bluuebunny/crossref_metadata_2025 huggingface dataset:
KINERJA KARYAWAN GENERASI MILENIAL BERDASARKAN DISIPLIN KERJA, TEAMWORK DAN KOMPENSASI
Authors: Rahayuningsih Nurjanah , Nurhayati Nunung , Friyanti Ita
Published: April 2024

Tujuan dari penelitian ini adalah untuk menentukan seberapa jauh kinerja karyawan generasi milenial dalam PT. XXX dinilai berdasarkan disiplin kerja, kolaborasi tim, dan kompensasi. Generasi milenial adalah salah satu generasi yang telah mengalami dunia kerja saat ini. Jika dibandingkan dengan generasi lain, generasi milenial memiliki karakteristik yang berbeda. Hal ini juga berdampak pada sifat generasi milenial, yang mudah menyesuaikan diri dengan perubahan dan perbedaan budaya. Dalam penelitian ini, sampel purposive diambil dari 62 responden. Peneliti menggunakan analisis regresi ganda. Menurut penelitian dan analisis data, variabel kinerja karyawan generasi milenial pada PT dipengaruhi oleh disiplin kerja, kolaborasi, dan kompensasi. XXX  menemukan bahwa variabel kinerja karyawan generasi milenial pada PT dipengaruhi secara bersamaan oleh disiplin kerja, teamwork, dan kompensasi. Dendelion Cantik Indonesia  Selain itu, variabel kinerja karyawan generasi milenial pada PT dipengaruhi secara parsial oleh disiplin kerja, teamwork, dan kompensasi. Dendelion Cantik Indonesia  Variabel kinerja karyawan generasi milenial pada PT terutama dipengaruhi oleh variabel kompensasi. Dendelion Cantik Indonesia  Nilai beta koefisien standar sebesar 0,459 menunjukkan dominasi variabel tersebut. Generasi milenial berfokus pada pencapaian karir terbaik dan mendapatkan gaji tertinggi, yang menyebabkan dominasi variabel kompensasi terhadap variabel kinerja karyawan
The relationship of changes in molecular subtypes with metastases and progression-free survival in breast cancer
Authors: Amansyah Fitran , Prihantono Prihantono , Hamid Firdaus , Syamsu Salman Ardi , Pieter Jr. John , Faruk Muhammad
Published: April 2024

BACKGROUND: Molecular subtyping of breast cancer cells is increasingly being developed as an initial step in selecting therapy and predicting the prognosis of breast cancer patients. During breast cancer, the molecular subtype of cancer cells can change. This study aimed to analyze the relationship between changes in the intrinsic subtype of breast cancer with metastasis and progression-free survival in breast cancer patients. METHODS: This was a retrospective cohort study of patients diagnosed with breast cancer from 2016 to 2021. The molecular subtypes from the immunohistochemical examination results were recorded twice, and metastasis and progression-free survival (PFS) were recorded. The data were analyzed using the chi-square test and SPSS 26. RESULTS: Of the 44 patients, 19 (43.2%) experienced a change in molecular subtype, and 25 (56.8%) did not. No significant relationship existed between changes in molecular subtype and metastasis (p = 0.405). No significant relationship existed between changes in molecular subtype and PFS (p = 0.900). A significant relationship was found between changes in the molecular subtype and PFS in the patients with changes in the molecular subtype (p = 0.022). CONCLUSIONS: Changes in the intrinsic subtype were associated with PFS in breast cancer patients. Patients with an intrinsic subtype that changed to triple-negative showed worse PFS.
Peripheral blood biomarkers associated with combination of immune checkpoint blockade plus chemotherapy in NSCLC
Authors: Kimura Nozomu , Tsukita Yoko , Ebina-Shibuya Risa , Miyauchi Eisaku , Yamada Mitsuhiro , Narita Daisuke , Saito Ryota , Inoue Chihiro , Fujino Naoya , Ichikawa Tomohiro , Tamada Tsutomu , Sugiura Hisatoshi
Published: April 2024

BACKGROUND: Biomarkers predicting clinical outcomes of treating non-small cell lung cancer (NSCLC) with combination of immune checkpoint inhibitors (ICIs) and chemotherapy would be valuable. OBJECTIVE: This study aims to seek predictors of combination of ICI/chemotherapy response in NSCLC patients using peripheral blood samples. METHODS: Patients diagnosed with advanced NSCLC between July 2019 and May 2021 receiving combination of ICI/chemotherapy were included and assessed for partial responses (PR), stable disease (SD) or progressive disease (PD). We measured circulating immune cells, plasma cytokines and chemokines. RESULTS: Nineteen patients were enrolled. The proportions of circulating natural killer (NK) cells within CD45 + cells, programmed death 1 (PD-1) + Tim-3 + T cells within CD4 + cells, and the amount of chemokine C-X-C ligand (CXCL10) in the plasma were significantly elevated in PR relative to SD/PD patients (median 8.1%-vs-2.1%, P= 0.0032; median 1.2%-vs-0.3%, P= 0.0050; and median 122.6 pg/ml-vs-76.0 pg/ml, P= 0.0125, respectively). Patients with 2 or 3 elevated factors had longer progression-free survival than patients with 0 or only one (not reached-vs-5.6 months, P= 0.0002). CONCLUSIONS: We conclude that NK cells, CD4 + PD-1 + Tim-3 + T cells, and CXCL10 levels in pre-treatment peripheral blood may predict the efficacy of combination of ICI/chemotherapy in NSCLC.
Predicting customer intention to adopt mobile commerce in Jordan
Authors: Gharaibeh Malik Khlaif
Published: April 2024

BACKGROUND

The progress made in wireless communications has led to a higher adoption of mobile devices among users and has spurred the development of mobile commerce technology.

