Differential Approach Research Articles

Identifying subtle differences : a radiomics model assessment for gastric schwannomas and gastrointestinal stromal tumors across risk grades

ObjectiveThis study aims to develop and validate an enhanced computed tomography (CT)-based radiomics model to differentiate gastric schwannomas (GS) from gastrointestinal stromal tumors (GIST) across various risk categories.MethodsThis retrospective analysis was conducted on 26 GS and 82 GIST cases, all confirmed by postoperative pathology. Data was divided into training and validation cohorts at a 7:3 ratio. We collected patient demographics, clinical presentations, and detailed CT imaging characteristics. Through univariable and multivariable logistic regression analyses, we identified independent predictors for discriminating between GS and GIST, facilitating the construction of a conventional model. Radiomic features were extracted and refined through manual 3D segmentation of venous phase thin-slice images to develop a radiomics model. Subsequently, we constructed a comprehensive combined model by integrating selected clinical and radiomics indicators. The diagnostic performances of all models in differentiating GS from GIST and stratifying GISTs according to malignancy risk were evaluated.ResultsWe identified several key independent variables distinguishing GS from GIST, including tumor location, cystic changes, degree of enhancement in arterial phase, and enhancement uniformity. The conventional model achieved AUCs of 0.939 and 0.869 in the training and validation cohort, respectively. Conversely, the radiomics model, predicated on eight pivotal radiomics features, demonstrated AUCs of 0.949 and 0.839. The combined model, incorporating tumor location, degree of enhancement in arterial phase, enhancement uniformity, and a radiomics model derived rad-score, significantly outperformed the traditional approach, achieving AUCs of 0.989 and 0.964 in the respective cohorts. The combined model showed superior diagnostic accuracy in distinguishing GS from GIST, as well as GS from high or low malignancy potential GISTs, as evidenced by IDI values of 0.2538, 0.2418, and 0.2749 (P<0.05 for all).ConclusionThe combined model based on CT imaging features and radiomics features presents a promising non-invasive approach for accurate preoperative differentiation between gastric schwannomas and gastrointestinal stromal tumors.

Systematic Literature Review of Epaxial Paraspinal Schwannomas: Differential Diagnosis and Treatment Approaches

Background: Schwannomas, predominantly benign nerve sheath tumors, are typically found within the intradural extramedullary space of the spinal cord with potential extradural expansion. Other typical localizations are the upper limbs and neck area. Pure epaxial paraspinal schwannomas are very rare, often asymptomatic, and predominantly occur in the thoracic region, with only a handful of cases reported globally. The range of differential diagnoses for paraspinal lesions is extensive, emphasizing the importance of accurate diagnosis to ensure optimal therapy and avoid unnecessary treatments. Method: We conducted a systematic literature review searching for published recommendations for paraspinal lesion management in addition to examining the case of a 49-year-old male patient who presented with a history of persistent back pain. A thorough medical history and physical examination were followed by ultrasound and MRI, revealing a well-defined paravertebral mass spanning from T7 to T9. A secure ultrasound-guided biopsy was performed, leading to a preliminary diagnosis of paraspinal schwannoma. Subsequently, complete surgical resection was performed. Results: pathological reports confirmed the initial diagnosis of paraspinal schwannoma. Further investigation using FMI and RNA sequencing did not detect any specific genetic anomalies aside from an NF2 gene mutation. A follow-up MRI conducted six months later showed no signs of recurrence. Conclusions: The broad spectrum of differential diagnoses for paraspinal lesions necessitates a multidisciplinary approach to ensure accurate diagnosis and tailored treatment. This approach involves meticulous imaging interpretation followed by a secure biopsy procedure to obtain preliminary pathology results, ultimately leading to the implementation of the most suitable surgical treatment.

