Indexing Scheme Research Articles

Estimating weight for multidimensional health poverty using Delphi method and analytic hierarchy process: a case of China

BackgroundTraditionally, poverty assessment has relied on a single income criterion, which is inadequate in contexts where absolute poverty has been mitigated. This study developed a weighting scheme for the Multidimensional Health Poverty Index (MHPI) based on sustainable livelihood framework (SLF), aimed at enhancing the accuracy of poverty identification in China.MethodsA two-round Delphi process was used to determine the indicators of the index system. The weight of each indicator was calculated using the analytic hierarchy process (AHP) method. The multidimensional poverty index incorporates SLF’s five dimensions: financial, natural, physical, social, and human capital.ResultsThe initial 46 indicators formed an indicator pool for the Delphi questionnaire. Based on the final consensus of the expert panel, the Delphi consultation resulted in an index system comprising six first-level and 23 second-level indicators. The weight values of the first-level indicators (economic security, health status, education, social capital, healthcare utilisation, and living conditions) were 0.2715, 0.2593, 0.0855, 0.0657, 0.1812, and 0.1363, respectively.ConclusionThis study established a scientific and effective index to evaluate generate weight for estimating multidimensional health poverty in China. Economic security, health status, and healthcare utilisation are the most crucial aspects of multidimensional health poverty. Moreover, the results indicated that vocational training and social capital should be emphasised.

SAFARI: Pangenome Alignment of Ancient DNA Using Purine/Pyrimidine Encodings.

Aligning DNA sequences retrieved from fossils or other paleontological artifacts, referred to as ancient DNA, is particularly challenging due to the short sequence length and chemical damage which creates a specific pattern of substitution (C→T and G→A) in addition to the heightened divergence between the sample and the reference genome thus exacerbating reference bias. This bias can be mitigated by aligning to pangenome graphs to incorporate documented organismic variation, but this approach still suffers from substitution patterns due to chemical damage. We introduce a novel methodology introducing the RYmer index, a variant of the commonly-used minimizer index which represents purines (A,G) and pyrimidines (C,T) as R and Y respectively. This creates an indexing scheme robust to the aforementioned chemical damage. We implemented SAFARI , an ancient DNA damage-aware version of the pangenome aligner vg giraffe which uses RYmers to rescue alignments containing deaminated seeds. We show that our approach produces more correct alignments from ancient DNA sequences than current approaches while maintaining a tolerable rate of spurious alignments. In addition, we demonstrate that our algorithm improves the estimate of the rate of ancient DNA damage, especially for highly damaged samples. Crucially, we show that this improved alignment can directly translate into better insights gained from the data by showcasing its integration with a number of extant pangenome tools.

Identification and hazard prioritization of hydrophobic organic chemicals in flowback and produced water particles: Implications for water management

Hydraulic fracturing flowback and produced water (HF-FPW) has raised significant concerns owing to its potential impact on aquatic organisms and human health. Understanding the chemical composition of HF-FPW is crucial for developing appropriate management and remediation strategies. Herein, we performed nontarget screening of hydrophobic organic chemicals in the particulate phase of FPW (P-FPW) using gas chromatography-Orbitrap mass spectrometry coupled with cheminformatic analysis. In total, 5807 features were discovered, with 209 annotated with positive confidence levels, which were further classified based on their chemical taxonomy and functional use information. We found that benzenoids dominated the chemical class, followed by hydrocarbons. The annotated chemicals were classified into fragrances, catalysts, antimicrobials, antioxidants, and other classes. Chemical overlap across countries (China vs. Canada) and wells was observed, with most chemicals reaching peak intensity within 24 or 48 h after the initial flowback and gradually decreasing. Approximately two-thirds of the identified or annotated chemicals have not been previously reported or included in existing HF-related databases, indicating expanded chemical coverage by the current screening workflow. A Toxicological Priority Index (ToxPi) scheme, which integrates chemical properties, ecological toxicities, and in vivo exposure potentials, was adopted to prioritize chemicals for further evaluation. Seven chemicals were proposed as prioritized compounds, of which the ester derivative of perfluorobutanoic acid, octacosyl heptafluorobutyrate (confidence level 2), exhibited the highest ToxPi score. Notably, most prioritized substances have limited toxicological data and are beyond the routine monitoring of the HF industry, highlighting significant gaps in our understanding of HF-related chemical content and environmental risk associated with water management.