OBJECTIVE

This research examines the factors influencing the intention to adopt mobile commerce in Jordan. These factors include perceived usefulness, perceived ease of use, trust, social influence, personal innovation, and mobility.

METHODS

The study relied on Technology Acceptance Model (TAM), incorporating additional variables to suit the Jordanian context. The researchers gathered data from 540 users, and the research hypotheses were evaluated using smartPLS analysis. The study utilized a convenience sample due to the absence of a comprehensive list of names of mobile commerce users.

RESULTS

The results reveal that perceived usefulness, trust, social influence, personal innovation, and mobility play a significant role in influencing adoption intentions of mobile commerce. Furthermore, the study finds that mobility significantly affects the perceived usefulness of mobile commerce. In contrast, perceived ease of use does not show a statistically significant relationship on the intention to adopt mobile commerce.

CONCLUSIONS

The outcomes of this research provide valuable insights for companies, service developers, and mobile marketers, enabling them to enhance the adoption rate of this service among individuals.
Cracking black-box models: Revealing hidden machine learning techniques behind their predictions
Authors: Fabra-Boluda Raül , Ferri Cèsar , Hernández-Orallo José , Ramírez-Quintana M. José , Martínez-Plumed Fernando
Published: April 2024

The quest for transparency in black-box models has gained significant momentum in recent years. In particular, discovering the underlying machine learning technique type (or model family) from the performance of a black-box model is a real important problem both for better understanding its behaviour and for developing strategies to attack it by exploiting the weaknesses intrinsic to the learning technique. In this paper, we tackle the challenging task of identifying which kind of machine learning model is behind the predictions when we interact with a black-box model. Our innovative method involves systematically querying a black-box model (oracle) to label an artificially generated dataset, which is then used to train different surrogate models using machine learning techniques from different families (each one trying to partially approximate the oracle’s behaviour). We present two approaches based on similarity measures, one selecting the most similar family and the other using a conveniently constructed meta-model. In both cases, we use both crisp and soft classifiers and their corresponding similarity metrics. By experimentally comparing all these methods, we gain valuable insights into the explanatory and predictive capabilities of our model family concept. This provides a deeper understanding of the black-box models and increases their transparency and interpretability, paving the way for more effective decision making.
Influence of toe flexor muscle fatigue on stiffness of the intrinsic foot muscles
Authors: Yoshida Kazushi , Katsuya Mikuto , Takano Shogo , Hayashi Kazuma , Hakozaki Taisei , Shinohara Junji
Published: April 2024

BACKGROUND:

Assessing intrinsic foot muscles (IFM) is important for understanding their role in loading movements. Additionally, knowledge of the impact of IFM following toe flexor muscle fatigue may aid the teaching of IFM exercises.

OBJECTIVE:

To examine the influence of toe flexor muscle fatigue on IFM stiffness using ultrasound shear-wave elastography.

METHODS:

This study included 19 college students. IFM stiffness at 10%, 50%, and 90% body weight was measured using ultrasound-based shear-wave elastography. IFM including the abductor hallucis (AbH), flexor hallucis brevis (FHB), flexor digitorum brevis (FDB), and quadratus plantae (QP) were assessed. The fatigue induction protocol comprised a series of toe flexions at a controlled pace of 40 beats per minute and an amplitude of 75% of the maximum toe flexor strength for a duration of 5 minutes.

RESULTS:

Muscle stiffness significantly increased with increasing load. Toe flexor muscle fatigue significantly increased the stiffness of the FDB.

CONCLUSIONS:

IFM stiffness significantly increased with increasing load, and the stiffness of FDB significantly increased in the toe flexor muscle fatigue condition at DLS and SLS loads. The findings of this study will contribute to the study and clinical setting of IFM exercises.
Real World Financial Mismanagement in Alzheimer’s Disease, Frontotemporal Dementia, and Primary Progressive Aphasia
Authors: Ngo Sang , Jackson Ashley J. , Manivannan Madhumitha , Young J. Clayton , Leggins Brandon , Cryns Noah G. , Tran Sheila T. , Grant Harli E. , Knudtson Marguerite V. , Chiong Winston
Published: April 2024

