Unique List Research Articles

The Association of Comorbidities With Total Knee Arthroplasty Healthcare Utilization

Patients undergoing total knee arthroplasty (TKA) tend to have various comorbidities such as obesity, hypertension, and cancer, which are partially associated with their advanced age. This study aimed to elucidate the associations of these comorbidities with hospital charge (HC), hospital costs (HCo), and length of stay (LOS). A national database was queried for patients who underwent primary TKA from 2012 to 2020. There were 30 comorbidities classified using the Elixhauser Comorbidity Index. For each comorbidity of interest, patients were matched to another patient by age, sex, and all other comorbidities except the comorbidity of interest. Matched cohorts were analyzed via multivariable regression analyses controlled for age, sex, procedure year, hospital location, and comorbidities to predict increases in HC, HCo, and LOS. A total of 1,014,831 patients were included. Mean HC, HCo, and LOS were $64,097, $17,299, and 2.5 days, respectively. Weight loss, blood loss anemia, coagulopathies, and fluid and electrolyte disorders were associated with the largest increases in HC, with 6, 5, 5, and 5% increases, respectively (P < 0.05). Pulmonary hypertension, non-metastatic tumors, paralysis, and obesity were associated with increased HCo, with 6, 4, 4, and 4% increases, respectively (P < 0.05). All comorbidities except hypothyroidism were associated with increased LOS. Pulmonary hypertension, weight loss, paralysis, fluid and electrolyte disorders, and human immunodeficiency virus/acquired immunodeficiency syndrome were associated with were associated with 53, 21, 15, 14, and 12%, respectively, increased odds of having LOS greater than or equal to four days' increase (P < 0.05). This study offers targets for the reduction of HC, HCo, and LOS for patients with these modifiable and non-modifiable ailments through protocol change. Optimization programs targeting malnourished patients, patients with pulmonary hypertension, and other comorbidities are encouraged. These results also provide clinicians with a quantifiable way to communicate financial burden and recovery time after TKA to patients' unique problem lists.

Effects of word presentation during treadmill walking on episodic memory and gait

Entrainment emerges when oscillatory movements synchronize with environmental stimuli processing. The purpose of this experiment was to assess how cognitive-motor entrainment during a dual-task would influence the quality of gait and affect episodic long-term memory. Twenty-one participants (22.56 y/o; 64 % F) walked at preferred paces while listening to 40-item word lists. In separate sessions, unique word lists were presented predictably on every fourth stride, unpredictably related to stepping, or predictably while standing. Memory tests administered 24-hr after encoding revealed that predictable word presentation led to better free-recall performance than unpredicted (p = .044); recognition memory was not impacted. Gait phase parameters during the predicted condition were more stable than the unpredicted condition or baseline assessments. Cognitive-motor entrainment may alleviate dual-task costs and enhance memory retention.

Alpha and theta oscillations on a visual strategic processing task in age-related hearing loss.

Emerging evidence suggests changes in several cognitive control processes in individuals with age-related hearing loss (ARHL). However, value-directed strategic processing, which involves selectively processing salient information based on high value, has been relatively unexplored in ARHL. Our previous work has shown behavioral changes in strategic processing in individuals with ARHL. The current study examined event-related alpha and theta oscillations linked to a visual, value-directed strategic processing task in 19 individuals with mild untreated ARHL and 17 normal hearing controls of comparable age and education. Five unique word lists were presented where words were assigned high- or low-value based on the letter case, and electroencephalography (EEG) data was recorded during task performance. The main effect of the group was observed in early time periods. Specifically, greater theta synchronization was seen in the ARHL group relative to the control group. Interaction between group and value was observed at later time points, with greater theta synchronization for high- versus low-value information in those with ARHL. Our findings provide evidence for oscillatory changes tied to a visual task of value-directed strategic processing in individuals with mild untreated ARHL. This points towards modality-independent neurophysiological changes in cognitive control in individuals with mild degrees of ARHL and adds to the rapidly growing literature on the cognitive consequences of ARHL.

A mass defect-based approach for the automatic construction of peak lists for databases of mass spectra with limited resolution: Application to time-of-flight secondary ion mass spectrometry data.

