bluuebunny/crossref_metadata_2025 huggingface dataset:
Neutrino Lorentz invariance violation from the $$\textit{CPT}$$-even SME coefficients through a tensor interaction with cosmological scalar fields
Authors: Cordero Rubén , Delgadillo Luis A. , Miranda O. G. , Moura C. A.
Published: January 2025

Numerous non-standard interactions between neutrinos and scalar fields have been suggested in the literature. In this work, we have outlined the case of tensorial neutrino non-standard interactions with scalar fields, which can be related to the effective $$\textit{CPT}$$
CPT
-even dimension-4 operators of the Standard Model Extension (SME). We illustrate how bounds placed on these parameters can be associated with limits on the energy scale of the proposed neutrino interactions with cosmic scalars. Besides, as a case study, we employ a DUNE-like experimental configuration to assess the projected sensitivities to the $$\textit{CPT}$$
CPT
-even isotropic $$c_{\alpha \beta }$$

c

α
β

and Z-spatial $$c_{\alpha \beta }^{ZZ}$$

c

α
β

ZZ

SME coefficients. For the case of the isotropic SME coefficients, an upper limit on the energy scale of the interaction can be placed. The current IceCube experiment and upcoming neutrino experiments such as DUNE, KM3NeT, IceCube-Gen2, and GRAND proposals, may clarify these classes of neutrino non-standard interactions.
Review of Anapolisia Piza, 1980 and Tropicophyllum Koçak & Kemal, 2008 stat. rev. (Orthoptera: Tettigoniidae: Phaneropterinae: Microcentrini)
Authors: MENDES DIEGO MATHEUS DE MELLO , RAFAEL JOSÉ ALBERTINO
Published: January 2025

Anapolisia Piza, 1980 is placed in Microcentrini Brunner von Wattenwyl, 1878 (Orthoptera, Tettigoniidae, Phaneropterinae) and currently comprises six species with distribution from Costa Rica to southern Brazil. In this work, the review of Anapolisia is provided, with a description of 19 new species and Anapolisia planiceps (Walker, 1869) nov. comb. was transferred from Ischyra Brunner von Wattenwyl, 1878; the revalidation of Tropicophyllum Koçak & Kemal, 2008 stat. rev., with a description of 12 new species and new combinations for four species: Tropicophyllum colosseum (Brunner von Wattenwyl, 1878) comb. nov., Tropicophyllum maculosum (Bowen-Jones, 2000) comb. nov., Tropicophyllum sentum (Grant, 1958) comb. nov. and Tropicophyllum zonatum (Giglio-Tos, 1898) comb. nov. Pictorial keys for species, maps and observations of natural history are provided.
Outcomes of Surgical Interventions for Patellofemoral Instability in the Presence of Trochlear Dysplasia: A Systematic Review and Meta-analysis
Authors: Aljadaan Bader Majed , Alhakbani Mohammed Saad A. , Almonaie Shahd Hamza , Cahusac Peter M.B.
Published: January 2025

Background:

Patellar instability is frequently encountered by orthopaedic surgeons. One of the major risk factors of this condition is underlying trochlear dysplasia (TD). Recent trends have indicated the use of multiple procedures to correct patellar instability under these conditions. Medial patellofemoral ligament reconstruction (MPFLR) and trochleoplasty (TP) are surgical strategies that have demonstrated successful outcomes. However, it is unclear whether isolated MPFLR suffices for patellar instability in the presence of severe TD. Furthermore, the need for TP or a combined approach, considering its invasiveness and technical demands, to achieve better clinical outcomes and patellofemoral stability remains uncertain.

Purpose:

To compare the outcomes of 3 surgical interventions for addressing patellar instability due to TD: MPFLR, TP, and combined MPFLR and TP.

Study Design:

Systematic review and meta-analysis; Level of evidence, 4.

Methods:

Studies reporting the clinical outcomes of MPFLR, TP, and both in combination, regardless of additional procedures, were identified using the MEDLINE, PubMed, Embase, Scopus, Cochrane Library, Cochrane Central Register of Controlled Trials, and Cumulative Index to Nursing and Allied Health Literature databases. An important inclusion criterion was that studies should include the mean preoperative and postoperative Kujala scores and any complications. A random-effects model was used for the meta-analysis.

Results:

Overall, 30 studies were included: 10 describing the outcomes of TP, 13 describing the outcomes of MPFLR, and 7 describing the outcomes of combined TP and MPFLR. Furthermore, 1457 patients (1571 knees) were evaluated with a mean follow-up of 42.4 ± 32.8 months. No statistically significant differences were found between the 3 methods regarding the mean difference using the Kujala score. However, combined MPFLR and TP had the highest weighted mean difference compared with the other 2 procedures (28.5 points; P < .001). Evidential analysis revealed a robust log-likelihood ratio ( S = 3.2) supporting the superiority of the combined procedure when comparing this mean and the weighted grand mean of 23.7. The redislocation rate after patellar stabilization remained minimal, irrespective of the surgical procedure (mean, 0.7%). However, the rate of residual patellar instability was the highest in the TP group (mean, 13.5%).

