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General General

A bibliometric review of global visibility, impact and adoption of electronic invoicing: The past and the future.

In Heliyon

The objective of this study is to conduct a bibliometric review of literature on electronic invoicing to provide an understanding of the growing field and valuable sources for future research. A total of 191 papers within the period of 1997 to July 2021 were included in our analysis. The systemic analysis revealed several insights in research progression over two decades, relevant authors and leading institutions including countries, most frequent keywords, and the principal methodologies and theories adopted. Although the field of electronic invoicing is still emerging, it is interesting to see trending keywords such as 'data mining', 'automation', 'blockchain', 'digital storage', and 'machine learning' as demonstrated in recent publications. The study also attempted to develop a framework and proposed an integrated theory of electronic invoicing since the general theoretical framework does not exist in the literature. Several research gaps were exposed related to more studies in the emerging field of electronic invoicing and how future studies could further shape the field by addressing yet unanswered questions. We anticipate that the findings in this study will be a valuable contribution and resource for e-invoicing research.

Olaleye Sunday Adewale, Sanusi Ismaila Temitayo, Dada Oluwaseun Alexander, Agbo Friday Joseph

2023-Mar

Automation, Bibliometric, Carbon footprint, Electronic invoicing, Electronic signature, Taxation

General General

Smaller is better? Unduly nice accuracy assessments in roof detection using remote sensing data with machine learning and k-fold cross-validation.

In Heliyon

Deriving the thematic accuracy of models is a fundamental part of image classification analyses. K-fold cross-validation (KCV), as an accuracy assessment technique, can be biased because existing built-in algorithms of software solutions do not handle the high autocorrelation of remotely sensed images, leading to overestimation of accuracies. We aimed to quantify the magnitude of the overestimation of KCV-based accuracies and propose a method to overcome this problem with the example of rooftops using a WorldView-2 (WV2) satellite image, and two orthophotos. Random split to training/testing subsets, independent testing and different types of repeated KCV sampling strategies were used to generate input datasets for classification. Results revealed that applying the random splitting of reference data to training/testing subsets and KCV methods had significantly biased the accuracies by up to 17%; overall accuracies (OAs) can incorrectly reach >99%. We found that repeated KCV can provide similar results to independent testing when spatial sampling is applied with a sufficiently large distance threshold (in our case 10 m). Coarser resolution of WV2 ensured more reliable results (up to a 5-9% increase in OA) than orthophotos. Object-based pixel purity of buildings showed that when using a majority filter for at least of 50% of objects the final accuracy approached 100% with each sampling method. The final conclusion is that KCV-based modelling ensures better accuracy than single models (with better pixel purity on the object level), but the accuracy metrics without spatially filtered sampling are not reliable.

Abriha Dávid, Srivastava Prashant K, Szabó Szilárd

2023-Mar

Accuracy assessment, Object-based pixel purity, Post-classification, Roof classification, Salt-and-pepper effect, Urban environment

Surgery Surgery

Development and validation of machine learning models for postoperative venous thromboembolism prediction in colorectal cancer inpatients: a retrospective study.

In Journal of gastrointestinal oncology

BACKGROUND : Colorectal cancer (CRC) is a heterogeneous group of malignancies distinguished by distinct clinical features. The association of these features with venous thromboembolism (VTE) is yet to be clarified. Machine learning (ML) models are well suited to improve VTE prediction in CRC due to their ability to receive the characteristics of a large number of features and understand the dataset to obtain implicit correlations.

METHODS : Data were extracted from 4,914 patients with colorectal cancer between August 2019 and August 2022, and 1,191 patients who underwent surgery on the primary tumor site with curative intent were included. The variables analyzed included patient-level factors, cancer-level factors, and laboratory test results. Model training was conducted on 30% of the dataset using a ten-fold cross-validation method and model validation was performed using the total dataset. The primary outcome was VTE occurrence in postoperative 30 days. Six ML algorithms, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), weighted support vector machine (SVM), a multilayer perception (MLP) network, and a long short-term memory (LSTM) network, were applied for model fitting. The model evaluation was based on six indicators, including receiver operating characteristic curve-area under the curve (ROC-AUC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and Brier score. Two previous VTE models (Caprini and Khorana) were used as the benchmarks.

RESULTS : The incidence of postoperative VTE was 10.8%. The top ten significant predictors included lymph node metastasis, C-reactive protein, tumor grade, anemia, primary tumor location, sex, age, D-dimer level, thrombin time, and tumor stage. In our results, the XGBoost model showed the best performance, with a ROC-AUC of 0.990, a SEN of 96.9%, a SPE of 96.1% in training dataset and a ROC-AUC of 0.908, a SEN of 77.5%, a SPE of 93.7% in validation dataset. All ML models outperformed the previously developed models (Caprini and Khorana).

CONCLUSIONS : This study developed postoperative VTE predictive models using six ML algorithms. The XGBoost VTE model might supply a complementary tool for clinical VTE prophylaxis decision-making and the proposed risk factors could shed some light on VTE risk stratification in CRC patients.

Qin Li, Liang Zhikun, Xie Jingwen, Ye Guozeng, Guan Pengcheng, Huang Yaoyao, Li Xiaoyan

2023-Feb-28

Surgical colorectal cancer patient, machine learning model, venous thromboembolism (VTE)

Pathology Pathology

Paving New Roads Using Allium sativum as a Repurposed Drug and Analyzing its Antiviral Action Using Artificial Intelligence Technology.