This study has developed a data processing protocol based on mass defect analysis for the automatic construction of unique peak lists addressing the need for the fast and efficient treatment of databases of mass spectra with limited mass resolution. The data processing protocol, implemented in MATLAB, is tested on a database of 126 mass spectra obtained from time-of-flight secondary ion mass spectrometry analysis of the exhaust of a laboratory diesel miniCAST burner deposited on Ti substrates. The data processing protocol converts the mass spectra into a data matrix suitable for chemometrics (peak list) by combining mass defect analysis and multivariate analysis. In particular, the role of the mass defect analysis is expanded to improve mass calibration and automate the construction of the peak list. In this context, mass defect analysis becomes an invaluable technique for the efficient processing of databases of mass spectra with limited mass resolution by allowing the fast and automated construction of a peak list common to all mass spectra, by improving the mass calibration, and finally by reducing the number of molecular formulae consistent with a given accurate mass, thus facilitating the identification of unknown ions.

Identification of MicroRNAs Binding Site in the 3’Untranslated Region of Long Non-Coding RNA, MIR497HG: A Bioinformatic Prediction

Introduction: Prediction and identification of miRNAs target genes are crucial for understanding the biology of miRNAs. Amidst reported long-coding RNA (lncRNA), the microRNA 195-497 cluster host gene (MIR497HG) regulation is mediated by multiple non-coding RNAs (ncRNAs) such as microRNAs (miRNAs). MIR497HG has been implicated as a tumour suppressor in various cancers. However, the impact of MIR497HG and its derived miRNAs is largely unknown and still needs to be further explored. Employing an experimental approach is often challenging since some lncRNAs are difficult to identify and isolate by the current isolation technique. Thus, bioinformatic tools are introduced to aid these problems. This study sought to search and identify the miRNAs targeting the 3’untranslated region (3’UTR) of MIR497HG. Methods: Here, bioinformatic tools were adopted to identify a unique list of miRNAs that potentially target the 3’UTR of MIR497HG. Results: A total of 57 candidate miRNAs that target the 3’UTR of MIR497HG were extracted using the miRDB. Meanwhile, STarMir predicted 291 miRNAs that potentially target the 3’UTR of MIR497HG. A common list of 36 miRNAs was obtained using the Venny 2.1.0 and further narrowed down using the LogitProb score of StarMir. Finally, a total 4 miRNAs (hsa-miR-3182, hsa-miR-7156-5p, hsa-miR-452-3p and hsa-miR-2117) were identified. The mRNA target of identified miRNAs was identified by TargetScan. Finally, Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of mRNA target was done using Enrichr. Conclusion: This finding could be useful in understanding the complex interaction between MIR497HG and its regulatory miRNA. In addition, a comparative analysis of computational miRNA-target predictions is provided in this study would potentially lay the foundations for miRNAs to be used for biomarkers in cancer research.

Unveiling the intricacy of gapmer oligonucleotides through advanced tandem mass spectrometry approaches and scan accumulation for 2DMS.

Antisense oligonucleotides (ASOs) are crucial for biological applications as they bind to complementary RNA sequences, modulating protein expression. ASOs undergo synthetic modifications like phosphorothioate (PS) backbone and locked nucleic acid (LNA) to enhance stability and specificity. Tandem mass spectrometry (MS) techniques were employed to study gapmer ASOs, which feature a DNA chain within RNA segments at both termini, revealing enhanced cleavages with ultraviolet photodissociation (UVPD) and complementary fragment ions from collision-induced dissociation (CID) and electron detachment dissociation (EDD). 2DMS, a data-independent analysis technique, allowed for comprehensive coverage and identification of shared fragments across multiple precursor ions. EDD fragmentation efficiency correlated with precursor ion charge states, with higher charges facilitating dissociation due to intramolecular repulsions. An electron energy of 22.8 eV enabled electron capture and radical-based cleavage. Accumulating multiple scans and generating average spectra improved signal intensity, aided by denoising algorithms. Data analysis utilised a custom Python script capable of handling modifications and generating unique mass lists.