E2E model from crop suggestion to harvesting time prediction in sugarcane plant utilizing machine learning and deep learning

AbstractThe automated system for enhancing plant growth presents an innovative approach to optimize quality of sugarcane cultivation for four main sugarcane growing zones. It includes issues like recommendation of crops based on soil nutrients, diagnosis of disease in the leaf and stem images of sugarcane, weed detection and harvesting time prediction. The research work proposed in the article presents an innovative two-stage approach for object detection and classification in agricultural imagery. Initially, YOLOv8 (You Only Look Once) is employed to accurately detect objects within images, delineating them with precise boundary boxes. Subsequently, the focus of hybrid model integrating Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, known as Contextual Long Short-Term Memory (CLSTM), is employed. This dual-stage methodology harnesses the speed and accuracy of YOLOv8 for robust object localization, while the CLSTM model ensures nuanced classification, contributing to comprehensive and accurate approach for object detection and crop-weed differentiation in agricultural scenarios. The proposed approach is compared with the four DL algorithms for identifying weeds in sugarcane crops and subsequently assessed their accuracy and F1 score performance. At a learning rate of 0.002, the findings of CLSTM showcase superior precision at 98.5%, recall at 97.8%, F1 score at 98.1%, and an overall accuracy of 97.7%. The subsequent task is harvesting time prediction, which entails identifying the best time to harvest sugarcane based on the planting period, weather predictions, and sugarcane brix value. The implementation of this automated system not only enhances the productivity of sugarcane cultivation but also serves as a model for sustainable and resource-efficient agriculture.

Peningkatan motivasi belajar siswa melalui pembelajaran berdiferensiasi dengan menggunakan media pembelajaran Google-Sites

Although differentiated learning has been recognized as an effective approach to meeting diverse learning needs in the classroom, it is challenging to use learning media that are interesting and efficient for all students, as is found by teachers. This study explores the impact of using Google Sites learning media in increasing students' learning motivation through a differentiated approach in the classroom. This research was conducted as classroom action research involving the students of Class VII A in SMP Negeri 9 Yogyakarta. This study was undertaken within two cycles, in which every cycle had stages of planning, implementation, observation, and reflection. Data collection methods included observation, interview, and analysis of learning outcomes. The results showed a significant increase in students' learning motivation after implementing the differentiation approach by using Google sites as learning media. The practical implication of this study is that using technology in learning differentiation can increase students' participation and overall interest in learning.

TWIST1/miR-199a axis promotes tumor aggressiveness through inhibiting oxidative phosphorylation in carcinomas

Background Metabolic reprogramming has emerged as a key hallmark of cancer progression, though its role in tumor aggressiveness is still evolving. Here, using a pan-cancer genome approach, we aimed to comprehensively assess the metabolic reprogramming involved in tumor aggressiveness in carcinomas and identify metabolic hubs which can be therapeutically targeted to treat aggressive tumors in the clinic. Methods In this study, we employed a stringent pan-cancer multi-omic metabolism-targeted differential expression approach to identify the metabolic hubs regulating tumor aggressiveness. mRNA, miRNA, DNA methylation and mutation profiling data of tumors representing 14 different types of carcinomas was downloaded from TCGA database. Cell line expression profiling and drug response data was downloaded from CCLE database. Pathway enrichment, GSEA, String protein-protein interaction, miRNA-mRNA prediction, network random-walk and CCLE drug response analyses were carried out. Results We identified downregulated expression of enzymes involved in oxidative phosphorylation as a key common factor across carcinomas, aligning with the Warburg effect. Additionally, we established that the decreased dependence on oxidative phosphorylation is driven by elevated expression of miR-199 family miRNAs that inhibit their expression at the post-transcriptional level. Furthermore, we identified the epithelial-to-mesenchymal transition-related transcription factor, TWIST1, as a master regulator of tumor aggressiveness by controlling miR-199a-3p and -5p expression. Random walk analysis of established miRNA-mRNA network identified NDUFA2, DLD, COX15, NDUFB5, and TIMM13 as crucial metabolic hubs downregulated as tumors become aggressive. Drug response analysis suggested that targeting PDGFR signaling may offer a novel therapeutic approach to counteract the aggressiveness driven by the loss of oxidative phosphorylation. Conclusion We identified TWIST1/miR-199a axis mediated suppression of oxidative phosphorylation as major metabolic contributor towards tumor aggressiveness in carcinomas. These insights underscore the critical interplay between metabolic reprogramming and tumor aggressiveness, opening avenues for potential metabolic therapies in clinical settings.