Background: Whereas clinical experience in dementia indicates high risk for financial mismanagement, there has been little formal study of real world financial errors in dementia. Objective: We aimed to compare caregiver-reported financial mistakes among people with Alzheimer’s disease, behavioral variant frontotemporal dementia (bvFTD), and primary progressive aphasia (PPA). Methods: Caregivers reported whether participants with dementia had made financial mistakes within the last year; and if so, categorized these as resulting from: (a) being too trusting or gullible, (b) being wasteful or careless with money, or (c) trouble with memory. In a pre-registered analysis https://archive.org/details/osf-registrations-vupj7-v1), we examined the hypotheses that (1) financial mistakes due to impaired socioemotional function and diminished sensitivity to negative outcomes are more prevalent in bvFTD than in Alzheimer’s disease, and (2) financial mistakes due to memory are more prevalent in Alzheimer’s disease than in bvFTD. Exploratory analyses addressed vulnerability in PPA and brain-behavior relationships using voxel-based morphometry. Results: Concordant with our first hypothesis, bvFTD was more strongly associated than Alzheimer’s disease with mistakes due to being too trusting/gullible or wasteful/careless; contrary to our second hypothesis, both groups were similarly likely to make mistakes due to memory. No differences were found between Alzheimer’s disease and PPA. Exploratory analyses indicated associations between financial errors and atrophy in right prefrontal and insular cortex. Conclusions: Our findings cohere with documented socioemotional and valuation impairments in bvFTD, and with research indicating comparable memory impairment between bvFTD and Alzheimer’s disease.
Examining a Preclinical Alzheimer’s Cognitive Composite for Telehealth Administration for Reliability Between In-Person and Remote Cognitive Testing with Neuroimaging Biomarkers
Authors: Duran Tugce , Gaussoin Sarah A. , Latham Lauren A. , Rundle Melissa M. , Espeland Mark A. , Williams Benjamin J. , Hughes Timothy M. , Craft Suzanne , Sachs Bonnie C. , Bateman James R. , Lockhart Samuel N.
Published: April 2024

Background: The preclinical Alzheimer’s cognitive composite (PACC) was developed for in-person administration to capture subtle cognitive decline. At the outset of the COVID-19 pandemic, cognitive testing was increasingly performed remotely by telephone or video administration. It is desirable to have a harmonized composite measurement derived from both in-person and remote assessments for identifying cognitive changes and to examine its relationship with common neuroimaging biomarkers. Objective: We defined a telehealth compatible PACC (tPACC) and examined its relationship with neuroimaging biomarkers related to neurodegeneration, brain function and perfusion, white matter integrity, and amyloid-β. Methods: We examined 648 participants’ neuroimaging and in-person and remote cognitive testing data from the Wake Forest Alzheimer’s Disease Research Center’s Clinical Core cohort (observational study) to calculate a modified PACC (PACC5-RAVLT) score and tPACC scores (in-person and remote). We performed Spearman/intraclass correlation coefficient (ICC) analyses for reliability of tPACC scores and linear regression models to evaluate associations between tPACC and neuroimaging. Bland-Altman plots for agreement were constructed across cognitively normal and impaired (mild cognitive impairment and dementia) participants. Results: There was a significant positive relationship between tPACCin - person and PACC5-RAVLT (Overall group: r2 = 0.94, N = 648), and tPACCin - person and tPACCremote (validation subgroup: ICC = 0.82, n = 53). Overall, tPACC showed significant associations with brain thickness/volume, gray matter perfusion, white matter free water, and amyloid-β deposition. Conclusions: There is a good agreement between tPACCand PACC5-RAVLTfor cognitively normal and impaired individuals. The tPACC is associated with common neuroimaging markers of Alzheimer’s disease.
The Association of Circulating Glucagon-Like Peptide-1 with Cognitive Functions and Biomarkers in Alzheimer’s Disease
Authors: Liu Mengqing , Ma Nenghong , Yang Xiao , Sun Miao , Li Xiaowen , Liu Yuhui , Chang Qing , Hei Changchun
Published: April 2024

Background: Alzheimer’s disease (AD) is an age-related neurodegenerative disease that is clinically characterized by progressive cognitive decline. Glucagon-like peptide-1 (GLP-1) is a hormone that belongs to the incretin family and is released in response to nutrient intake. It plays a role in maintaining metabolic homeostasis and has been suggested to be involved in maintaining the brain microenvironment. However, the role of GLP-1 in AD pathogenesis has not been fully illustrated. Objective: This study aims to investigate the clinical relevance of GLP-1 in AD and the effects of GLP-1 in amyloid-β (Aβ) metabolism in vitro. Methods: In this study, 39 AD patients and 120 cognitively intact controls were included. Plasma levels of GLP-1 were measured using ELISA. SH-SY5Y cells overexpressing human amyloid precursor protein (APP) were treated with GLP-1. Western blot analysis was used to assess the effects of GLP-1 on the metabolism of Aβ. Results: Plasma GLP-1 levels were decreased with aging. Plasma GLP-1 levels were lower in AD patients in comparison with healthy older adults. Plasma GLP-1 levels were positively associated with Mini-Mental State Examination scores but negatively associated with plasma pTau181 levels. GLP-1 dose-dependently increased the area fraction of mitochondrial staining in vitro. Furthermore, GLP-1 dose-dependently promoted the α-cleavage of APP, thus reducing the generation of Aβ. Conclusions: GLP-1 has neuroprotective effects in AD, and therefore the decrease in GLP-1 levels during aging might contribute to the development of AD.
Impact of Apolipoprotein E4 on the Locus Coeruleus Functional Connectivity in Preclinical Alzheimer’s Disease
Authors: Um Yoo Hyun , Wang Sheng-Min , Kang Dong Woo , Kim Sunghwan , Lee Chang Uk , Kim Donghyeon , Choe Yeong Sim , Kim Regina E.Y. , Lee Soyoung , Lee Min-Kyung , Lim Hyun Kook
Published: April 2024

