Search

Abstract Alzheimer’s disease (AD) ranks among the leading causes of morbidity and mortality worldwide. The objective was to evaluate the burden of AD and other dementias among the countries of the Middle East and North Africa (MENA) region by age and sex from 1990 to 2021. The data were sourced from the Global Burden of Disease (GBD) study 2021. The estimates are presented as counts and age-standardised rates per 100,000 accompanied by 95% uncertainty intervals (UIs). In 2021, AD and other dementias recorded an age-standardised prevalence of 772.7 per 100,000 in the MENA region (95% UI 671.2–877.6 per 100,000). This rate decreased by 4.9% in comparison to 1990, marking a statistically significant change. AD and other dementias also accounted for approximately 73.79 thousand deaths in the region in 2021, with the age-standardised rate decreasing by 8.6% compared to 1990. Moreover, the disability-adjusted life years (DALY) rate was 476.3 per 100,000 population (95% UI 225.6–1004.2), representing a 7.7% decrease from 1990 to 2021. Lebanon registered the highest point prevalence per 100,000 at 828.25, while the United Arab Emirates recorded the lowest at 652.43. The age-standardised point prevalence decreased from 1990 to 2021 in 13 of the MENA countries, while no significant changes were observed in eight of countries. Additionally, in 2021, women experienced higher prevalence rates, DALYs, compared to men. In the MENA region, age-standardised dementia prevalence rose with age in both sexes. The burden of dementia in MENA has decreased from 1990 to 2021, but it remains higher than global estimates. Furthermore, the findings indicate that dementia imposes a greater burden on the female population compared to males. To achieve a more accurate estimation of the burden of Alzheimer’s disease and other dementias, more systematic studies in low- to middle-income countries within the MENA region are required.

Abstract Background Work organization significantly impacts occupational incidents and fatigue in hospital settings, particularly among nurses. This study aimed to evaluate the psychometric properties of instruments measuring work organization and fatigue and to examine their relationship with occupational accidents. Methods A cross-sectional study was conducted in 2019 with 200 nurses working in hospitals in Qom, Iran using the stratified sampling method. Data were collected using three standardized tools: the Work Organization Questionnaire, the Fatigue Checklist, and a demographic information questionnaire. Structural equation modeling was employed to analyze the data, while instrument validity and reliability were assessed through Cronbach’s alpha, composite reliability, and average variance extracted (AVE). Analysis was performed using Smart PLS and SPSS V20. Results The analysis revealed a significant relationship between work organization and occupational accidents (t = 3.22, p

Abstract Background Future breeding and selection of Cannabis sativa L. for both drug production and industrial purposes require a source of germplasm with wide genetic variation, such as that found in wild relatives and progenitors of highly cultivated plants. Limited directional selection and breeding have occurred in this crop, especially informed by molecular markers. Results This study investigated the population genomics of a natural cannabis collection comprising male and female individuals from various climatic zones in Iran. Using Genotyping-By-Sequencing (GBS), we sequenced 228 individuals from 35 populations. The data obtained enabled an association analysis, linking genotypes with key phenotypes such as inflorescence characteristics, flowering time, plant morphology, tetrahydrocannabinol (THC) and cannabidiol (CBD) content, and sex. We detected approximately 23,266 significant high-quality Single Nucleotide Polymorphisms (SNPs), establishing associations between markers and traits. The population structure analysis revealed that Iranian cannabis plants fall into five distinct groups. Additionally, a comparison with global data suggested that the Iranian populations is distinctive and generally closer to marijuana than to hemp, with some populations showing a closer affinity to hemp. The GWAS identified novel genetic loci associated with sex, yield, and chemotype traits in cannabis, which had not been previously reported. Conclusion The study's findings highlight the distinct genetic structure of Iranian Cannabis populations. The identification of novel genetic loci associated with important traits suggests potential targets for future breeding programs. This research underscores the value of the Iranian cannabis germplasm as a resource for breeding and selection efforts aimed at improving Cannabis for various uses.

Abstract Background Many countries agreed to reduce CO2 emissions to limit global warming under the terms of the Paris Agreement. In Europe, this agreement is supported by the climate targets introduced under the European Green Deal and the Fit for 55 package. Although Germany has made substantial progress in reducing emissions across various sectors, the transport sector remains a notable exception, showing little improvement. It is therefore essential to reevaluate the transport sector to strengthen its contribution to achieving the emission reduction targets. The aim of this study is to identify and propose strategies for shifting from fossil fuel-based transport to a more sustainable mode centred on alternative fuels. To investigate the potential pathways, an integrated approach is developed using a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis and multi-criteria decision analysis (MCDA). Results A two-step survey was used to collect data from different stakeholders in order to derive the key factors for the implementation of alternative fuels and devise transition strategies. The findings show that reducing GHG emissions, resource competition, and the impacts of environmental regulations are the most important factors for evaluating the transition strategies. On the other hand, reducing the competitiveness of fossil fuels through increased prices, as well as technical and infrastructural support, are the most promising strategies. Conclusions The sustainable transition in the transport sector is fundamentally driven by the use of renewable fuel alternatives as sustainable energy carriers to replace fossil fuels. The use and deployment of renewable fuel alternatives will play the most significant role in the defossilization of the transport sector, on course to achieve a 55% reduction by 2030 and reaching climate-neutrality by 2050. However, identification of the proper transition strategies in the phase-out of fossil fuels and their replacement with renewable fuel alternatives necessitates a comprehensive evaluation framework. This work contributes to this by developing a holistic evaluation framework, enabling the incorporation of multiple stakeholders within the identification and evaluation of the transition strategies. While several strategies are identified, stakeholders agree that reducing the competitiveness of fossil fuels through increased prices and lower subsidies would be the best strategy.

In this paper, firstly we obtain characterization for boundedness of the weighted differentiation composition operator from $Q_K(p,q) $ space into weighted Zygmund space. Then we give an estimation for the essential norm of such an operator on the mentioned spaces. As an application, we present a characterization for the compactness of the above operator.

Abstract Benzene separation from hydrocarbon mixtures is a challenge in the refining and petrochemical industries. The application of liquid–liquid extraction process using ionic liquids (I.Ls) is an option for this separation. The selection of the most appropriate I.L. for this application is a challenging task due to the variety of anion and cation structures. In the current study, the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases has been evaluated using the Quantitative Structure–Property Relationship (QSPR) method. A dataset comprising of 112 ternary systems (namely, I.L., benzene, and aliphatic hydrocarbon) was compiled after an extensive review of literature. The primary dataset consists of 17 anions, 20 cations, and 12 aliphatic hydrocarbons. Therefore, the impact of the structure of anion, cation, or aliphatic hydrocarbon on the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases has been investigated. The linear QSPR models were constructed using Multiple Linear Regression (MLR). The statistical evaluation of the final linear model showed that the constructed model (R2 = 0.900) has an acceptable capability to predict the mole fraction of benzene in the I.L.-rich phase. Additionally, non-linear QSPR models were developed using Genetic Programming (GP) and Artificial Neural Network (ANN) machine learning methods. The statistical evaluation of the GP model (R2 = 0.927) and ANN model (R2 = 0.939) showed that non-linear models had slightly higher prediction accuracy compared to the linear model. The final QSPR model was developed using the BELe3 cation descriptor which is a 2D Burden eigenvalues descriptor and HTm anion descriptor which is a 3D GETAWAY descriptor. After model construction, the selected molecular descriptors of anion and cation structures has been interpreted. The results showed that the size and the electronegativity of the atoms in the anion and cation structure are probably important parameters that affect the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases. Additionally, the anion shape can be considered as an effective parameter in the benzene extraction process.