An Efficient and Secure Blockchain Consensus Protocol for Internet of Vehicles

Conventional blockchain consensus protocols tailored for the Internet of Vehicles (IoV) usually face low transaction throughput, high latency, and elevated communication overhead issues. To address these issues, in this paper, we propose ESBCP, an efficient and secure blockchain consensus protocol for the IoV environment. Firstly, considering the significant performance differences among nodes in the IoV, we designed a blockchain consensus model for the IoV. Roadside units execute a trust evaluation mechanism to select high-quality vehicle nodes for the consensus process, thereby reducing the likelihood of malicious nodes in the consensus cluster. Secondly, we designed a node partition strategy to adapt to the dynamic feature of the IoV. Finally, addressing the mobility of nodes in the IoV, we introduced a dynamic unique node list. Vehicle nodes can promptly select nodes with high reliability from the list of communicable nodes to join their unique node list, while also promptly removing nodes with low reliability from their unique node list. Combining these strategies, we propose DK-PBFT, an improved Practical Byzantine Fault Tolerance consensus algorithm. The algorithm meets the efficiency and mobility requirements of vehicular networks. Through theoretical analysis, ESBCP could prevent external and internal security risks while reducing communication overhead. Experimental verification demonstrated that ESBCP effectively reduces consensus latency and improves transaction throughput. Our proposed ESBCP can be used in other application scenarios that require high consensus efficiency.

Identifying Similar Allergens and Potentially Cross-Reacting Areas Using Structural Database of Allergenic Proteins (SDAP) Tools and D-Graph.

The Structural Database of Allergenic Proteins (SDAP) provides rapid search tools to identify similarities among allergens, their IgE epitopes, and to determine the potential allergenicity of any novel protein. Many labs have identified IgE-binding proteins and their antibody binding or T cell epitopes using dotspots or microarrays. This chapter describes how to determine the relationship of these proteins and peptides to known allergens using the tools implemented in SDAP. One can also search with these smaller peptide similarity search tool implemented in SDAP to find similar sequences with low property distance (PD) values in the over 1500 sequences of allergens. The sequences can be compared by mapping on the surface of the protein structures provided for nearly all the allergens in SDAP. Once the user has a unique list of similar sequences, they can be graphed in 2D according to interpeptide PD values calculated automatically by the D-graph program. This chapter provides a step-by-step description of how to do this, starting from a protein similar to the Ole e 1 (olive pollen) allergen family.

Impact of ICME- and SIR/CIR-Driven Geomagnetic Storms on the Ionosphere over Hungary

We investigate the differences between the effects of geomagnetic storms due to Interplanetary Coronal Mass Ejections (ICME) and due to Stream Interaction Regions or Corotating Interaction Regions (SIR/CIR) on the ionospheric F2-layer during the maximum of solar cycle 24. We have created a unique list of the ICME- and SIR/CIR-driven geomagnetic storm events for the time interval between November 2012 and October 2014. Finally, 42 clear ICME and 34 clear SIR/CIR events were selected for this analysis. The individual geomagnetic storm periods were grouped by seasons, time of day, and local time of Dstmin and were analyzed using three different methods: linear correlation analysis using 4-h averages of foF2 parameters and the geomagnetic indices (1st), daily variation of deltafoF2 (2nd), and 3D plotting: geomagnetic indices vs. time vs. deltafoF2 (3rd). The main phase day of the ICME- and SIR/CIR-induced geomagnetic storms was our main focus. We used manually evaluated ionospheric foF2 parameters measured at the Sopron ionosonde station and the geomagnetic indices (Kp, Dst, and AE) for this analysis. We have found that in most cases, the variation of the Dst index is the best indicator of the impact caused in the F2 layer. We conclude as well that the representation of the data by the third method gives a better description of the ICME and SIR/CIR-triggered storm behavior. In addition, our investigation shows that the SIR/CIR-related perturbations can be predicted with greater accuracy with the second method.

Features of the Image of the USSR on the Pages of the American Press in the Last Years of the Soviet Union

The article discusses the features of the image of the Soviet Union in the American press in 1990–1991. Based on the analysis of the articles from the liberal weekly news magazine “Time”, the research aims to identify the characteristic features of the representation of the image of the USSR on the pages of the American press in the last years of the Soviet Union and to analyze the dominant stereotypes and the specifics of their formation. The author resorts to quantitative and semantic analysis of texts from “Time” magazine covering events in the USSR.The paper examines the problems of the collapse of the USSR but for the first time from the perspective of the imagological study. Based on the frequency analysis of lexemes, the author proposes a unique thematic list of rubrics that reflects the focus of interest of American journalists when covering events related to the Soviet Union. On the pages of “Time”, the concept of “Russian people” is presented as a passive object that has nothing to do with politics. There is also a division of political forces into positive ones, represented by M. S. Gorbachev and B. N. Yeltsin, and negative, pursuing a repressive policy, presented as abstractions “Kremlin”, “Moscow”, “Soviet Union”. The interpretation of the events of the August putsch deserves special attention - the persistent drawing of parallels with the revolution of 1917.