A Differentiable Physics-Informed Machine Learning Approach to Model Laser-based Micro-Manufacturing Process

Abstract Advanced manufacturing processes are often based on complex multiphysics phenomena that are either poorly understood or are computationally too expensive to simulate in the context of process design, control or planning. Traditionally, simplified physics models with prescribed heuristics or purely data-driven surrogate models are used as alternatives in such applications. The concept of physics-informed machine learning (PIML) has been shown to have unique advantages over both of these alternatives in various fields of complex system analysis. In this paper, a new physics-informed machine learning (PIML) approach is presented to model the geometry of the cut produced by a Magnetically-assisted Laser Induced Plasma Micro Machining (M-LIPMM) process. This PIML architecture uses a neural network to auto-adapt the parametric boundary condition and physical properties used in a simplified finite difference based physics model (of 2D heat conduction), as a function of the inputs namely the laser settings. This network also estimates the scaling and shifting parameters used by a convolutional neural network that takes the temperature profile predicted by the simplified heat conduction model to predict the width and depth of the machined cut. Trained on physical experiment data, the PIML approach compares favorably to a pure data-driven neural network model in extrapolation tests, while also providing physical insights (that the latter cannot). The PIML approach also provides an 85% better accuracy overall compared to the simplified physics model with heuristic settings.

Dreams and fears in the process of creative thinking in children from areas affected by the armed conflict in Colombia

The development of creativity and creative thinking in early school-age children is a strategy to foster future capabilities for innovation and change. It is also a strategy to instill resilience in these children, enabling them to face the challenges posed by the various environments that surround teaching and learning processes. This article analyzes the dreams and fears expressed by children from educational institutions located in municipalities affected by the armed conflict in Colombia, within the framework of a route designed to develop creativity and innovative thinking. A qualitative narrative analysis methodology was used to characterize and analyze the narratives of 68 co-creativity teams, involving 408 children. The results show that the narratives are influenced not only by the context in which the children live but also by the consequences of the armed conflict in the areas where the schools are located. The findings suggest that educational strategies like the one studied must incorporate elements that allow for adaptation to the context from the design stage, with a differential approach, to generate the expected positive effects in the territories where they are implemented.

FEVERLESS: Fast and Secure Vertical Federated Learning Based on XGBoost for Decentralized Labels

Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without leaking private local information. Tree-based models, like XGBoost and LightGBM, have been widely used in VFL to enhance the interpretation and efficiency of training. However, there is a fundamental lack of research on how to conduct VFL securely over distributed labels. This work is the first to fill this gap by designing a novel protocol, called FEVERLESS, based on XGBoost. FEVERLESS leverages secure aggregation via information masking technique and global differential privacy provided by a fairly and randomly selected noise leader to prevent private information from being leaked in the training process. Furthermore, it provides label and data privacy against honest-but-curious adversaries even in the case of collusion of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n - 2$</tex-math></inline-formula> out of n clients. We present a comprehensive security and efficiency analysis for our design, and the empirical results from our experiments demonstrate that FEVERLESS is fast and secure. In particular, it outperforms the solution based on additive homomorphic encryption in runtime cost and provides better accuracy than the local differential privacy approach.