Accounting for black carbon refractive index in atmospheric radiation

AbstractBecause of the fractal aggregated structure of black carbon (BC), black carbon refractive index measurements are difficult. There are substantial differences among the over 40 existing measurement schemes and no two schemes are the same. Three typical BC refractive index schemes are chosen to explore the difference in black carbon optical properties and the consequences of the radiative effect. Two schemes are widely used in climate models, and the third is from a newer measurement in 2016. It is shown that black carbon optical properties are sensitive to different refractive indices. The relative differences in extinction coefficient and single scattering albedo can be over 100%. In addition, by using Maxwell–Garnett and Bruggeman mixing rules, it has been found that the effect of internal mixing on aerosol optical properties depends strongly on the choice of refractive index. Using a one‐dimensional radiative transfer model under clear‐sky conditions, we demonstrate that the choice of black carbon refractive index influences the inferred radiative effect. Using the more recent (2016) scheme for pure black carbon can increase the top‐of‐atmosphere radiative effect by 20% relative to the currently widely used lowest‐absorbing scheme. For internally mixed aerosol, the sign of the radiative effect can change depending on which refractive index is used.

SSF-CDW: achieving scalable, secure, and fast OLAP query for encrypted cloud data warehouse

Implementing a cloud-based data warehouse to store sensitive or critical strategic data presents challenges primarily related to the security of the stored information and the exchange of OLAP queries between the cloud server and users. Although encryption is a viable solution for safeguarding outsourced data, applying it to OLAP queries involving multidimensional data, measures, and Multidimensional Expressions (MDX) operations on encrypted data poses difficulties. Existing searchable encryption solutions are inadequate for handling such complex queries, which complicates the use of business intelligence tools that rely on efficient and secure data processing and analysis.This paper proposes a new privacy-preserving cloud data warehouse scheme called SSF-CDW which facilitates a secure and scalable solution for an encrypted cloud data warehouse. Our SSF-CDW improves the OLAP queries accessible only to authorized users who can decrypt the query results with improved query performance compared to traditional OLAP tools. The approach involves utilizing symmetric encryption and Ciphertext Policy Attribute-Based Encryption (CP-ABE) to protect the privacy of the dimension and fact data modeled in Multidimensional OLAP (MOLAP). To support efficient OLAP query execution, we proposed a new data cube retrieval mechanism using a Redis schema which is an in-memory database. This technique dynamically compiles queries by disassembling them down into multiple levels and consolidates the results mapped to the corresponding encrypted data cube. The caching of dimensional and fact data associated with the encrypted cube is also implemented to improve the speed of frequently queried data. Experimental comparisons between our proposed indexed search strategy and other indexing schemes demonstrate that our approach surpasses alternative techniques in terms of search speed for both ad-hoc and repeated OLAP queries, all while preserving the privacy of the query results.

Integration of Nontarget Screening and QSPR Models to Identify Novel Organophosphate Esters of High Priority in Aquatic Environment.

With the development of large numbers of novel organophosphate esters (OPEs) alternatives, it is imperative to screen and identify those with high priority. In this study, surface water, biofilms, and freshwater snails were collected from the flow-in rivers of Taihu Lake Basin, China. Screened by target, suspect, and nontarget analysis, 11 traditional and 14 novel OPEs were identified, of which 5 OPEs were first discovered in Taihu Lake Basin. The OPE concentrations in surface water ranged from 196 to 2568 ng/L, with the primary homologue tris(2,4-ditert-butylphenyl) phosphate (TDtBPP) being newly identified, which was likely derived from the transformation of tris(2,4-ditert-butylphenyl) phosphite. The majority of the newly identified OPEs displayed substantially higher bioaccumulation and biomagnification potentials in the biofilm-snail food chain than the traditional ones. Quantitative structure-property relationship models revealed both hydrophobicity and polarity influenced the bioaccumulation and biomagnification of the OPEs, while electrostatic attraction also had a contribution to the bioaccumulation in the biofilm. TDtBPP was determined as the utmost priority by toxicological priority index scheme, which integrated concentration, bioaccumulation, biomagnification, acute toxicity, and endocrine disrupting potential of the identified OPEs. These findings provide novel insights into the behaviors of OPEs and scientific bases for better management of high-risk pollutants in aquatic ecosystem.

