fracture inlet. Third, an increase in acid viscosity can enlarge the geometric size of the hydraulic fracture and reduce the propagation distance of wormholes. In addition, an increase in the acid viscosity blocks the acid flow from fracture inlet to tip, reducing the effective distance of acid fracturing. Fourth, the natural fracture is the vital inducement of wormhole growth, and wormholes are apt to grow in the natural fracture area. Moreover, the geometric size of the hydraulic fracture and the effective distance of acid fracturing decrease with an increasing number of natural fractures. This research can provide a reference for field applications of acid fracturing without prepad.