Abstract Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases. In this retrospective development and validation study, we developed an ML model based on dynamic ensemble selection (DES) for LC detection. The model leverages standard blood sample analysis and smoking history data from a large population at risk in Denmark. The study includes all patients examined on suspicion of LC in the Region of Southern Denmark from 2009 to 2018. We validated and compared the predictions by the DES model with diagnoses provided by five pulmonologists. Among the 38,944 patients, 9,940 had complete data of which 2,505 (25%) had LC. The DES model achieved an area under the roc curve of 0.77±0.01, sensitivity of 76.2%±2.04%, specificity of 63.8%±2.3%, positive predictive value of 41.6%±1.2%, and F1-score of 53.8%±1.0%. The DES model outperformed all five pulmonologists, achieving a sensitivity 6.5% higher than their average. The model identified smoking status, lactate dehydrogenase, age, total calcium levels, low values of sodium, leucocytes, neutrophil count, and C-reactive protein as the most important factors for LC detection. The results highlight the successful application of the ML approach in detecting LC, surpassing pulmonologists’ performance. Incorporating clinical and laboratory data in future risk assessment models can improve decision-making and facilitate timely referrals.

The Triarchic Psychopathy Measure (TriPM) assesses the Boldness, Meanness, and Disinhibition dimensions of psychopathy. This study evaluated the psychometric properties of the Persian TriPM in university students (n = 941, aged 18–67, Mage = 28.36, SD = 9.09; 39.1 % male) and a male clinical sample (n = 188, aged 18–65, Mage = 35.05, SD = 10.75) in Iran. Exploratory Structural Equation Modeling (ESEM) supported the original three-factor model only in the clinical sample, though with notable instances of poor item functioning (e.g., low loadings and high cross-loadings). In the students' sample, Exploratory Factor Analysis (EFA) identified a four-factor model: Leadership, Callousness, Impulsivity, and Antisocial which also fit in the clinical sample and demonstrated acceptable internal consistency across both samples. Furthermore, the Meanness and Disinhibition dimensions and their equivalent domains in the four-factor model yielded expected associations with external correlates in both samples. However, the Boldness and Leadership domains showed inconsistent associations with external correlates in the clinical sample, unlike the hypothesized correlations observed for Leadership in the student sample. Overall, findings highlight inconsistencies in the TriPM's alignment with the triarchic model of psychopathy and suggest a need for further refinement of the measure based on theoretical and empirical insights. We discuss directions for future research.

IntroductionHeavy metal contamination in soil refers to the excessive accumulation of elements called heavy metals, which are capable of causing significant levels of biological toxicity. These metals, in concentrations higher than of tolerable threshold of plants, often cause metabolic disorders, inhibition of growth and physiological process and eventually lead to death of plants. Lead (Pb) and zinc (Zn), as the two common coexisting mineral heavy metals around the world, accumulate in the soil due to mining activity and can adversely affect the crop productivity, the agro-ecosystem components and ultimately the human and environmental health. Biochar, as a potent soil amendment agent, is often obtained by pyrolysis of agricultural organic wastes such as crop residues under low oxygen pressure condition. Trichoderma harzianum, as a plant growth promoter, has capability in stimulating plant growth under varying environmental stresses and therefore, inoculation of plant roots with this fungus could be a conceivable approach to cope with the heavy metal stress. Hairy vetch (Vicia villosa Roth) is a winter annual forage species that shows better growth performance than the other forage legumes such as alfalfa or clover under the severe environmental conditions, especially winter frost and drought.phosphorus supplementation and their interactions on the growth, physiology and P, Pb and Zn uptake by hairy vetch in heavy metal-polluted soil.Materials and methodsThe experiment was carried out in the greenhouse of Faculty of Agriculture and Natural Resources of Mohaghegh Ardabili University as a factorial experiment based on a completely randomized design (CRD) in three replications. Heavy metal contaminated soil was collected from wheat fields around Angouran village (Angouran Rural District, Angouran District, Mahneshan County, Zanjan Province, Iran), downstream of the Zanjan's lead and zinc processing plant (Calcimin® Co.; 36°34'13.2"N 47°37'21.4"E), from a depth of about 0 to 25 cm. Two levels of biochar application (non-biochar control, 5% peanut hull-derived biochar), two levels of Trichoderma inoculation (non-inoculation control and inoculation with conidial suspension) and three levels of P supplementation (0P, non-supplementation; 11P, 11 mg P.kg-1 and 22P, 22 mg P.kg-1) were applied as the three experimental factors. The feedstock of peanut hull (PH) used in the present experiment were purchased from the local market. After thorough washing, PHs were air-dried and then well ground with a lab-scale mill and sieved using a 100 mesh sieve size. T. harzianum Rifai - T22 isolate acquired from the Laboratory of Plant Pathology, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili was revived on PDA (potato dextrose agar) medium and further incubated for at least one week at 28 °C. This experiment was conducted in an open-air vegetation yard with mean day/night temperatures of 25/17 °C, respectively. The experiment lasted for 50 days, in the period from May 16, 2020 (sowing the seeds) until July 5, 2020 (final sampling). In each plastic pot (25 cm in diameter × 20 cm in height), pre-filled with the pot mixture according to the treatments, 20 surface-sterilized hairy vetch (Vicia villosa Roth., Local landrace from Ardabil, Iran) seeds were sown and irrigated with tap water (EC = 0.21 dS m-1 and pH = 7.2) to 100% field capacity. Throughout the experiment, all the pots were re-irrigated when the soil water content dropped to 70% of the field capacity.Results and discussionApplication of biochar and Trichoderma fungus at not using phosphorus (P0), resulted in higher aerial biomass. Trichoderma fungus alone or with P11 level had a better result in increasing plant height. The concentration of chlorophyll a was in highest level by biochar combined Trichoderma, and P0 phosphorus applied, as well as the application of Trichoderma caused the highest concentration of chlorophyll b. The changes in relative leaf water content ranged from 85.5 to 90.5% among the treatment combinations. Phosphorus consumption at the maximum level (P22) showed the same effect in improving leaf protein compared to the P0 level. Moderate phosphorus consumption (P11) combined with biochar and Trichoderma caused the highest soluble sugar content. The highest proline content of leaves was obtained by using Trichoderma without applying biochar and phosphorus. The use of biochar alone or with Trichoderma could improve the amount of phosphorus absorption as much as the use of phosphorus fertilizer alone. Combined application of biochar and Trichoderma fungus with P11 and P22 fertilizer levels increased phosphorus absorption by 41.6% and 22.3% respectively compared to P0 level. Trichoderma fungus had no significant effect on the amount of Pb and Zn absorption in the aerial parts, but biochar significantly reduced their absorption in the aerial parts of Hairy vetch plants.ConclusionBased on the results, the application of biochar alone or in combination with Trichoderma fungus improved the vegetative traits and physiological characteristics, including increasing the concentration of photosynthetic pigments, the relative content of leaf water and soluble sugar, the content of protein and proline in the cluster flower vetch. Phosphorus absorption increased significantly when using biochar and Trichoderma. It seems that biochar helps to increase photosynthesis by creating suitable conditions in soil porosity, humidity, increasing cation exchange capacity and reducing the concentration of heavy elements, with better absorption of elements and thus improving plant growth and development. Trichoderma fungus with biological control increases the availability of elements including phosphorus by increasing the transfer of sugar and amino acids and creating induction resistance and stimulating the growth of beneficial microorganisms, and by making changes in the physiological characteristics, it increases the resistance of the plant against the stress of heavy elements.

Abstract Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.