A joint international consensus statement for measuring quality of survival for patients with childhood cancer.

The aim of treating childhood cancer remains to cure all. As survival rates improve, long-term health outcomes increasingly define quality of care. The International Childhood Cancer Outcome Project developed a set of core outcomes for most types of childhood cancers involving relevant international stakeholders (survivors; pediatric oncologists; other medical, nursing or paramedical care providers; and psychosocial or neurocognitive care providers) to allow outcome-based evaluation of childhood cancer care. A survey among healthcare providers (n = 87) and online focus groups of survivors (n = 22) resulted in unique candidate outcome lists for 17 types of childhood cancer (five hematological malignancies, four central nervous system tumors and eight solid tumors). In a two-round Delphi survey, 435 healthcare providers from 68 institutions internationally (response rates for round 1, 70-97%; round 2, 65-92%) contributed to the selection of four to eight physical core outcomes (for example, heart failure, subfertility and subsequent neoplasms) and three aspects of quality of life (physical, psychosocial and neurocognitive) per pediatric cancer subtype. Measurement instruments for the core outcomes consist of medical record abstraction, questionnaires and linkage with existing registries. This International Childhood Cancer Core Outcome Set represents outcomes of value to patients, survivors and healthcare providers and can be used to measure institutional progress and benchmark against peers.

Analysis of 200 unrelated individuals with a constitutional NF1 deep intronic pathogenic variant reveals that variants flanking the alternatively spliced NF1 exon 31 [23a] cause a classical neurofibromatosis type 1 phenotype while altering predominantly NF1 isoform type II

Neurofibromatosis type 1 results from loss-of-function NF1 pathogenic variants (PVs). Up to 30% of all NF1 PVs disrupt mRNA splicing, including deep intronic variants. Here, we retrospectively investigated the spectrum of NF1 deep intronic PVs in a cohort of 8,090 unrelated individuals from the University of Alabama at Birmingham (UAB) dataset with a molecularly confirmed neurofibromatosis type 1. All variants were identified through their effect on the NF1 transcript, followed by variant characterization at the DNA-level. A total of 68 distinct variants, which were ≥ 20 nucleotides away from the closest exon–intron junction, were identified in 2.5% unrelated individuals with NF1 (200/8,090). Nine different pathogenic splice variants, identified in 20 probands, led to exonization of different parts of intron 30 [23.2] or 31 [23a]. The two major NF1 transcript isoforms, distinguished by the absence (type I) or presence (type II) of the alternatively spliced cassette exon 31 [23a], are equally expressed in blood in control individuals without NF1 or NF1-affected individuals carrying their PV not in the introns flanking exon 31 [23a]. By fragment and cloning analysis we demonstrated that the exonization of intron 31 [23a] sequences due to deep intronic PV predominantly affects the NF1 isoform II. Seven additional (likely) pathogenic NF1 deep intronic variants not observed in the UAB dataset were found by classification of 36 variants identified by a literature search. Hence, the unique list of these 75 deep intronic (likely) PVs should be included in any comprehensive NF1 testing strategy.

Who can verify this? Finding authorities for rumor verification in Twitter

A large body of research work has proposed verification techniques for rumors spreading in social media that mainly relied on subjective evidence, e.g., propagation networks or user interactions. Alternatively, in this work, we introduce the task of authority finding in social media, in which we aim to find authorities, for given rumors spreading specifically in Twitter, who can help verify them by providing exclusive/convincing evidence that supports or denies those rumors. We release the first test collection for Authority FINding in Arabic Twitter (AuFIN). The collection comprises 150 rumors (expressed in tweets) associated with a total of 1,044 authority accounts and a user collection of 395,231 Twitter accounts (members of 1,192,284 unique Twitter lists). Moreover, we propose a hybrid model that employs pre-trained language models and combines lexical, semantic, and network signals to find authorities. Our experiments show that the textual representation of users is insufficient, and incorporating the Twitter network features improved the recall of authorities by 34%. Moreover, semantic ranking is inferior to the lexical and network-based ranking in terms of precision, but superior in terms of recall. Therefore, combining both the semantic and network-based ranking achieved the best overall performance achieving a precision of 0.413 and 0.213 at depth 1 and 5 respectively. We show that rumor expansion by exploiting Knowledge Bases improves the recall of authorities by up to 15%. Furthermore, we find that SOTA models for topic expert finding perform poorly on finding authorities. Finally, drawing upon our experiments, we discuss failure factors and make recommendations for future research directions in addressing this task.