Conclusion:

This study found no statistically significant difference between the outcomes of the 3 procedures. However, the combined approach of MPFLR and TP was associated with the most promising clinical outcomes and a low redislocation rate in patients with varying severities of TD.
Exploring the Relationship Between Combined, Acetabular, and Femoral Version on Postoperative Outcomes 2 Years After Hip Arthroscopy for Femoroacetabular Impingement Syndrome
Authors: Larson Jordan H. , Allahabadi Sachin , Kaplan Daniel , Chapman Reagan , Kazi Omair , Brusalis Christopher M. , Nho Shane J.
Published: January 2025

Background:

Many studies have examined the prevalence of acetabular version (AV) and femoral version (FV) abnormalities and their effect on patient-reported outcomes (PROs) after hip arthroscopy for femoroacetabular impingement syndrome (FAIS), but few have explored the prevalence and influence of combined version (CV) abnormalities.

Purpose:

To (1) describe the distribution of AV, FV, and CV in the largest cohort to date and (2) determine the relationship between AV, FV, and CV and PROs after hip arthroscopy for FAIS.

Study Design:

Cohort study; Level of evidence, 3.

Methods:

Patients were identified who underwent primary hip arthroscopy for FAIS between 2012 and 2018 and received computed tomography containing the pelvis and transcondylar knee slices. AV and FV were assessed on computed tomography, with CV calculated as their sum. PROs were collected preoperatively and 2 years postoperatively. The distributions of AV, FV, CV, and combinations thereof were described based on published ranges. The relationships between version measurements and PROs were analyzed based on a given measurement's distance from a normative value, defined as the mean within the study population (eg, relative acetabular anteversion [AAr]). These relationships were first assessed using univariate natural (restricted) cubic regression splines to account for nonlinearity. The relationship between each relative version group (to the mean) and PROs was then assessed using multiple linear regression, with the other 2 version measurements held constant.

Results:

In total, 566 patients were included (66.4% female; mean age, 32.6 ± 11.9 years; mean body mass index, 25.2 ± 5.1). The mean follow-up was 28.0 months. The mean AV, FV, and CV were 17.1°± 5.2°, 12.1°± 9.2°, and 29.3°± 11°, respectively. Univariate natural cubic regression splines demonstrated that AAr had a significant negative association with 4 of 5 PROs preoperatively and 3 of 5 PROs 2 years postoperatively ( P≤ .018). All other relative version groups did not have a significant relationship with any PRO at either time point ( P > .05). The association between AAr and relatively worse PROs was maintained after controlling for relative FV and CV via multiple linear regression, particularly in patients with relative femoral retroversion.

Conclusion:

FV and CV, as distinct measures, are not associated with outcomes after hip arthroscopy for FAIS. AAr is associated with worse preoperative status and less improvement at 2 years postoperatively, particularly in patients with relative femoral retroversion.
Midterm Functional Outcomes After Retear of an Arthroscopic Rotator Cuff Repair: A Propensity Score–Matched Comparative Study
Authors: Kim Jae Soo , Kim Su Cheol , Park Jong Hun , Kim Hyun Gon , Jung Hyoung Seok , Lee Sang Min , Yoo Jae Chul
Published: January 2025

Background:

Studies are still limited on the isolated effect of retear after arthroscopic rotator cuff repair (ARCR) on functional outcomes after the midterm period.

Purpose:

To assess the effect of retear at midterm follow-up after ARCR and to identify factors associated with the need for revision surgery.

Study Design:

Cohort study; Level of evidence, 3.

Methods:

A retrospective review was conducted on patients who underwent ARCR between 2014 and 2018, with a minimum 5-year follow-up. The structural integrity was evaluated using magnetic resonance imaging. After propensity score matching, 65 patients for the retear group and 65 for the healed group were included. Functional outcomes, including visual analog scale for pain and function (PVAS and FVAS) and American Shoulder and Elbow Surgeons (ASES) score, were compared between the groups at 1 year and final follow-up. Multivariate analysis was conducted on the retear group to identify factors associated with the final ASES score.

Results:

The mean ± SD follow-up period was 6.9 ± 1.4 years. All outcomes at the final follow-up improved as compared with the preoperative status, regardless of retear (all P < .001). In the retear group, PVAS at the final follow-up showed deterioration when compared with postoperative 1 year ( P = .044). While there were no significant differences in all outcomes at 1 year postoperatively between the groups (all P > .05), the retear group demonstrated significantly worse outcomes than the healed group in scores on the PVAS (2.5 ± 1.5 vs 1.9 ± 1.5; P = .011), FVAS (7.3 ± 1.4 vs 7.8 ± 1.3; P = .020), and ASES (73.2 ± 12.7 vs 79.9 ± 15.4; P = .008) at the final follow-up. The revision rate in the retear group was 12.8%, and multivariate analysis showed that larger anteroposterior retear size ( P = .017) and retear of the subscapularis ( P = .047) were negatively associated with the final ASES score.

Conclusion:

Functional outcomes after ARCR improved during midterm follow-up, regardless of retear. While the effect of retear itself on functional outcomes was minimal in the short term, it became more pronounced after the midterm period. A retear involving the subscapularis or with a larger anteroposterior size was associated with a poorer functional outcome.
Advancing a machine learning-based decision support tool for pre-hospital assessment of dyspnoea by emergency medical service clinicians: a retrospective observational study
Authors: Kauppi Wivica , Imberg Henrik , Herlitz Johan , Molin Oskar , Axelsson Christer , Magnusson Carl
Published: January 2025

Background

In Sweden with about 10 million inhabitants, there are about one million primary ambulance missions every year. Among them, around 10% are assessed by Emergency Medical Service (EMS) clinicians with the primary symptom of dyspnoea. The risk of death among these patients has been reported to be remarkably high, at 11,1% and 13,2%. The aim was to develop a Machine Learning (ML) model to provide support in assessing patients in pre-hospital settings and to compare them with established triage tools.