APB-tree: An Adaptive Pre-built Tree Indexing Scheme for NVM-based IoT Systems

APB-tree: An Adaptive Pre-built Tree Indexing Scheme for NVM-based IoT Systems

Optimizing subgraph retrieval and matching with an efficient indexing scheme

Optimizing subgraph retrieval and matching with an efficient indexing scheme

Fast retrieval of multi-modal embeddings for e-commerce applications

In this paper, we introduce a retrieval framework designed for e-commerce applications, which employs a multi-modal approach to represent items of interest. This approach incorporates both textual descriptions and images of products, alongside a locality-sensitive hashing (LSH) indexing scheme for rapid retrieval of potentially relevant products. Our focus is on a data-independent methodology, where the indexing mechanism remains unaffected by the specific dataset, while the multi-modal representation is learned beforehand. Specifically, we utilize a multi-modal architecture, CLIP, to learn a latent representation of items by combining text and images in a contrastive manner. The resulting item embeddings encapsulate both the visual and textual information of the products, which are then subjected to various types of LSH for balancing between result quality and retrieval speed. We present the findings of our experiments conducted on two real-world datasets sourced from e-commerce platforms, comprising both product images and textual descriptions. Promising results have been achieved, demonstrating favorable retrieval time and average precision. These results were obtained through testing the approach with a specifically selected set of queries and with synthetic queries generated using a Large Language Model.

The Ring: Worst-case Optimal Joins in Graph Databases using (Almost) No Extra Space

We present an indexing scheme for triple-based graphs that supports join queries in worst-case optimal (wco) time within compact space. This scheme, called a ring , regards each triple as a cyclic string of length 3. Each rotation of the triples is lexicographically sorted and the values of the last attribute are stored as a column, so we obtain the order of the next column by stably re-sorting the triples by its attribute. We show that, by representing the columns with a compact data structure called a wavelet tree, this ordering enables forward and backward navigation between columns without needing pointers. These wavelet trees further support wco join algorithms and cardinality estimations for query planning. While traditional data structures such as B-Trees, tries, and so on, require 6 index orders to support all possible wco joins over triples, we can use one ring to index them all. This ring replaces the graph and uses only sublinear extra space, thus supporting wco joins in almost no space beyond storing the graph itself. Experiments querying a large graph (Wikidata) in memory show that the ring offers nearly the best overall query times while using only a small fraction of the space required by several state-of-the-art approaches. We then turn our attention to some theoretical results for indexing tables of arity d higher than 3 in such a way that supports wco joins. While a single ring of length d no longer suffices to cover all d ! orders, we need much fewer rings to index them all: O (2 d ) rings with a small constant. For example, we need 5 rings instead of 120 orders for d =5. We show that our rings become a particular case of what we dub order graphs , whose nodes are attribute orders and where stably sorting by some attribute leads us from an order to another, thereby inducing an edge labeled by the attribute. The index is then the set of columns associated with the edges, and a set of rings is just one possible graph shape. We show that other shapes, like for example a single ring instead of several ones of length d , can lead us to even smaller indexes, and that other more general shapes are also possible. For example, we handle d =5 attributes within space equivalent to 4 rings.

Assessing the spatial distribution of elemental concentrations in surface sediments of Lake Victoria, Kenya: implications for ecological health and management.

Lacustrine sediment quality indicates the effects of both natural and anthropogenic activities on the ecosystem and communities. Despite its ecological importance, myriad complexities, and potential contaminant sources, the spatial distribution of surficial sediments in Lake Victoria's Winam Gulf has never been comprehensively documented. The purpose of this study was to assess the spatial distribution, pathways, and ecological risk of metal elements in the lake using a sediment matrix. Sediment samples were collected throughout the gulf in November 2022. The concentrations of Al, As, Cd, Co, Cr, Cu, Fe, K, Mn, Mo, Ni, P, Pb, Sb, Sn, Ti, Tl, U, and Zn were compared to different contamination metrics and ecological risk assessment indices. The average concentrations were in the following decreasing order: Zn > > > Cr > > Cu > Ni > Pb > Co > As > Cd with mean (± SD) of 185 ± 45mgkg-1, 56 ± 15mgkg-1, 45 ± 16mgkg-1, 37 ± 11mgkg-1, 24 ± 5mgkg-1, 20 ± 7mgkg-1, 3.9 ± 1.3mgkg-1, 0.30 ± 0.09mgkg-1, respectively, with strong indications of anthropogenic sources. Average concentrations were in the following decreasing order: Zn > > > Cr, Cu, Ni, Pb, Co, As, and Cd levels (mean ± SD) were 185 ± 45mgkg-1, 56 ± 15mgkg-1, 45 ± 16mgkg-1, 37 ± 11mgkg-1, 24 ± 5mgkg-1, 20 ± 7mgkg-1, 3.9 ± 1.3mgkg-1 and 0.30 ± 0.09mgkg-1 with strong indications of anthropogenic sources. The geo-accumulation index (Igeo) and enrichment factor categorisation schemes, respectively, classified these as uncontaminated (level 0) and depletion to minimal enrichment (level 1), while the ecological risk analysis classified them as "low risk". The mouth of the Nyando River, as well as Kisumu, Kendu, and Homa bays, were the most element-enriched and should be prioritised for focused monitoring and remediation. As a result, targeted land management of urban, industrial, transportation, and agricultural areas offers the opportunity to reduce sediment inputs into the lake ecosystem.