Background: Recent interest has surged in the locus coeruleus (LC) for its early involvement in Alzheimer’s disease (AD), notably concerning the apolipoprotein ɛ4 allele (APOE4). Objective: This study aimed to discern LC functional connectivity (FC) variations in preclinical AD subjects, dissecting the roles of APOE4 carrier status and amyloid-β (Aβ) deposition. Methods: A cohort of 112 cognitively intact individuals, all Aβ-positive, split into 70 APOE4 noncarriers and 42 carriers, underwent functional MRI scans, neuropsychological assessments, and APOE genotyping. The research utilized seed to voxel analysis for illustrating LC rsFC discrepancies between APOE4 statuses and employed a general linear model to examine the interactive influence of APOE4 carrier status and Aβ deposition on LC FC values. Results: The investigation revealed no significant differences in sex, age, or SUVR between APOE4 carriers and noncarriers. It found diminished LC FC with the occipital cortex in APOE4 carriers and identified a significant interaction between APOE4 carrier status and temporal lobe SUVR in LC FC with the occipital cortex. This interaction suggested a proportional increase in LC FC for APOE4 carriers. Additional notable interactions were observed affecting LC FC with various brain regions, indicating a proportional decrease in LC FC for APOE4 carriers. Conclusions: These findings confirm that APOE4 carrier status significantly influences LC FC in preclinical AD, showcasing an intricate relationship with regional Aβ deposition. This underscores the critical role of genetic and pathological factors in early AD pathophysiology, offering insights into potential biomarkers for early detection and intervention strategies.
From the chokeberry fruit products and by-products to health-promoting effects through multifaceted in vitro bioactivity evaluation and molecular docking studies
Authors: Milutinović Milica , Ćujić Nikolić Nada , Radan Milica , Mihajilov Krstev Tatjana , Šavikin Katarina , Petrović Predrag , Randjelović Milica , Miladinović Bojana , Branković Suzana , Kitić Dušanka
Published: April 2024

BACKGROUND:

Polyphenolic-rich chokeberry extracts and juice could provide health benefits in humans. Moreover, by-products after processing stand out as a potential new source of valuable compounds.

OBJECTIVE:

The research aimed to evaluate in vitro bioactivity of chokeberry fruit extract, juice, and waste extract (material remaining after juice processing) based on their chemical composition and molecular docking analysis.

METHODS:

Spectrophotometric methods were used to determine the phenolics, anthocyanins, flavonoids, and proanthocyanins content in freeze-dried samples. Additionally, individual anthocyanins, flavonoids, and sugars were identified through HPLC analysis. The antioxidant capacity was tested using two in vitro methods and assessing antimicrobial activity. The effects of tyrosinase and acetylcholinesterase inhibition were examined using spectrophotometric methods. Molecular docking analysis identified the interaction of chokeberry components with the active sites of these enzymes.

RESULTS:

The waste extract exhibited the most robust antioxidant activity within the DPPH system. While bactericidal activity was absent across all chokeberry preparations, they demonstrated inhibitory effects on specific microbiological strains relevant to the gastrointestinal tract. Results revealed the chokeberry products’ ability to inhibit tyrosinase and acetylcholinesterase. Molecular docking confirmed the strong interactions between chokeberry compounds and these enzymes.

CONCLUSION:

The results indicate the potential of chokeberry products and by-products as valuable resources for pharmaceutical applications.
Design of a load balancing Objective Function for RPL
Authors: Elmahi M.Y. , Osman N.I.M.
Published: April 2024

Routing protocols for Internet of Things (IoT) play a major role in the performance of the network. The standard Routing Protocol for Low-Power and Lossy Networks (RPL) suffers from a number of limitations including congestion of higher-level nodes and unbalanced topology. This paper proposes a novel Objective Function called Load Balanced Minimum Rank with Hysteresis Objective Function (LB_MRHOF), which assigns child nodes to the most suitable parent in the topology. The Objective Function utilizes a weight of the Expected Transmission Count (ETX) and number of children to calculate the Composite ETX and Number of Children (CENOC) which estimates the load on each node. The attained CENOC is used to select the optimum parent for each node in the topology, where nodes with high CENOC are avoided in the parent selection process. The proposed Objective Function has been evaluated under random and hierarchical network topologies. In addition, the evaluation has investigated the influence of the number of nodes by testing for small, medium and large-scale networks. Results have shown that the proposed Objective Function outperforms MRHOF, OF_FUZZY and OF-EC in terms of Packet Delivery Ratio (PDR) and reduces nodal hop-count under all tested scenarios, with no compromise in energy consumption. They have also revealed that the best performance achieved by LB_MRHOF is attained under large-scale networks. The resulting network topology which is formed by the proposed Objective Function has shown improved balance and more depth.
Felis bee optimization-based localization for energy-efficient communication in IoT-assisted WSN
Authors: Sawant Surabhi Sushant , Helonde Jagdish B. , Burade Prakash G.
Published: April 2024