Methods

This was a retrospective observational study including 6,354 patients who called the Swedish emergency telephone number (112) between January and December 2017. Patients presenting with the main symptom of dyspnoea were included which were recruited from two EMS organisations in Göteborg and Södra Älvsborg. Serious Adverse Event (SAE) was used as outcome, defined as any of the following:1) death within 30 days after call for an ambulance, 2) a final diagnosis defined as time-sensitive, 3) admitted to intensive care unit, or 4) readmission within 72 h and admitted to hospital receiving a final time-sensitive diagnosis. Logistic regression, LASSO logistic regression and gradient boosting were compared to the Rapid Emergency Triage and Treatment System for Adults (RETTS-A) and National Early Warning Score2 (NEWS2) with respect to discrimination and calibration of predictions. Eighty percent (80%) of the data was used for model development and 20% for model validation.

Results

All ML models showed better performance than RETTS-A and NEWS2 with respect to all evaluated performance metrics. The gradient boosting algorithm had the overall best performance, with excellent calibration of the predictions, and consistently showed higher sensitivity to detect SAE than the other methods. The ROC AUC on test data increased from 0.73 (95% CI 0.70–0.76) with RETTS-A to 0.81 (95% CI 0.78–0.84) using gradient boosting.

Conclusions

Among 6,354 ambulance missions caused by patients suffering from dyspnoea, an ML method using gradient boosting demonstrated excellent performance for predicting SAE, with substantial improvement over the more established methods RETTS-A and NEWS2.
Chemical, biological and nerve gas attacks: need for education among healthcare personnel and medical students; a Swedish regional cross-sectional web-survey
Authors: Castellheim Albert Gyllencreutz , Persson Gustav , Kuikka Juuso , Babinski Karol , Robinson Yohan , Taube Fabian
Published: January 2025

Background

Chemical, biological and nerve gas events have a significant impact on public health, necessitating proper education and training. This study investigated the educational needs as perceived by two groups, frontline healthcare workers and medical students, in relation to chemical, biological, and nerve gas events.

Methods

Three distinct web-based cross-sectional surveys were conducted, one each for chemical, biological, and nerve gas events, with each survey following the same structural format including sections on (a) theoretical knowledge assessment, using multiple-choice questions regarding identification, protection, and treatment, (b) perception of threat, using questions based on a 5-point Likert scale to gauge views on threat/preparedness and (c) perception of existing competency, with questions regarding prior education and the need for additional education and training.

Results

The surveys on chemical, biological, and nerve agents received responses from 44, 36, and 59 participants respectively, comprising both frontline healthcare workers and medical students. The total response rate was approximately 16%. In the theoretical knowledge section of each survey, neither group of participants reached 51% correct answer rate in any of the three surveys. In the section on perception of threat, the percentages of responses in the low half of the Likert Scale were 43.2%, 53.0%, and 96.4% for biological, chemical, and nerve gas surveys, respectively. For the same surveys, 56.8%, 78.7%, and 87.6% of responses were in the middle of the Likert Scale. Regarding perception of competency, 146.2%, 143.1%, and 170.4% of combined responses indicated low existing competency for biological, chemical, and nerve gas surveys, respectively. High and middle ratings for competency were low across all surveys. The perception of need for education were high in the high half and low in the low half of the Likert Scale. The were no statistically significant differences across the sections among the study groups.

Conclusions

The results indicate a widespread tendency to downplay the presence of significant threat and a perception of low existing competency. There is a broad agreement on the need or education and training in managing events of chemical, biological, and nerve gas agents, for frontline healthcare personnel as well as medical students in Sweden.
Matrix-free human 2D organoids recapitulate duodenal barrier and transport properties
Authors: Masete Kopano Valerie , Günzel Dorothee , Schulzke Jörg-Dieter , Epple Hans-Jörg , Hering Nina A.
Published: January 2025

Background

Traditionally, transformed cell line monolayers have been the standard model for studying epithelial barrier and transport function. Recently, intestinal organoids were proposed as superior in recapitulating the intestine. Typically, 3D organoids are digested and seeded as monolayers on gelatinous matrix pre-coated surfaces for anchorage. As this coat could potentially act as a diffusion barrier, we aimed to generate robust human duodenum-derived organoid monolayers that do not need a gelatinous matrix for anchorage to improve upon existing models to study epithelial transport and barrier function.

Results

We characterized these monolayers phenotypically regarding polarization, tight junction formation and cellular composition, and functionally regarding uptake of nutrients, ion transport and cytokine-induced barrier dysfunction. The organoid monolayers recapitulated the duodenum phenotypically as well as functionally regarding glucose and short-chain fatty acid uptake. Tumour necrosis factor-alpha induced paracellular transport of 4-kDa Dextran and transcytosis of 44-kDa horseradish peroxidase. Notably, forskolin-stimulated chloride secretion was consistently lower when organoid monolayers were seeded on a layer of basement membrane extract (BME).