TT-HEALpix: A New Data Indexing Strategy for Efficient Cross-match of Large-scale Astronomical Catalogs

Cross-matching is an indispensable operation in the data preparation, analysis, and research processes of multi-band astronomy and time-domain astronomy. Multi-catalog time-series data reconstruction is an important part of time-domain astronomy. In the large-scale distributed reconstruction process, boundary problems have always affected the accuracy of time-series data. To optimize these boundary problems and improve data precision, this paper proposes a new hybrid astronomical data indexing method called Translated Transformation based HEALPix Dual Index (TT-HEALPix). Under the reasonable Healpix division level, by translation transformation, the two indexes before and after the transformation form a unique pseudo-hybrid index strategy, which not only retains the advantages of the hybrid index scheme suitable for large-scale parallel computing, but also compensates for its shortage of high omission at the block boundary position. Based on TT-HEALPix, this paper completes the multi-catalog time-series reconstruction process on the Spark platform and compares it with the HEALPix+HTM hybrid indexing strategy. The experiments demonstrate that TT-HEALPix has significant advantages over the traditional HEALPix+HTM hybrid indexing method in terms of data accuracy and cross-matching efficiency. At level 9 of the Healpix index, TT-HEALPix achieves a 6%–19% improvement in cross-matching efficiency in a distributed environment compared to HEALPix+HTM. In terms of data accuracy, for the AST3-II dataset at level 9, TT-HEALPix has 62.2% accuracy improvement over HEALPix and 45.5% improvement over HEALPix+HTM. In conclusion, the proposed novel indexing strategy, TT-HEALPix, is better suited to the efficiency and accuracy requirements of cross-match.

Assessment of terrestrial ecosystem sensitivity to climate change in arid, semi-arid, sub-humid, and humid regions using EVI, LAI, and SIF products

Arid and humid ecosystems are undergoing significant changes over the past decades due to the warming temperature and frequent occurrence of climate extremes. Quantifying climatic vegetation sensitivity in regions with different climate classifications is crucial for understanding the interaction mechanisms between vegetation and ongoing climate change. However, such knowledge about the spatiotemporal variations of vegetation sensitivity and its climate driving factors in arid and humid regions is still unclear. Based on this consideration, a moving-window-based Vegetation Sensitivity Index (VSI) scheme was applied to Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Solar-induced Chlorophyll Fluorescence (GOSIF) data to evaluate the spatiotemporal variations of terrestrial ecosystem in response to climate changes and explore its driving climatic factors from 2003 to 2020. Results indicated that high VSI (>60) was observed for tropical rainforests in humid regions and central Eurasia in semi-arid regions. By contrast, low VSI (<40) was typically observed in arid regions. Spatially, we found VSI exhibited a nonlinear parabolic variation along the gradient of aridity index in the arid and semi-arid regions but generally presented a linear variation with an upward trend in the humid regions. Temporally, VSI showed a significant declining trend over the past two decades but displayed a significantly increasing trend in arid areas. Temperature was the dominant climatic factor to explain the spatial variations of VSI globally, while precipitation mainly dominated the temporal variations of VSI. The dominant climatic factor of VSI varied across different aridity regions as temperature and precipitation were the main climate factor to drive spatiotemporal variations of VSI in humid and arid classifications, respectively. Our results provide unique insights into the vegetation sensitivity in response to ongoing and future climate change in regions with different climate classifications.

Optimisation and Quality Evaluation of Teaching Models in Primary and Secondary Schools Based on Deep Learning

Abstract This paper examines the concept of deep learning and evaluates the quality of teaching models in primary and secondary schools. Based on the comprehensive evaluation application of fuzzy theory, the fuzzy model is established, the evaluation index scheme is selected, and the quality evaluation model of primary and secondary school teaching mode is constructed. The evaluation index system is divided into indexes, and the weights of indexes are calculated, and the first-level indexes are divided into teaching content, teaching management, teaching effect, and teaching attitude. Calculate the fuzzy comprehensive evaluation score of primary and secondary school teaching mode and adjust the primary and secondary school teaching mode based on the score. The final score of a comprehensive evaluation of teaching mode is 4.32, teaching content Q 1 = 4.33, teaching attitude Q 2 = 4.26, teaching management Q 3 = 4.43, and teaching effect Q 4 = 4.05. In the quality evaluation of primary and secondary school teaching mode, teaching management &gt; teaching content &gt; teaching attitude &gt; teaching effect.

SH2O: Efficient Data Access for Work-Sharing Databases

Interactive applications require processing tens to hundreds of concurrent analytical queries within tight time constraints. In such setups, where high concurrency causes contention, work-sharing databases are critical for improving scalability and for bounding the increase in response time. However, as such databases share data access using full scans and expensive shared filters, they suffer from a data-access bottleneck that jeopardizes interactivity. We present SH2O: a novel data-access operator that addresses the data-access bottleneck of work-sharing databases. SH2O is based on the idea that an access pattern based on judiciously selected multidimensional ranges can replace a set of shared filters. To exploit the idea in an efficient and scalable manner, SH2O uses a three-tier approach: i) it uses spatial indices to efficiently access the ranges without overfetching, ii) it uses an optimizer to choose which filters to replace such that it maximizes cost-benefit for index accesses, and iii) it exploits partitioning schemes and independently accesses each data partition to reduce the number of filters in the access pattern. Furthermore, we propose a tuning strategy that chooses a partitioning and indexing scheme that minimizes SH2O's cost for a target workload. Our evaluation shows a speedup of 1.8-22.2 for batches of hundreds of data-access-bound queries.

Abstract 14262: Cancer - A Driver of Heart Failure in People With Diabetes

Introduction: Despite the risk of heart failure (HF) in the survivorship phase of some cancers, the risk is not high enough to justify HF screening. However, people with diabetes mellitus (DM) have an increased risk of both HF and cancer. Examining this at-risk population may allow for better understanding of the effect cancer has on the development of HF. Hypothesis: Among people with DM, HF is more common in those with than without prior cancer. Methods: Data from the Australian National Diabetes Services Scheme (NDSS; 1,114,356 DM, types 1 and 2) from 2010 to 2019, was linked with public hospital data, the National Death Index and national Pharmaceutical Benefits Scheme (PBS) database. People with cancer were identified by the prescription of chemotherapy agents on the PBS dataset or a hospital admission with cancer. Incident HF was detected via first hospitalisation with HF as the primary diagnosis or death due to HF. The model was adjusted for age and sex. Results: Of the total cohort (1,114,356 people with DM), 48,014 experienced HF during 6,663,046 person-years of follow up from 2010-2019. Specifically, of 175,656 people with DM and cancer, 8,464 experienced HF during 644,620 person-years of follow up. The HF incidence rate ratio (IRR) of people with DM and cancer was 1.63 (95%CI: 1.59-1.67), compared to people with DM but no cancer. Cancer admission alone had an IRR of 1.97 (1.90-2.05), with an IRR of 1.50 (1.46-1.54) in DM treated with chemotherapy with or without admission. Conclusion: Cancer diagnosis is associated with an increased incidence of HF among people with DM. The higher IRR of people with cancer admission alone supports the association of cancer with HF irrespective of chemotherapy. Further studies controlling for confounders of cardiovascular risk and refinements by age are required to justify grounds for HF screening.

An efficient searchable scheme for encrypted high dimensional data

The growing prominence and progress of cloud computing has prompted data owners to delegate data management to cloud servers, leading to substantial cost reduction and enhanced accessibility. To address privacy concerns, sensitive data must be encrypted before outsourcing, which impairs usability and renders traditional plaintext-based keyword search techniques obsolete. Several search schemes based on encrypted data have been proposed. However, most of these schemes use inverted indexes and tree indexes, resulting in a linear correlation between the index size and the size of the keyword dictionary, which is insufficient in terms of search efficiency. In this paper, we propose a secure multi-keyword ranking search scheme based on Singular Value Decomposition technology to provide more precise and efficient results for secure multi-keyword ranking searches. Experimental results show that our scheme can save at least 50% of the time compared to the traditional indexing scheme.