Localization of WSN sensor nodes gains significance in order to grasp the idea for dead node replacement and re-establishing the network from data loss. In this research, the node localization and the position estimation of the dead nodes during routing are more effectively identified by the Felis bee localization protocol. The node localization is significant for effective communication and data transfer throughout the network with high signal strength. The developed approach assists in identifying the dead nodes and replaces them with less localization error thus, the signal strength of the receiver side is maximum. By utilizing the information on the exact position of the anchor nodes, the unknown location of the dead nodes is estimated by the Felis bee localization protocol. The Felis bee localization protocol enhances the potential of node localization concerning the energy, localization error, and multi-objective function, and attains the minimum RMSE as 0.566 at the 25th round, maximum RSSI as −47.925 dBm as well as the Energy at round 25 as 0.6517 J and the Throughput as 0.2952bps over the simulation area of 10 × 100. Further the established localization reaches the minimum RMSE as 0.673, the maximum Energy as 0.5141 J, maximum throughput as 0.5682,and the maximum RSSI as −53.911 dBm, at the 50th round over simulation area 200 × 200 and surpasses other conventional localization techniques.
Towards bridging the semantic gap between image and text: An empirical approach
Authors: Javed Hira , Sufyan Beg M.M. , Akhtar Nadeem , Alroobaea Roobaea
Published: April 2024

Vlogs, Recordings, news, sport coverages are huge sources of multimodal information that do not just limit to text but extend to audio, images and videos. Applications such as summary generation, image/video captioning, multimodal sentiment analysis, cross modal retrieval requires Computer Vision along with Natural Language Processing techniques to extract relevant information. Information from different modalities must be leveraged in order to extract quality content. Hence, reducing the gap between different modalities is of utmost importance. Image to text conversion is an emerging field and employs the use of encoder decoder architecture. Deep CNNs extract the feature of images and sequence to sequence models are used to generate text description. This paper is a contribution towards the growing body of research in multimodal information retrieval. In order to generate the textual description of images, we have performed 5 experiments using the benchmark Flickr8k dataset. In these experiments we have utilized different architectures - simple sequence to sequence model, attention mechanism, transformer-based architecture to name a few. The results have been evaluated using BLEAU score. Results show that the best descriptions are attained by making use of transformer architecture. We have also compared our results with the pretrained visual model vit-gpt2 that incorporates visual transformer.
Fetal region contour and crown-rump length estimation using modified U-Net
Authors: Sriraam Natarajan , Chinta Babu , Suresh Seshadhri , Sudharshan Suresh
Published: April 2024

Assessing fetal growth and development requires accurate identification of the fetal area contour and measurement of the Crown-Rump Length (CRL). In this paper, we presented a unique method for autonomously segmenting the fetal region in ultrasound images and calculating the CRL based on the U-Net architecture. Because of its capacity to capture both global and local information, the U-Net model is a popular choice for image segmentation tasks. Our method employs the U-Net model to extract the fetal region contour and measure the CRL, resulting in a dependable and efficient prenatal evaluation solution.
Leveraging domain-adapted transformer based models for disaster-specific tweet classification
Authors: Singh Pardeep , Singh Monika , Singh Nitin Kumar , Das Prativa , Chand Satish
Published: April 2024

Social media platforms play vital roles in disseminating information during crisis situations. Many rescue agencies, media outlets, and volunteers regularly monitor this data to identify and analyze disasters, ultimately mitigating life risks. However, effectively categorizing these messages based on information types is crucial for enhancing the situational awareness of emergency responders. This paper addresses the challenge of analyzing informal crisis-related social media texts by classifying disaster event tweets into 10 humanitarian categories associated with 19 major natural disaster events. We fine-tune seven state-of-the-art pre-trained transformer models and compare their performance with the recently introduced domain-specific models, i.e., CrisisTransformers. We empirically found that CrisisTransformers outperform seven strong baseline transformer models in classifying disaster-specific tweets from the HumAID dataset, achieving a macro-averaged F1 score of 0.77. Our work contributes to the crisis computing field by improving the classification of disaster-related tweets and enhancing the capabilities of emergency responders and disaster management organizations.
RETRACTED: Selection and application of building material suppliers based on the 2-tuple linguistic neutrosophic number combined grey relational analysis method
Authors: Yang Cheng , Xu Xinrui
Published: April 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
RETRACTED: Classification of mammogram breast cancer using customized deep learning model
Authors: Anbumani A. , Jayanthi P.
Published: April 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
RETRACTED: Remaining life prediction of aircraft engines based on IGOA-LSTM-FNN
Authors: Bai Xiaojun , Jia Haiyang , Fu Yanfang , Ji Yu , Li Suyang
Published: April 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
RETRACTED: A color 3D reconstruction method for waste mechanical parts based on improved SGM
Authors: Liu Dapeng
Published: April 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
A new framework for building agricultural domain-based ontologies from text documents using natural language processing and artificial intelligence techniques
Authors: Saravanan Krithikha Sanju , Bhagavathiappan Velammal
Published: April 2024