Conclusions

BME-free organoid monolayers represent an improved model for studying transcytotic, paracellular but especially transcellular transport. As BME is extracted from mice, our model furthers efforts to make organoid culture more animal-free.
Predicting hepatocellular carcinoma outcomes and immune therapy response with ATP-dependent chromatin remodeling-related genes, highlighting MORF4L1 as a promising target
Authors: Xu Chao , Liang Litao , Liu Guoqing , Feng Yanzhi , Xu Bin , Zhu Deming , Jia Wenbo , Wang Jinyi , Zhao Wenhu , Ling Xiangyu , Zhou Yongping , Ding Wenzhou , Kong Lianbao
Published: January 2025

Background

Hepatocellular carcinoma (HCC) continues to be a major cause of cancer-related death worldwide, primarily due to delays in diagnosis and resistance to existing treatments. Recent research has identified ATP-dependent chromatin remodeling-related genes (ACRRGs) as promising targets for therapeutic intervention across various types of cancer. This development offers potential new avenues for addressing the challenges in HCC management.

Methods

This study integrated bioinformatics analyses and experimental approaches to explore the role of ACRRGs in HCC. We utilized data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), applying machine learning algorithms to develop a prognostic model based on ACRRGs’ expression. Experimental validation was conducted using quantitative real-time Polymerase Chain Reaction (qRT-PCR), Western blotting, and functional assays in HCC cell lines and xenograft models.

Results

Our bioinformatics analysis identified four key ACRRGs—MORF4L1, HDAC1, VPS72, and RUVBL2—that serve as prognostic markers for HCC. The developed risk prediction model effectively distinguished between high-risk and low-risk patients, showing significant differences in survival outcomes and predicting responses to immunotherapy in HCC patients. Experimentally, MORF4L1 was demonstrated to enhance cancer stemness by activating the Hedgehog signaling pathway, as supported by both in vitro and in vivo assays.

Conclusion

ACRRGs, particularly MORF4L1, play crucial roles in modulating HCC progression, offering new insights into the molecular mechanisms driving HCC and potential therapeutic targets. Our findings advocate for the inclusion of chromatin remodeling dynamics in the strategic development of precision therapies for HCC.
Impact of circulating lymphoma cells at diagnosis on outcomes in patients with newly diagnosed de novo diffuse large B-cell lymphoma
Authors: Chowdhury Sayan Mullick , Bhatta Subodh , Voorhees Timothy J. , Annunzio Kaitlin , Bond David A. , Sawalha Yazeed , Sigmund Audrey , Hanel Walter , Sehgal Lalit , Alinari Lapo , Baiocchi Robert , Maddocks Kami , Christian Beth , Jones Dan , Epperla Narendranath
Published: January 2025

Diffuse large B-cell lymphoma (DLBCL), the most common B-cell non-Hodgkin lymphoma rarely presents with circulating lymphoma cells (CL) at diagnosis. Previous studies were limited by small sample size precluding robust analysis. Hence, we evaluated the prognostic relevance of CL cells in newly diagnosed DLBCL patients. Based on peripheral blood (PB) immunophenotyping, patients were grouped into CL + and CL−. CL was defined as detectable clonally restricted B-cells that matched the actual or expected B-cell immunophenotype of DLBCL. The primary endpoint was progression-free survival (PFS), and secondary endpoints included overall survival (OS) and diagnosis-to-treatment interval (DTI). Among the 1266 patients with DLBCL, 621 had PB flow at diagnosis, and after excluding patients not meeting eligibility criteria, 588 cases remained. Among these, 85 (15%) were CL + and 503 were CL- (85%). Patients in CL + group were younger (67 vs. 70 years, p = 0.03) with a higher proportion of non-bulky disease (85% vs. 56%, p < 0.0001), normal albumin (79% vs. 54%, p < 0.0001), and MYC/BCL2 and/or BCL6 rearrangements (18% vs. 7%, p = 0.003) compared to the CL − group. Patients with CL at diagnosis had significantly inferior PFS and OS compared with those without CL. After adjusting for factors associated with inferior PFS and OS in univariable analysis, presence of CL remained significantly associated with inferior PFS (HR = 2.04, 95%CI = 1.47–2.84, p < 0.0001) and OS (HR = 1.61, 95%CI = 1.1–2.36, p = 0.01), respectively. There was no significant difference in DTI between the two groups. Given the prognostic relevance associated with presence of CL, clinicians should consider checking PB flow at diagnosis in all newly diagnosed DLBCL patients.
Clinical predictors for surfactant retreatment in preterm infants with respiratory distress syndrome: the results of a pooled analysis
Authors: Dani Carlo , Poggi Chiara , Agosti Massimo , Bellettato Massimo , Betta Pasqua , Biban Paolo , Corvaglia Luigi , Falsaperla Raffaele , Forcellini Carlo , Gazzolo Diego , Gitto Eloisa , Gizzi Camilla , Lago Paola , Lista Gianluca , Maffei Gianfranco , Mosca Fabio , Napolitano Marcello , Scarpelli Gianfranco , Sandri Fabrizio , Trevisanuto Daniele , Vento Giovanni , Corsini Iuri , Pratesi Simone , Boni Luca
Published: January 2025

Background

The issue of retreatment with surfactant of infants with respiratory distress syndrome (RDS) has been poorly investigated. Our aim was to identify possible clinical predictors of the need for multiple doses of surfactant in a large cohort of very preterm infants.