Precision parameter estimation in Proton Exchange Membrane Fuel Cells using depth information enhanced Differential Evolution

Proton Exchange Membrane Fuel Cell (PEMFC) models require parameter tuning for their design and performance improvement. In this study, Depth Information-Based Differential Evolution (Di-DE) algorithm, a novel and efficient metaheuristic approach, is applied to the complex, nonlinear optimization problem of PEMFC parameter estimation. The Di-DE algorithm was tested on twelve PEMFCs (BCS 500 W PEMFC, Nedstack 600 W PS6 PEMFC, SR-12 500 W PEMFC, H-12 PEMFC, STD 250 W PEMFC, HORIZON 500 W PEMFC and four 250W PEMFC and two H-12 12W PEMFC) and showed excellent accuracy. The Di-DE algorithm is was compared with other advanced evolutionary algorithms like iwPSO, CLPSO, DNLPSO, SLPSO, SaDE, SHADE, JADE, QUATRE, LSA, QUATRE-EMS and C-QUATRE, which obtained a minimum objective function value of 0.0255 and an average runtime improvement of 98.8%. The optimized parameters of the proposed method yielded the Sum of Squared Errors (SSE) as low as 0.00002 in some cases, which indicates better precision and stability. Moreover, the voltage–current (V–I) and power–voltage (P–V) characteristics predicted by Di-DE were within 1% error relative to the experimental data for all tested PEMFCs. The results of this work highlight the potential of the Di-DE algorithm to enable more sophisticated modelling and optimization of PEMFCs, which in turn will help to broaden the use of PEMFCs in clean energy applications.

Bone Tumors of the Jaw - the "Blind Spot" for Radiologists Experienced with Tumors? - Part II.

Primary bone tumours of the jaw are rare tumoral entities and do substantially differ from other bone tumours of the human body with respect of their frequently encountered unusual radiological appearances. The reason for that may be confined to the co-existence of two closely neighbored but different anatomical structures (i.e., tooth-forming apparatus and jaw bones with adjacent gingiva) and some tumour pathologies which are nearly excusively encountered in the jaw bones only (e.g., ameloblastoma, ossifying fibroma, ghost cell carcinoma).This paper would like to highlight some basic principles of the diagnostic approach and possibilities of radiological differentiation of such tumour-suspicious changes within the gnathic system are elucidated and discussed.The paper presented here is substantially based on the most recent classification of odontogenic and maxillofacial tumours (5th edition, 2022) which serves as a scaffold for the selection of typical tumour entities. Due to the educational character of this paper, only important jaw tumours worth mentioning and their characteristics are subject to be extracted from the literature and further discussed.The main focus was put onto both the description of radiological tumoral appearance and the rational selection of a radiological diagnostic work-up. In order to better visualize this difficult field of tumour entities, much attention has been paid on a comprehensive pictorial essay.For radiologists, it is their foremast task to detect, describe, and to classify bone tumours of the jaw when they are found intentionally or accidentally, resp. A close co-operation with their clinical partners is of upmost importance to gain information about patient´s history and clinical presentation. It is readily reasonable that radiologists are mostly able to provide only a suggestion of the presented tumour entity but this expert opinion would be very helpful to further narrow down the list of potential differential diagnoses (e.g., differentiation of a cyst vs. solid tumour osteolysis, identification of jaw osteomyelitis vs. tumoral infiltration, recognizing of secondary tumour involvement of the jaw). · primary bone tumours of the jaw are very rare, moreover difficult to differentiate radiologically, and do need therefore histological proof;. · profound knowledge about tumour characteristics (location within the jaw, relationship to the tooth, bony destructive pattern) may allow a rough orientation and classification;. · matrix-forming tumours and dysplasias of the jaw facilitates their radiological differentiation and classification;. · in contrary, osteolyses should be thoroughly scrutinized for the more frequent gnathic cysts in differentiation of rather rare solid primary tumours;. · an interdisciplinary round-table discussion amongst well-experienced maxillofacial surgeons and specialized radiologists may be appropriate to avoid severe misinterpretations.. · Grieser T, Hirsch E, Tödtmann N. Tumours of the jaw bones - Part II. Fortschr Röntgenstr 2024; DOI 10.1055/a-2416-1282.