High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning

This study combines measurements of dissolved inorganic carbon (DIC), total alkalinity (TA), pH, earth observation (EO), and ocean model products with deep learning to provide a good step forward in detecting changes in the ocean carbonate system parameters at a high spatial and temporal resolution in the North Atlantic region (Long. −61.00° to −50.04° W; Lat. 24.99° to 34.96° N). The in situ reference dataset that was used for this study provided discrete underway measurements of DIC, TA, and pH collected by M/V Equinox in the North Atlantic Ocean. A unique list of co-temporal and co-located global daily environmental drivers derived from independent sources (using satellite remote sensing, model reanalyses, empirical algorithms, and depth soundings) were collected for this study at the highest possible spatial resolution (0.04° × 0.04°). The resulting ANN-estimated DIC, TA, and pH obtained by deep learning shows a high correspondence when verified against observations. This study demonstrates how a select number of geophysical information derived from EO and model reanalysis data can be used to estimate and understand the spatiotemporal variability of the oceanic carbonate system at a high spatiotemporal resolution. Further methodological improvements are being suggested.

Bringing biophysics outreach to a county fair

In an effort to promote more STEM engagement with hard-to-reach rural school children, we designed and implemented a distinctive set of interactive biophysics-related displays for our local county fair. Due to the location and the audience, these demonstrations had a unique list of design criteria, including being attractive to an average fairgoer, being exceptionally rugged, transportable, and being able to fit into the fair's indoor booth. Through a combination of using pop culture to engage students, building fun and interactive displays, and emphasizing real-world applications in our lessons, we were able to engage with hundreds of local children. We envision that others hoping to do similar projects will create their own unique displays that best reflect their community, school, and research interests. Therefore, to best facilitate the creation of other fair booths, here we present the hurdles and pitfalls, list the resulting design requirements, and describe the results that one can expect from undertaking this kind of outreach activity.

Validating Four Hmong Word Recognition Tests With Normal-Hearing Bilingual Hmong Individuals

The purpose of this study was to validate four digitally recorded and phonetically balanced 50-word recognition lists in the White Hmong dialect with normal-hearing bilingual Hmong adults. Using a randomized, incomplete-block design, each participant listened to and repeated four unique Hmong lists delivered by a female and a male talker. Participants were also tested with an English word list-List 1A of the Northwestern University Auditory Test No. 6. Participants' correct pronunciation of each word was scored. A nonparametric Mann-Whitney U Location Difference Test for Equivalence using two one-sided tests equivalence hypothesis: -0.02 < [(List_1) - (List_2)] < 0.02 was conducted to assess equivalence among all four Hmong and the English lists. Seventy Hmong speakers participated in this study (35 women, 35 men; M age = 29.5 years, SD = 7.1). In all four Hmong lists, 93.5% (187/200) words met the validation criteria for ≥ 92% correct pronunciation. The 13 difficult words were deemed adequate by a Hmong panel and, therefore, were included to maintain four unique, balanced word lists. The test revealed that the Hmong and English word lists were considered equivalent at the 2% bound. The four Hmong word lists were validated to ensure an equal range of word difficulty across the lists.