Methods

Data were analyzed from three previous studies on infants born between 25+ 0 and 31+ 6 weeks of gestation with RDS who were treated with surfactant.

Results

We studied 448 infants. Among them 306 (68%) were treated with a single dose of surfactant and 142 (32%) were treated with multiple doses. Multivariable mixed effects logistic regression analysis showed that the odd of requiring multiple doses of surfactant was significantly lower in patients with higher gestational age (27–28 vs. 25–26 wks: OR 0.46, 95% C.l. 0.26–0.79; ≥29 vs. 25–26 wks: OR 0.34, 95% C.l. 0.13–0.85; overall P = 0.013), while it increased in infants born to mothers with hypertensive disorders of pregnancy (OR 2.53, 95% C.l. 1.49–4.31; P < 0.001) and with hemodynamically significant PDA (OR 2.74, 95% C.l. 1.66–4.53, P < 0.001).

Conclusions

Gestational age, hypertension in pregnancy, and hemodynamically significant PDA can predict the need for multiple doses of surfactant. Further investigation is needed to evaluate if these sub-groups of preterm infants represent specific phenotypes of RDS who deserve a peculiar surfactant treatment.
Nickel-induced labial angioedema in a pediatric patient with orthodontic braces: a case report
Authors: Leone Fabrizio , Gori Alessandra , Cinicola Bianca Laura , Coletti Giorgio , Pignataro Elia , Martina Capponi , Giulia Brindisi , Anania Caterina , Zicari Anna Maria
Published: January 2025

Background

Angioedema is a condition marked by sudden, intense swelling of the subcutaneous and submucosal tissues, typically associated with hypersensitivity reactions, genetic mutations, or reactions to medications. It can also result from contact with allergens such as nickel, leading to dermatitis.

Case presentation

: A 12-year-old girl presented at our Pediatric Immunology and Allergology service with recurrent labial angioedema for over a year, linked to the consumption of legumes and tomatoes, and following the use of a metal flute. Despite a nickel-positive patch test and subsequent avoidance of nickel, her symptoms persisted. Further investigations to rule out other causes of angioedema were unproductive. It was later discovered that she had been wearing a nickel-containing orthodontic device applied a year earlier. The removal of this orthodontic device led to a cessation of the angioedema episodes, highlighting nickel as the likely trigger.

Conclusions

This case underscores the importance of considering prolonged nickel exposure from dental devices as a potential cause of angioedema. For patients predisposed to nickel hypersensitivity, using nickel-free alternatives such as ceramic for orthodontic appliances is crucial. Additionally, comprehensive allergen screening, including latex testing, should be conducted before the placement of such devices to prevent similar adverse reactions.
Multi-level lag scheme significantly improves training efficiency in deep learning: a case study in air quality alert service over sub-tropical area
Authors: Tam Benedito Chi Man , Tang Su-Kit , Cardoso Alberto
Published: January 2025

In environmental monitoring, deep learning models are used where we can either use past observations or extrapolated values with high uncertainty as input. The lag scheme is commonly applied during the modeling and construction process, in the application of deep learning models in multivariate time series prediction. For an adaptive feature engineering, an automated lag scheme is essential for improving the training efficiency. In multivariate time series (MTS) models, the predictive accuracy of artificial neural network ANN-type models can be improved by including more features. It is assumed that when processing a certain number of multivariate features, the timeliness and lag time of the inter-influencing between any pair of elements are different. This research aims to adopt an adaptive approach to solve it, namely, multi-level lag scheme. The research methods include literature review, searching for relevant technology frontiers, feasibility studies, selection and design solutions, modeling, data collection and pre-processing, experiments, evaluation, comprehensive analysis and conclusions. In proof of concept, we demonstrated a practical case of seasonal ANN type MTS model and public service on air quality. In terms of models, ANN type models were attempted with ARIMA as the comparing baseline. We used public data set of more than two base stations with pollution varying from low to high and including southern to northern district of a small city. Conclusions can be drawn from the analysis of multiple experimental results, proving that the proposed solution can effectively improve the training efficiency of the model. This is of great significance, so that most such models can be implemented to adaptively use lagged past measured data as input, instead of synchronously inputting future prediction values, which can greatly improve the practical application of the model in predictive ability.
Assessment of constructed wetland projects as a multifunction landscape: a case study in Egypt
Authors: ElMeligy Aya , Gaber Rasha Mahmoud , Mostafa Hind , Haron Ahmed , Ismaeel Walaa S. E.
Published: January 2025