Bone Tumors of the Jaw - the "Blind Spot" for Radiologists Experienced with Tumors? - Part I.

Primary bone tumours of the jaw are rare tumoral entities and do substantially differ from other bone tumours of the human body with respect of their frequently encountered unusual radiological appearances. The reason for that may be confined to the co-existence of two closely neighbored but different anatomical structures (i.e., tooth-forming apparatus and jaw bones with adjacent gingiva) and some tumour pathologies which are nearly excusively encountered in the jaw bones only (e.g., ameloblastoma, ossifying fibroma, ghost cell carcinoma).This paper would like to highlight some basic principles of the diagnostic approach and possibilities of radiological differentiation of such tumour-suspicious changes within the gnathic system are elucidated and discussed.The paper presented here is substantially based on the most recent classification of odontogenic and maxillofacial tumours (5th edition, 2022) which serves as a scaffold for the selection of typical tumour entities. Due to the educational character of this paper, only important jaw tumours worth mentioning and their characteristics are subject to be extracted from the literature and further discussed.The main focus was put onto both the description of radiological tumoral appearance and the rational selection of a radiological diagnostic work-up. In order to better visualize this difficult field of tumour entities, much attention has been paid on a comprehensive pictorial essay.For radiologists, it is their foremast task to detect, describe, and to classify bone tumours of the jaw when they are found intentionally or accidentally, resp. A close co-operation with their clinical partners is of upmost importance to gain information about patient's history and clinical presentation. It is readily reasonable that radiologists are mostly able to provide only a suggestion of the presented tumour entity but this expert opinion would be very helpful to further narrow down the list of potential differential diagnoses (e.g., differentiation of a cyst vs. solid tumour osteolysis, identification of jaw osteomyelitis vs. tumoral infiltration, recognizing of secondary tumour involvement of the jaw). · primary bone tumours of the jaw are very rare, moreover difficult to differentiate radiologically, and do need therefore histological proof;. · profound knowledge about tumour characteristics (location within the jaw, relationship to the tooth, bony destructive pattern) may allow a rough orientation and classification;. · matrix-forming tumours and dysplasias of the jaw facilitates their radiological differentiation and classification;. · in contrary, osteolyses should be thoroughly scrutinized for the more frequent gnathic cysts in differentiation of rather rare solid primary tumours;. · an interdisciplinary round-table discussion amongst well-experienced maxillofacial surgeons and specialized radiologists may be appropriate to avoid severe misinterpretations.. · Grieser T, Hirsch E, Tödtmann N. Bone Tumors of the Jaw - the "Blind Spot" for Radiologists Experienced with Tumors? - Part I. Fortschr Röntgenstr 2024; DOI 10.1055/a-2415-8880.

Low-Power 8T SRAM Compute-in-Memory Macro for Edge AI Processors

The traditional Von Neumann architecture creates bottlenecks due to data movement. The compute-in-memory (CIM) architecture performs computations within memory bit-cell arrays, enhancing computational performance. Edge devices utilizing artificial intelligence (AI) address real-time problems and have established themselves as groundbreaking technology. The 8T structure proposed in this paper has strengths over other existing structures in that it better withstands environmental changes within the SRAM and consumes lower power during memory operation. This structure minimizes reliance on complex ADCs, instead utilizing a simplified voltage differential approach for multiply-and-accumulate (MAC) operations, which enhances both power efficiency and stability. Based on these strengths, it can achieve higher battery efficiency in AI edge devices and improve system performance. The proposed integrated circuit was simulated in a 90 nm CMOS process and operated on a 1 V supply voltage.