Abstract Background Prompt and accurate diagnosis of prostate cancer (PCa) is of paramount importance for effective treatment planning. While Gallium-68 labeled prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) has proven efficacy in detecting PCa, limited availability poses challenges. As a potential alternative, [99mTc]Tc-PSMA single photon emission computed tomography (SPECT)/computed tomography (CT) holds promise. This systematic review and meta-analysis aimed to evaluate the diagnostic value of [99mTc]Tc-PSMA SPECT/CT for prostate cancer. Methods A comprehensive search of PubMed, Cochrane, EMBASE, Scopus, Ovid, and Web of Science databases was conducted until July 2024. Sensitivity and specificity data were extracted to assess the diagnostic accuracy of [99mTc]Tc-PSMA SPECT/CT, while the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to evaluate study quality. Statistical analyses were performed using STATA 18, with MetaDisc 1.4 employed to detect threshold effects. Diagnostic accuracy indicators, including sensitivity, specificity, diagnostic odds ratio (DOR), negative likelihood ratio (LR-), and positive likelihood ratio (LR+), were pooled. The area under the curve (AUC) of the combined model was calculated using summary receiver-operating characteristic (SROC) curves. Results Seven studies meeting the inclusion criteria were identified from an initial pool of 1467 articles, with no publication bias observed. The pooled sensitivity, specificity, and AUC of [99mTc]Tc-PSMA SPECT/CT were found to be 0.89 (95% CI, 0.84–0.93), 0.92 (95% CI, 0.67–0.99), and 0.93 (95% CI, 0.90–0.95), respectively. Additionally, the comprehensive diagnostic odds ratio, diagnostic score, positive likelihood ratio, and negative likelihood ratio were calculated as 95.24 (95% CI, 17.30-524.41), 4.56 (95% CI, 2.85–6.26), 11.35 (95% CI, 2.31–55.71), and 0.12 (95% CI, 0.08–0.18), respectively. Conclusions In conclusion, our findings demonstrate that [99mTc]Tc-PSMA SPECT/CT exhibits favorable diagnostic performance for prostate cancer and can provide valuable supplementary information, particularly in regions and settings where [68Ga]Ga-PSMA PET/CT availability is limited, such as remote areas. These results highlight the potential of [99mTc]Tc-PSMA SPECT/CT as a valuable tool in the diagnosis and management of prostate cancer, warranting further investigation and validation in larger patient cohorts.

Abstract Brain cancer is one of the deadliest diseases, although many efforts have been made to treat it, there is no comprehensive and effective treatment approach yet. In recent years, the use of network-based analysis to identify important biological genes and pathways involved in various complex diseases, including brain cancer, has attracted the attention of researchers. The goal of this manuscript is to perform a comprehensive analysis of the various results presented related to brain cancer. For this purpose, firstly, based on the CORMINE medical database, collected all the genes related to brain cancer with a valid P-value. Then the structural and functional relationships between the above gene sets have been identified based on the STRING database. Next, in the PPI network, hub centrality analysis was performed to determine the proteins that have many connections with other proteins. After the modularization of the network, the module with the most hub vertices is considered as the most relevant module to the formation and progression of brain cancer. Since the driver vertices play an important role in biological systems, the edges of the selected module were oriented, and by analyzing the controllability of complex networks, a set of five proteins with the highest control power has been identified. Finally, based on the drug-gene interaction, a set of drugs effective on each of the driver genes has been obtained, which can potentially be used as new combination drugs. Validation of the hub and driver proteins shows that they are mainly essential proteins in the biological processes related to the various cancers and therefore the drugs that affect them can be considered as new combination therapy. The presented procedure can be used for any other complex disease.

Background: Colorectal cancer ranks as the third most prevalent cancer type globally. In addition to surgery, chemotherapy, and radiation therapy, being the foremost efficacious and all-encompassing treatment modalities for cancer, pelvic chemoradiotherapy is known to precipitate adverse effects, notably intestinal inflammation. This study delves into assessing the impact of curcumin on the prophylaxis and amelioration of chemoradiotherapy-induced enterocolitis in colorectal cancer patients. Method: This randomized study encompassed 44 colorectal cancer patients undergoing standard pelvic chemoradiotherapy, allocated to either curcumin treatment (22 patients) or placebo (22 patients) groups. Patients were administered oral curcumin capsules at a daily dosage of 500 mg commencing one week before baseline and extending throughout the standard treatment regimen, adhering to the same schedule. Subsequently, patients were subjected to biweekly evaluations encompassing demographics, clinical characteristics, and manifestations of enterocolitis, with statistical analysis employing Mann-Whitney and chi-square tests. A significance threshold of P < 0.05 was employed in the study for statistical significance. Results: The incidence of complications exhibited no statistically significant disparity between the two cohorts across diverse disease stages. Furthermore, there were no discernible discrepancies in the manifestation of varying grades of intestinal complications between the curcumin-treated and placebo groups. Predominantly, both groups experienced the most pronounced side-effects during the initial two weeks of treatment. Additionally, there was no statistically significant distinction in the prevalence of adverse drug reactions between the two groups, with figures standing at 31% versus 40% (P = 0.17). Conclusion: Even though 500 mg/day of curcumin over a six-week duration did not engender a statistically significant reduction in the adverse effects of chemoradiotherapy, it was well-tolerated and deemed safe in this patient cohort.

Background: Hypertensive disorders of pregnancy (HDP) are significant drivers of maternal and neonatal morbidity and mortality. Current management strategies include early identification and initiation of risk mitigating interventions facilitated by a rules-based checklist. Advanced analytic techniques, such as machine learning, can potentially offer improved and refined predictive capabilities. Objective: To develop and internally validate a machine learning prediction model for hypertensive disorders of pregnancy (HDP) when initiating prenatal care. Study Design: We developed a prediction model using data from the prospective multisite cohort Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) among low-risk individuals without a prior history of aspirin utilization for preeclampsia prevention. The primary outcome was the development of HDP. Random forest modeling was utilized to develop predictive models. Recursive feature elimination (RFE) was employed to create a reduced model for each outcome. Area under the curve (AUC), 95% confidence intervals (CI), and calibration curves were utilized to assess discrimination and accuracy. Sensitivity analyses were conducted to compare the sensitivity and specificity of the reduced model compared to existing risk factor-based algorithms. Results: Of 9,124 assessed low risk nulliparous individuals, 21% (n=1,927) developed HDP. The prediction model for HDP had satisfactory discrimination with an AUC of 0.73 (95% CI: 0.70, 0.75). After RFE, a parsimonious reduced model with 30 features was created with an AUC of 0.71 (95% CI: 0.68, 0.74). Variables included in the model after RFE included body mass index at the first study visit, pre-pregnancy weight, first trimester complete blood count results, and maximum systolic blood pressure at the first visit. Calibration curves for all models revealed relatively stable agreement between predicted and observed probabilities. Sensitivity analysis noted superior sensitivity (AUC 0.80 vs 0.65) and specificity (0.65 vs 0.53) of the model compared to traditional risk factor-based algorithms. Conclusion: In cohort of low-risk nulliparous pregnant individuals, a prediction model may accurately predict HDP diagnosis at the time of initiating prenatal care and aid employment of close interval monitoring and prophylactic measures earlier in pregnancy.

The main purpose of this research is to investigate the effect of the company's position in the life cycle on real earning management, emphasizing the moderating role of family ownership. The method of research is applied in terms of purpose with a causal nature which takes a post-experimental method. Data source is the information included in the financial statements of the Tehran Stock Exchange listed companies. The time frame of research includes eight consecutive years from 2014 to 2021. The statistical sample embraces 141 TSE-listed companies. The final analysis of data was done in Eviews and SPSS software. Firstly, the entire sample is divided into two groups of companies with family ownership and without family ownership. Then, the effect of the life cycle on earning management variables is tested using multivariate regression analysis in both samples, independently. Finally, the effect coefficients obtained in two samples are compared with each other by two-sample t-test (paired comparison). The obtained results show that there is a significant relationship between the life cycle stages of companies and earning management through sales management, production cost management and investment management. Also, the earnings management strategies used in the life cycle of family-owned firms are different from others. Based on the findings of this research, companies without family ownership use production cost management and sales management in the stages of introduction and maturity, respectively, and in the final stages of life (decline), they put earning management using investment management into practice. While family-owned companies do not use profit management in the early stages (introduction and growth), and in the maturity stages, they manage real earnings by using sales management.

Abstract Background Smoking is a critical risk factor responsible for over eight million annual deaths worldwide. It is essential to obtain information on smoking habits to advance research and implement preventive measures such as screening of high-risk individuals. In most countries, including Denmark, smoking habits are not systematically recorded and at best documented within unstructured free-text segments of electronic health records (EHRs). This would require researchers and clinicians to manually navigate through extensive amounts of unstructured data, which is one of the main reasons that smoking habits are rarely integrated into larger studies. Our aim is to develop machine learning models to classify patients’ smoking status from their EHRs. Methods This study proposes an efficient natural language processing (NLP) pipeline capable of classifying patients’ smoking status and providing explanations for the decisions. The proposed NLP pipeline comprises four distinct components, which are; (1) considering preprocessing techniques to address abbreviations, punctuation, and other textual irregularities, (2) four cutting-edge feature extraction techniques, i.e. Embedding, BERT, Word2Vec, and Count Vectorizer, employed to extract the optimal features, (3) utilization of a Stacking-based Ensemble (SE) model and a Convolutional Long Short-Term Memory Neural Network (CNN-LSTM) for the identification of smoking status, and (4) application of a local interpretable model-agnostic explanation to explain the decisions rendered by the detection models. The EHRs of 23,132 patients with suspected lung cancer were collected from the Region of Southern Denmark during the period 1/1/2009-31/12/2018. A medical professional annotated the data into ‘Smoker’ and ‘Non-Smoker’ with further classifications as ‘Active-Smoker’, ‘Former-Smoker’, and ‘Never-Smoker’. Subsequently, the annotated dataset was used for the development of binary and multiclass classification models. An extensive comparison was conducted of the detection performance across various model architectures. Results The results of experimental validation confirm the consistency among the models. However, for binary classification, BERT method with CNN-LSTM architecture outperformed other models by achieving precision, recall, and F1-scores between 97% and 99% for both Never-Smokers and Active-Smokers. In multiclass classification, the Embedding technique with CNN-LSTM architecture yielded the most favorable results in class-specific evaluations, with equal performance measures of 97% for Never-Smoker and measures in the range of 86 to 89% for Active-Smoker and 91–92% for Never-Smoker. Conclusion Our proposed NLP pipeline achieved a high level of classification performance. In addition, we presented the explanation of the decision made by the best performing detection model. Future work will expand the model’s capabilities to analyze longer notes and a broader range of categories to maximize its utility in further research and screening applications.

Background: Premature menopause (PM) is the cessation of ovarian function before age 40. PM women are more likelyto have cardiovascular diseases (CVDs), diabetes, and mental disorders. This is the first study that assessed the associationof single nucleotide polymorphisms (SNPs) with anti-heat shock protein 27 (Hsp27), High-sensitivity C-reactive protein(hs-CRP), and PM and serum pro-oxidant-antioxidant balance (PAB), as putative risk factors for CVDs. We aimed toexplore the association of oxidative stress markers with eight different SNPs shown to be related to premature menopause.Materials and Methods: In this cross-sectional research, we included 183 healthy women and 117 premature menopausalwomen. We determined baseline characteristics for all participants and measured serum hs-CRP, anti-HSP-27 antibody titer, and PAB levels using the established methods. Genotyping for eight SNPs was done usingthe tetra amplification refractory mutation system polymerase chain reaction (Tetra-ARMS PCR) and allele-specificoligonucleotide PCR (ASO-PCR) methods.Results: We found a significant difference between mean serum PAB levels and the genetic variant of rs16991615(P=0.03). ANCOVA showed a significant effect of the genotypes rs4806660 and rs10183486 on hs-CRP serum levelsin the case and control groups, respectively (P=0.04 and P=0.007). ANCOVA also showed an association betweenrs244715 genotypes and anti-hsp27 serum levels in the case group (P=0.02). There was a significant effect of thegenotypes of rs451417 on the serum hs-CRP level in the control group (P=0.03).Conclusion: There was a significant association of the genetic variants related to PM with oxidative stress and inflammatorymarkers (serum PAB, anti-hsp27 antibody, and hs-CRP). Accordingly, this seems to be an effective approach topredicting susceptible subjects for cardiovascular and mental disorders as well as various cancers.

Abstract Background Cesarean section (C-section) rates, deemed a critical health indicator, have experienced a historical increase. The advent of the COVID-19 pandemic significantly impacted healthcare patterns including delays or lack of follow-up in treatment and an increased number of patients with acute problems in hospitals. This study aimed to explore whether the observed surge is a genuine consequence of pandemic-related factors. Methods This study employs an Interrupted Time Series (ITS) design to analyze monthly C-section rates from March 2018 to January 2023 in Kurdistan province, Iran. Segmented regression modeling is utilized for robust data analysis. Results The C-section rate did not show a significant change immediately after the onset of COVID-19. However, the monthly trend increased significantly during the post-pandemic period (p

Abstract Background During the Covid-19 pandemic, a number of hotlines/helplines/call centers was implemented to provide remote services and support public health. The objective of this study was to investigate the characteristics, challenges and lessons learned of implementing Covid-19 hotlines/helplines/call centers during the pandemic. Methods PubMed, Web of Science, Scopus, the Cochrane Library, IEEE Xplore, and ProQuest databases as well as Google Scholar were searched between 1st January 2020 and 31st December 2023 to retrieve relevant articles published in English. The quality and risk of bias of the studies were assessed using the Appraisal tool for Cross-Sectional Studies (AXIS), the Mixed Methods Appraisal Tool (MMAT), and Critical Appraisal Skills Programme (CASP) Checklist. Results In total, 43 out of 1440 articles were included in this study. About half of the hotlines/helplines/call centers were launched in March 2020 (n = 19). Providing psychological support (n = 23), reliable information about Covid-19 (n = 10), healthcare advices about Covid-19 (n = 8), and triage (n = 7) were the most common purposes of implementing these services. The most common challenges included a lack of physical examination, unavailability of hotlines/helplines/call centers at the point of need, and delay in updating Covid-19 information. The most common lessons learned were employing qualified staff, providing proper training, and getting feedback from the callers and operators. Conclusion According to the results, most of the Covid-19 hotlines/helplines/call centers were launched in the early months of the pandemic, and about half of them were active seven days a week. Most of the operators were mental health providers and clinicians. The findings show the importance of continuous psychological support during crises, particularly when adequate information about the situation is not available. The challenges experienced by the callers and operators as well as the lessons learned by the service providers also need to be considered for future crises to increase the effectiveness of similar services.

We introduce a new subclass of convex functions as follows:\[ \mathcal{K}_{IC}:=\left\{f\in \mathcal{A}:{\rm Re}\left(1+\frac{zf''(z)}{f'(z)}\right)>\left|f'(z)-1\right|,\quad |z|

In today’s world, the Internet of Things has become an integral part of our lives. The increasing number of intelligent devices and their pervasiveness has made it challenging for developers and system architects to plan and implement systems of Internet of Things and Industrial Internet of Things effectively. The primary objective of this work is to automate the design process of Industrial Internet of Things systems while optimizing the quality of service parameters, battery life, and cost. To achieve this goal, a general four-layer fog-computing model based on mathematical sets, constraints, and objective functions is introduced. This model takes into consideration the various parameters that affect the performance of the system, such as network latency, bandwidth, and power consumption. The Non-dominated Sorting Genetic Algorithm II is employed to find Pareto optimal solutions, while the Technique for Order of Preference by Similarity to Ideal Solution is used to identify compromise solutions on the Pareto front. The optimal solutions generated by this approach represent servers, communication links, and gateways whose information is stored in a database. These resources are chosen based on their ability to enhance the overall performance of the system. The proposed strategy follows a three-stage approach to minimize the dimensionality and reduce dependencies while exploring the search space. Additionally, the convergence of optimization algorithms is improved by using a biased initial population that exploits existing knowledge about how the solution should look. The algorithms used to generate this initial biased population are described in detail. To illustrate the effectiveness of this automated design strategy, an example of its application is presented.

High quality standards in vegetables is one of the most important expectation of consumers presence of weeds would also reduce the quantity and quality of crops produced. Use of cover crops could be an environmentally friendly alternative to conventional chemical and mechanical methods. To this end for controlling weeds, a factorial experiment based on a randomized complete block was conducted at the Research Farm of Mohaghegh Ardabili University with three replications in 2017. Experimental treatments were the first factor, consisting of mono culture and intercropping of rye (Secale cereal L.), hairy vetch (Vicia villosa Roth.) and control treatment (no cover crop) and the second factor consisting of one time hand weeding and two times weeding, with 15 days intervals, and without hand weeding, as control. The results showed that the highest dry biomass of cover crops obtained from rye mono culture (530.59 g.m-2) and the lowest biomass (47.85 g.m-2) from hairy vetch mono culture. The highest reduction of total weed biomass (83.24%) obtained from rye with hairy vetch without hand weeding. The highest yield of iceberg lettuce was obtained from hairy vetch mono culture with one-time hand weeding or from without hand weeding treatment (5025 and 4965 g.m-2, respectively). Also, the highest plant height, number of leaves and diameter of lettuce stem was not statistically significant difference from mono culture and intercropping of cover crops. The results showed that the combination of hand weeding and cover crop residues were more effective in controlling and reducing weed biomass, but the presence of cover crop residues in the shortron also increased the yield of iceberg lettuce through weed control.

Vitamin D deficiency is a worldwide condition, which has been linked to a variety of health-related issues. Vitamin D can be beneficial to cardiovascular patients and those suffering from depression and anxiety, based on Survey of Ultraviolet Intake by Nutritional Approach (SUVINA study), ,showing the potential effects of vitamin D-fortified dairy products on anxiety, depression, and stress in subjects with cardiovascular disease (CVD) risk factors. Methods: It was a quadruple-blind randomized controlled trial. Individuals were randomly allocated to one of four groups: fortified low-fat milk (FM), non-fortified low-fat milk (NFM), fortified low-fat yogurt (FY), and non-fortified low-fat yogurt (NFY). FM and FY groups were fortified with 1500 IU nano-encapsulated vitamin D3. Anthropometric parameters as well as depression, anxiety, and stress scores were measured at baseline and after a ten-week trial in Mashhad, Iran. Results: Totally, 289 participants (143 men, 146 women) with a mean age of 41.86±7.81years were enrolled in the study. There was no statistical difference between the scores of depression, anxiety, and stress in participants with and without CVD risk factors (P>0.05). No statistical difference was found in the subgroup analysis based on milk and yogurt consumption. Conclusions: Fortified low-fat milk containing 1,500 IU of vitamin D has no impact on improving depression, anxiety, and stress during ten weeks. However, further studies with higher vitamin D doses for a longer duration are recommended.

Abstract Background Consecutive droughts and quantitative and qualitative reduction of surface and underground water resources have caused an increase in greenhouse and hydroponic cultivation for most garden crops, including strawberries, in Iran. On the other hand, most of the inputs of greenhouse crops in Iran are imported. To possibility of replacing vermicompost with peat moss under hydroponic cultivation, an experiment was done in a split plot based on randomized complete blocks design in three replications in Isfahan (Iran) Agricultural and Natural Resources Research Center in 2019. The main treatment was substrate at four levels included different levels of vermicompost (30 and 50%) and peat moss (30 and 50%) in combination with perlite and sub-treatment were Selva and Camarosa cultivars. Results The results showed that Camarosa cultivar and Selva cultivar in (perlite/ peat moss 50:50) and Selva cultivar in (perlite / vermicompost 70:30) had maximum yield. Leaf number and chlorophyll index were maximum in Camarosa cultivar in peat moss substrates. Strawberry cultivars had the highest root fresh weight, the content of vitamin C and total soluble solids (TSS) in substrates containing vermicompost. Camarosa cultivar in (perlite / peat moss50:50) and Selva cultivar in (perlite /vermicompost 50:50) had maximum root dry weight. Also, the highest number of inflorescences was related to substrates containing peat moss and (perlite /vermicompost 70:30). Maximum amount of fresh and dry weight of shoots were observed in (perlite/ peat moss70:30). Selva cultivar had more inflorescences (16.5%) than Camarosa cultivar and Camarosa cultivar produced more fresh and dry weight of shoots (16.5%, 23.01%) than Selva cultivar. Conclusion Expriment results highlighted the importance of considering both main and sub-treatments in agricultural research, as they interacted to influence various growth and yield parameters. 50% vermicompost treatment combined with perlite had a positive impact on plant growth and in quality index such as vitamin C content and TSS was highest. while the choice of cultivar affected different aspects of plant development. Selva cultivar was known to be more tolerant to salinity caused by vermicompost. Vermicompost is local and more economical, also salt resistant cultivars are recommended in a controlled (30%) amount of vermicompost.

Abstract The design and synthesis of novel cytotoxic agents is still an interesting topic for medicinal chemistry researchers due to the unwanted side effects of anticancer drugs. In this study, a novel series of uracil–azole hybrids were designed and synthesized. The cytotoxic activity, along with computational studies: molecular docking, molecular dynamic simulation, density functional theory, and ADME properties were also, evaluated. The compounds were synthesized by using 3-methyl-6-chlorouracil as the starting material. Cytotoxicity was determined using MTT assay in the breast carcinoma cell line (MCF-7) and Hepatocellular carcinoma cell line (HEPG-2). These derivatives demonstrated powerful inhibitory activity against breast and hepatocellular carcinoma cell lines in comparison to Cisplatin as positive control. Among these compounds, 4j displayed the best selectivity profile and good activity with IC50 values of 16.18 ± 1.02 and 7.56 ± 5.28 µM against MCF-7 and HEPG-2 cell lines respectively. Structure–activity relationships revealed that the variation in the cytotoxic potency of the synthesized compounds was affected by various substitutions of benzyl moiety. The docking output showed that 4j bind well in the active site of EGFR and formed a stable complex with the EGFR protein. DFT was used to investigate the reactivity descriptors of 4a and 4j. The outputs demonstrated that these uracil–azole hybrids can be considered as potential cytotoxic agents.

Abstract Protein function hinges on small shifts of three-dimensional structure. Elevating temperature or pressure may provide experimentally accessible insights into such shifts, but the effects of these distinct perturbations on protein structures have not been compared in atomic detail. To quantitatively explore these two axes, we report the first pair of structures at physiological temperature versus. high pressure for the same protein, STEP (PTPN5). We show that these perturbations have distinct and surprising effects on protein volume, patterns of ordered solvent, and local backbone and side-chain conformations. This includes interactions between key catalytic loops only at physiological temperature, and a distinct conformational ensemble for another active-site loop only at high pressure. Strikingly, in torsional space, physiological temperature shifts STEP toward previously reported active-like states, while high pressure shifts it toward a previously uncharted region. Altogether, our work indicates that temperature and pressure are complementary, powerful, fundamental macromolecular perturbations.

Abstract Corona virus 19 (Covid-19) has caused many problems in public health, economic, and even cultural and social fields since the beginning of the epidemic. However, in order to provide therapeutic solutions, many researches have been conducted and various omics data have been published. But there is still no early diagnosis method and comprehensive treatment solution. In this manuscript, by collecting important genes related to COVID-19 and using centrality and controllability analysis in PPI networks and signaling pathways related to the disease; hub and driver genes have been identified in the formation and progression of the disease. Next, by analyzing the expression data, the obtained genes have been evaluated. The results show that in addition to the significant difference in the expression of most of these genes, their expression correlation pattern is also different in the two groups of COVID-19 and control. Finally, based on the drug-gene interaction, drugs affecting the identified genes are presented in the form of a bipartite graph, which can be used as the potential drug combinations.

Abstract In dealing with the real-world optimization problems, a decision-maker has to frequently face the ambiguity and hesitancy due to various uncontrollable circumstances. Rough set theory has emerged as an indispensable tool for representing this ambiguity because of its characteristic of incorporating agreement and understanding of all the involved specialists and producing more realistic conclusions. This paper studies an application of the rough set theory for a multi-objective non-linear programming problem that originates for the management of solid wastes. Municipal solid waste management is a global problem that affects every country. Because of the poor waste management system in many nations, the bulk of municipal solid waste is disposed of in open landfills with no recovery mechanism. Hence, an effective and long term waste management strategy is the demand of the day. This research offers an incinerating, composting, recycling, and disposing system for the long-term management of the municipal solid waste. A model for the municipal solid waste management with the goal of minimizing the cost of waste transportation, cost of waste treatment and maximizing the revenue generated from various treatment facilities is developed under rough interval environment. To tackle the conflicting nature of different objectives, an approach is proposed that gives the optimistic and pessimistic views of the decision-maker for optimizing the proposed model. Also, the biasness/preference of the decision-maker for a specific objective is handled by establishing the respective non-linear membership and non-membership functions instead of the linear ones. Finally, to demonstrates the practicality of the proposed methodology, a case study is solved and the obtained Pareto-optimal solution has been compared to those obtained by the existing approaches.