This research aims to develop a conceptual framework and assessment tool to assess sustainability of Multifunction Constructed Wetlands Projects (MCWP). First, by literature review to analyze the main points and identify the gaps in existing research to what concerns viewing constructed wetlands as multifunction sustainable landscape projects. To assess the performance of MCWP, urban sustainability indicators are proposed examining interconnections between environmental, economic and social aspects and their effects on each other. 12 environmental, 9 socio-cultural and 7 economic indicators are selected according to their relevance to the United Nations and National Sustainable Development Goals, the impacts of their weights according to a distributed questionnaire showed these percentages: environmental aspects 42%, Socio-cultural aspects 29% and the economic aspects 28%. Also, performance-oriented assessment tools for MCWPs were designed for wastewater treatment. The impacts of proposed indicators are then assessed using the adapted Leopold Matrix method. Hence, this study aims to establish an assessment model to evaluate the sustainability features of MCWPs, by proposing sustainability indicators to be assessed by measurement metrics and respective weights for indicators and sub-indicators.
Advanced Security and Privacy in Cloud Computing: Enhancing Data Protection with Multikeyword Ranked Search in Encrypted Environments
Authors: Joshi Narendra Shyam , Sambrekar Kuldeep P. , Patankar Abhijit J. , Rajawat Anand Singh , Muqeem Mohd
Published: January 2025

As cloud services become more popular, encryption becomes more important for user privacy. Establishing reliable solutions for secure and fast data retrieval is crucial. This research article proposes a novel way to search encrypted cloud data. The suggested method optimises queries with multiple terms and synonyms using a greedy depth-first search (DFS) algorithm and a sophisticated rating system. The suggested architecture assumes users would search using many keywords, some of which may be synonyms for article terms. A search algorithm that uses user query synonyms was created to solve this problem. Despite the constant increase of the search universe, greedy methods help us find the most relevant information. Our depth-first search strategy improves the likelihood of finding relevant information. Our study also uses a unique ranking system that considers keyword frequency, synonym precision, and keyword proximity to determine a text’s relevance to a search query. Our suggested methodology outperforms state-of-the-art methods in simulated cloud architecture experiments using encrypted datasets and industry-standard protocols. Runtime, accuracy, and recall show this superiority. The greedy Depth-First Search (DFS) algorithm optimises resources, improving efficiency. A grading method helps users quickly find the most relevant publications by naturally arranging the results. This synonym-enhanced search strategy in encrypted cloud storage systems may improve privacy and usability today.
Review on the Use of Federated Learning Models for the Security of Cyber-Physical Systems
Authors: Rafeeq War Muhammed , Singh Yashwant , Sheikh Zakir Ahmad , Singh Pradeep Kumar
Published: January 2025

The field of critical infrastructure has undergone significant expansion over the past three decades, spurred by global economic liberalization and the pursuit of development, industrialization, and privatization by nations worldwide. This rapid growth has led to a proliferation of critical infrastructure across various sectors, necessitating decentralization efforts to manage the associated burdens effectively. With the advent of artificial intelligence and machine learning, computer scientists have sought innovative approaches to detect and respond to the evolving landscape of cyber threats. Despite efforts to subscribe to these changes, attackers continually devise new methods to evade detection, requiring constant vigilance and adaptation from cybersecurity professionals. Traditional centralized models of machine and deep learning demand substantial data and computational resources, making them susceptible to single-point failures. To address these challenges, scientists have introduced federated learning—a decentralized technique that minimizes computational costs while prioritizing data privacy and preservation. This review article delves into recent research and review papers concerning critical infrastructure security and federated learning, exploring various architectures, threats, vulnerabilities, and attack vectors. Through our analysis, we provide a comprehensive overview of federated learning, cyber-physical systems security, and the advantages of integrating federated learning into critical infrastructure environments. By synthesizing insights from diverse sources, our study contributes to a deeper understanding of federated learning's applications and implications in safeguarding critical infrastructures. We highlight the potential of federated learning to enhance cybersecurity measures while addressing the unique challenges posed by modern-day threats. As organizations and nations navigate the complexities of securing their critical assets, the adoption of federated learning emerges as a promising strategy to bolster resilience and protect against emerging cyber risks.
A GPS- enabled Fuel Sensor based Vehicle Tracking System for Fleet Management using the Internet of Things
Authors: Nagesh A Sri , Aparna Ch. , Rao MVP Chandra Sekhar
Published: January 2025

The key objective of this paper is to monitor the Fuel consumption, route deviations, driving habits, breakdown details, vehicle maintenance conditions, and other vital performance details of a Heavy vehicle especially a college bus using a pre-fitted fuel sensor needed for an extensive fleet management system as operational costs and maintenance are escalating day by day. In particular, vehicle owners are confronting fuel break-ins, spare parts theft and illegal parking or route diversions in transport vehicles. In addition to this, vehicle proprietors don't ascertain operational details daily if they are illiterate. Various models are available which Standalone, Onboard diagnostics-based, GPS GPS-based fuel tracking Systems. This research project leverages IoT technology, GPS-based sensors, and a GSM tracking system to provide vehicle owners with real-time information on fuel consumption. This cloud-based and mobile application tracks the vehicle in real time. Most of the models are web-based and custom-built only. When the driver initiates fuel filling, the Fuel sensor in the tank automatically activates and senses the amount of fuel in the diesel tank by transmitting fuel input data to a connected cloud web server for storage and analysis along with other data. The gathered values are cross-verified with the database, and an alert message is promptly sent to the vehicle owner based on consumption patterns. This mobile app-based Vehicle Tracking System (VTS) application, using SMS and Vehicle tracking features offers numerous advantages to the vehicle owner, primarily focusing on preventing fuel theft.
Genome Sequence Analysis of Severe Acute Respiratory Syndrome Using GenoAnalytica Model
Authors: Dubey Shivendra , Verma Dinesh Kumar , Kumar Mahesh
Published: January 2025