In Iranian journal of pharmaceutical research : IJPR

CONTEXT : The whole universe is facing a coronavirus catastrophe, and prompt treatment for the health crisis is primarily significant. The primary way to improve health conditions in this battle is to boost our immunity and alter our diet patterns. A common bulb veggie used to flavor cuisine is garlic. Compounds in the plant that are physiologically active are present, contributing to its pharmacological characteristics. Among several food items with nutritional value and immunity improvement, garlic stood predominant and more resourceful natural antibiotic with a broad spectrum of antiviral potency against diverse viruses. However, earlier reports have depicted its efficacy in the treatment of a variety of viral illnesses. Nonetheless, there is no information on its antiviral activities and underlying molecular mechanisms.

OBJECTIVES : The bioactive compounds in garlic include organosulfur (allicin and alliin) and flavonoid (quercetin) compounds. These compounds have shown immunomodulatory effects and inhibited attachment of coronavirus to the angiotensin-converting enzyme 2 (ACE2) receptor and the Mpro of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Further, we have discussed the contradictory impacts of garlic used as a preventive measure against the novel coronavirus.

METHOD : The GC/MS analysis revealed 18 active chemicals, including 17 organosulfur compounds in garlic. Using the molecular docking technique, we report for the first time the inhibitory effect of the under-consideration compounds on the host receptor ACE2 protein in the human body, providing a crucial foundation for understanding individual compound coronavirus resistance on the main protease protein of SARS-CoV-2. Allyl disulfide and allyl trisulfide, which make up the majority of the compounds in garlic, exhibit the most potent activity.

RESULTS : Conventional medicine has proven its efficiency from ancient times. Currently, our article's prime spotlight was on the activity of Allium sativum on the relegation of viral load and further highlighted artificial intelligence technology to study the attachment of the allicin compound to the SARS-CoV-2 receptor to reveal its efficacy.

CONCLUSIONS : The COVID-19 pandemic has triggered interest among researchers to conduct future research on molecular docking with clinical trials before releasing salutary remedies against the deadly malady.

Atoum Manar Fayiz, Padma Kanchi Ravi, Don Kanchi Ravi

2022-Dec

Allium sativum, Flavonoid, Immunomodulatory, SARS-CoV-2

oncology Oncology

Investigating nurses' acceptance of patients' bring your own device implementation in a clinical setting: A pilot study.

In Asia-Pacific journal of oncology nursing

OBJECTIVE : The popularity of the ​"bring your own device (BYOD)" ​concept has grown in recent years, and its application has extended to the healthcare field. This study was aimed at examining nurses' acceptance of a BYOD-supported system after a 9-month implementation period.

METHODS : We used the technology acceptance model to develop and validate a structured questionnaire as a research tool. All nurses (n ​= ​18) responsible for the BYOD-supported wards during the study period were included in our study. A 5-point Likert scale was used to assess the degree of disagreement and agreement. Statistical analysis was performed in SPSS version 24.0.

RESULTS : The questionnaire was determined to be reliable and well constructed, on the basis of the item-level content validity index and Cronbach α values above 0.95 and 0.87, respectively. The mean constant values for all items were above 3.95, thus suggesting that nurses had a positive attitude toward the BYOD-supported system, driven by the characteristics of the tasks involved.

CONCLUSIONS : We successfully developed a BYOD-supported system. Our study results suggested that nursing staff satisfaction with BYOD-supported systems could be effectively increased by providing practical functionalities and reducing clinical burden. Hospitals could benefit from the insights generated by this study when implementing similar systems.

Chien Shuo-Chen, Chen Chun-You, Chien Chia-Hui, Iqbal Usman, Yang Hsuan-Chia, Hsueh Huei-Chia, Weng Shuen-Fu, Jian Wen-Shan

2023-Mar

Bring your own device, Internet of things, Nurse acceptance, Smart hospital, Technology acceptance model

General General

Automated pancreatic islet viability assessment for transplantation using bright-field deep morphological signature.

In Computational and structural biotechnology journal

Islets transplanted for type-1 diabetes have their viability reduced by warm ischemia, dimethyloxalylglycine (DMOG; hypoxia model), oxidative stress and cytokine injury. This results in frequent transplant failures and the major burden of patients having to undergo multiple rounds of treatment for insulin independence. Presently there is no reliable measure to assess islet preparation viability prior to clinical transplantation. We investigated deep morphological signatures (DMS) for detecting the exposure of islets to viability compromising insults from brightfield images. Accuracies ranged from 98 % to 68 % for; ROS damage, pro-inflammatory cytokines, warm ischemia and DMOG. When islets were disaggregated to single cells to enable higher throughput data collection, good accuracy was still obtained (83-71 %). Encapsulation of islets reduced accuracy for cytokine exposure, but it was still high (78 %). Unsupervised modelling of the DMS for islet preparations transplanted into a syngeneic mouse model was able to predict whether or not they would restore glucose control with 100 % accuracy. Our strategy for constructing DMS' is effective for the assessment of islet pre-transplant viability. If translated into the clinic, standard equipment could be used to prospectively identify non-functional islet preparations unable to contribute to the restoration of glucose control and reduce the burden of unsuccessful treatments.

Habibalahi Abbas, Campbell Jared M, Walters Stacey N, Mahbub Saabah B, Anwer Ayad G, Grey Shane T, Goldys Ewa M

2023

AI, artificial intelligence, DMOG, dimethyloxalylglycine, DMS, deep morphological signatures, Deep morphological signature, ECG, electrocardiogram, EEG, electroencephalogram, EMCCD, electron multiplying charge coupling device, FD, Fisher Distance, GSIS, glucose stimulated insulin secretion, IoU, intersection over union, MEG, magnetoencephalography, MRI, magnetic resonance imaging, PCA, principal component analysis, Pancreatic islet, ROS, reactive oxygen species, SI, swarm intelligence, SVM, support vector machine, Transplantation