Competency Test Scheme Development to Meet Competency Achievements of Graduates In Industry

A competency test scheme at LSP PIP Semarang needs to be developed to increase the competitiveness of graduates and job opportunities. This study aims to formulate a competency test scheme that has the potential to be developed at LSP PIP Semarang in accordance with the needs of industry, the business world, and the world of work (IDUKA) and to determine the impact of competency certificate ownership on labor absorption. Data analysis was carried out descriptively using satisfaction index, GPA, and potential competency schemes to be developed at LSP PIP Semarang. The research was conducted through an online survey involving cadets and graduated users as respondents. The results showed that LSP PIP Semarang had a good competency test service performance. It can be seen from the high achievement of satisfaction index, which is 3.74 – 3.84. Moreover, the GPA also shows the trust level of cadets in the management of competency test services, as indicated by the achievement of an index of 5.57 – 5.68. The investigation results demonstrate that the absorption rate of the PIP Semarang graduates with competency certificates is very high, ranging from 95 – 100%. Several types of competencies are of interest to cadets, which are needed in the job market but are not yet available at LSP PIP Semarang, so they have the potential to be developed, such as K3, PPJK, and logistics. This refers to cadets' interests and the job market's current needs.

Designing aperture [formula omitted] hexagonal grids and their indexing as factor rings of Eisenstein integers

This paper proposes apertures of size 22k for hexagonal grids together with their indexing scheme, where k may be any positive integer. Hexagonal grid can be represented by an ideal I(2+ω) of a unit-ring Z(ω) of Eisenstein integers, where, for the set Z of integers and the cube root ω of 1, Z(ω) denotes {a+bω|a,b∈Z}, and, for any λ∈Z(ω), I(λ) denotes an ideal {κλ|κ∈Z(ω)} of Z(ω). We show that a factor ring I(2+ω)/I(2k(2+ω)) represents an aperture 22k hexagonal grid, and prove that it is isomorphic to a factor ring Z(ω)/I(2k). The latter represents a trigonal grid in a rhombus-shaped aperture with its edge length 2k, where we can address each of its 22k grid points as i+jω using two k-bit positive binary integers. We show that each grid point α=a+bω in I(2+ω)/I(2k(2+ω)) can be indexed by the address of its isomorphic image in Z(ω)/I(2k), and that this index is identical to the pair of the k-bit 2′s complements of a and b. Based on these findings, we clarify how indices may change for different resolutions, how to define the hierarchical indexing, how to geometrically interpret algebraic operations on indices, and, as one of the applications, how the quantization of trajectories of 106∼107 mobile objects on hexagonal grids may reduce the communication capacity required for their quasi-real-time simultaneous monitoring and sharing through the Internet. We also show how our indexing scheme can be applied to the design of DGGSs (Discrete Global Grid Systems) that cover the entire Earth's surface and 3D regular grids.

A High-performance RDMA-oriented Learned Key-value Store for Disaggregated Memory Systems

Disaggregated memory systems separate monolithic servers into different components, including compute and memory nodes, to enjoy the benefits of high resource utilization, flexible hardware scalability, and efficient data sharing. By exploiting the high-performance RDMA (Remote Direct Memory Access), the compute nodes directly access the remote memory pool without involving remote CPUs. Hence, the ordered key-value (KV) stores (e.g., B-trees and learned indexes) keep all data sorted to provide range query services via the high-performance network. However, existing ordered KVs fail to work well on the disaggregated memory systems, due to either consuming multiple network roundtrips to search the remote data or heavily relying on the memory nodes equipped with insufficient computing resources to process data modifications. In this article, we propose a scalable RDMA-oriented KV store with learned indexes, called ROLEX, to coalesce the ordered KV store in the disaggregated systems for efficient data storage and retrieval. ROLEX leverages a retraining-decoupled learned index scheme to dissociate the model retraining from data modification operations via adding a bias and some data movement constraints to learned models. Based on the operation decoupling, data modifications are directly executed in compute nodes via one-sided RDMA verbs with high scalability. The model retraining is hence removed from the critical path of data modification and asynchronously executed in memory nodes by using dedicated computing resources. ROLEX efficiently alleviates the fragmentation and garbage collection issues, due to allocating and reclaiming space via fixed-size leaves that are accessed via the atomic-size leaf numbers. Our experimental results on YCSB and real-world workloads demonstrate that ROLEX achieves competitive performance on the static workloads, as well as significantly improving the performance on dynamic workloads by up to 2.2× over state-of-the-art schemes on the disaggregated memory systems. We have released the open-source codes for public use in GitHub.