We proposed a GenoAnalytica model for examining the SARS’s genomics sequences. The technologies make proper data extraction from genomics sequences of viruses. We use the GenoAnalytica model, i.e. GenoCompute, and IGMiner Algorithm; to classify the range of genomics sequences, including recognizing the sequence variation from the datasets. The projected algorithm computes the nucleotide patterns and represents the nucleotide genome sequence of SARS (airborne virus) by IGMiner technique and works out on the GenoCompute to calculate computation time with minimum count in second. Along with this, we proposed a UMRA algorithm to compute the mutation rate of the genome sequence with minimum count in seconds as compared to traditional method. Furthermore, we work out the different datasets (China and Algeria datasets) and determine the whole variation at the index level inside the all genome sequence. This learning also signifies the performance evaluation on altering minsup using IGMiner and Aprori-based SPM. Also, we calculate the mutation rate of the genome sequence of airborne virus using Unique Mutation Rate Analysis algorithm. The severe acute respiratory syndrome coronavirus 2 has been responsible for the deadly COVID-19 pandemic. It has ruined limitless individuals all over the globe, and along with this, it continues to harm well-being and people’s health. Healthcare specialists and Researchers can obtain insight into COVID-19’s inherited variation or SAR-CoV-2 through cutting-edge Artificial Intelligence and genome sequence analysis tools.
Development and Application of Sporadic Material Inventory Optimization Management System based on Artificial Intelligence
Authors: Li Qian , Hou Wenjun , Wu Yatong , Hou Yaqi , Zhu Jian
Published: January 2025

This paper introduces the overall framework of the intelligent electrical sporadic materials management system based on the ubiquitous Internet of Things. The system includes storage equipment and an intelligent management terminal. It makes optimal design for storage equipment and intelligent management terminals. The dynamic modeling of a three-layer supply chain system of power materials is constructed and composed of manufacturers, suppliers, and raw materials. The inventory control strategy of each link in the supply chain of power materials under different cooperation levels is studied by introducing the corresponding adjustment variables. Through the experiment and analysis of the system, it is proved that the system has good performance in label management, resource management, inventory management, disposal management, system management and essential management. The number of concurrent users, response speed, stability and other aspects of the system are excellent. The database is complete, independent, and secure. And the data is objectively reasonable and repairable. The system can lay a specific technical foundation for intelligent management of electrical sporadic materials.
Recovery Modeling and Robustness Study after Cascading Failures in Logistics-based Networks
Authors: Qian Xiaodong , Wang Sichen
Published: January 2025

In order to ensure the normal operation of the logistics network and improve the robustness of the network in case of cascading failure faults, this study introduces the concept of node recovery threshold based on the existing failure model to optimize the recovery time lag. Further, a criticality-first recovery model is proposed, which defines the capacity of a recovery node as a function related to its original capacity and opens the recovery node selectively to critical neighboring nodes to reduce the risk of secondary failure. Finally, a postal logistics network in Northwest China is used as a case study to investigate the recovery robustness of this network when it encounters cascading failures. The effects of various parameter variations on the network robustness are examined through experimental simulations. The experimental results show that timely recovery measures can significantly reduce the number of failed nodes when cascade failure occurs in the logistics network; setting a higher recovery threshold can reduce the impact of cascade failure on the network, effectively reduce the scale of network failure, and thus significantly improve the robustness of the logistics network; at the same time, increasing the capacity parameter can effectively delay the time of cascade failure in the network, and can slightly improve the robustness of the network.
Smart Street Light System Integrated with Internet-of-things based Sensors for Energy Monitoring
Authors: Mishra Sadhana , Dhakad Bhupendra , Ojha Shailendra Singh , Akashe Shyam
Published: January 2025

In this proposed work, two prototypes are implemented, one is for smart parking and the other is for monitoring the energy consumption of the same. With the implementation of IoT based Street Light System in the field, nearly 60-70 percent of electricity saving can be achieved as compared to conventional systems. Here, this system is not only controlling the switching of the street lights ON/ OFF but these electricity consumption details can be monitored remotely through another developed prototype. It is demonstrated that for a single pole electricity consumption is nearly 1.2KW load per pole which results in power consumption of 14.4KWh from evening 6:00 PM to morning 6:00AM with conventional system. The implemented system is energy efficient in terms of energy saving, which is nearly 9.6KWh can be achieved per day on each pole with same specifications. Moreover, real-time implementation of the proposed system is also demonstrated. IoT is a key technology in healthcare sector for managing, monitoring and controlling the medical devices, services and processes with the basic sensing and actuating components.
Exploring the Role of Artificial Intelligence in Sports Injury Prevention and Rehabilitation
Authors: Zou Rongchao
Published: January 2025