Objective: Dams play a key role in controlling floods and minimizing associated losses. They reduce downstream losses by lowering the flood peak and enable a safer flood stream. Despite the significant advantages of dams, dam failure may cause dramatic casualties and financial losses. Overtopping is a major dam failure that can be induced by one or more geophysical events, e.g., floods, landslides, earthquakes, and intense winds. Overtopping is particularly crucial for earth and aggregate dams as they are more likely to be destroyed due to the flow over the crest. Therefore, the overtopping risk evaluation of dams is important, and it is necessary to bring a trade-off between the reservoir level and overtopping risk. Methods: The risk management of earth dams is complex since multiple factors are involved. System dynamics (SD) can be used to evaluate the effects of a set of complex, interrelated factors on the overtopping risk of dams under several scenarios. Floods and winds are the main causes of overtopping, with each being dependent on a number of variables. This paper used an SD model to evaluate the independent and combined effects of uncertainties with floods and winds of different return periods on the overtopping risk of the Vanyar Dam, Iran. This would help implement efficient measures to minimize the overtopping risk and maximize safety. To measure the effects of overtopping floods on the downstream of the dam, a hydraulic model was developed in HEC-RAS. The geometric parameters/data of the river and the non-steady stream data along with the initial and boundary conditions were introduced as inputs to the hydraulic model. The most critical scenario to identify the overtopping flood zone corresponded to the probable maximum flood (PMF), under which a broad area of the downstream lands would be flooded. Results: This study analyzed the overtopping risk of the Vanyar Dam, Iran, for different flood and wind return periods by modeling the dam reservoir through system dynamics (SD) under initial reservoir level and dam height uncertainties using the Monte Carlo method. The effects of floods and the combined effects of floods and winds on the overtopping risk were evaluated. The results demonstrated that the probable maximum flood (PMF) with a wind return period of 100 years under reservoir level and dam height uncertainties maximized the overtopping risk to 0.122931. The overtopping risk converged in 200,000 iterations. The analysis of the overtopping flood downstream of the dam showed that many villages and facilities would be completely flooded, and it is necessary to implement management measures, such as controlling the reservoir level, to minimize the overtopping risk.

The emergence of commercially accessible depth sensors has driven the widespread adoption of 3D data, offering substantial benefits across diverse applications, ranging from human activity recognition to augmented reality. However, indoor environments present significant challenges for 3D computer vision applications, particularly in cluttered and dynamic scenes where background bounding surfaces hinder the detection and analysis of foreground objects. We introduce a novel perspective-independent point cloud simplifier (PiPCS) for complex 3D indoor scenes. PiPCS streamlines 3D computer vision workflows by contextually segmenting and subtracting background bounding surfaces and preserving segmented foreground objects within indoor scenes, effectively reducing the size of indoor point clouds and enhancing 3D indoor scene perception. Methodologically, we use estimated surface normals to intelligently divide an input point cloud into distinct zones, which are then split into multiple distinct parallel clusters. Next, we find the largest plane in each cluster and sort the fitted planes within each zone based on their distance along the zone’s normal vector to identify the bounding surfaces. Finally, we segment the 3D background, simplify the point cloud by employing a voxel-based background subtraction technique, and segment 3D foreground objects via a cluster-based segmentation approach. We evaluated PiPCS on the Stanford S3DIS dataset and our own challenging dataset, achieving average values of 97.08% for specificity and 91.27% for F1 score on the S3DIS dataset and size reductions averaging 74.11% overall. Our experimental and evaluation results demonstrate that PiPCS robustly simplifies and reduces the size of unorganized indoor point clouds.

Affiliation Correction [...]

In this paper, we employ the theory of differential subordination to establish a theorem that delineates certain sufficient conditions for starlikeness, convexity, close-to-convexity, and quasi-convexity in relation to functions with fixed initial coefficients. Furthermore, we introduce some results derived from these conditions. Building upon this framework, we derive an extension of Nunokawa’s lemma for analytic functions with fixed initial coefficients.

In this study, the significance of temperature within the range of 25 to 70 degrees Celsius and the particle size of calcium carbonate at 237 μm, 348.5 μm, and 497.5 μm on the hydrochloric acid reaction rate with calcium carbonate stone was deliberated. The slow reaction rate helps a retarded action, and therefore, HCl penetrates deeper, and the zones farther from the wellbore will be affected by the acidizing. Therefore, the exploration of variables influencing the reaction rate holds considerable significance. The reaction rate is controlled by influential factors such as temperature and particle size, where the effect of these two factors is discussed in detail. According to the empirical findings, the reaction rate exhibits an upward trend as temperature rises and as the size of calcium carbonate particles decreases. It is worth mentioning that the highest and lowest reaction rates were observed at temperatures of 70 and 25 degrees Celsius, and with calcium carbonate particle sizes of 237 and 497.5 micrometers, respectively. Consequently, this research, considering the examined parameters and the obtained results, contributes to a better understanding and more efficient design of the acidizing process.

Introduction: Cardiovascular disease (CVD) is the leading cause of mortality and one of the main challenges for health systems worldwide. In this study, we aimed to evaluate the association of socio-demographic, lifestyle, psychological and anthropometric factors and underlying diseases such as hypertension (HTN), diabetes mellitus (DM) and metabolic syndrome (MS) with CVD risk among a subpopulation of Iranian adults.Methods: In this prospective study, a total of 235 CVD patients along with 8405 healthy and non-symptomatic individuals who participated in MASHAD cohort study were enrolled. CVD diagnosis was performed by taking electrocardiogram (ECG) and medical history and performing physical examination for each participant. Health and lifestyle questionnaires, the Beck’s anxiety inventory (BAI), Beck’s depression inventory (BDI) and the James and Schofield human energy requirements equations were completed for all participants. Anthropometric measurements were also recorded for all subjects. All statistical analyses including chi-square and student independent T-test were performed using SPSS 16.0 software (SPSS Inc., Chicago, IL, USA) at a significant level of 0.05.Results: We found that there were significant associations between CVD risk and age, body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), diabetes mellitus (DM) and family history (FH) of CVD in both genders; though, there was a significant negative correlation between physical activity level (PAL) and risk of CVD among men and women. Also hypertension (HTN), metabolic syndrome (MS), depression and anxiety were positively and higher education level was negatively associated with CVD events only in females. While, waist-to-hip ratio (WHR) was an independent predictor of CVD among males (P-value< 0.05).Conclusion: There are several modifiable and non-modifiable risk factors that are independently considered as CVD predictors among the MASHAD study population. It is recommended to prioritize the lifestyle modification, development of local risk calculators and gender-related stratified strategies in order to prevent and manage CVD among the Iranian population.

By considering a fixed point in the punctured unit disk and using the $q$--derivative, a new subfamily of meromorphic and univalent functions is defined. Also, the first and second order {$q$--derivative} of meromorphic functions are introduced. Coefficient bounds, extreme points, radii of starlikeness and convexity are obtained. Furthermore, the convexity and preserving under convolution with some restrictions on parameters are investigated

Extended Abstract Introduction and Objective: The impact of drought stress on crops is devastating annually, resulting in yield losses of 17%. Research seems, therefore, necessary to improve crop tolerance to drought stress and minimize water losses in agriculture. This study aimed to investigate the relationships between yield and yield components with physiological traits and use these relationships to select high-yielding cultivars under normal irrigation and drought stress conditions at the flowering stage. Material and Methods: To explore the effect of drought stress at the flowering stage, four genotypes were investigated as factor A by factorial experiment based on the randomized complete block design (RCBD) with three replications under the normal and drought stress conditions at the flowering stage as factor B in the research greenhouse of Azerbaijan Shahid Madani University during the 2016-2017 crop season. The relationship between yield and yield components with physiological traits and the relative importance of traits affecting yield were investigated using an analysis of variance, mean comparison, and canonical correlation analysis after measuring the traits of the studied genotypes. Results: Analysis of variance results revealed significant differences between genotypes for most traits studied. Under normal irrigation conditions and drought stress, the Arum genotype was the best in terms of biological yield, awn length, grain number per spike, and grain number in spikelet traits and the Mihan genotype outperformed others concerning plant dry weight, number of grains per spike, days to spike formation, root dry weight, root volume, and number of grains per spikelet traits. The results of this canonical correlation analysis showed a significant correlation between the canonical variables of yield and yield components with the canonical variables of physiological traits under both normal and stress conditions. To increase grain yield and 1000-grain weight under normal irrigation conditions in the greenhouse, traits of malondialdehyde concentrations, hydrogen peroxide content, proline content, peroxide, and catalase content, and chlorophyll a and total chlorophyll concentrations may be appropriate selection criteria. The results of canonical correlation analysis, under stress conditions in the greenhouse, revealed that to increase 1000-grain weight, number of grains per spike, and grain yield, traits such as malondialdehyde, chlorophyll b concentration, protein content, peroxidase, proline content, chlorophyll a concentration, and catalase are important and influential factors. Conclusion: According to canonical correlation analysis, it can be argued that under normal irrigation and drought stress conditions in the greenhouse, traits of malondialdehyde concentration, peroxidase content, and chlorophyll a concentration are appropriate selection criteria to increase grain yield.

Since the emergence of the coronavirus and the international community's attention to this virus, researchers and medical staff in hospitals have put approaches and methods for dealing with it and early diagnosis on their agenda. In this study, one of the method that called the Impact Dose program has been presented to calculate the absorbed and effective dose of people infected with the Covid-19 virus using CT scans of the respiratory tract. This is an analytical cross-sectional study that was conducted at Isfahan Shariati Hospital. The study is included of the 42 patients with Covid-19, who were selected based on doctor's opinion. The effective and absorbed dose were estimated by using the Impact Dose program, which is based on Monte Carlo. The data were analyzed using SPSS-24 software. The effective dose and DLP were reported as 1.67 mSv and 80.08 mGy.cm, respectively. The absorbed dose and effective dose were found to be higher in women than in men. The multiple CT scans of patients for the diagnosis of Covid-19 resulted in a very high absorbed dose, and lung sonography was recommended as an alternative method due to its high accuracy, similar to CT scans, in diagnosing pneumonia.

Abstract Background Alcohol use disorder (AUD) causes significant morbidity, mortality, and injuries. According to reports, approximately 5% of all registered deaths in Denmark could be due to AUD. The problem is compounded by the late identification of patients with AUD, a situation that can cause enormous problems, from psychological to physical to economic problems. Many individuals suffering from AUD never undergo specialist treatment during their addiction due to obstacles such as taboo and the poor performance of current screening tools. Therefore, there is a lack of rapid intervention. This can be mitigated by the early detection of patients with AUD. A clinical decision support system (DSS) powered by machine learning (ML) methods can be used to diagnose patients’ AUD status earlier. Methods This study proposes an effective AUD prediction model (AUDPM), which can be used in a DSS. The proposed model consists of four distinct components: (1) imputation to address missing values using the k-nearest neighbours approach, (2) recursive feature elimination with cross validation to select the most relevant subset of features, (3) a hybrid synthetic minority oversampling technique-edited nearest neighbour approach to remove noise and balance the distribution of the training data, and (4) an ML model for the early detection of patients with AUD. Two data sources, including a questionnaire and electronic health records of 2571 patients, were collected from Odense University Hospital in the Region of Southern Denmark for the AUD-Dataset. Then, the AUD-Dataset was used to build ML models. The results of different ML models, such as support vector machine, K-nearest neighbour, decision tree, random forest, and extreme gradient boosting, were compared. Finally, a combination of all these models in an ensemble learning approach was selected for the AUDPM. Results The results revealed that the proposed ensemble AUDPM outperformed other single models and our previous study results, achieving 0.96, 0.94, 0.95, and 0.97 precision, recall, F1-score, and accuracy, respectively. In addition, we designed and developed an AUD-DSS prototype. Conclusion It was shown that our proposed AUDPM achieved high classification performance. In addition, we identified clinical factors related to the early detection of patients with AUD. The designed AUD-DSS is intended to be integrated into the existing Danish health care system to provide novel information to clinical staff if a patient shows signs of harmful alcohol use; in other words, it gives staff a good reason for having a conversation with patients for whom a conversation is relevant.

Objective: To examine the associations between dietary patterns and cardiometabolic risk factors among type 1 diabetic (T1D) patients. Methods: This cross-sectional study was conducted on 229 Iranian T1D patients. Data on dietary intakes were collected using a 168-item food frequency questionnaire. To identify major dietary patterns, we merged data on the 168 food items to obtain 23 food groups. Then, we constructed major dietary patterns using factor analysis with varimax rotation. We used binary logistic regression to assess the association between dietary patterns and cardiometabolic risk factors, in which potential confounders were adjusted. Results: Four dietary patterns were identified: Western, unhealthy, traditional, and semi-healthy patterns. After adjusting for confounders including demographic variables, physical activity, energy intake, and medical history, participants in the highest tertile of the Western dietary pattern had 2.53 (95 % CI: 1.03–6.22) and 3.37 (95 % CI: 1.18–9.63) times more odds of elevated HbA1c and low estimated glucose disposal rate (eGDR), respectively, compared with those in the lowest tertile. Such the positive association was also seen for elevated fasting blood glucose (FBG). Moreover, individuals in the top tertile of unhealthy diet had more odds of elevated LDL-c and abdominal obesity than those in the lowest tertile. Regarding the semi-healthy diet, higher adherence was associated with 51 % lower odds of elevated FBG (OR: 0.49, 95 % CI: 0.24–0.99). For other outcomes, no significant association was found. Conclusion: We found that T1D patients may take benefit from adherence to a semi-healthy diet with a low amount of unhealthy and Western-related foods.

سابقه و هدف: با توجه به اینکه متغیّر ادراک خدا با بسیاری از مؤلفه‌های روان‌شناختی در ارتباط است و متغیّر پیچیده‌ای محسوب می‌شود، پژوهش حاضر با هدف بررسی تأثیر معنی‌درمانی بر کیفیت رشد ادراک از خدا با توجه به متغیّرهای فردی در میان بیماران نامزد جراحی قلب انجام شد. روش کار: پژوهش حاضر از زمرۀ پژوهش‌های نیمه‌تجربی و از نوع پیش‌آزمون-پس‌آزمون با گروه کنترل است. جامعۀ آماری پژوهش تمامی بیماران نامزد جراحی قلب باز مراجعه‌کننده به بیمارستان مرکز قلب تهران بود که از میان این جامعه، نمونه‌ای به حجم 60 نفر (30 نفر گروه آزمون و 30 نفر گروه کنترل) به‌شیوۀ تصادفی انتخاب شدند. مطالعه با کمک ابزار پرسش‌نامۀ سه‌قسمتی جمعیت‌شناختی، تصور فرد از خدا (بازسازی مقیاس لارنس) و پرسش‌نامۀ وضعیت مذهبی خانواده گرنکویست اجرا شد. داده‌ها با استفاده از ضریب همبستگی پیرسون، اسپیرمن، تی، من ویتنی، تحلیل واریانس و کروسکال والیس تجزیه و تحلیل شد. در این پژوهش همۀ موارد اخلاقی رعایت شده است و مؤلفان مقاله تضاد منافعی گزارش نکرده‌اند. یافته‌ها: نتایج نشان داد که در گروه مداخله و کنترل رابطۀ معنی‌داری بین متغیّرهای سن، جنسیت، وضعیت تأهل و سطح تحصیلات با نمرۀ کل ادراک خدا وجود نداشت (05/0