The advancements in technology, particularly in the field of Natural Language Processing (NLP) and Artificial Intelligence (AI) can be advantageous for the agricultural sector to enhance the yield. Establishing an agricultural ontology as part of the development would spur the expansion of cross-domain agriculture. Semantic and syntactic knowledge of the domain data is required for building such a domain-based ontology. To process the data from text documents, a standard technique with syntactic and semantic features are needed because the availability of pre-determined agricultural domain-based data is insufficient. In this research work, an Agricultural Ontologies Construction framework (AOC) is proposed for creating the agricultural domain ontology from text documents using NLP techniques with Robustly Optimized BERT Approach (RoBERTa) model and Graph Convolutional Network (GCN). The anaphora present in the documents are resolved to produce precise ontology from the input data. In the proposed AOC work, the domain terms are extracted using the RoBERTa model with Regular Expressions (RE) and the relationships between the domain terms are retrieved by utilizing the GCN with RE. When compared to other current systems, the efficacy of the proposed AOC method achieves an exceptional result, with precision and recall of 99.6% and 99.1% respectively.
RETRACTED: Cost-based distributionally robust tourist attraction recommendation with demand uncertainty
Authors: Li Zexin , Li Qiulin , Li Zepeng , Huang Lixia , Pu Song , Luo Zunhao
Published: April 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
An adaptive differential evolution algorithm with multi-strategy for solving complex optimization problem
Authors: Li Yuangang , Gao Xinrui , Ni Hongcheng , Song Yingjie , Deng Wu
Published: April 2024

In this paper, an adaptive differential evolution algorithm with multi-strategy, namely ESADE is proposed to solve the premature convergence and high time complexity for complex optimization problem. In the ESADE, the population is divided into several sub-populations after the fitness value of each individual is sorted. Then different mutation strategies are proposed for different populations to balance the global exploration and local optimization. Next, a new self-adaptive strategy is designed adjust parameters to avoid falling into local optimum while the convergence accuracy has reached its maximum value. And a complex airport gate allocation multi-objective optimization model with the maximum flight allocation rate, the maximum near gate allocation rate, and the maximum passenger rate at near gate is constructed, which is divided into several single-objective optimization model. Finally, the ESADE is applied solve airport gate allocation optimization model. The experiment results show that the proposed ESADE algorithm can effectively solve the complex airport gate allocation problem and achieve ideal airport gate allocation results by comparing with the current common heuristic optimization algorithms.
A novel attribute reduction approach using coverage-credibility-based rough decision entropy for interval-valued data
Authors: Liu Xia , Zhang Xianyong , Chen Jiaxin , Chen Benwei
Published: April 2024

Attribute reduction is an important method in data analysis and machine learning, and it usually relies on algebraic and informational measures. However, few existing informational measures have considered the relative information of decision class cardinality, and the fusion application of algebraic and informational measures is also limited, especially in attribute reductions for interval-valued data. In interval-valued decision systems, this paper presents a coverage-credibility-based condition entropy and an improved rough decision entropy, further establishes corresponding attribute reduction algorithms for optimization and applicability. Firstly, the concepts of interval credibility, coverage and coverage-credibility are proposed, and thus, an improved condition entropy is defined by virtue of the integrated coverage-credibility. Secondly, the fused rough decision entropy is constructed by the fusion of improved condition entropy and roughness degree. By introducing the coverage-credibility, the proposed uncertainty measurements enhance the relative information of decision classes. In addition, the nonmonotonicity of the improved condition entropy and rough decision entropy is validated by theoretical proofs and experimental counterexamples, with respect to attribute subsets and thresholds. Then, the two rough decision entropies drive monotonic and nonmonotonic attribute reductions, and the corresponding reduction algorithms are designed for heuristic searches. Finally, data experiments not only verify the effectiveness and improvements of the proposed uncertainty measurements, but also illustrate the reduction algorithms optimization through better classification accuracy than four comparative algorithms.
Emergence of the neutrosophic ratio-type estimators for estimating the population mean evidence: From energy dissipated Okara district Pakistan
Authors: Faraz Ansar Ali , Khan Hina , Aslam Muhammad , Albassam Mohammed
Published: April 2024

When data are hazy or uncertain, estimators given under classical statistics are ineffective. Given that it deals with uncertainty, neutrosophic statistics is the sole alternative. Due to the vast range of applications, extensive research has been done in this area. The objective of this study is to determine the most accurate predictions for the population mean with the least amount of mean square error. We have created neutrosophic ratio type estimators, when working with ambiguous, hazy, and neutrosophic-type data, the proposed estimation methods are very useful for computing results. These estimators produce findings that are not single-valued but rather have an interval form, where our population parameter may lie more frequently. Since we have an estimated interval with the unknown population mean value given a minimal mean square error, it improves the estimators’ efficiency. Real life neutrosophic line losses data and simulation are both used to analyze the effectiveness of the proposed neutrosophic ratio-type estimators. Additionally, a comparison is made to show how helpful Neutrosophic ratio type estimator is in comparison to existing estimators.
Prediction of soil organic carbon using machine learning techniques and geospatial data for sustainable agriculture
Authors: Mundada Shyamal , Jain Pooja , Kumar Nirmal
Published: April 2024