This research examines the utilisation of artificial intelligence (AI) in sports damage anticipation and recovery, pointing to optimising competitor care and execution. Leveraging different datasets comprising execution measurements, biomechanical estimations, damage histories, physiological parameters, and natural components, four AI calculations were actualised and compared: Support Vector Machines (SVM), Random Forest, Recurrent Neural Networks (RNN), and Slope Boosting Machines (GBM). It comes about illustrating critical viability overall calculations, with RNN accomplishing the most elevated execution measurements. Exactness values for SVM, Irregular Timberland, RNN, and GBM were 0.85, 0.88, 0.90, and 0.87 separately, with comparing accuracy, recall, and F1-score values demonstrating strong prescient capabilities. These discoveries emphasise the potential of AI-driven approaches to precisely distinguish damage dangers and personalise recovery conventions custom-made to personal competitor needs. The comparative examination against existing strategies highlights the prevalent execution of AI calculations, emphasising the transformative effect of progressed advances in sports science and pharmaceuticals.
Design of Automatic Error Correction System for English Translation based on Reinforcement Learning Algorithm
Authors: Liu Hui
Published: January 2025

The paper investigates the mix of support learning calculations for automatic blunder adjustment in English interpretation, expecting to further develop interpretation exactness and familiarity. Through trial and error with Deep Q-Learning, Policy Gradient, Entertainer Pundit, and Deep Deterministic Policy Gradient (DDPG) calculations, it shows the adequacy of support learning in improving interpretation quality. Results show that DDPG accomplishes the most elevated typical award of 0.96 and meets quicker contrasted with different calculations. Moreover, the examination of various prize designs uncovers that molded award fundamentally further develops interpretation exactness and familiarity, with specialists prepared with formed reward accomplishing 82.6% precision and a familiarity score of 0.88. Similar examinations with standard techniques affirm the predominance of the proposed approach, with support learning-based blunder adjustment frameworks outflanking rule-based heuristics and administered learning draws near. The mix of manufactured and genuine world datasets guarantees the power and speculation of the blunder adjustment framework. Generally speaking, this examination adds to propelling machine interpretation by offering an information-driven and versatile answer for further developing interpretation quality, with expected applications in cross-lingual correspondence and regular language handling.
Fault Diagnosis of CNC Machine Tools based on Support Vector Machine Optimized by Genetic Algorithm
Authors: Wang Yong , Wang Chunsheng
Published: January 2025

To enhance the accuracy of CNC machine tool fault diagnosis, this study proposes an intelligent optimization method based on the combination of Particle Swarm Optimization (PSO) and Bacterial Foraging Algorithm (BFA), referred to as PSO-BFA. By simulating the local foraging behavior of bacteria, the PSO-BFA algorithm demonstrates characteristics of local convergence, replicability, and migratory properties during parameter selection, effectively improving the local optimization capability and fitness value of the model. This leads to faster convergence to the optimal solution in the fault data training process. The study utilizes a Deep Confidence Network (DCN) model, known for its strong adjustability of model structure, for training the fault feature set. The PSO algorithm is employed to search for the optimal value in the global range. Simulation data indicate that the PSO-BFA intelligent optimization method significantly outperforms traditional swarm intelligence methods in multi-fault diagnosis and classification, achieving the peak fitting value in fewer iterations.
Investigation into the Optimisation of Cold Chain Logistics Distribution Paths using the Hybrid Ant Colony Method
Authors: Xu Weiwei
Published: January 2025

China's cold chain logistics market has been growing quickly in recent years. Cold chain logistics helps minimize food loss and waste during transit in addition to meeting people's need for fresh food. As the idea of "green logistics" has gained traction, we created a better ant colony algorithm with a multi-objective heuristic function to address the issue. Specifically, we combined the A* algorithm with the ACO algorithm to address the issue of insufficient pheromone in the early stages of the ACO algorithm, and the resulting improved multi-objective ACO algorithm was able to solve the vehicle path distribution problem with a multi-objective optimisation model more successfully than the traditional ACO algorithm, yielding more Pareto efficient solutions. Ultimately, simulation studies demonstrate that the distribution paths produced by the multi-objective model and algorithm presented in this paper can concurrently optimize for lowering distribution costs, cutting carbon emissions, and raising customer satisfaction, ultimately resulting in a more ecologically friendly and greener distribution solution.
Teaching Quality Evaluation and Improvement Based on Big Data Analysis
Authors: Zhuang Xueqiu , Song Meijing
Published: January 2025

To address the limitations of Problem-Based Learning (PBL) and to foster student initiative while enhancing teaching quality, the author suggests a novel approach: leveraging big data analysis for teaching quality evaluation and improvement. This method involves conducting diverse and dynamic evaluations, randomly and repeatedly, involving students, teachers, and supervisors. By applying an enhanced Dempster evidence synthesis formula and weights derived from the Analytic Hierarchy Process, the system dynamically calculates each teacher's rating in their respective courses, allowing for continuous improvement. Additionally, personalized feature indicators and teaching quality evaluation metrics are developed to provide a comprehensive assessment. The results indicate that in the coarse evidence set algorithm, is obtained through experience. If is used as the weight alone, the subjectivity is too heavy, and is added for fusion operation, as well as the intervention of experience factor, a balance point between subjectivity and objectivity is found. The final score of 4.2878 was obtained by combining the weights between subjects obtained through Analytic Hierarchy Process, which is consistent with the survey and the public's opinion. This method avoids the deficiency of traditional evidence theory that treats all evidence equally, enhances the ability of information fusion, and obtains more realistic conclusions. Further validated the feasibility and usability of the personalized teaching quality evaluation and improvement model for software engineering.