ETHNOPHARMACOLOGICAL AND PHYTOCHEMICAL STUDY OF MAJOR SPECIES FROM LABIATAE FAMILY IN SAUDI ARABIA: A SYSTEMATIC REVIEW

A review on a medicinal plant belong to family Labiatae has been made in the Kingdom of Saudi Arabia. Among the reported flora in the Kingdom of Saudi Arabia, Labiatae family has the highest species. A total of 23 species of Labiatae family has been reported with high medicinal value. A detail review on 12 species of Labiatae family had done to know their phytochemical constituents, medicinal uses and pharmacological properties. In this review we also explore the total chemical constituents present in each plant with a unique list of 3D chemical structures. (E)-2-Hexenal, 1,8-Cineole, Bicyclogermacrene, Borneol, Bornyl acetate, Bulnesol, Caffeic acid, Camphene, Camphor, Eugenol, Gallic acid, Quercetin, Terpinene, Thymol, etc are the important constituent in these 12 species of Labiatae family. This study also shows that Marrubium vulgare L, Ocimum basilicum L, Origanum majorana and Otostegia fruticosa plants had reported with many medicinal uses and pharmacological properties. Medicinal constituents include the highest phenolic, flavonoid content and strong aromatic essential oil. Further scientific investigations on these 12 species are needed to explore toxicity and clinical efficacy and as well as to explore the therapeutic effects of major secondary metabolites. The summarized information from the current review may be a useful tool for researchers to carry out further study and explore other scientific aspects of the herbs.

Learning Strong Substitutes Demand via Queries

This article addresses the computational challenges of learning strong substitutes demand when given access to a demand (or valuation) oracle. Strong substitutes demand generalises the well-studied gross substitutes demand to a multi-unit setting. Recent work by Baldwin and Klemperer shows that any such demand can be expressed in a natural way as a finite list of weighted bid vectors. A simplified version of this bidding language has been used by the Bank of England. Assuming access to a demand oracle, we provide an algorithm that computes the unique list of weighted bid vectors corresponding to a bidder’s demand preferences. In the special case where their demand can be expressed using positive bids only, we have an efficient algorithm that learns this list in linear time. We also show super-polynomial lower bounds on the query complexity of computing the list of bids in the general case where bids may be positive and negative. Our algorithms constitute the first systematic approach for bidders to construct a bid list corresponding to non-trivial demand, allowing them to participate in “product-mix” auctions.

Medically integrated dispensing (MID) clinical and cost outcomes compared to specialty pharmacies (SP).

e18645 Background: Oral oncolytics MID offers potential care and cost advantages compared to a centralized SP model. A pharmacy benefit manger and 3 health plans created a pilot program to enable and encourage oncology practice MID with the goal of improving care, enhancing the provider and patient experience, and reducing drug waste. This study assesses clinical outcomes and drug waste differences between MID and central SP dispensing using real-world pharmacy claims data. Methods: The MID pilot program was implemented early 2021 within 3 oncology practices across 3 plans commercially insured lives. Members were required to have continuous enrollment for the adherence and discontinuation (d/c) measures. Adherence was measured using the CMS industry standard proportion of days covered with a &gt; / = 80% cut point. D/c was measured in new initiators defined as no claim evidence of the drug in the previous 90 days with discontinuation defined as having a &gt; / = 45-day therapy gap within the first 180 days. Dose changes were defined as a new unit strength being dispensed for a previously filled drug. Each measure used a unique drug list: 3 CDK4/6i for adherence; 10 highly utilized oral brand and generic oncolytics for d/c; and 26 oral oncology brand drugs having multiple strengths and labeled dose change directions for dose change waste. The adherence and waste measures assessment end date was Dec 31, 2021 and for d/c was Jan 28, 2022. Statistical testing: adherence- Fischer’s exact test; d/c- chi-square; and waste– t-test. Results: For adherence, individuals were followed for an average of 243 days, range 21-333 days, without a statistical difference between MID and SP follow-up. CDK4/6i adherence was 82% (9 of 11) for MID and 73% (421 of 574) for SP, p = 0.74. D/C was 29% (15 of 52) for MID and 48% (360 of 753) for SP, p = 0.01. Waste occurred in 29% (6 of 21) of MID dose changes and 50% (315 of 627) of SP dose changes, p = 0.05; resulting in an average cost of $937 and $2,733 per dose change at the MID and SP, respectively, for an SP average dose change additional cost of $1,796. Conclusions: MID demonstrated numerically higher adherence, significantly lower d/c rate with MID having a number need to treat of 5 to prevent one less d/c event, and significantly lower waste expense with an estimated savings of $1.1 million, if all 627 individuals with a dose change at SP had used a MID. These real-world findings have resulted in a national PBM network to encourage MID use.

An effective method for clustering-based web service recommendation

Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets.