bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Involvement in Social Networks and Prudence in Establishing the Persistence of Online Customers Who Have Experienced Fraud
Authors: Rahayu Aneke Dewi
Published: January 2022

Internet network had been developed over the years and its utilization has also increased and extended to various fields including midwives venturing into online business. Online transaction can be performed through digital media and do not require a direct meeting between the seller and the buyer. Online purchase is called with an e-commerce where the system continues to experience growth. As this growth there is a lot of cheating and fraud. The number does not decrease the negative impact of buying interest someone through e-commerce. There are two variables are taken into consideration, namely the emergence of persistent involvement in social networks and the prudence of a person due to lack of trust after fraud. The purpose of this study was to determine the effect of a person's involvement in social networks and caution against the persistence of online customers who have experienced fraud. Methods of analysis using path analysis with AMOS program. The results of this study are involved in social networking direct or indirect effect on the variable persistence through prudential variable.
An Overview of Suicide Risk for Homeless Women at Pondok Social Surabaya: Case Study
Authors: Astrini Retno Ayu , Herdiana Ike
Published: January 2022

This case study describes an interview on the risk of suicide to a homeless woman in a social lodge in Surabaya, referred to as YS, which shows the symptoms of suicidal tendencies. The interview assessment used is a structured interview guide from the NSW department of health (2010) about the risk of suicide in individuals. The results of the suicidal risk assessment interview on the subject of YS ie the subject has ever thought about committing suicide. The originator is related to the problem in the sticky figure, namely the family. Other triggers include neglect from close relatives, until finally the subject is entrusted to Liponsos. The subject explained that he had tried to kill himself and hurt himself in the early months of 2017. The subject had tried to wash his hands until his hands were bleeding. The subject also stabbed himself using a pin even today. The subject considered death a scary thing, but on the other hand the subject also felt awry. The subject claimed that he already had a plan to end his life, but he was confused because it was impossible to end his life in Liponsos, even though he thought that in Liponsos there were also residents who died. The dynamics of YS suicidal risk trends will be explained in this case study report.
INSIGHTS FROM FOSSIL FISH TAPHONOMY INTO THE DEPOSITIONAL ENVIRONMENT OF THE LOWER MILWAUKEE FORMATION (BERTHELET MEMBER, LATEST MIDDLE DEVONIAN)
Authors: RICH ANDREW J. , ZAMBITO JAMES J.
Published: January 2022

The fossiliferous, argillaceous dolostone of the latest Middle Devonian (Givetian) Milwaukee Formation contains abundant disarticulated fish skeletal elements. This study refines depositional environment reconstruction of the Milwaukee Formation through taphonomic analysis of this fish assemblage. Robust skeletal elements of placoderm grinding teeth, fin spines, and armored plating dominate the assemblage. Specimens display variation in taphonomic attributes including color, luster, corrasion (chemical corrosion and/or physical abrasion), presence of pyrite and phosphate permineralization, and epibionts. Rare occurrences of teeth belonging to osteichthyan fish are also taphonomically variable. Associated invertebrates are diverse and include abundant cephalopod internal molds and pyritized brachiopods often randomly oriented in shell hashes. In sum, these observations suggest deposition on a marine shelf between normal and storm wave base. During low-energy background periods, fish plates exposed on the seafloor would undergo initial disarticulation, epibiont attachment, and color- and luster-altering corrasion and phosphatization. Buried skeletal elements were ‘protected' from epibiont attachment and corrasion, but occasional low oxygen conditions in the substrate led to early diagenetic pyrite permineralization. Periodic high-energy storm events were responsible for complete disarticulation as well as reworking and reexposure of buried skeletal elements (and burial of others), resulting in a time-averaged assemblage with a wide range of taphonomic attributes. Robust placoderm skeletal elements were best suited to survive fossilization in this setting and therefore the placoderm-dominated fossil assemblage appears to be an artifact of taphonomic processes and energy of the depositional environment.
AnchoisFert: A New Organic Fertilizer from Fish Processing Waste for Sustainable Agriculture
Authors: Muscolo Adele , Mauriello Francesco , Marra Francesca , Calabrò Paolo Salvatore , Russo Mariateresa , Ciriminna Rosaria , Pagliaro Mario
Published: January 2022

The “AnchoisFert” solid residue comprised of milled anchovy leftovers after fish oil extraction with biobased limonene is a powerful organic fertilizer. Employed to promote the growth of Tropea’s red onion (Allium cepa), the fertilizer turns out to largely superior to commonly used organic (manure) and chemical (NPK) fertilizers. Rich in proteins, organic carbon, flavonoids, magnesium, potassium, phosphate and sulphate, and devoid of antibiotics and antibiotic resistance genes, the new organic fertilizer can replace both conventional organic and inorganic fertilizers. The discovery closes the fishing material cycle for one of the most fished species across the seas opening the route to a new class of organic fertilizers of exceptional performance derived from abundant biowaste via a low-cost, environmentally-friendly circular economy process.
Individual Risk Factors of Mycetoma Occurrence in Eastern Sennar Locality, Sennar State, Sudan: A Case-Control Study
Authors: Hassan Rowa , Deribe Kebede , Simpson Hope , Bremner Stephen , Bakheet Osama Elhadi , Alnour Mustafa , Fahal Ahmed Hassan , Newport Melanie , Bakhiet Sahar
Published: January 2022

Mycetoma is a serious chronic subcutaneous granulomatous inflammatory disease that is endemic in tropical and subtropical regions where it impacts profoundly on patients, families, and communities. Individual-level risk factors for the disease are poorly understood. To address this, a case-control study was conducted based on data collected from 60 villages in Eastern Sennar Locality, Sennar State, Sudan. Based on the presence of swelling in any part of the body, or sinus formation with or without grain discharge evident from the lesion by ultrasound examination, we diagnosed 359 cases of mycetoma. For each case, we included three healthy sex-matched persons, with no evidence of mycetoma, from the same village as the controls group (n=1077). The odds for mycetoma were almost three times higher in individuals in the age group 16-30 years (Adjusted Odds Ratio (AOR) = 2.804, 95% CI= 1.424 – 5.523) compared to those in age group ≤15 years. Other factors odds of mycetoma were history of local trauma (AOR =1.892, 95% CI= 1.425 – 2.513), being unmarried (AOR =3.179, 95% CI= 2.339 – 4.20) and owning livestock (AOR =3.941, 95% CI= 2.874 – 5.405). In conclusion, certain factors found to be associated with mycetoma in this study could inform a high index of suspicion for mycetoma diagnosis, which would improve early case detection. Other factors found to be associated could inform the development of an interventional program for mycetoma control in Sudan, including education on healthy farming practices and the risks of puncture wounds for individuals residing in endemic areas. However, this work was conducted in one endemic state, while mycetoma cases occur in all states of Sudan. Replicating this study over a wider area would give a fuller picture of the situation, providing the control program with more comprehensive information on the risk factors for the disease.
Health-care Supplier Selection using Hybrid Multi-criteria Decision Making Methods : A Case Study from Morocco
Authors: AZIZ layla , ÇALIK Ahmet
Published: January 2022

Several studies have been conducted in the context of selecting the best supplier. In literature, the supplier selection problem has been widely studied using different approaches. Multi Criteria decision making(MCDM) is one of the frequently exploited tools for achieving this study. Health-care sector represents a vital priority for decreasing risks. However, it is rarely treated by the research community. In this work, we present three hybrid multi-criteria methods (Analytic hierarchy process-technique for order preference by similarity to ideal solution) AHP-TOPSIS, ( Pythagorean fuzzy analytic hierarchy process-technique for order preference by similarity to ideal solution (PFAHP-TOPSIS) and Best worst method-technique for order preference by similarity to ideal solution(BWM-TOPSIS)) for making the efficient health-care supplier selection.This study is mainly composed of two stages: the first one consists of calculating of the weight priority of each criterion and sub-criterion using AHP, BWM, and PFAHP methods, while the second stage aims to integrate the weights priorities for making the ranking of the alternatives using TOPSIS method. Moreover, we present the sensitivity analysis for the three integrated tools to evaluate the ranking of suppliers under the variation of the weights of criteria. This study was conducted in a public hospital from Marrakech city
In Silico Mutational Analysis of ACE2 to Check the Susceptibility of Lung Cancer Patients Towards COVID-19
Authors: Khalid Zumama , Khan Abeedha Tu-Allah , Alnajjar Radwan , Santali Eman , Shakoori Abdul Rauf
Published: January 2022

Being the second major cause of death worldwide, lung cancer might pose a significant threat to the health of patients especially during this era of pandemic as they are found to be more prone to COVID-19. Although many recent studies imply a high frequency of COVID-19 infection associated severe outcome, however the molecular studies are still lacking in this respect. Hence the current study is designed to investigate the binding affinities of ACE2 lung cancer mutants with the viral spike protein to find the susceptibility of respective mutants carrying patients in catching the virus. Quite interestingly, our study found lesser binding affinities of all the selected mutants thus implying that these cancer patients might be less affected by the virus than others. These results are opposed to the recent studies’ propositions and open new avenues for more in-depth studies.
Feature Extraction from MRI ADC Images for Brain Tumor Classification Using Machine Learning Techniques
Authors: Vijithananda Sahan M. , Jayatilake Mohan L. , Hewavithana Badra , Gonçalves Teresa , Rato Luis M. , Weerakoon Bimali S. , Kalupahana Tharindu D. , Silva Anil D. , Dissanayake Karuna D.
Published: January 2022

Background: Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors. Methods: This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed. Results: According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process. Conclusion: This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures such as brain biopsies.
Increased Risk of Primary Ovarian Insufficiency by High-Fructose Diet: A 90 Day Hormonal and Immunohistochemical Study in Wistar Rats
Authors: Mirzaei Roya , Bidgoli Sepideh Arbabi , Khosrokhavar Roya , Shoeibi Shahram , Ashtiani Hamidreza Ahmadi
Published: January 2022

There is ambiguous evidence that high-fructose diet can induce toxicity in different organ systems but its endocrine disrupting effects by abnormal changes in female reproductive organs is poorly evidenced. This study aimed to address the reproductive safety of high fructose diet through clinical, necropsy biochemical, hormonal, histopathological and immunohistochemical analysis. For this purpose, 5-6 weeks mature female Wistar rats were divided in three groups and each five animals/group exposed to standard chow+ water+ HFCS-55, standard chow+ water +sucrose 75 %w/v and standard chow+ water for 90 days. Remarkable increase in most lipid profile factors and total body weights of HFCS-55 fed rats and sucrose fed rats were detected in similar pattern compared to control .At the same time a battery of differential signs and symptoms in HFCS fed groups including destructive endometrial and ovarian changes , significant increase in FSH and LH levels, meaningful decreased serum testosterone and estradiol levels and strong AR expression in reproductive tissues of HFCS group of animals were recorded compared to other two study groups . These thought-provoking signs and signals of fructose induced reproductive toxicity in this model emphasis the contribution of HFCS-55 to deteriorated ovarian and endometrial health. More human population studies is necessary to find any possible association between fructose-rich diet and growing incidence of Primary Ovarian Deficiency (POI) in reproductive aged women .Further concern on this public health issue is required to revise existing regulatory standards and prevent this possible threat in human society.
The Positive Effects of Arbuscular Mycorrhizal Fungi Inoculation And/Or Additional Aeration On The Purification Efficiency of Combined Heavy Metals In Vertical Flow Constructed Wetlands
Authors: Xu Zhouying , Li Kaiguo , Li Wenxuan , Wu Chen , Chen Xi , Huang Jun , Zhang Xiangling , Ban Yihui
Published: January 2022

The extent to which, and mechanisms by which, arbuscular mycorrhizal fungi (AMF) and additional aeration (AA) purify wetlands polluted by combined heavy metals (HMs) are not well understood. In this study, vertical flow constructed wetlands (VFCWs) with the Phragmites australis (reeds)-AMF/AA were used to treat combined HMs-polluted wastewater. The results showed that (1) AA improved the AMF colonization in VFCWs, and AMF accumulated the combined HMs in their structures; (2) AMF inoculation and/or AA significantly promoted the reeds growth and antioxidant enzymes activities, thereby alleviating oxidative stress; (3) AMF inoculation and AA significantly enhanced the removal rates of Pb, Zn, Cu and Cd under the stress of high combined HMs concentrations comparing to the CK treatment (autoclaved AMF inoculation and no aeration), which increased by 22.72%, 30.31%, 12.64%, and 50.22%, respectively; (4) AMF inoculation and/or AA significantly promoted the combined HMs accumulation in plant roots and substrates, and altered the distribution of HMs at the subcellular level. We therefore conclude that AMF inoculation and/or AA in VFCWs improves the purification of combined HMs-polluted wastewater, and the VFCWs-reeds-AMF/AA associations exhibit great potential for application in remediation of combined HMs-polluted wastewater.
Who’s your data? Primary immune deficiency differential diagnosis prediction via machine learning and data mining of the USIDNET registry
Authors: Barrera Jose Alfredo Mendez , Guzman Samuel Rocha , Cascajares Elisa Hierro , Garabedian Elizabeth K , Fuleihan Ramsay L , Sullivan Kathleen E , Reyes Saul Oswaldo Lugo
Published: January 2022

INTRODUCTION: There are currently more than 450 primary immune deficiency (PID) diseases, and about 7,000 rare diseases that together afflict around 1 in every 17 humans. Computational aids based on data mining and machine learning might facilitate the diagnostic task by extracting rules from large datasets and making predictions when faced with new problem cases. OBJECTIVE: In a proof-of-concept data mining study, we aimed to predict PID diagnoses with a supervised machine learning algorithm based on classification tree boosting. METHODS: Through a data query at the USIDNET registry we obtained a database of 2,396 patients with common diagnoses of PID, including their clinical and laboratory features. We kept 12 diagnoses and 286 features that were included in the model. We used the XGBoost package with parallel tree boosting for the supervised classification model, and SHAP for variable importance interpretation, on Python v3.7. The patient database was split into training and testing subsets, and after boosting through gradient descent, the predictive model provides measures of diagnostic prediction accuracy and individual feature importance. To correct for imbalanced classification, after a baseline performance test, we used the Class Weighting Hyperparameter, or scale_pos_weight. RESULTS: The twelve PID diagnoses were CVID (1,098 patients), DiGeorge syndrome, Chronic granulomatous disease, Congenital agammaglobulinemia, ID not otherwise classified, Specific antibody deficiency, Complement deficiency, Hyper-IgM, Leukocyte adhesion deficiency, ectodermal dysplasia with immune deficiency, Severe combined immune deficiency, and Wiskott-Aldrich syndrome. For CVID, the model found an accuracy on the train sample of 0.80, with an area under the ROC curve (AUC) of 0.80, and a Gini coefficient of 0.60. In the test subset, accuracy was 0.76, AUC 0.75, and Gini 0.51. The positive feature value to predict CVID was highest for upper respiratory infections, asthma, autoimmunity and hypogammaglobulinemia. Features with the highest negative predictive value were high IgE, growth delay, abscess, lymphopenia, and congenital heart disease. For the rest of the diagnoses, accuracy stayed between 0.75 and 0.99, AUC 0.46-0.87, Gini 0.07-0.75, and LogLoss 0.09-8.55. See tables and figures.DISCUSSION: Clinicians should remember to consider the negative predictive features together with the positives. We are calling this a proof-of-concept study to continue with our explorations. A good performance is encouraging, and the feature importance might aid feature selection for future endeavors. In the meantime, we can learn from the rules derived by the model and build a user-friendly decision tree to generate differential diagnoses.
Water Allocation Management Under Scarcity: A Bankruptcy Approach
Authors: Zheng Yang , Sang Xuefeng , Liu Zhiwu , Zhang Siqi , Liu Pan
Published: January 2022

This study hopes to develop multi-criteria decision making (MCDM) method for equitable and efficient allocation water resource under scarcity. Based on the Bankruptcy problems, five classic plus one proposed allocation rules are introduced to generate water distribution alternatives. The "Core" solution of Cooperative Game Theory (CGT) and Security Restriction have been used to select the feasible alternatives. Additionally, five voting methods in Social Choice Theory (SCT) are launched to aggregate preferences and obtain "win" alternative. Apply this model on 2030 water allocation management project of Ezhou City, China, as a case study. Under the proposed rule, adjust minimal overlap rule (AMO), five regions, Urban Area, Gedian DZ and three counties, hold the water deficit rate of 5.9%, 15.8% and 4.7%-6.1%, respectively. By voting technique, AMO wins four fifths of aggregating processes and came in the second for the last, which provides some insights for allocating water in a fair and feasible way.
Industrial co-agglomeration, green technological innovation and total factor energy efficiency
Authors: Yang Haochang , Xu Xiezu , Zhang Faming
Published: January 2022

The double-wheel-driven of manufacturing and producer services industrial co-agglomeration is of great significance for transforming the economic growth mode driven by a single industry, integrating and extending regional resources, and improving energy efficiency. Based on panel data from 2004 to 2019, this paper uses the spatial Dubin model to analyze the impact of industrial co-agglomeration on total factor energy efficiency (TFEE) and its regional heterogeneity; Moreover, the mediating model is employed to examine the mediating effect of green technological innovation in the industrial co-agglomeration affects TFEE. Last but not least, the threshold panel regression model is conducted to verify the nonlinear relationship between industrial co-agglomeration and TFEE. The results show that: There is a U-shaped curve relationship between industrial co-agglomeration and TFEE, namely that industrial co-agglomeration first shows a certain inhibitory effect on TFEE, and then plays a significant role in promoting. Moreover, there are obvious regional heterogeneities in the impact of industrial co-agglomeration on TFEE and its spatial spillover effect. Industrial co-agglomeration has a significant indirect impact on TFEE through green technological innovation. In addition, there is a single threshold effect in the impact of industrial co-agglomeration on TFEE, only when the industrial co-agglomeration degree crosses the threshold value of 0.6329, can it positively promote the improvement of TFEE.
CLS, PLS and PCR Methods in Different Sets of Data for Simultaneous Determination of Paracetamol and Orphenadrine Citrate in Their Combined Pharmaceutical Dosage Forms
Authors: sebaiy mahmoud , Fawzy Michael , mattar amr , eladl sobhy
Published: January 2022

Three simple, specific, accurate and precise chemometric methods have been developed for the analysis of paracetamol and orphenadrine citrate in their mixture form without prior separation. CLS, PLS and PCR methods were used for simultaneous determination of paracetamol and orphenadrine citrate by using different sets of data in which better results were produced. The validity of these chemometrics has been carried out by using 8 synthetic mixtures for determination of the power of prediction for each method. Latent variable number is different from one model to another concurrent with changing the set of data. PRESS (predicted residual error sum of squares) and RMSEP (Root mean square error of prediction) were used for the comparison between different methods and for determination the predictive power of each set of data. Statistical comparison between the proposed chemometric methods was also performed.
The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: A comparison with radiologists
Authors: Urushibara Aiko , Saida Tsukasa , Mori Kensaku , Ishiguro Toshitaka , Inoue Kei , Satoh Toyomi , Masumoto Tomohiko , Nakajima Takahito
Published: January 2022

Purpose: To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions. Methods: This retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and 2020. In Experiment 1, single and combined image sets of several sequences from 204 patients with cancer and 184 patients with non-cancerous lesions were used to train CNNs. Subsequently, testing was performed using 97 images from 51 patients with cancer and 46 patients with non-cancerous lesions. The test image sets were independently interpreted by three blinded radiologists. Experiment 2 investigated whether the addition of different types of images for training using single image sets improved the diagnostic performance of CNNs. Results: The AUC of the CNNs pertaining to single and combined image sets were 0.88–0.95 and 0.87-0.93, respectively, indicating better or equivalent diagnostic performance than the radiologists. The AUC of the CNNs trained with the addition of other types of images was 0.88–0.95. Conclusion: CNNs demonstrated high diagnostic performance for the diagnosis of endometrial cancer using MRI. Although there were no significant differences, adding other types of images improved the diagnostic performance for some image sets.
Restorative Biodegradable Two-layered Hybrid Microneedles for Melanoma Photothermal/Chemo Co-therapy and Wound Healing
Authors: Liao Jinfeng , Shan Yue , Tan Bowen , Zhang Min , Xie Xi
Published: January 2022

Tumor killing and wound healing are two complementary and influential processes during the treatment of melanoma. Herein, a two-layered microneedle platform was developed with bifunctional effect of chemo-photothermal synergistic melanoma therapy and skin regeneration. The platform composed of embeddable curcumin nanodrugs/new Indocyanine Green/hyaluronic acid (Cur NDs/IR820/HA) microneedles and sodium alginate/gelatin/hyaluronic acid (SA/Ge/HA) supporting back layer was prepared through a two-step casting process. With uniform incorporation of Cur NDs and IR820, the microneedles exhibited nice photothermal performance under external near-infrared (NIR) light stimulation and excellent tumor co-therapy ability. Once the embeddable microneedles insert into skin, they rapidly dissolve and activate drug release successfully for tumor treatment. Moreover, the SA/Ge/HA supporting back layer left behind to cover the wound and promote the proliferation of endothelial and fibroblasts cells for enhanced skin regeneration. The two-layered microneedles platform can simultaneously eliminate the tumor and accelerate wounding healing, which may be potentially employed as a competitive strategy for the treatment of melanoma.
Metoprolol Mitigates Ischemic Heart Remodeling and Fibrosis by Increasing the Expression of akap5 in Ischemic Heart
Authors: Zhu Feng , Wang Zhi , Zhang Benkai , Zhang Xue , Wang Hegui
Published: January 2022

Background The purpose of this study was to verify whether metoprolol regulates AKAP5 expression and test the role of AKAP5 post-injury in mitigating cardiac infarction-associated tissue remodeling and fibrosis.Methods and results Sprague-Dawley (SD) rats underwent coronary artery ligation (CAL), which was followed immediately with metoprolol daily. HW/BW ratio and cardiac expression of COL1 and COL3 were increased in rats following CAL compared with shams. Treatment with metoprolol post-injury was associated with a decrease in HW/BW ratio and COL1/COL3 expression compared to uncontrol rats. CAL resulted in decreased cardiac AKAP5 expression compared to the control group, while metoprolol treatment restored levels compared to baseline shams. Cardiac expression levels of NFATc3/p-NFATc3 and GATA4 were modest at baseline and increased with injury, whereas metoprolol suppressed gene expression to below injury-associated changes. Immunoprecipitation indicated that AKAP5 could bind and regulate PP2B. Conclusions The results indicate that metoprolol mitigates ischemic cardia remodeling and fibrosis, which mechanism of mitigating remodeling likely to improve cardiac AKAP5 expression and AKAP5-PP2B interaction.
Challenges in treating cancer in post-conflict Kosovo, a systematic review
Authors: Buleshkaj Shqipe Hyseni , Rugova Naser
Published: January 2022

Background: The direct and side effects of the impact of the conflict on the general health condition of the population and the health infrastructure are evidenced, measurable as well as fact-based as is the case of Kosovo. These findings are also confirmed by interviews with interviewed health professionals who address this issue. In these 22 years after the conflict and 13 years following the independence, the consequences of the conflict in the health system of Kosovo are obvious in all three levels of its organization. Cancer treatment and palliative care continue to be among the weakest links in Kosovo's health system. Case presentation: Since the end of the conflict there is a significant increase of cancer cases in Kosovo. This has proven to be a difficult extra burden for an already shattered health system. The main purpose of this study is to explore and analyze the available data on cancer treatment during this post-conflict period in Kosovo, including the causes, age and gender of those endangered by cancer, and measures taken by the Kosovo authorities to treat and contain these cases. Conclusions: The consequences of the conflict significantly complicate the enhancement and reforms in the health system of a country, especially in the areas of tertiary health care that require political will, sustainable and continuous funding, continuous education of health personnel, therapy based on modern protocols and advanced equipment, which the Kosovo health system and palliative care currently lack.
How are parental practices and attitudes towards corporal punishment related to child academic, developmental, or psychological-emotional dysfunctioning?
Authors: Grujicic Roberto , Toskovic Oliver , Lazarevic Ljiljana , Maravic Vanja Mandic , Voncina Marija Mitkovic , Radanovic Ana , Kircanski Jelena Radosavljev , Videnovic Marina , Pekmezovic Tatjana , Milovancevic Milica Pejovic
Published: January 2022

Corporal punishment (CP) is a widely spread disciplining technique among parents and caregivers globally. The aim of our paper was to explore the relationship between the parental attitudes towards CP, expected outcomes of CP, and parenting practices on one hand, with the reported dysfunctions of their children. In addition, we aimed to explore the relationship between the use of CP and the reported academic, developmental and psychological-emotional dysfunctions of their children. The present study involved a nationally representative sample of 1186 parents in Serbia, who had at least one child aged 0 to 18 years in the moment of interviewing. The parents filled out a series of interviews on their attitudes towards CP, expectations of CP outcomes and their parental practices. Our findings indicate that parents that report having a child with dysfunctions have positive attitudes towards CP and expect positive outcomes of CP. These parents also report using more CP as a disciplining method, as well as other harsh disciplining techniques. We also identified significant predictors of reported child dysfunctions. When these measured effects combine, this puts the children with health-related and school-related issues at a significant risk of further maltreatment and abuse.
HAPLN1, A Potential Promising Biomarker for Heart Failure
Authors: Xu Sichi , Chen Peipei , Xiao Ying , Wu Wei , Zhang Shuyang
Published: January 2022

Objective: Heart failure (HF) is the end pathophysiological process of many cardiovascular diseases. Although brain natriuretic peptide (BNP) and N-terminal pro B type natriuretic peptide, (NT-proBNP) have been widely used to diagnose HF, their expression levels are easily affected by age, weight, obesity, and renal function. Therefore, more accurate markers of HF are needed. This study aimed to explore common new biomarkers and mechanisms for HF based on microarray and bioinformatics analysis at the transcriptome level.Methods: Microarray datasets of HF were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were selected by using R software. Tissue or organ-specific genes were predicted by BioGPS. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used to conduct enrichment analysis. Hub genes were selected by a protein-protein interaction (PPI) network, and competitive endogenous RNA (ceRNA) networks were constructed by Cytoscape. The HF model was induced by aortic banding (AB) surgery. Furthermore, effective DEGs were further validated using reverse transcription-quantitative polymerase chain reaction (RT-PCR) analysis. Results: Three datasets were downloaded from the GEO. As many as 950 DEGs were recoded from GSE57338 and GSE141910, and 43 common DEGs were identified. DEGs were mostly enriched in extracellular matrix structural components, extracellular adhesion, oxygen binding and carrying, apoptosis, and phagosomes. Four hub genes were identified from the intersection of the PPI and BioGPS system. The diagnostic level of natriuretic peptide A (NPPA), haemoglobin subunit beta (HBB), and hyaluronan and proteoglycan link protein 1(HAPLN1) were validated by a GSE116250 dataset. SNHG16-miR-23-3p/miR-103-5p/HAPLN1 and XIST-miR-23-3p/ miR-103-5p/HAPLN1 might regulate the disease progression of HF at the transcriptional level. Moreover, the mRNA levels of HAPLN1 and CD163 were also validated in an animal model of HF. Conclusions: HAPLN1 might be a new potential biomarker of HF and CD163 may participate in the progression of HF. SNHG16-miR-23-3p/ miR-103-5p / HAPLN1 and XIST-miR-23-3p/miR-103-5p/HAPLN1 may be involved in the transcriptional regulation during HF.
Energy R&D Trends And Sustainable Energy Strategies In IEA Countries: Efficiency, Dependency And Environmental Dynamics
Authors: Bicil İbrahim Murat , Erkul Abdullah , Türköz Kumru
Published: January 2022

Understanding the factors affecting R&D trends in the energy sector has a key role in overcoming environmental concerns such as combating climate change, as well as other economic and political problems related to energy. Based on such concerns, this study aims to analyze fundamental factors that determine the energy R&D trends of 29 International Energy Agency (IEA) countries. The data set, consisting of annual indicators for the period 1990-2015, is analyzed with the Driscoll-Kraay panel data estimator. Empirical findings for overall sample show that efficiency, import dependency, and the share of renewable energy use are positively related with R&D expenditure in energy sector. CO2 intensity is found to be statistically insignificant. When countries are grouped considering their energy composition structures, the dynamics of energy R&D expenditures differ between groups. In overall evaluation, our findings illustrate efficiency and dependency to have greater priority compared to environmental dynamics on energy R&D expenditures for IEA countries during the period.
Evaluation of Highway Collapse Hazard Based on Rough Set and Support Vector Machine
Authors: He Hujun , Quan Guorong , Zhu Haolei , Li Wei , Xing Rui , Zhao Yichen
Published: January 2022

The prediction of possibility and risk classification of collapse is an important issue in the process of highway construction in mountain area. Based on the principle of rough set and support vector machine, a landslide hazard prediction model was established. First of all, according to field investigation, an evaluation index system and a sample set of evaluation index data were established, the rough set decision table was constructed by preprocessing the original data based on the function classification of standard evaluation index, and then, the influence indexes of the collapse activity were reduced by rough set theory, and the main 9 indexes affecting the collapse activity as the key discriminant factors of support vector machine model, namely slope shape of slope, aspect of slope, slope of slope, height of slope, exposed structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate and weathering degree of rock were extracted. Then, taking the data of 13 post earthquake collapses provided by the author etc in Yingxiu-Wolong highway of Hanchuan County as training samples, the optimal model parameters were analyzed and calculated. When the penalty parameter C is 8 and the kernel parameter g is 0.5, the correct rate of cross-validation is 100%, and the model is optimal. At last, 4 other landslide data were tested, the discriminant results of the test sample data were compared with the results obtained by uncertainty measure and distance discriminant analysis. The results show that the discriminant results of the test sample data by RS-SVM were consistent with the results obtained by uncertainty measure and distance discriminant analysis, the accurate rate is 100%. The collapse hazard analysis model based on rough set and support vector machine can reduce the computation while ensuring the accuracy of evaluation, and better solve the small sample and nonlinear problems, can provide certain a good idea for collapse hazard evaluation in the future.
The Linear Behavior of the Joint Initial-Boundary-Value Predictability of the Climate System
Authors: Yong Jianlin , Zhang Shaoqing , Liu Zhengyu , Gao Yang , Wu Lixin , Li Jianping , Lu Lv , Jiang Yingjing , Yu Xiaolin , Li Mingkui , Zhao Haoran , Lin Xiaopei
Published: January 2022

The primary sources of predictability of the Earth climate system come from initial conditions and external radiative forcings, which is a joint initial-boundary-value problem. It is crucial to clearly understand the contributions of initial conditions and external forcings (i.e. initial-value and boundary-value) to the total predictability, for which the traditional predictability study examines the growth of system uncertainties is however difficult to quantitatively identify. This study uses the variations of prediction skills in the space of prediction lead times to systematically examines the linear behaviors of initial-value, boundary-value and joint initial-boundary-value predictability, which are measured by anomaly correlation coefficients between system states in a series of perturbed experiments on initial and boundary conditions. The lead time scale at which the prediction skill induced from external forcing exceeds the skill from the initial condition is a key measurement of system uncertainty, after which the boundary-value predictability efficiently extends the total predictability. These results provide a guideline to understand how the accurate setting of external forcing caused by human activities promotes the prediction skill when a comprehensive Earth climate system model is initialized to make climate predictions.
Characteristics of the Turkana Low-Level Jet Stream and the Associated Rainfall in Cmip6 Models
Authors: lino oscar , Nzau Mutemi J. , Ellen Dyer , Franklin Opijah , Rachel James , Richard Washington , Webb Tom
Published: January 2022

The Turkana low-level jet stream (TJ) is important to climatic conditions over northern Kenya and East Africa. The representation of the TJ in climate models varies due to the TJ interaction with Turkana channel that is influenced by model resolution and influences the model representation of the regional climate. This study compares features of the TJ in CMIP6 AMIP model simulations with ERA5. Models reveal wind speeds that match those of the reanalysis from the ERA5 at the jet entrance (13 m/s) but lower magnitudes of wind speed and vertical shears compared to ERA5 within the Turkana channel. The models with slowest wind speeds, have a flattened Turkana channel and fail to exhibit the terrain constriction at 37°E which otherwise aids in accelerating winds to form a jet core. Furthermore, they fail to represent the narrowing of the channel as in ERA5, thereby forming blocking walls in the channel, forcing vertical ascent and mixing, and weakening shear. Stronger ascent is conducive for rainfall formation, enhancing wet anomalies by up to 8% above the observed climatology at the exit of the TJ when the jet stream is weaker. By applying a new narrowing index, we demonstrate the need to improve topography details in the CMIP6 models, particularly those with resolution coarser than 1.5°, in order to properly simulate the TJ and the observed rainfall over the northwestern areas of eastern Africa.
Which Muscle Mass Measurement Should we Prefer in the Global Leadership Initiative on Malnutrition Criteria?
Authors: Sahin Gulsah Gunes , Akin Sibel , Ergul Serap Sahin , Ozer Nurhayat Tugra
Published: January 2022

Objectives: The first aim of our study was to evaluate the malnutrition rates of patients separately with four different phenotypic methods (Calf Circumference (CC), Middle Arm Circumference (MAMC), Fat Free Mass Index (FFMI), and Handgrip Strength (HGS)) that define reduced muscle mass with the Global Leadership Initiative on Malnutrition (GLIM).The study's first aim was to determine the frequency of MN according to (Mini Nutritional Assessment) MNA and the GLIM criteria. The second aim was to evaluate the relationship between the energy and content of the foods consumed daily by older adults and four different phenotypic methods.
Methods: The study included 238 outpatients. Anthropometric measurements, body composition and HGS of the patients were evaluated. The dietician recorded the 24-hour food consumption records of the patients.
Results: The median age of outpatients included in the study was 68(60-85). The median Basal metabolic Index (BMI) value of the outpatients was 30.7(16.9-61.2). GLIM-MAMC was 26.9%, GLIM-CC was 28.1%, GLIM-FFMI was 31.5%, GLIM-HGS was 31.9%, and GLIM-2F+2E, 21.4% of outpatients.
Conclusion: One of the most practical and straightforward methods to determine malnutrition in geriatric outpatients is the MAMC, which is more meaningful with food consumption recording. However, the method that determines the highest rate of MN according to HGS is the GLIM. By using these methods, the malnutrition of patients will not be neglected.
Minimising Multi-Centre Radiomics Variability Through Image Normalisation: A Pilot Study
Authors: Campello Víctor M. , Martín-Isla Carlos , Izquierdo Cristian , Guala Andrea , Rodríguez-Palomares José F. , Viladés David , Descalzo Martín L. , Karakas Mahir , Çavus Ersin , Raisi-Estabragh Zahra , Petersen Steffen E. , Escalera Sergio , Seguí Santi , Lekadir Karim
Published: January 2022

Radiomics is an emerging technique for the quantification of imaging data that has recently shown great promise for deeper phenotyping of cardiovascular disease. Thus far, the technique has been mostly applied in single-centre studies. However, one of the main difficulties in multi-centre imaging studies is the inherent variability of image characteristics due to centre differences. In this paper, a comprehensive analysis of radiomics variability under several image-and feature-based normalisation techniques was conducted using a multi-centre cardiovascular magnetic resonance dataset. 218 subjects divided into healthy (n=112) and hypertrophic cardiomyopathy (n=106, HCM) groups from five different centres were considered. First and second order texture radiomic features were extracted from three regions of interest, namely the left and right ventricular cavities and the left ventricular myocardium. Two methods were used to assess features’ variability. First, feature distributions were compared across centres to obtain a distribution similarity index. Second, two classification tasks were proposed to assess: 1) the amount of centre-related information encoded in normalised features (centre identification) and 2) the generalisation ability for a classification model when trained on these features (healthy versus HCM classification). The results showed that the feature-based harmonisation technique ComBat is able to remove the variability introduced by centre information from radiomic features, at the expense of degrading classification performance. Piecewise linear histogram matching normalisation gave features with greater generalisation ability for classification (0.75 ± 0.09). Models trained with features from images without normalisation showed the worst performance overall (0.47 ± 0.13). In conclusion, centre-related information removal did not imply good generalisation ability for classification.
Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation
Authors: PENG Anbang , TIAN Yuanyang , XU Wei , ZHANG Xiaoli
Published: January 2022

With the rapid development of Artificial Intelligence (AI) technology, an increasing number of intelligent algorithms have been used for simulating and forecasting hydrological process, among which the Long Short-Term Memory (LSTM) network is widely studied. The training of artificial intelligence networks often entails a large amount of training data, which contradicts the limitation of hydrological data. In this study, the effect of training data amount on the performance of LSTM network for runoff simulation are evaluated. First, the runoff series of 130 years are randomly generated by K-Nearest Neighbour (KNN) algorithm and SWAT model. The K-Nearest Neighbour (KNN) algorithm is employed for generating the meteorological data series based on the observed data, and the SWAT model is used to obtain the runoff series with the generated meteorological data series. Then, the LSTM models are developed and evaluated, with the 5-year, 10-year, 20-year, 40-year and 80-year data series of rainfall and runoff as training data respectively, and the 50-year data serves as validating data. The results obtained in Yalong River, Minjiang River and Jialing River show that (1) increasing the training data amount can effectively reduce the over-fittings of LSTM network; (2) increasing the training data amount can also improve the prediction accuracy and stability of LSTM network.
Applying the Optimal Injected Noise to Signal Estimation via Adaptive Stochastic Resonance
Authors: Pan Yan , Xu Liyan , Duan Fabing , Chapeau-Blondeau Francois
Published: January 2022

In signal estimation, an optimal estimator is frequently unachievable, because its closed form may not be analytically tractable or is too complex to implement. Alternatively, one can turn to suboptimal yet easily implementable estimators for practical signal estimation tasks. In this paper, a general noise-boosted estimator is designed and the adaptive stochastic resonance method is implemented to simultaneously exploit the beneficial role of the injected noise as well as the learning ability of the estimator parameter. Aiming to effectively improve the estimation performance, we use the kernel function method to find an approximate solution for the probability density function (PDF) of the optimal injected noise. During this process, the noise PDF and the estimator parameter establish a finite dimensional non-convex optimization space for maximizing the estimation performance, which is adaptively searched by the sequential quadratic programming algorithm at each iteration.

Two representative signal estimation problems are explored, consisting in estimating a random signal from low-resolution observations, or a deterministic parameter in a heavy-tailed noisy environment. The obtained results demonstrate that this adaptive stochastic resonance method can improve the performance of the suboptimal estimators and bring it very close to that of the optimal estimator.