Sustainable agriculture revolves around soil organic carbon (SOC), which is essential for numerous soil functions and ecological attributes. Farmers are interested in conserving and adding additional soil organic carbon to certain fields in order to improve soil health and productivity. The relationship between soil and environment that has been discovered and standardized throughout time has enhanced the progress of digital soil-mapping techniques; therefore, a variety of machine learning techniques are used to predict soil properties. Studies are thriving at how effectively each machine learning method maps and predicts SOC, especially at high spatial resolutions. To predict SOC of soil at a 30 m resolution, four machine learning models—Random Forest, Support Vector Machine, Adaptive Boosting, and k-Nearest Neighbour were used. For model evaluation, two error metrics, namely R2 and RMSE have been used. The findings demonstrated that the calibration and validation sets’ descriptive statistics sufficiently resembled the entire set of data. The range of the calculated SOC content was 0.06 to 1.76 %. According to the findings of the study, Random Forest showed good results for both cases, i.e. evaluation using cross validation and without cross validation. Using cross validation, RF confirmed highest R2 as 0.5278 and lowest RMSE as 0.1683 for calibration dataset while without cross validation it showed R2 as 0.8612 and lowest RMSE as 0.0912 for calibration dataset. The generated soil maps will help farmers adopt precise knowledge for decisions that will increase farm productivity and provide food security through the sustainable use of nutrients and the agricultural environment.
RETRACTED: Spotted hyena optimizer with ensemble deep learning based intrusion detection in internet of things environment
Authors: Srinivasan Manohar , Senthilkumar N.C.
Published: April 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Attribute selection for partially labeled data based on uncertainty measure
Authors: Zheng Lina , Wang Yini , Wang Sichun
Published: April 2024

Due to the relatively high cost of labeling data, only a fraction of the available data is typically labeled in machine learning. Some existing research handled attribute selection for partially labeled data by using the importance of an attribute subset or uncertainty measure (UM). Nevertheless, it overlooked the missing rate of labels or the choice of the UM with optimal performance. This study uses discernibility relation and the missing rate of labels to UM for partially labeled data and applies it to attribute selection. To begin with, a decision information system for partially labeled data (pl-DIS) can be used to induce two equivalent decision information systems (DISs): a DIS is constructed for labeled data (l-DIS), and separately, another DIS is constructed for unlabeled data (ul-DIS). Subsequently, a discernibility relation and the percentage of missing labels are introduced. Afterwards, four importance of attribute subset are identified by taking into account the discernibility relation and the missing rate of labels. The sum of their importance, which is determined by the label missing rates of two DISs, is calculated by weighting each of them and adding them together. These four importance may be seen as four UMs. In addition, numerical simulations and statistical analyses are carried out to showcase the effectiveness of four UMs. In the end, as its application for UM, the UM with optimal performance is used to attribute selection for partially labeled data and the corresponding algorithm is proposed. The experimental outcomes demonstrate the excellence of the proposed algorithm.
RETRACTED: Enhanced X-wave recognition in ECG signal using max–min thresholds and detection of QRS complex
Authors: Velusamy Saravanan , Murugan Pallikonda Rajasekaran , Vishnuvarthanan G. , Thiyagarajan Arunprasath , Ramaraj Kottaimalai , Kamalakkannan Vidyavathi
Published: April 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
RETRACTED: Empowering modern E-commerce consumers: Optimized deep learning for product image classification in home shopping
Authors: Lamani Dharmanna , Shanthi T.S. , Kirubakaran M.K. , Roopa R.
Published: April 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
RETRACTED: Building recognition and classification using deep learning in civil engineering projects
Authors: Rong Guo , Xi Wang
Published: April 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Upper Limbs Muscle Co-Contraction Changes Correlate With The Physical Motor Impairments in CMT
Authors: Lencioni Tiziana , Bandini Virginia , Schenone Cristina , Lagostina Maria , Aiello Alessia , Schenone Angelo , Ferrarin Maurizio , Trompetto Carlo , Mori Laura ,   CMT-RF Study Group, Germanotta Marco , Pazzaglia Costanza , Massucco Sara , Marinelli Lucio , Grandis Marina
Published: April 2024

Background:

Subjects with Charcot-Marie-Tooth (CMT) disease show hands impairment which is a relevant problem affecting the quality of life. This symptom is related to muscle weakness and reduced motor coordination of the upper limb. However, most studies focus on lower limb impairment, therefore the investigation of upper limb disability is necessary to identify biomarkers able to monitor disease-specific features and to tailor rehabilitation.

Objective:

This study aimed at characterizing upper limb muscle co-contraction using the co-contraction index (CCI) in CMT population.

Methods:

Upper limb kinematic and electromyography (EMG) data were collected from fourteen CMT subjects (6-CMT1A and 8-CMT1X) during motor tasks typical of daily living activities. Rudolph’s CCI was used to quantify muscle co-contraction of four muscle pairs acting on shoulder, elbow and wrist. All CMT subjects underwent clinical examination. Thirteen healthy subjects served as the normative reference (HC).

Results:

CMT1X and CMT1A showed a significant reduction in CCI for distal and proximal muscle pairs compared to HC. Furthermore, CMT1A showed greater values of CCI compared to CMT1X mainly for the axial and axial-to-proximal muscle pairs. Movement speed and smoothness were not altered compared to HC. In addition, EMG metrics showed moderate-to-strong significant correlations with clinical outcomes.