Enfoque diferencial: otros caminos pedagógicos para fortalecer la educación contable desde aproximaciones a la interculturalidad

This document constitutes a reflection on the differential approach and interculturality as tools that make it possible to find other pedagogical paths to strengthen accounting education. It is a bet, which leads to approaches from to the territories and theinterweaving of knowledge with rural communities, to understand other economic logics and contribute to some extent to organizational strengthening and local development. Methodologically, it has a descriptive-participatory orientation, since the communities are vital actors in this process. It is evident from this proposal that it is possible to transcend barriers, to promote epistemic threads, between communities and academia, which enable the configuration of identity strengthening dynamics and the exercise of their autonomy, as well as the enrichment of the accounting discipline.

A comparative study of Chinese and foreign research relevant to the diffusion drift model in the last decade

Abstract. This study evaluates the differential research approaches of Chinese and international scholars regarding the drift diffusion model in mental processes through a literature review from the last decade. The application of DDM theory in cognitive research varies between Chinese and foreign study methodologies. The differences in decision-making mechanisms among cultural groups result in this outcome. Inconsistency has been identified in international academic research. They cover reward rate optimization, decision thresholds, decision time, and more. On the contrary, advancements in Chinese studies involve the development of innovative models. They are Bayesian and semi-parametric. These are designed for mixtures combining drift-diffusion processes and the inverse Gaussian distribution. In addition, they encompass elucidating the neural processes and implementing DDM techniques. This use of DDM reveals the rewarding perception and decision-making linked to depression. It also reveals the brain patterns of social anxiety. These comparisons aim to summarize the differences between Chinese and foreign studies. They also analyze them. It will find the causes of these differences. Moreover, it will determine how Chinese clinical care can use them. The DDM studies show its importance in decision-making patterns. Scholars have the opportunity to apply these tools in a variety of scientific fields, including neuroscience, psychology, artificial intelligence, and cognitive research.

Cooperative Learning Teams Games Tournament (TGT) with Content Differentiation Approach: An Effort to Improve Student Learning Outcomes in Whole Number Topics

Students often perceive mathematics as difficult and abstract, negatively affecting their interest and motivation to learn. The Teams Games Tournament (TGT) model was chosen because it incorporates healthy competition into the learning process, encouraging students to engage more actively. The main objective of this study is to improve the learning outcomes of fourth-grade students in a whole number topics by applying the Cooperative Learning Teams Games Tournament (TGT) model with a content differentiation approach. This study used Classroom Action Research (CAR) carried out in two cycles. Each cycle consists of four stages: planning, acting, observing, and reflecting. The research subjects were 27 fourth-grade students at SDN Sidosermo I Surabaya in the 2023/2024 academic year. Learning outcome data were collected through pre-tests and post-tests to assess students’ understanding before and after the intervention. The data were analyzed using descriptive analysis methods to describe the average scores and the percentage of student mastery in each cycle. Based on the pre-test results, the average student score was 61.48, with a mastery rate of 40.74%, which is below the minimum passing criterion (KKM) of 75. After the intervention in Cycle 1, the average score increased to 80.85, with a mastery rate of 52%. In Cycle 2, following strategy improvements, the average score reached 86.78, with 85% mastery. These results indicate that the TGT model with a content differentiation approach is effective in improving students' understanding and learning outcomes, surpassing the 80% mastery criterion.

Site index determination using a time series of airborne laser scanning data

Site index (SI) is determined from the top height development and is a proxy for forest productivity, defined as the expected top height for a given species at a certain index age. In Norway, an index age of 40 years is used. By using bi-temporal airborne laser scanning (ALS) data, SI can be determined using models estimated from SI observed on field plots (the direct approach) or from predicted top heights at two points in time (the height differential approach). Time series of ALS data may enhance SI determination compared to conventional methods used in operational forest inventory by providing more detailed information about the top height development. We used longitudinal data comprising spatially consistent field and ALS data collected from training plots in 1999, 2010, and 2022 to determine SI using the direct and height differential approaches using all combinations of years and performed an external validation. We also evaluated the use of data assimilation. Values of root mean square error obtained from external validation were in the ranges of 16.3%–21.4% and 12.8%–20.6% of the mean field-registered SI for the direct approach and the height differential approach, respectively. There were no statistically significant effects of time series length or the number of points in time on the obtained accuracies. Data assimilation did not result in any substantial improvement in the obtained accuracies. Although a time series of ALS data did not yield greater accuracies compared to using only two points in time, a larger proportion of the study area could be used in ALS-based determination of SI when a time series was available. This was because areas that were unsuitable for SI determination between two points in time could be subject to SI determination based on data from another part of the time series.