Introduction: Metabolic syndrome (MetS) is comprised of a clustering of various cardiovascular risk factors that can also affect bone health. We aimed the associations between MetS and bone mineral density (BMD) and trabecular bone score (TBS) in subjects with abdominal adiposity.Methods: Individuals with body mass index less than 25 kg/m2 were enrolled from the SUVINA study and allocated into two groups according criteria of IDF for metabolic syndrome. TBS T-score and Z/T score of fore skeletal region were measured using dual-energy X-ray absorptiometry (DXA). SPSS software was used for statistical analysis and p value 0

Abstract In this study, CO2 solubility in different choline chloride-based deep eutectic solvents (DESs) has been investigated using the Quantitative Structure–Property Relationship (QSPR). In this regard, the effect of different structures of the hydrogen bond donor (HBD) in choline chloride (ChCl) based deep eutectic solvents (DESs) has been studied in different temperatures and different molar ratios of ChCl as hydrogen bond acceptor (HBA) to HBD. 12 different datasets with 390 data on the CO2 solubility were chosen from the literature for the model development. Eight predictive models, which contain the pressure and one structural descriptor, have been developed at the fixed temperature (i.e. 293, 303, 313, or 323 K), and the constant molar ratio of ChCl to HBD equal to 1:3 or 1:4. Moreover, two models were also introduced, which considered the effects of pressure, temperature, and HBD structures, simultaneously in the molar ratios equal to 1:3 or 1:4. Two additional datasets were used only for the further external validation of these two models at new temperatures, pressures, and HBD structures. It was identified that CO2 solubility depends on the “EEig02d” descriptor of HBD. “EEig02d” is a molecular descriptor derived from the edge adjacency matrix of a molecule that is weighted by dipole moments. This descriptor is also related to the molar volume of the structure. The statistical evaluation of the proposed models for the unfixed and fixed temperature datasets confirmed the validity of the developed models.

This article discusses the replicating kernel interpolation collocation method related to Jacobi polynomials to solve a class of fractional system of equations. The reproducing kernel function that is executed as an (RKM) was first created in the form of Jacobi polynomials. To prevent Schmidt orthogonalization, researchers compare the numerical solutions achieved by varying the parameter value. Through various numerical examples, it is demonstrated that this technique is practical and precise.

In this paper, we introduce a newly defined subclass $\mathcal{S}_{\Sigma}(\vartheta,\gamma,\eta;\varphi) $ of bi-univalent functions by using the Tremblay differential operator satisfying subordinate conditions in the unit disk. Moreover, we use the Faber polynomial expansion to derive bounds for the Fekete-Szego problem and first two \emph{Taylor-Maclaurin coefficients} $|a_2|$ and $|a_3|$ for functions of this class.

Abstract Using ab initio density functional theory, we demonstrated the possibility of controlling the magnetic ground-state properties of bilayer CrCl $$_{3}$$ 3 by means of mechanical strains and electric fields. In principle, we investigated the influence of these two fields on parameters describing the spin Hamiltonian of the system. The obtained results show that biaxial strains change the magnetic ground state between ferromagnetic and antiferromagnetic phases. The mechanical strain also affects the direction and amplitude of the magnetic anisotropy energy (MAE). Importantly, the direction and amplitude of the Dzyaloshinskii–Moriya vectors are also highly tunable under external strain and electric fields. The competition between nearest-neighbor exchange interactions, MAE, and Dzyaloshinskii–Moriya interactions can lead to the stabilization of various exotic spin textures and novel magnetic excitations. The high tunability of magnetic properties by external fields makes bilayer CrCl $$_{3}$$ 3 a promising candidate for application in the emerging field of two-dimensional quantum spintronics and magnonics.

Objective(s): One of the most important neurotrophins is brain-derived neurotrophic factor (BDNF) that is closely associated with insulin resistance and Type 2 Diabetes Mellitus (T2DM). This study was done under the objective of investigating the effects of curcumin (CUR) and nano-curcumin (nCUR) on serum levels of BDNF, fasting blood sugar (FBS), water and food intake, and body weight in T2DM rats. Materials and Methods: Our work required the division of 48 male Wistar rats into the 6 groups of Control, diabetic, diabetic treated with two doses of CUR and nCUR (100 and 200 mg/kg), T2DM induced rats by intraperitoneal injection of Streptozotocin, and nicotinamide in the fasting state. Once the rats received nCUR and CUR doses through gavage for 28 days, their Serum level of BDNF was measured at the end of intervention period, while their body weight, FBS, and food and water intake were also examined at both onset and ending of this period.Results: Considering the effect of curcumin and nano-curcumin administration on the induced significant increase in serum BDNF and decrease in FBS, food, and water intake in T2DM rats (P

Abstract In the literature, fuzzy arithmetic approach has been used for solving fuzzy data envelopment analysis (DEA) model in the presence of undesirable outputs by use of ideal and anti-ideal decision-making units (DMUs). In order to obtain the best and the worst fuzzy efficiencies, such an approach allows each DMU to choose the most favorable weights via optimizing its own evaluation that needs to solve various mathematical models. On the one hand, solving one mathematical model to find the best fuzzy efficiency and solving another mathematical model to find the worst fuzzy efficiency for each DMU increase the computational complexity significantly. On the other hand, the fully flexibility of weights leads to the different set of weights that may not be desirable. This paper proposes a common-weight method from two optimistic and pessimistic perspectives in fuzzy environment to determine the common sets of weights (CWS) to compute the best and the worst fuzzy efficiencies of all DMUs. The advantages of using common-weight DEA method based upon fuzzy arithmetic approach are reduction of the computational complexities and evaluation of equitably the best and the worst fuzzy efficiencies on the same base. The proposed approach first solves one linear programming model to compute each of the best and the worst fuzzy efficiencies of all DMUS, and then combines them to find a relative closeness index for the overall assessment. The developed approach is illustrated by a numerical example form the literature and the obtained results are compared with those from the existing ones.

In this paper, a new method for flocking quadcopters is introduced. For this purpose, the idea of two-level control has been used, where the high-level controller is the same as the flocking algorithm and acts as the path designer of quadcopters. Moreover, tracking of the generated desired path is performed by a low-level controller. The main focus of this paper is on the high-level controller. A novel leaderless flocking algorithm is introduced, where new potential functions are generated using fuzzy logic to achieve a proper lattice. The introduced potential functions have a minimum value in the lattice positions. Therefore, the control signal minimizes its value by using the gradient-descent method to reach the desired situation. A safety radius is defined for every agent such that using the proposed flocking algorithm, the quadcopters do not enter each other's safety region. The stability and convergence of the structural and transitional dynamics of the system are shown. Finally, the proposed method is evaluated through simulations of five quadcopters. The results show that The proposed method provides better performance in creating a lattice, and maintaining obstacle as compared with recently published methods in literature.

Designing green stormwater infrastructure (GSI) requires an accurate estimate of the contributing drainage area and a model for runoff generation. We examined some factors that add to the uncertainty associated with these two design steps in the urban environment. Delineated drainage areas at five GSI sites in southeastern Pennsylvania (PA) were compared for digital elevation model (DEM) resolutions (grid cell sizes) ranging from 8 to 300 cm. The findings point to an optimal DEM resolution range of 30–60 cm, with up to 100 cm resolution providing acceptable results for some sites. The delineated areas were validated with the observed flow and rainfall records at three sites by examining curve number (CN) values calculated for individual storms. The calculated CNs decreased with increasing rainfall volume, which supports a recommendation to consider a range of CNs in the GSI design process. The variation in calculated CNs was higher for the overestimated drainage areas derived from coarser DEM resolutions. We hypothesize that the observed continued decrease of CNs at high rainfall is the result of inlet bypass, a potentially significant factor in urban hydrology. The findings from this study provide insight into the variability in expected delineated drainage areas using standard methods in GSI design. HIGHLIGHTS Evaluate the optimal DEM resolution for urban drainage area delineation.; Inform design decisions for urban green stormwater infrastructure.; Validate area delineation with observed flows and the curve number method.;