Conclusions:

CCI was able to quantify disease-specific deficits with respect to the normative reference, highlighting motor control alterations even before motor output impairment. CCI was also sensitive in detecting CMT subtypes-based differences and adopted compensatory strategies. Our findings suggest that CCI can be an outcome measure for CMT disease monitoring and interventional studies.
Efficacy of serial casting protocols in idiopathic toe-walking
Authors: Shirel Tyler , Sylvanus Tonye , Cho Kelly , Authement Amy , Krach Linda E.
Published: April 2024

OBJECTIVE: Idiopathic toe-walking (ITW) is a diagnosis of exclusion. A relationship between ITW and decreased range of motion (ROM) is postulated. Treatments focus on increasing ankle dorsiflexion including serial casting. There is no consensus for duration of serial casting. This study aimed to determine ROM changes with cast change intervals of one vs. two weeks, and the rate of ITW recurrence. METHODS: This was a retrospective study of 86 patients, ages 0–9 years with ITW undergoing weekly casting (N = 29) and two-week casting (N = 57) at a children’s hospital from 2014–2020. ROM at baseline, two weeks, four weeks, and final cast removal were collected. Statistical analyses included chi-squared tests, two-sample t-tests, and linear mixed regression. Outcome distributions were assessed for normality. P-values < 0.05 were considered statistically significant. RESULTS: After adjusting for baseline ROM, the mean change in ROM from baseline to two weeks was 10.6∘ vs 7.5∘ in the one-week vs. two-week casting interval, respectively (p < 0.001). The baseline to final measurement was 13.4∘ vs 9.8∘ in the one-week vs. two-week casting interval, respectively (p < 0.001). The rate of recurrence of ITW was similar between the two groups. CONCLUSION: This study suggests greater improvement in ROM in the one-week vs. two-week casting interval group.
An investigation into the convergence of economic growth among Indian States and the path ahead
Authors: Sinha Jitendra Kumar
Published: April 2024

This study offers a comprehensive examination of economic growth and convergence in the Indian States and Union Territories (U.T.) over the period from 1991 to 2020. It investigates absolute, sigma, and conditional convergence within this diverse set of states and utilizes the augmented Solow and extended Solow models to explore conditional convergence dynamics. The empirical findings reveal several significant insights. First, there is no substantial negative correlation between the initial per capita GDP ratio and the average annual growth rate, indicating the absence of absolute convergence across the Indian States and U.T. economies during the study period. These results align with those obtained from sigma convergence analysis, reinforcing the absence of widespread convergence. However, conditional convergence is observed, as evidenced by the rate of conditional convergence (coefficient of initial GDP per capita) estimated at 0.038 among the Indian States and U.T. The presence of conditional convergence implies that while initial conditions matter, other factors, including physical and human capital, population growth, and additional variables, significantly contribute to the growth and convergence of Indian regions. The study concludes that policies aimed at promoting economic growth in the Indian States should prioritize the expansion of the labor force, investments in physical and human capital, and prudent government consumption. Furthermore, fostering an environment that encourages access to new technologies and ideas, maintaining sound macroeconomic management, and increasing investments in human capital formation are essential for sustained growth. Effective resource allocation through prudent budgetary policies and heightened investments in the health sector are recommended. Incentives to reduce fertility rates and adept monetary policy management are also identified as crucial elements for ensuring stability and sustainable growth. In summary, this research underscores the importance of adopting a holistic approach to foster economic growth and convergence in the Indian States. The suggested policy measures create a conducive environment for sustained development and prosperity in this diverse and dynamic region.
Enhanced acrylic gauge with five eccentric circles for optimizing CT angiography spatial resolution via Taguchi’s methodology
Authors: Shen Cheng-Mao , Lin Ya-Hui , Li Dian-Fong , Pan Lung-Kwang , Peng Bing-Ru
Published: April 2024

BACKGROUND: Cerebral examination via CTA is always the first choice for patients with unexpected brain injury or different types of brain lesions to detect ruptured hemangiomas, vascular infarcts, or other brain tissue lesions. OBJECTIVE: This study innovated the acrylic gauge with five eccentric circles for computed tomography angiography (CTA) analysis to optimize the spatial resolution via Taguchi’s methodology. METHODS: The customized gauge was revised from the V-shaped slit gauge and transferred into five eccentric circles’ slit gauge. The gauge was assembled with another six acrylic layers to simulate the human head. Taguchi’s L18 orthogonal array was adopted to optimize the spatial resolution of CTA imaging quality. In doing so, six essential factors of CTA are kVp, mAs, spiral rotation pitch, FOV, rotation time of the CT and reconstruction filter, and each factor has either two or three levels to organize into eighteen combinations to simulate the full factor combination of 486 (21 × 35 = 486) times according to Taguchi’s recommendation. Three well-trained radiologists ranked the gauge’s 18 CTA scanned imaging qualities according to contrast, sharpness, and spatial resolution and derived the unique fish-bone-plot of six factors for further analysis. The optimal factor combination of CTA was proven by follow-up verification and ANOVA to obtain this study’s dominant or minor factor. RESULTS: The optimal factor combination of CTA was A2 (120 kVp), B3 (200 mAs), C1 (Pitch 0.6), D2 (FOV 220 mm2), E1 (rotation time 0.33 s), and F3 (Brain sharp, UC). Furthermore, deriving a quantified MDD (minimum detectable difference) to imply the spatial resolution of CTA, a semiauto profile analysis program run in MATLAB and OriginPro was recommended to evaluate the MDD and to suppress the manual error in calculation. Eventually, the derived MDDs of the conventional and optimal factor combinations of CTA were 2.35 and 2.26 mm, respectively, in this study. CONCLUSION: Taguchi’s methodology was found applicable for quantifying the CTA imaging quality in practical applications.