Role of phenylpropanoid pathway in genetic regulation and physiological adaptation in arsenic stressed rice genotypes

This study investigates the role of the phenylpropanoid pathway in arsenic (As) contaminated rice genotypes under natural conditions, exploring the intricate relationship between genetic regulation and physiological adaptation. Differential approaches adapted by rice genotypes to counteract As exposure are elucidated here through analysis of enzyme activities and related gene expression patterns, docking simulations, and nutrient dynamics. Enzymatic analysis from the phenylpropanoid pathway revealed significant variations across rice genotypes, with Mini mansoori exhibiting notably higher activity levels of key enzymes (PAL, C4H, 4CL, CHI, DFR and F3H) compared to Sampoorna and Pioneer. Additionally, the gene expression profiling unveiled differential responses, with Mini mansoori and Pioneer demonstrating higher expression of genes (OsPAL, OsCHS, OsCHI, OsF3H, OsF3′H, OsFLS, OsDFR, and OsLAR) associated with As resistance and tolerance, compared to Sampoorna. Enrichment analysis emphasized the involvement of cinnamic acid biosynthesis and related pathways. Molecular docking depicted certain proteins, such as Os4CL, OsFLS, OsDFR, and OsLAR susceptible to As binding, potentially affecting enzymatic activity. Ionomic analysis unveiled that Mini mansoori maintained higher levels of essential nutrients such as Na, Ca, P, Mn, Mg, and Zn in grains. However, this contrasted with Pioneer and Sampoorna, which experienced nutrient imbalance likely due to higher As accumulation. Chlorophyll fluorescence analysis depicted that Mini mansoori and Pioneer maintained better photosynthetic efficiency under As toxicity compared to Sampoorna. Moreover, network analysis highlights the critical role of Mg and Na interaction with essential phenolics and flavonoids, in combating the stress. Harnessing this understanding, targeted breeding effort could yield As-resistant rice varieties with enhanced nutrient and flavonoid contents, addressing both food safety and malnutrition in affected regions.

Improved convective cloud differential (CCD) tropospheric ozone from S5P-TROPOMI satellite data using local cloud fields

Abstract. We present the CHORA (Cloud Height Ozone Reference Algorithm) for retrieving tropospheric-ozone columns from S5P-TROPOMI (Sentinel-5 Precursor–TROPOspheric Monitoring Instrument). The method uses a local-cloud reference sector (CLC – CHORA Local Cloud) to determine the stratospheric (above-cloud) column, which is subtracted from the total column in clear-sky scenes in the same zonal band to retrieve the tropospheric column. The standard CCD (convective cloud differential) approach uses cloud data from the Pacific region (CPC – CHORA Pacific Cloud) instead. An important assumption for the standard method is the zonal invariance of stratospheric ozone. The local-cloud approach is the first step to diminish this constraint in order to extend the CCD method to mid-latitudes, where stratospheric-ozone variability is larger. An iterative approach has been developed for the automatic selection of an optimal local-cloud reference sector around each retrieval grid box varying latitudinally by ± 1° and longitudinally between ± 5 and ± 50°. The optimised CLCT (CHORA Local Cloud Theil–Sen) algorithm, a follow-up from the CLC, employs a homogeneity criterion for total ozone from the cloud reference sector in order to overcome the inhomogeneities in stratospheric ozone. It directly estimates the above-cloud column ozone for a common reference altitude of 270 hPa using the Theil–Sen regression. The latter allows for the combination of the CCD method with the cloud-slicing algorithm that retrieves upper-tropospheric ozone volume mixing ratios. Monthly averaged tropospheric-column ozone (TCO) using the Pacific cloud reference sector (CPC) and the local-cloud reference sector (CLC, CLCT) has been determined over the tropics and subtropics (26° S–22° N) using TROPOMI for the time period from 2018 to 2022. The accuracy of the various methods was investigated by means of comparisons with spatially collocated NASA/GSFC SHADOZ (Southern Hemisphere Additional Ozonesondes) measurements and the ESA TROPOMI level-2 tropospheric-ozone product. At eight out of nine tropical stations, tropospheric-ozone columns using the CLCT yield better agreement with ozonesondes than the CPC. In the tropical region (20° S–20° N), the CLCT shows a significantly lower overall mean bias and dispersion of 1 ± 7 %, outperforming both the CPC (12 ± 10 %) and CCD-ESA (22 ± 10 %). The CLCT surpasses the ESA operational product, providing more accurate tropospheric-ozone retrievals at eight out of nine stations in the tropics. For the Hilo station, with a larger stratospheric-ozone variability due to its proximity to the subtropics, the bias of +30 % (CPC) is effectively reduced to −5 % (CLCT). Similarly, in the subtropics (Reunion, Irene, Hanoi, and King's Park), the CLCT algorithm provides an overall bias and scatter of −11 ± 9 % with respect to sondes. The CLCT effectively reduces the impact of stratospheric-ozone inhomogeneity, typically at higher latitudes. These results demonstrate the advantage of the local-cloud reference sector in the subtropics. The algorithm is therefore an important basis for subsequent systematic applications in current and future missions of geostationary satellites, like GEMS (Geostationary Environment Monitoring Spectrometer, Korea), ESA Sentinel-4, and NASA TEMPO (Tropospheric Emissions: Monitoring of POllution), predominantly covering the middle latitudes.

Study on the New Path of Chinese Commercial Banks' Globalization Development

The globalization process of Chinese commercial banks stands at a significant historical juncture. With the increasing integration of global financial markets, China's economic power is progressively infiltrating the international arena. As vital agents of economic development, commercial banks must adapt to this trend and accelerate their internationalization efforts. However, amid the wave of globalization, Chinese commercial banks face unprecedented challenges and opportunities. On one hand, the intensifying competition in global markets and the growing complexity and uncertainty of the international financial system necessitate higher-level strategic planning. On the other hand, the rapid advancement of technology and the rise of the digital economy offer new avenues for development. Identifying an appropriate strategic position globally and achieving sustainable business growth has become a pressing issue for Chinese commercial banks. This study focuses on exploring how Chinese commercial banks can navigate the globalization process through innovative strategies, regional differentiation approaches, and multinational collaboration to find new paths that adapt to the global market, thereby securing a competitive edge in the international arena.

Grandfathering or benchmarking? The impact of carbon quota allocation rule on the joint emission reduction supply chain

The choice of carbon quota allocation rule (CQA rule) is crucial for the efficient operation of cap-and-trade policy. This paper analyses the impact of grandfathering (GF) rule and benchmarking (BM) rule on the joint emission reduction (JER) supply chain by using a differential game approach. In the proposed model, the upstream manufacturer achieves its emission reduction target through green technology and recycling, and the downstream retailer promotes these products. Our results show that compared with the benchmark case, both CQA rules do not always encourage the manufacturer to increase emission reduction investments. Moreover, the environmental and economic performance of different CQA rules is time-varying. For low-emission products, both CQA rules may yield higher total carbon emissions. For medium and high-emission products, the BM rule has a better short-term environmental performance, while the GF rule always has the best long-term environmental performance. As such, the government should prioritise the inclusion of medium and high-emission manufacturers in the carbon trading market and adopt the BM rule at the initial stage, and then transition to the GF rule. We also provide practical insights on the timing of transition, setting free carbon quotas, balancing economic and environmental performance, and dealing with uncertain market changes.